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Merge branch 'develop' of github.com:PaddlePaddle/DeepSpeech into add_mbmelgan

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tools/Montreal-Forced-Aligner/ tools/Montreal-Forced-Aligner/
tools/sctk tools/sctk
tools/sctk-20159b5/ tools/sctk-20159b5/
tools/kaldi
tools/OpenBLAS/
*output/ *output/
README.md
浏览文件 @ 950d17cb
# PaddlePaddle Speech toolkit English | [简体中文](README_ch.md)
# PaddleSpeech
<p align="center">
<img src="./docs/images/PaddleSpeech_log.png" />
</p>
<div align="center">
<h3>
<a href="https://github.com/Mingxue-Xu/DeepSpeech#quick-start"> Quick Start </a>
| <a href="https://github.com/Mingxue-Xu/DeepSpeech#tutorials"> Tutorials </a>
| <a href="https://github.com/Mingxue-Xu/DeepSpeech#model-list"> Models List </a>
</div>
------------------------------------------------------------------------------------
![License](https://img.shields.io/badge/license-Apache%202-red.svg) ![License](https://img.shields.io/badge/license-Apache%202-red.svg)
![python version](https://img.shields.io/badge/python-3.7+-orange.svg) ![python version](https://img.shields.io/badge/python-3.7+-orange.svg)
![support os](https://img.shields.io/badge/os-linux-yellow.svg) ![support os](https://img.shields.io/badge/os-linux-yellow.svg)
*DeepSpeech* is an open-source implementation of end-to-end Automatic Speech Recognition engine, with [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) platform. Our vision is to empower both industrial application and academic research on speech recognition, via an easy-to-use, efficient, samller and scalable implementation, including training, inference & testing module, and deployment. <!---
why they should use your module,
how they can install it,
how they can use it
-->
**PaddleSpeech** is an open-source toolkit on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) platform for two critical tasks in Speech - **Automatic Speech Recognition (ASR)** and **Text-To-Speech Synthesis (TTS)**, with modules involving state-of-art and influential models.
Via the easy-to-use, efficient, flexible and scalable implementation, our vision is to empower both industrial application and academic research, including training, inference & testing module, and deployment. Besides, this toolkit also features at:
- **Fast and Light-weight**: we provide a high-speed and ultra-lightweight model that is convenient for industrial deployment.
- **Rule-based Chinese frontend**: our frontend contains Text Normalization (TN) and Grapheme-to-Phoneme (G2P, including Polyphone and Tone Sandhi). Moreover, we use self-defined linguistic rules to adapt Chinese context.
- **Varieties of Functions that Vitalize Research**:
- *Integration of mainstream models and datasets*: the toolkit implements modules that participate in the whole pipeline of both ASR and TTS, and uses datasets like LibriSpeech, LJSpeech, AIShell, etc. See also [model lists](#models-list) for more details.
- *Support of ASR streaming and non-streaming data*: This toolkit contains non-streaming/streaming models like [DeepSpeech2](http://proceedings.mlr.press/v48/amodei16.pdf), [Transformer](https://arxiv.org/abs/1706.03762), [Conformer](https://arxiv.org/abs/2005.08100) and [U2](https://arxiv.org/pdf/2012.05481.pdf).
Let's install PaddleSpeech with only a few lines of code!
>Note: The official name is still deepspeech. 2021/10/26
``` shell
# 1. Install essential libraries and paddlepaddle first.
# install prerequisites
sudo apt-get install -y sox pkg-config libflac-dev libogg-dev libvorbis-dev libboost-dev swig python3-dev libsndfile1
# `pip install paddlepaddle-gpu` instead if you are using GPU.
pip install paddlepaddle
# 2.Then install PaddleSpeech.
git clone https://github.com/PaddlePaddle/DeepSpeech.git
cd DeepSpeech
pip install -e .
```
## Table of Contents
The contents of this README is as follow:
- [Alternative Installation](#installation)
- [Quick Start](#quick-start)
- [Models List](#models-list)
- [Tutorials](#tutorials)
- [FAQ and Contributing](#faq-and-contributing)
- [License](#license)
- [Acknowledgement](#acknowledgement)
## Alternative Installation
## Features The base environment in this page is
- Ubuntu 16.04
- python>=3.7
- paddlepaddle==2.1.2
See [feature list](docs/source/asr/feature_list.md) for more information. If you want to set up PaddleSpeech in other environment, please see the [ASR installation](docs/source/asr/install.md) and [TTS installation](docs/source/tts/install.md) documents for all the alternatives.
## Setup ## Quick Start
All tested under: > Note: `ckptfile` should be replaced by real path that represents files or folders later. Similarly, `exp/default` is the folder that contains the pretrained models.
* Ubuntu 16.04
* python>=3.7
* paddlepaddle==2.1.2
Please see [install](docs/source/asr/install.md). Try a tiny ASR DeepSpeech2 model training on toy set of LibriSpeech:
## Getting Started ```shell
cd examples/tiny/s0/
# source the environment
source path.sh
# prepare librispeech dataset
bash local/data.sh
# evaluate your ckptfile model file
bash local/test.sh conf/deepspeech2.yaml ckptfile offline
```
Please see [Getting Started](docs/source/asr/getting_started.md) and [tiny egs](examples/tiny/s0/README.md). For TTS, try FastSpeech2 on LJSpeech:
- Download LJSpeech-1.1 from the [ljspeech official website](https://keithito.com/LJ-Speech-Dataset/) and our prepared durations for fastspeech2 [ljspeech_alignment](https://paddlespeech.bj.bcebos.com/MFA/LJSpeech-1.1/ljspeech_alignment.tar.gz).
- Assume your path to the dataset is `~/datasets/LJSpeech-1.1` and `./ljspeech_alignment` accordingly, preprocess your data and then use our pretrained model to synthesize:
```shell
bash ./local/preprocess.sh conf/default.yaml
bash ./local/synthesize_e2e.sh conf/default.yaml exp/default ckptfile
```
## More Information
If you want to try more functions like training and tuning, please see [ASR getting started](docs/source/asr/getting_started.md) and [TTS Basic Use](/docs/source/tts/basic_usage.md).
## Models List
PaddleSpeech ASR supports a lot of mainstream models, which are summarized as follow. For more information, please refer to [ASR Models](./docs/source/asr/released_model.md).
<!---
The current hyperlinks redirect to [Previous Parakeet](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples).
-->
<table>
<thead>
<tr>
<th>ASR Module Type</th>
<th>Dataset</th>
<th>Model Type</th>
<th>Link</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="6">Acoustic Model</td>
<td rowspan="4" >Aishell</td>
<td >2 Conv + 5 LSTM layers with only forward direction </td>
<td>
<a href = "https://deepspeech.bj.bcebos.com/release2.1/aishell/s0/aishell.s0.ds_online.5rnn.debug.tar.gz">Ds2 Online Aishell Model</a>
</td>
</tr>
<tr>
<td>2 Conv + 3 bidirectional GRU layers</td>
<td>
<a href = "https://deepspeech.bj.bcebos.com/release2.1/aishell/s0/aishell.s0.ds2.offline.cer6p65.release.tar.gz">Ds2 Offline Aishell Model</a>
</td>
</tr>
<tr>
<td>Encoder:Conformer, Decoder:Transformer, Decoding method: Attention + CTC</td>
<td>
<a href = "https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.release.tar.gz">Conformer Offline Aishell Model</a>
</td>
</tr>
<tr>
<td >Encoder:Conformer, Decoder:Transformer, Decoding method: Attention</td>
<td>
<a href = "https://deepspeech.bj.bcebos.com/release2.1/librispeech/s1/conformer.release.tar.gz">Conformer Librispeech Model</a>
</td>
</tr>
<tr>
<td rowspan="2"> Librispeech</td>
<td>Encoder:Conformer, Decoder:Transformer, Decoding method: Attention</td>
<td> <a href = "https://deepspeech.bj.bcebos.com/release2.1/librispeech/s1/conformer.release.tar.gz">Conformer Librispeech Model</a> </td>
</tr>
<tr>
<td>Encoder:Transformer, Decoder:Transformer, Decoding method: Attention</td>
<td>
<a href = "https://deepspeech.bj.bcebos.com/release2.1/librispeech/s1/transformer.release.tar.gz">Transformer Librispeech Model</a>
</td>
</tr>
<tr>
<td rowspan="3">Language Model</td>
<td >CommonCrawl(en.00)</td>
<td >English Language Model</td>
<td>
<a href = "https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm">English Language Model</a>
</td>
</tr>
<tr>
<td rowspan="2">Baidu Internal Corpus</td>
<td>Mandarin Language Model Small</td>
<td>
<a href = "https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm">Mandarin Language Model Small</a>
</td>
</tr>
<tr>
<td >Mandarin Language Model Large</td>
<td>
<a href = "https://deepspeech.bj.bcebos.com/zh_lm/zhidao_giga.klm">Mandarin Language Model Large</a>
</td>
</tr>
</tbody>
</table>
PaddleSpeech TTS mainly contains three modules: *Text Frontend*, *Acoustic Model* and *Vocoder*. Acoustic Model and Vocoder models are listed as follow:
<table>
<thead>
<tr>
<th>TTS Module Type</th>
<th>Model Type</th>
<th>Dataset</th>
<th>Link</th>
</tr>
</thead>
<tbody>
<tr>
<td> Text Frontend</td>
<td colspan="2"> &emsp; </td>
<td>
<a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/other/text_frontend">chinese-fronted</a>
</td>
</tr>
<tr>
<td rowspan="7">Acoustic Model</td>
<td >Tacotron2</td>
<td rowspan="2" >LJSpeech</td>
<td>
<a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/ljspeech/tts0">tacotron2-vctk</a>
</td>
</tr>
<tr>
<td>TransformerTTS</td>
<td>
<a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/ljspeech/tts1">transformer-ljspeech</a>
</td>
</tr>
<tr>
<td>SpeedySpeech</td>
<td>CSMSC</td>
<td >
<a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/csmsc/tts2">speedyspeech-csmsc</a>
</td>
</tr>
<tr>
<td rowspan="4">FastSpeech2</td>
<td>AISHELL-3</td>
<td>
<a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/aishell3/tts3">fastspeech2-aishell3</a>
</td>
</tr>
<tr>
<td>VCTK</td>
<td> <a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/vctk/tts3">fastspeech2-vctk</a> </td>
</tr>
<tr>
<td>LJSpeech</td>
<td> <a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/ljspeech/tts3">fastspeech2-ljspeech</a> </td>
</tr>
<tr>
<td>CSMSC</td>
<td>
<a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/csmsc/tts3">fastspeech2-csmsc</a>
</td>
</tr>
<tr>
<td rowspan="4">Vocoder</td>
<td >WaveFlow</td>
<td >LJSpeech</td>
<td>
<a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/ljspeech/voc0">waveflow-ljspeech</a>
</td>
</tr>
<tr>
<td rowspan="3">Parallel WaveGAN</td>
<td >LJSpeech</td>
<td>
<a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/ljspeech/voc1">PWGAN-ljspeech</a>
</td>
</tr>
<tr>
<td >VCTK</td>
<td>
<a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/vctk/voc1">PWGAN-vctk</a>
</td>
</tr>
<tr>
<td >CSMSC</td>
<td>
<a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/csmsc/voc1">PWGAN-csmsc</a>
</td>
</tr>
<tr>
<td rowspan="2">Voice Cloning</td>
<td>GE2E</td>
<td >AISHELL-3, etc.</td>
<td>
<a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/other/ge2e">ge2e</a>
</td>
</tr>
<tr>
<td>GE2E + Tactron2</td>
<td>AISHELL-3</td>
<td>
<a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/aishell3/vc0">ge2e-tactron2-aishell3</a>
</td>
</td>
</tr>
</tbody>
</table>
## Tutorials
Normally, [Speech SoTA](https://paperswithcode.com/area/speech) gives you an overview of the hot academic topics in speech. If you want to focus on the two tasks in PaddleSpeech, you will find the following guidelines are helpful to grasp the core ideas.
The original ASR module is based on [Baidu's DeepSpeech](https://arxiv.org/abs/1412.5567) which is an independent product named [DeepSpeech](https://deepspeech.readthedocs.io). However, the toolkit aligns almost all the SoTA modules in the pipeline. Specifically, these modules are
* [Data Prepration](docs/source/asr/data_preparation.md) * [Data Prepration](docs/source/asr/data_preparation.md)
* [Data Augmentation](docs/source/asr/augmentation.md) * [Data Augmentation](docs/source/asr/augmentation.md)
...@@ -33,16 +304,18 @@ Please see [Getting Started](docs/source/asr/getting_started.md) and [tiny egs]( ...@@ -33,16 +304,18 @@ Please see [Getting Started](docs/source/asr/getting_started.md) and [tiny egs](
* [Benchmark](docs/source/asr/benchmark.md) * [Benchmark](docs/source/asr/benchmark.md)
* [Relased Model](docs/source/asr/released_model.md) * [Relased Model](docs/source/asr/released_model.md)
The TTS module is originally called [Parakeet](https://github.com/PaddlePaddle/Parakeet), and now merged with DeepSpeech. If you are interested in academic research about this function, please see [TTS research overview](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/docs/source/tts#overview). Also, [this document](https://paddleparakeet.readthedocs.io/en/latest/released_models.html) is a good guideline for the pipeline components.
## Questions and Help
You are welcome to submit questions in [Github Discussions](https://github.com/PaddlePaddle/DeepSpeech/discussions) and bug reports in [Github Issues](https://github.com/PaddlePaddle/DeepSpeech/issues). You are also welcome to contribute to this project. ## FAQ and Contributing
You are warmly welcome to submit questions in [discussions](https://github.com/PaddlePaddle/DeepSpeech/discussions) and bug reports in [issues](https://github.com/PaddlePaddle/DeepSpeech/issues)! Also, we highly appreciate if you would like to contribute to this project!
## License ## License
DeepSpeech is provided under the [Apache-2.0 License](./LICENSE). PaddleSpeech is provided under the [Apache-2.0 License](./LICENSE).
## Acknowledgement ## Acknowledgement
We depends on many open source repos. See [References](docs/source/asr/reference.md) for more information. PaddleSpeech depends on a lot of open source repos. See [references](docs/source/asr/reference.md) for more information.
deepspeech/decoders/recog.py
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...@@ -24,21 +24,23 @@ from .utils import add_results_to_json ...@@ -24,21 +24,23 @@ from .utils import add_results_to_json
from deepspeech.exps import dynamic_import_tester from deepspeech.exps import dynamic_import_tester
from deepspeech.io.reader import LoadInputsAndTargets from deepspeech.io.reader import LoadInputsAndTargets
from deepspeech.models.asr_interface import ASRInterface from deepspeech.models.asr_interface import ASRInterface
from deepspeech.models.lm_interface import dynamic_import_lm
from deepspeech.utils.log import Log from deepspeech.utils.log import Log
# from espnet.asr.asr_utils import get_model_conf
# from espnet.asr.asr_utils import torch_load
# from espnet.nets.lm_interface import dynamic_import_lm
logger = Log(__name__).getlog() logger = Log(__name__).getlog()
# NOTE: you need this func to generate our sphinx doc # NOTE: you need this func to generate our sphinx doc
def get_config(config_path):
confs = CfgNode(new_allowed=True)
confs.merge_from_file(config_path)
return confs
def load_trained_model(args): def load_trained_model(args):
args.nprocs = args.ngpu args.nprocs = args.ngpu
confs = CfgNode() confs = get_config(args.model_conf)
confs.set_new_allowed(True)
confs.merge_from_file(args.model_conf)
class_obj = dynamic_import_tester(args.model_name) class_obj = dynamic_import_tester(args.model_name)
exp = class_obj(confs, args) exp = class_obj(confs, args)
with exp.eval(): with exp.eval():
...@@ -49,6 +51,16 @@ def load_trained_model(args): ...@@ -49,6 +51,16 @@ def load_trained_model(args):
return model, char_list, exp, confs return model, char_list, exp, confs
def load_trained_lm(args):
lm_args = get_config(args.rnnlm_conf)
lm_model_module = lm_args.model_module
lm_class = dynamic_import_lm(lm_model_module)
lm = lm_class(**lm_args.model)
model_dict = paddle.load(args.rnnlm)
lm.set_state_dict(model_dict)
return lm
def recog_v2(args): def recog_v2(args):
"""Decode with custom models that implements ScorerInterface. """Decode with custom models that implements ScorerInterface.
...@@ -78,12 +90,7 @@ def recog_v2(args): ...@@ -78,12 +90,7 @@ def recog_v2(args):
preprocess_args={"train": False}, ) preprocess_args={"train": False}, )
if args.rnnlm: if args.rnnlm:
lm_args = get_model_conf(args.rnnlm, args.rnnlm_conf) lm = load_trained_lm(args)
# NOTE: for a compatibility with less than 0.5.0 version models
lm_model_module = getattr(lm_args, "model_module", "default")
lm_class = dynamic_import_lm(lm_model_module, lm_args.backend)
lm = lm_class(len(char_list), lm_args)
torch_load(args.rnnlm, lm)
lm.eval() lm.eval()
else: else:
lm = None lm = None
......
deepspeech/decoders/recog_bin.py
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...@@ -21,8 +21,6 @@ from distutils.util import strtobool ...@@ -21,8 +21,6 @@ from distutils.util import strtobool
import configargparse import configargparse
import numpy as np import numpy as np
from deepspeech.decoders.recog import recog_v2
def get_parser(): def get_parser():
"""Get default arguments.""" """Get default arguments."""
...@@ -359,7 +357,7 @@ def main(args): ...@@ -359,7 +357,7 @@ def main(args):
if args.num_encs == 1: if args.num_encs == 1:
# Experimental API that supports custom LMs # Experimental API that supports custom LMs
if args.api == "v2": if args.api == "v2":
from deepspeech.decoders.recog import recog_v2
recog_v2(args) recog_v2(args)
else: else:
raise ValueError("Only support --api v2") raise ValueError("Only support --api v2")
......
deepspeech/decoders/scorers/ctc_prefix_score.py
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...@@ -318,6 +318,18 @@ class CTCPrefixScore(): ...@@ -318,6 +318,18 @@ class CTCPrefixScore():
r[0, 0] = xs[0] r[0, 0] = xs[0]
r[0, 1] = self.logzero r[0, 1] = self.logzero
else: else:
# Although the code does not exactly follow Algorithm 2,
# we don't have to change it because we can assume
# r_t(h)=0 for t < |h| in CTC forward computation
# (Note: we assume here that index t starts with 0).
# The purpose of this difference is to reduce the number of for-loops.
# https://github.com/espnet/espnet/pull/3655
# where we start to accumulate r_t(h) from t=|h|
# and iterate r_t(h) = (r_{t-1}(h) + ...) to T-1,
# avoiding accumulating zeros for t=1~|h|-1.
# Thus, we need to set r_{|h|-1}(h) = 0,
# i.e., r[output_length-1] = logzero, for initialization.
# This is just for reducing the computation.
r[output_length - 1] = self.logzero r[output_length - 1] = self.logzero
# prepare forward probabilities for the last label # prepare forward probabilities for the last label
......
deepspeech/frontend/augmentor/augmentation.py
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...@@ -13,6 +13,7 @@ ...@@ -13,6 +13,7 @@
# limitations under the License. # limitations under the License.
"""Contains the data augmentation pipeline.""" """Contains the data augmentation pipeline."""
import json import json
import os
from collections.abc import Sequence from collections.abc import Sequence
from inspect import signature from inspect import signature
from pprint import pformat from pprint import pformat
...@@ -90,9 +91,8 @@ class AugmentationPipeline(): ...@@ -90,9 +91,8 @@ class AugmentationPipeline():
effect. effect.
Params: Params:
augmentation_config(str): Augmentation configuration in json string. preprocess_conf(str): Augmentation configuration in `json file` or `json string`.
random_seed(int): Random seed. random_seed(int): Random seed.
train(bool): whether is train mode.
Raises: Raises:
ValueError: If the augmentation json config is in incorrect format". ValueError: If the augmentation json config is in incorrect format".
...@@ -100,11 +100,18 @@ class AugmentationPipeline(): ...@@ -100,11 +100,18 @@ class AugmentationPipeline():
SPEC_TYPES = {'specaug'} SPEC_TYPES = {'specaug'}
def __init__(self, augmentation_config: str, random_seed: int=0): def __init__(self, preprocess_conf: str, random_seed: int=0):
self._rng = np.random.RandomState(random_seed) self._rng = np.random.RandomState(random_seed)
self.conf = {'mode': 'sequential', 'process': []} self.conf = {'mode': 'sequential', 'process': []}
if augmentation_config: if preprocess_conf:
process = json.loads(augmentation_config) if os.path.isfile(preprocess_conf):
# json file
with open(preprocess_conf, 'r') as fin:
json_string = fin.read()
else:
# json string
json_string = preprocess_conf
process = json.loads(json_string)
self.conf['process'] += process self.conf['process'] += process
self._augmentors, self._rates = self._parse_pipeline_from('all') self._augmentors, self._rates = self._parse_pipeline_from('all')
......
deepspeech/io/collator.py
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...@@ -105,7 +105,7 @@ class SpeechCollatorBase(): ...@@ -105,7 +105,7 @@ class SpeechCollatorBase():
self._local_data = TarLocalData(tar2info={}, tar2object={}) self._local_data = TarLocalData(tar2info={}, tar2object={})
self.augmentation = AugmentationPipeline( self.augmentation = AugmentationPipeline(
augmentation_config=aug_file.read(), random_seed=random_seed) preprocess_conf=aug_file.read(), random_seed=random_seed)
self._normalizer = FeatureNormalizer( self._normalizer = FeatureNormalizer(
mean_std_filepath) if mean_std_filepath else None mean_std_filepath) if mean_std_filepath else None
......
deepspeech/io/reader.py
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...@@ -17,7 +17,7 @@ import kaldiio ...@@ -17,7 +17,7 @@ import kaldiio
import numpy as np import numpy as np
import soundfile import soundfile
from deepspeech.frontend.augmentor.augmentation import AugmentationPipeline from deepspeech.frontend.augmentor.augmentation import AugmentationPipeline as Transformation
from deepspeech.utils.log import Log from deepspeech.utils.log import Log
__all__ = ["LoadInputsAndTargets"] __all__ = ["LoadInputsAndTargets"]
...@@ -66,8 +66,7 @@ class LoadInputsAndTargets(): ...@@ -66,8 +66,7 @@ class LoadInputsAndTargets():
raise ValueError("Only asr are allowed: mode={}".format(mode)) raise ValueError("Only asr are allowed: mode={}".format(mode))
if preprocess_conf is not None: if preprocess_conf is not None:
with open(preprocess_conf, 'r') as fin: self.preprocessing = Transformation(preprocess_conf)
self.preprocessing = AugmentationPipeline(fin.read())
logger.warning( logger.warning(
"[Experimental feature] Some preprocessing will be done " "[Experimental feature] Some preprocessing will be done "
"for the mini-batch creation using {}".format( "for the mini-batch creation using {}".format(
......
deepspeech/models/asr_interface.py
浏览文件 @ 950d17cb
...@@ -18,7 +18,7 @@ from deepspeech.utils.dynamic_import import dynamic_import ...@@ -18,7 +18,7 @@ from deepspeech.utils.dynamic_import import dynamic_import
class ASRInterface: class ASRInterface:
"""ASR Interface for ESPnet model implementation.""" """ASR Interface model implementation."""
@staticmethod @staticmethod
def add_arguments(parser): def add_arguments(parser):
...@@ -103,14 +103,14 @@ class ASRInterface: ...@@ -103,14 +103,14 @@ class ASRInterface:
@property @property
def attention_plot_class(self): def attention_plot_class(self):
"""Get attention plot class.""" """Get attention plot class."""
from espnet.asr.asr_utils import PlotAttentionReport from deepspeech.training.extensions.plot import PlotAttentionReport
return PlotAttentionReport return PlotAttentionReport
@property @property
def ctc_plot_class(self): def ctc_plot_class(self):
"""Get CTC plot class.""" """Get CTC plot class."""
from espnet.asr.asr_utils import PlotCTCReport from deepspeech.training.extensions.plot import PlotCTCReport
return PlotCTCReport return PlotCTCReport
......
deepspeech/models/lm/__init__.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
deepspeech/models/lm/transformer.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any
from typing import List
from typing import Tuple
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from deepspeech.decoders.scorers.scorer_interface import BatchScorerInterface
from deepspeech.models.lm_interface import LMInterface
from deepspeech.modules.encoder import TransformerEncoder
from deepspeech.modules.mask import subsequent_mask
from deepspeech.utils.log import Log
logger = Log(__name__).getlog()
class TransformerLM(nn.Layer, LMInterface, BatchScorerInterface):
def __init__(self,
n_vocab: int,
pos_enc: str=None,
embed_unit: int=128,
att_unit: int=256,
head: int=2,
unit: int=1024,
layer: int=4,
dropout_rate: float=0.5,
emb_dropout_rate: float=0.0,
att_dropout_rate: float=0.0,
tie_weights: bool=False,
**kwargs):
nn.Layer.__init__(self)
if pos_enc == "sinusoidal":
pos_enc_layer_type = "abs_pos"
elif pos_enc is None:
pos_enc_layer_type = "no_pos"
else:
raise ValueError(f"unknown pos-enc option: {pos_enc}")
self.embed = nn.Embedding(n_vocab, embed_unit)
if emb_dropout_rate == 0.0:
self.embed_drop = None
else:
self.embed_drop = nn.Dropout(emb_dropout_rate)
self.encoder = TransformerEncoder(
input_size=embed_unit,
output_size=att_unit,
attention_heads=head,
linear_units=unit,
num_blocks=layer,
dropout_rate=dropout_rate,
attention_dropout_rate=att_dropout_rate,
input_layer="linear",
pos_enc_layer_type=pos_enc_layer_type,
concat_after=False,
static_chunk_size=1,
use_dynamic_chunk=False,
use_dynamic_left_chunk=False)
self.decoder = nn.Linear(att_unit, n_vocab)
logger.info("Tie weights set to {}".format(tie_weights))
logger.info("Dropout set to {}".format(dropout_rate))
logger.info("Emb Dropout set to {}".format(emb_dropout_rate))
logger.info("Att Dropout set to {}".format(att_dropout_rate))
if tie_weights:
assert (
att_unit == embed_unit
), "Tie Weights: True need embedding and final dimensions to match"
self.decoder.weight = self.embed.weight
def _target_mask(self, ys_in_pad):
ys_mask = ys_in_pad != 0
m = subsequent_mask(ys_mask.size(-1)).unsqueeze(0)
return ys_mask.unsqueeze(-2) & m
def forward(self, x: paddle.Tensor, t: paddle.Tensor
) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
"""Compute LM loss value from buffer sequences.
Args:
x (paddle.Tensor): Input ids. (batch, len)
t (paddle.Tensor): Target ids. (batch, len)
Returns:
tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]: Tuple of
loss to backward (scalar),
negative log-likelihood of t: -log p(t) (scalar) and
the number of elements in x (scalar)
Notes:
The last two return values are used
in perplexity: p(t)^{-n} = exp(-log p(t) / n)
"""
xm = x != 0
xlen = xm.sum(axis=1)
if self.embed_drop is not None:
emb = self.embed_drop(self.embed(x))
else:
emb = self.embed(x)
h, _ = self.encoder(emb, xlen)
y = self.decoder(h)
loss = F.cross_entropy(
y.view(-1, y.shape[-1]), t.view(-1), reduction="none")
mask = xm.to(dtype=loss.dtype)
logp = loss * mask.view(-1)
logp = logp.sum()
count = mask.sum()
return logp / count, logp, count
# beam search API (see ScorerInterface)
def score(self, y: paddle.Tensor, state: Any,
x: paddle.Tensor) -> Tuple[paddle.Tensor, Any]:
"""Score new token.
Args:
y (paddle.Tensor): 1D paddle.int64 prefix tokens.
state: Scorer state for prefix tokens
x (paddle.Tensor): encoder feature that generates ys.
Returns:
tuple[paddle.Tensor, Any]: Tuple of
paddle.float32 scores for next token (n_vocab)
and next state for ys
"""
y = y.unsqueeze(0)
if self.embed_drop is not None:
emb = self.embed_drop(self.embed(y))
else:
emb = self.embed(y)
h, _, cache = self.encoder.forward_one_step(
emb, self._target_mask(y), cache=state)
h = self.decoder(h[:, -1])
logp = F.log_softmax(h).squeeze(0)
return logp, cache
# batch beam search API (see BatchScorerInterface)
def batch_score(self,
ys: paddle.Tensor,
states: List[Any],
xs: paddle.Tensor) -> Tuple[paddle.Tensor, List[Any]]:
"""Score new token batch (required).
Args:
ys (paddle.Tensor): paddle.int64 prefix tokens (n_batch, ylen).
states (List[Any]): Scorer states for prefix tokens.
xs (paddle.Tensor):
The encoder feature that generates ys (n_batch, xlen, n_feat).
Returns:
tuple[paddle.Tensor, List[Any]]: Tuple of
batchfied scores for next token with shape of `(n_batch, n_vocab)`
and next state list for ys.
"""
# merge states
n_batch = len(ys)
n_layers = len(self.encoder.encoders)
if states[0] is None:
batch_state = None
else:
# transpose state of [batch, layer] into [layer, batch]
batch_state = [
paddle.stack([states[b][i] for b in range(n_batch)])
for i in range(n_layers)
]
if self.embed_drop is not None:
emb = self.embed_drop(self.embed(ys))
else:
emb = self.embed(ys)
# batch decoding
h, _, states = self.encoder.forward_one_step(
emb, self._target_mask(ys), cache=batch_state)
h = self.decoder(h[:, -1])
logp = F.log_softmax(h)
# transpose state of [layer, batch] into [batch, layer]
state_list = [[states[i][b] for i in range(n_layers)]
for b in range(n_batch)]
return logp, state_list
if __name__ == "__main__":
tlm = TransformerLM(
n_vocab=5002,
pos_enc=None,
embed_unit=128,
att_unit=512,
head=8,
unit=2048,
layer=16,
dropout_rate=0.5, )
# n_vocab: int,
# pos_enc: str=None,
# embed_unit: int=128,
# att_unit: int=256,
# head: int=2,
# unit: int=1024,
# layer: int=4,
# dropout_rate: float=0.5,
# emb_dropout_rate: float = 0.0,
# att_dropout_rate: float = 0.0,
# tie_weights: bool = False,):
paddle.set_device("cpu")
model_dict = paddle.load("transformerLM.pdparams")
tlm.set_state_dict(model_dict)
tlm.eval()
#Test the score
input2 = np.array([5])
input2 = paddle.to_tensor(input2)
state = None
output, state = tlm.score(input2, state, None)
input3 = np.array([5, 10])
input3 = paddle.to_tensor(input3)
output, state = tlm.score(input3, state, None)
input4 = np.array([5, 10, 0])
input4 = paddle.to_tensor(input4)
output, state = tlm.score(input4, state, None)
print("output", output)
"""
#Test the batch score
batch_size = 2
inp2 = np.array([[5], [10]])
inp2 = paddle.to_tensor(inp2)
output, states = tlm.batch_score(
inp2, [(None,None,0)] * batch_size)
inp3 = np.array([[100], [30]])
inp3 = paddle.to_tensor(inp3)
output, states = tlm.batch_score(
inp3, states)
print("output", output)
#print("cache", cache)
#np.save("output_pd.npy", output)
"""
deepspeech/models/lm_interface.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Language model interface."""
import argparse
from deepspeech.decoders.scorers.scorer_interface import ScorerInterface
from deepspeech.utils.dynamic_import import dynamic_import
class LMInterface(ScorerInterface):
"""LM Interface model implementation."""
@staticmethod
def add_arguments(parser):
"""Add arguments to command line argument parser."""
return parser
@classmethod
def build(cls, n_vocab: int, **kwargs):
"""Initialize this class with python-level args.
Args:
idim (int): The number of vocabulary.
Returns:
LMinterface: A new instance of LMInterface.
"""
args = argparse.Namespace(**kwargs)
return cls(n_vocab, args)
def forward(self, x, t):
"""Compute LM loss value from buffer sequences.
Args:
x (torch.Tensor): Input ids. (batch, len)
t (torch.Tensor): Target ids. (batch, len)
Returns:
tuple[torch.Tensor, torch.Tensor, torch.Tensor]: Tuple of
loss to backward (scalar),
negative log-likelihood of t: -log p(t) (scalar) and
the number of elements in x (scalar)
Notes:
The last two return values are used
in perplexity: p(t)^{-n} = exp(-log p(t) / n)
"""
raise NotImplementedError("forward method is not implemented")
predefined_lms = {
"transformer": "deepspeech.models.lm.transformer:TransformerLM",
}
def dynamic_import_lm(module):
"""Import LM class dynamically.
Args:
module (str): module_name:class_name or alias in `predefined_lms`
Returns:
type: LM class
"""
model_class = dynamic_import(module, predefined_lms)
assert issubclass(model_class,
LMInterface), f"{module} does not implement LMInterface"
return model_class
deepspeech/models/st_interface.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""ST Interface module."""
from .asr_interface import ASRInterface
from deepspeech.utils.dynamic_import import dynamic_import
class STInterface(ASRInterface):
"""ST Interface model implementation.
NOTE: This class is inherited from ASRInterface to enable joint translation
and recognition when performing multi-task learning with the ASR task.
"""
def translate(self,
x,
trans_args,
char_list=None,
rnnlm=None,
ensemble_models=[]):
"""Recognize x for evaluation.
:param ndarray x: input acouctic feature (B, T, D) or (T, D)
:param namespace trans_args: argment namespace contraining options
:param list char_list: list of characters
:param paddle.nn.Layer rnnlm: language model module
:return: N-best decoding results
:rtype: list
"""
raise NotImplementedError("translate method is not implemented")
def translate_batch(self, x, trans_args, char_list=None, rnnlm=None):
"""Beam search implementation for batch.
:param paddle.Tensor x: encoder hidden state sequences (B, Tmax, Henc)
:param namespace trans_args: argument namespace containing options
:param list char_list: list of characters
:param paddle.nn.Layer rnnlm: language model module
:return: N-best decoding results
:rtype: list
"""
raise NotImplementedError("Batch decoding is not supported yet.")
predefined_st = {
"transformer": "deepspeech.models.u2_st:U2STModel",
}
def dynamic_import_st(module):
"""Import ST models dynamically.
Args:
module (str): module_name:class_name or alias in `predefined_st`
Returns:
type: ST class
"""
model_class = dynamic_import(module, predefined_st)
assert issubclass(model_class,
STInterface), f"{module} does not implement STInterface"
return model_class
deepspeech/models/u2_st/__init__.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .u2_st import U2STInferModel
from .u2_st import U2STModel
deepspeech/modules/embedding.py
浏览文件 @ 950d17cb
...@@ -22,10 +22,52 @@ from deepspeech.utils.log import Log ...@@ -22,10 +22,52 @@ from deepspeech.utils.log import Log
logger = Log(__name__).getlog() logger = Log(__name__).getlog()
__all__ = ["PositionalEncoding", "RelPositionalEncoding"] __all__ = [
"PositionalEncodingInterface", "NoPositionalEncoding", "PositionalEncoding",
"RelPositionalEncoding"
]
class PositionalEncoding(nn.Layer): class PositionalEncodingInterface:
def forward(self, x: paddle.Tensor,
offset: int=0) -> Tuple[paddle.Tensor, paddle.Tensor]:
"""Compute positional encoding.
Args:
x (paddle.Tensor): Input tensor (batch, time, `*`).
Returns:
paddle.Tensor: Encoded tensor (batch, time, `*`).
paddle.Tensor: Positional embedding tensor (1, time, `*`).
"""
raise NotImplementedError("forward method is not implemented")
def position_encoding(self, offset: int, size: int) -> paddle.Tensor:
""" For getting encoding in a streaming fashion
Args:
offset (int): start offset
size (int): requried size of position encoding
Returns:
paddle.Tensor: Corresponding position encoding
"""
raise NotImplementedError("position_encoding method is not implemented")
class NoPositionalEncoding(nn.Layer, PositionalEncodingInterface):
def __init__(self,
d_model: int,
dropout_rate: float,
max_len: int=5000,
reverse: bool=False):
nn.Layer.__init__(self)
def forward(self, x: paddle.Tensor,
offset: int=0) -> Tuple[paddle.Tensor, paddle.Tensor]:
return x, None
def position_encoding(self, offset: int, size: int) -> paddle.Tensor:
return None
class PositionalEncoding(nn.Layer, PositionalEncodingInterface):
def __init__(self, def __init__(self,
d_model: int, d_model: int,
dropout_rate: float, dropout_rate: float,
...@@ -40,7 +82,7 @@ class PositionalEncoding(nn.Layer): ...@@ -40,7 +82,7 @@ class PositionalEncoding(nn.Layer):
max_len (int, optional): maximum input length. Defaults to 5000. max_len (int, optional): maximum input length. Defaults to 5000.
reverse (bool, optional): Not used. Defaults to False. reverse (bool, optional): Not used. Defaults to False.
""" """
super().__init__() nn.Layer.__init__(self)
self.d_model = d_model self.d_model = d_model
self.max_len = max_len self.max_len = max_len
self.xscale = paddle.to_tensor(math.sqrt(self.d_model)) self.xscale = paddle.to_tensor(math.sqrt(self.d_model))
...@@ -85,7 +127,7 @@ class PositionalEncoding(nn.Layer): ...@@ -85,7 +127,7 @@ class PositionalEncoding(nn.Layer):
offset (int): start offset offset (int): start offset
size (int): requried size of position encoding size (int): requried size of position encoding
Returns: Returns:
paddle.Tensor: Corresponding encoding paddle.Tensor: Corresponding position encoding
""" """
assert offset + size < self.max_len assert offset + size < self.max_len
return self.dropout(self.pe[:, offset:offset + size]) return self.dropout(self.pe[:, offset:offset + size])
......
deepspeech/modules/encoder.py
浏览文件 @ 950d17cb
...@@ -24,6 +24,7 @@ from deepspeech.modules.activation import get_activation ...@@ -24,6 +24,7 @@ from deepspeech.modules.activation import get_activation
from deepspeech.modules.attention import MultiHeadedAttention from deepspeech.modules.attention import MultiHeadedAttention
from deepspeech.modules.attention import RelPositionMultiHeadedAttention from deepspeech.modules.attention import RelPositionMultiHeadedAttention
from deepspeech.modules.conformer_convolution import ConvolutionModule from deepspeech.modules.conformer_convolution import ConvolutionModule
from deepspeech.modules.embedding import NoPositionalEncoding
from deepspeech.modules.embedding import PositionalEncoding from deepspeech.modules.embedding import PositionalEncoding
from deepspeech.modules.embedding import RelPositionalEncoding from deepspeech.modules.embedding import RelPositionalEncoding
from deepspeech.modules.encoder_layer import ConformerEncoderLayer from deepspeech.modules.encoder_layer import ConformerEncoderLayer
...@@ -76,7 +77,7 @@ class BaseEncoder(nn.Layer): ...@@ -76,7 +77,7 @@ class BaseEncoder(nn.Layer):
input_layer (str): input layer type. input_layer (str): input layer type.
optional [linear, conv2d, conv2d6, conv2d8] optional [linear, conv2d, conv2d6, conv2d8]
pos_enc_layer_type (str): Encoder positional encoding layer type. pos_enc_layer_type (str): Encoder positional encoding layer type.
opitonal [abs_pos, scaled_abs_pos, rel_pos] opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos]
normalize_before (bool): normalize_before (bool):
True: use layer_norm before each sub-block of a layer. True: use layer_norm before each sub-block of a layer.
False: use layer_norm after each sub-block of a layer. False: use layer_norm after each sub-block of a layer.
...@@ -101,6 +102,8 @@ class BaseEncoder(nn.Layer): ...@@ -101,6 +102,8 @@ class BaseEncoder(nn.Layer):
pos_enc_class = PositionalEncoding pos_enc_class = PositionalEncoding
elif pos_enc_layer_type == "rel_pos": elif pos_enc_layer_type == "rel_pos":
pos_enc_class = RelPositionalEncoding pos_enc_class = RelPositionalEncoding
elif pos_enc_layer_type == "no_pos":
pos_enc_class = NoPositionalEncoding
else: else:
raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type) raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
...@@ -370,6 +373,41 @@ class TransformerEncoder(BaseEncoder): ...@@ -370,6 +373,41 @@ class TransformerEncoder(BaseEncoder):
concat_after=concat_after) for _ in range(num_blocks) concat_after=concat_after) for _ in range(num_blocks)
]) ])
def forward_one_step(
self,
xs: paddle.Tensor,
masks: paddle.Tensor,
cache=None, ) -> Tuple[paddle.Tensor, paddle.Tensor]:
"""Encode input frame.
Args:
xs (paddle.Tensor): (Prefix) Input tensor. (B, T, D)
masks (paddle.Tensor): Mask tensor. (B, T, T)
cache (List[paddle.Tensor]): List of cache tensors.
Returns:
paddle.Tensor: Output tensor.
paddle.Tensor: Mask tensor.
List[paddle.Tensor]: List of new cache tensors.
"""
if self.global_cmvn is not None:
xs = self.global_cmvn(xs)
#TODO(Hui Zhang): self.embed(xs, masks, offset=0), stride_slice not support bool tensor
xs, pos_emb, masks = self.embed(xs, masks.astype(xs.dtype), offset=0)
#TODO(Hui Zhang): remove mask.astype, stride_slice not support bool tensor
masks = masks.astype(paddle.bool)
if cache is None:
cache = [None for _ in range(len(self.encoders))]
new_cache = []
for c, e in zip(cache, self.encoders):
xs, masks, _ = e(xs, masks, output_cache=c)
new_cache.append(xs)
if self.normalize_before:
xs = self.after_norm(xs)
return xs, masks, new_cache
class ConformerEncoder(BaseEncoder): class ConformerEncoder(BaseEncoder):
"""Conformer encoder module.""" """Conformer encoder module."""
......
deepspeech/modules/encoder_layer.py
浏览文件 @ 950d17cb
...@@ -71,7 +71,7 @@ class TransformerEncoderLayer(nn.Layer): ...@@ -71,7 +71,7 @@ class TransformerEncoderLayer(nn.Layer):
self, self,
x: paddle.Tensor, x: paddle.Tensor,
mask: paddle.Tensor, mask: paddle.Tensor,
pos_emb: paddle.Tensor, pos_emb: Optional[paddle.Tensor]=None,
mask_pad: Optional[paddle.Tensor]=None, mask_pad: Optional[paddle.Tensor]=None,
output_cache: Optional[paddle.Tensor]=None, output_cache: Optional[paddle.Tensor]=None,
cnn_cache: Optional[paddle.Tensor]=None, cnn_cache: Optional[paddle.Tensor]=None,
...@@ -82,8 +82,8 @@ class TransformerEncoderLayer(nn.Layer): ...@@ -82,8 +82,8 @@ class TransformerEncoderLayer(nn.Layer):
mask (paddle.Tensor): Mask tensor for the input (#batch, time). mask (paddle.Tensor): Mask tensor for the input (#batch, time).
pos_emb (paddle.Tensor): just for interface compatibility pos_emb (paddle.Tensor): just for interface compatibility
to ConformerEncoderLayer to ConformerEncoderLayer
mask_pad (paddle.Tensor): does not used in transformer layer, mask_pad (paddle.Tensor): not used here, it's for interface
just for unified api with conformer. compatibility to ConformerEncoderLayer
output_cache (paddle.Tensor): Cache tensor of the output output_cache (paddle.Tensor): Cache tensor of the output
(#batch, time2, size), time2 < time in x. (#batch, time2, size), time2 < time in x.
cnn_cache (paddle.Tensor): not used here, it's for interface cnn_cache (paddle.Tensor): not used here, it's for interface
......
deepspeech/modules/subsampling.py
浏览文件 @ 950d17cb
...@@ -60,7 +60,8 @@ class LinearNoSubsampling(BaseSubsampling): ...@@ -60,7 +60,8 @@ class LinearNoSubsampling(BaseSubsampling):
self.out = nn.Sequential( self.out = nn.Sequential(
nn.Linear(idim, odim), nn.Linear(idim, odim),
nn.LayerNorm(odim, epsilon=1e-12), nn.LayerNorm(odim, epsilon=1e-12),
nn.Dropout(dropout_rate), ) nn.Dropout(dropout_rate),
nn.ReLU(), )
self.right_context = 0 self.right_context = 0
self.subsampling_rate = 1 self.subsampling_rate = 1
...@@ -83,7 +84,12 @@ class LinearNoSubsampling(BaseSubsampling): ...@@ -83,7 +84,12 @@ class LinearNoSubsampling(BaseSubsampling):
return x, pos_emb, x_mask return x, pos_emb, x_mask
class Conv2dSubsampling4(BaseSubsampling): class Conv2dSubsampling(BaseSubsampling):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
class Conv2dSubsampling4(Conv2dSubsampling):
"""Convolutional 2D subsampling (to 1/4 length).""" """Convolutional 2D subsampling (to 1/4 length)."""
def __init__(self, def __init__(self,
...@@ -134,7 +140,7 @@ class Conv2dSubsampling4(BaseSubsampling): ...@@ -134,7 +140,7 @@ class Conv2dSubsampling4(BaseSubsampling):
return x, pos_emb, x_mask[:, :, :-2:2][:, :, :-2:2] return x, pos_emb, x_mask[:, :, :-2:2][:, :, :-2:2]
class Conv2dSubsampling6(BaseSubsampling): class Conv2dSubsampling6(Conv2dSubsampling):
"""Convolutional 2D subsampling (to 1/6 length).""" """Convolutional 2D subsampling (to 1/6 length)."""
def __init__(self, def __init__(self,
...@@ -187,7 +193,7 @@ class Conv2dSubsampling6(BaseSubsampling): ...@@ -187,7 +193,7 @@ class Conv2dSubsampling6(BaseSubsampling):
return x, pos_emb, x_mask[:, :, :-2:2][:, :, :-4:3] return x, pos_emb, x_mask[:, :, :-2:2][:, :, :-4:3]
class Conv2dSubsampling8(BaseSubsampling): class Conv2dSubsampling8(Conv2dSubsampling):
"""Convolutional 2D subsampling (to 1/8 length).""" """Convolutional 2D subsampling (to 1/8 length)."""
def __init__(self, def __init__(self,
......
deepspeech/training/extensions/plot.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import os
import numpy as np
from . import extension
class PlotAttentionReport(extension.Extension):
"""Plot attention reporter.
Args:
att_vis_fn (espnet.nets.*_backend.e2e_asr.E2E.calculate_all_attentions):
Function of attention visualization.
data (list[tuple(str, dict[str, list[Any]])]): List json utt key items.
outdir (str): Directory to save figures.
converter (espnet.asr.*_backend.asr.CustomConverter):
Function to convert data.
device (int | torch.device): Device.
reverse (bool): If True, input and output length are reversed.
ikey (str): Key to access input
(for ASR/ST ikey="input", for MT ikey="output".)
iaxis (int): Dimension to access input
(for ASR/ST iaxis=0, for MT iaxis=1.)
okey (str): Key to access output
(for ASR/ST okey="input", MT okay="output".)
oaxis (int): Dimension to access output
(for ASR/ST oaxis=0, for MT oaxis=0.)
subsampling_factor (int): subsampling factor in encoder
"""
def __init__(
self,
att_vis_fn,
data,
outdir,
converter,
transform,
device,
reverse=False,
ikey="input",
iaxis=0,
okey="output",
oaxis=0,
subsampling_factor=1, ):
self.att_vis_fn = att_vis_fn
self.data = copy.deepcopy(data)
self.data_dict = {k: v for k, v in copy.deepcopy(data)}
# key is utterance ID
self.outdir = outdir
self.converter = converter
self.transform = transform
self.device = device
self.reverse = reverse
self.ikey = ikey
self.iaxis = iaxis
self.okey = okey
self.oaxis = oaxis
self.factor = subsampling_factor
if not os.path.exists(self.outdir):
os.makedirs(self.outdir)
def __call__(self, trainer):
"""Plot and save image file of att_ws matrix."""
att_ws, uttid_list = self.get_attention_weights()
if isinstance(att_ws, list): # multi-encoder case
num_encs = len(att_ws) - 1
# atts
for i in range(num_encs):
for idx, att_w in enumerate(att_ws[i]):
filename = "%s/%s.ep.{.updater.epoch}.att%d.png" % (
self.outdir, uttid_list[idx], i + 1, )
att_w = self.trim_attention_weight(uttid_list[idx], att_w)
np_filename = "%s/%s.ep.{.updater.epoch}.att%d.npy" % (
self.outdir, uttid_list[idx], i + 1, )
np.save(np_filename.format(trainer), att_w)
self._plot_and_save_attention(att_w,
filename.format(trainer))
# han
for idx, att_w in enumerate(att_ws[num_encs]):
filename = "%s/%s.ep.{.updater.epoch}.han.png" % (
self.outdir, uttid_list[idx], )
att_w = self.trim_attention_weight(uttid_list[idx], att_w)
np_filename = "%s/%s.ep.{.updater.epoch}.han.npy" % (
self.outdir, uttid_list[idx], )
np.save(np_filename.format(trainer), att_w)
self._plot_and_save_attention(
att_w, filename.format(trainer), han_mode=True)
else:
for idx, att_w in enumerate(att_ws):
filename = "%s/%s.ep.{.updater.epoch}.png" % (self.outdir,
uttid_list[idx], )
att_w = self.trim_attention_weight(uttid_list[idx], att_w)
np_filename = "%s/%s.ep.{.updater.epoch}.npy" % (
self.outdir, uttid_list[idx], )
np.save(np_filename.format(trainer), att_w)
self._plot_and_save_attention(att_w, filename.format(trainer))
def log_attentions(self, logger, step):
"""Add image files of att_ws matrix to the tensorboard."""
att_ws, uttid_list = self.get_attention_weights()
if isinstance(att_ws, list): # multi-encoder case
num_encs = len(att_ws) - 1
# atts
for i in range(num_encs):
for idx, att_w in enumerate(att_ws[i]):
att_w = self.trim_attention_weight(uttid_list[idx], att_w)
plot = self.draw_attention_plot(att_w)
logger.add_figure(
"%s_att%d" % (uttid_list[idx], i + 1),
plot.gcf(),
step, )
# han
for idx, att_w in enumerate(att_ws[num_encs]):
att_w = self.trim_attention_weight(uttid_list[idx], att_w)
plot = self.draw_han_plot(att_w)
logger.add_figure(
"%s_han" % (uttid_list[idx]),
plot.gcf(),
step, )
else:
for idx, att_w in enumerate(att_ws):
att_w = self.trim_attention_weight(uttid_list[idx], att_w)
plot = self.draw_attention_plot(att_w)
logger.add_figure("%s" % (uttid_list[idx]), plot.gcf(), step)
def get_attention_weights(self):
"""Return attention weights.
Returns:
numpy.ndarray: attention weights. float. Its shape would be
differ from backend.
* pytorch-> 1) multi-head case => (B, H, Lmax, Tmax), 2)
other case => (B, Lmax, Tmax).
* chainer-> (B, Lmax, Tmax)
"""
return_batch, uttid_list = self.transform(self.data, return_uttid=True)
batch = self.converter([return_batch], self.device)
if isinstance(batch, tuple):
att_ws = self.att_vis_fn(*batch)
else:
att_ws = self.att_vis_fn(**batch)
return att_ws, uttid_list
def trim_attention_weight(self, uttid, att_w):
"""Transform attention matrix with regard to self.reverse."""
if self.reverse:
enc_key, enc_axis = self.okey, self.oaxis
dec_key, dec_axis = self.ikey, self.iaxis
else:
enc_key, enc_axis = self.ikey, self.iaxis
dec_key, dec_axis = self.okey, self.oaxis
dec_len = int(self.data_dict[uttid][dec_key][dec_axis]["shape"][0])
enc_len = int(self.data_dict[uttid][enc_key][enc_axis]["shape"][0])
if self.factor > 1:
enc_len //= self.factor
if len(att_w.shape) == 3:
att_w = att_w[:, :dec_len, :enc_len]
else:
att_w = att_w[:dec_len, :enc_len]
return att_w
def draw_attention_plot(self, att_w):
"""Plot the att_w matrix.
Returns:
matplotlib.pyplot: pyplot object with attention matrix image.
"""
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
plt.clf()
att_w = att_w.astype(np.float32)
if len(att_w.shape) == 3:
for h, aw in enumerate(att_w, 1):
plt.subplot(1, len(att_w), h)
plt.imshow(aw, aspect="auto")
plt.xlabel("Encoder Index")
plt.ylabel("Decoder Index")
else:
plt.imshow(att_w, aspect="auto")
plt.xlabel("Encoder Index")
plt.ylabel("Decoder Index")
plt.tight_layout()
return plt
def draw_han_plot(self, att_w):
"""Plot the att_w matrix for hierarchical attention.
Returns:
matplotlib.pyplot: pyplot object with attention matrix image.
"""
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
plt.clf()
if len(att_w.shape) == 3:
for h, aw in enumerate(att_w, 1):
legends = []
plt.subplot(1, len(att_w), h)
for i in range(aw.shape[1]):
plt.plot(aw[:, i])
legends.append("Att{}".format(i))
plt.ylim([0, 1.0])
plt.xlim([0, aw.shape[0]])
plt.grid(True)
plt.ylabel("Attention Weight")
plt.xlabel("Decoder Index")
plt.legend(legends)
else:
legends = []
for i in range(att_w.shape[1]):
plt.plot(att_w[:, i])
legends.append("Att{}".format(i))
plt.ylim([0, 1.0])
plt.xlim([0, att_w.shape[0]])
plt.grid(True)
plt.ylabel("Attention Weight")
plt.xlabel("Decoder Index")
plt.legend(legends)
plt.tight_layout()
return plt
def _plot_and_save_attention(self, att_w, filename, han_mode=False):
if han_mode:
plt = self.draw_han_plot(att_w)
else:
plt = self.draw_attention_plot(att_w)
plt.savefig(filename)
plt.close()
class PlotCTCReport(extension.Extension):
"""Plot CTC reporter.
Args:
ctc_vis_fn (espnet.nets.*_backend.e2e_asr.E2E.calculate_all_ctc_probs):
Function of CTC visualization.
data (list[tuple(str, dict[str, list[Any]])]): List json utt key items.
outdir (str): Directory to save figures.
converter (espnet.asr.*_backend.asr.CustomConverter):
Function to convert data.
device (int | torch.device): Device.
reverse (bool): If True, input and output length are reversed.
ikey (str): Key to access input
(for ASR/ST ikey="input", for MT ikey="output".)
iaxis (int): Dimension to access input
(for ASR/ST iaxis=0, for MT iaxis=1.)
okey (str): Key to access output
(for ASR/ST okey="input", MT okay="output".)
oaxis (int): Dimension to access output
(for ASR/ST oaxis=0, for MT oaxis=0.)
subsampling_factor (int): subsampling factor in encoder
"""
def __init__(
self,
ctc_vis_fn,
data,
outdir,
converter,
transform,
device,
reverse=False,
ikey="input",
iaxis=0,
okey="output",
oaxis=0,
subsampling_factor=1, ):
self.ctc_vis_fn = ctc_vis_fn
self.data = copy.deepcopy(data)
self.data_dict = {k: v for k, v in copy.deepcopy(data)}
# key is utterance ID
self.outdir = outdir
self.converter = converter
self.transform = transform
self.device = device
self.reverse = reverse
self.ikey = ikey
self.iaxis = iaxis
self.okey = okey
self.oaxis = oaxis
self.factor = subsampling_factor
if not os.path.exists(self.outdir):
os.makedirs(self.outdir)
def __call__(self, trainer):
"""Plot and save image file of ctc prob."""
ctc_probs, uttid_list = self.get_ctc_probs()
if isinstance(ctc_probs, list): # multi-encoder case
num_encs = len(ctc_probs) - 1
for i in range(num_encs):
for idx, ctc_prob in enumerate(ctc_probs[i]):
filename = "%s/%s.ep.{.updater.epoch}.ctc%d.png" % (
self.outdir, uttid_list[idx], i + 1, )
ctc_prob = self.trim_ctc_prob(uttid_list[idx], ctc_prob)
np_filename = "%s/%s.ep.{.updater.epoch}.ctc%d.npy" % (
self.outdir, uttid_list[idx], i + 1, )
np.save(np_filename.format(trainer), ctc_prob)
self._plot_and_save_ctc(ctc_prob, filename.format(trainer))
else:
for idx, ctc_prob in enumerate(ctc_probs):
filename = "%s/%s.ep.{.updater.epoch}.png" % (self.outdir,
uttid_list[idx], )
ctc_prob = self.trim_ctc_prob(uttid_list[idx], ctc_prob)
np_filename = "%s/%s.ep.{.updater.epoch}.npy" % (
self.outdir, uttid_list[idx], )
np.save(np_filename.format(trainer), ctc_prob)
self._plot_and_save_ctc(ctc_prob, filename.format(trainer))
def log_ctc_probs(self, logger, step):
"""Add image files of ctc probs to the tensorboard."""
ctc_probs, uttid_list = self.get_ctc_probs()
if isinstance(ctc_probs, list): # multi-encoder case
num_encs = len(ctc_probs) - 1
for i in range(num_encs):
for idx, ctc_prob in enumerate(ctc_probs[i]):
ctc_prob = self.trim_ctc_prob(uttid_list[idx], ctc_prob)
plot = self.draw_ctc_plot(ctc_prob)
logger.add_figure(
"%s_ctc%d" % (uttid_list[idx], i + 1),
plot.gcf(),
step, )
else:
for idx, ctc_prob in enumerate(ctc_probs):
ctc_prob = self.trim_ctc_prob(uttid_list[idx], ctc_prob)
plot = self.draw_ctc_plot(ctc_prob)
logger.add_figure("%s" % (uttid_list[idx]), plot.gcf(), step)
def get_ctc_probs(self):
"""Return CTC probs.
Returns:
numpy.ndarray: CTC probs. float. Its shape would be
differ from backend. (B, Tmax, vocab).
"""
return_batch, uttid_list = self.transform(self.data, return_uttid=True)
batch = self.converter([return_batch], self.device)
if isinstance(batch, tuple):
probs = self.ctc_vis_fn(*batch)
else:
probs = self.ctc_vis_fn(**batch)
return probs, uttid_list
def trim_ctc_prob(self, uttid, prob):
"""Trim CTC posteriors accoding to input lengths."""
enc_len = int(self.data_dict[uttid][self.ikey][self.iaxis]["shape"][0])
if self.factor > 1:
enc_len //= self.factor
prob = prob[:enc_len]
return prob
def draw_ctc_plot(self, ctc_prob):
"""Plot the ctc_prob matrix.
Returns:
matplotlib.pyplot: pyplot object with CTC prob matrix image.
"""
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
ctc_prob = ctc_prob.astype(np.float32)
plt.clf()
topk_ids = np.argsort(ctc_prob, axis=1)
n_frames, vocab = ctc_prob.shape
times_probs = np.arange(n_frames)
plt.figure(figsize=(20, 8))
# NOTE: index 0 is reserved for blank
for idx in set(topk_ids.reshape(-1).tolist()):
if idx == 0:
plt.plot(
times_probs,
ctc_prob[:, 0],
":",
label="<blank>",
color="grey")
else:
plt.plot(times_probs, ctc_prob[:, idx])
plt.xlabel(u"Input [frame]", fontsize=12)
plt.ylabel("Posteriors", fontsize=12)
plt.xticks(list(range(0, int(n_frames) + 1, 10)))
plt.yticks(list(range(0, 2, 1)))
plt.tight_layout()
return plt
def _plot_and_save_ctc(self, ctc_prob, filename):
plt = self.draw_ctc_plot(ctc_prob)
plt.savefig(filename)
plt.close()
deepspeech/training/triggers/__init__.py
浏览文件 @ 950d17cb
...@@ -11,18 +11,3 @@ ...@@ -11,18 +11,3 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
from .interval_trigger import IntervalTrigger
def never_fail_trigger(trainer):
return False
def get_trigger(trigger):
if trigger is None:
return never_fail_trigger
if callable(trigger):
return trigger
else:
trigger = IntervalTrigger(*trigger)
return trigger
deepspeech/training/triggers/compare_value_trigger.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..reporter import DictSummary
from .utils import get_trigger
class CompareValueTrigger():
"""Trigger invoked when key value getting bigger or lower than before.
Args:
key (str) : Key of value.
compare_fn ((float, float) -> bool) : Function to compare the values.
trigger (tuple(int, str)) : Trigger that decide the comparison interval.
"""
def __init__(self, key, compare_fn, trigger=(1, "epoch")):
self._key = key
self._best_value = None
self._interval_trigger = get_trigger(trigger)
self._init_summary()
self._compare_fn = compare_fn
def __call__(self, trainer):
"""Get value related to the key and compare with current value."""
observation = trainer.observation
summary = self._summary
key = self._key
if key in observation:
summary.add({key: observation[key]})
if not self._interval_trigger(trainer):
return False
stats = summary.compute_mean()
value = float(stats[key]) # copy to CPU
self._init_summary()
if self._best_value is None:
# initialize best value
self._best_value = value
return False
elif self._compare_fn(self._best_value, value):
return True
else:
self._best_value = value
return False
def _init_summary(self):
self._summary = DictSummary()
deepspeech/training/triggers/time_trigger.py
浏览文件 @ 950d17cb
...@@ -30,3 +30,12 @@ class TimeTrigger(): ...@@ -30,3 +30,12 @@ class TimeTrigger():
return True return True
else: else:
return False return False
def state_dict(self):
state_dict = {
"next_time": self._next_time,
}
return state_dict
def set_state_dict(self, state_dict):
self._next_time = state_dict['next_time']
deepspeech/training/triggers/utils.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .interval_trigger import IntervalTrigger
def never_fail_trigger(trainer):
return False
def get_trigger(trigger):
if trigger is None:
return never_fail_trigger
if callable(trigger):
return trigger
else:
trigger = IntervalTrigger(*trigger)
return trigger
deepspeech/transform/__init__.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
deepspeech/transform/add_deltas.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
def delta(feat, window):
assert window > 0
delta_feat = np.zeros_like(feat)
for i in range(1, window + 1):
delta_feat[:-i] += i * feat[i:]
delta_feat[i:] += -i * feat[:-i]
delta_feat[-i:] += i * feat[-1]
delta_feat[:i] += -i * feat[0]
delta_feat /= 2 * sum(i**2 for i in range(1, window + 1))
return delta_feat
def add_deltas(x, window=2, order=2):
"""
Args:
x (np.ndarray): speech feat, (T, D).
Return:
np.ndarray: (T, (1+order)*D)
"""
feats = [x]
for _ in range(order):
feats.append(delta(feats[-1], window))
return np.concatenate(feats, axis=1)
class AddDeltas():
def __init__(self, window=2, order=2):
self.window = window
self.order = order
def __repr__(self):
return "{name}(window={window}, order={order}".format(
name=self.__class__.__name__, window=self.window, order=self.order)
def __call__(self, x):
return add_deltas(x, window=self.window, order=self.order)
deepspeech/transform/channel_selector.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy
class ChannelSelector():
"""Select 1ch from multi-channel signal"""
def __init__(self, train_channel="random", eval_channel=0, axis=1):
self.train_channel = train_channel
self.eval_channel = eval_channel
self.axis = axis
def __repr__(self):
return ("{name}(train_channel={train_channel}, "
"eval_channel={eval_channel}, axis={axis})".format(
name=self.__class__.__name__,
train_channel=self.train_channel,
eval_channel=self.eval_channel,
axis=self.axis, ))
def __call__(self, x, train=True):
# Assuming x: [Time, Channel] by default
if x.ndim <= self.axis:
# If the dimension is insufficient, then unsqueeze
# (e.g [Time] -> [Time, 1])
ind = tuple(
slice(None) if i < x.ndim else None
for i in range(self.axis + 1))
x = x[ind]
if train:
channel = self.train_channel
else:
channel = self.eval_channel
if channel == "random":
ch = numpy.random.randint(0, x.shape[self.axis])
else:
ch = channel
ind = tuple(
slice(None) if i != self.axis else ch for i in range(x.ndim))
return x[ind]
deepspeech/transform/cmvn.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import io
import h5py
import kaldiio
import numpy as np
class CMVN():
"Apply Global/Spk CMVN/iverserCMVN."
def __init__(
self,
stats,
norm_means=True,
norm_vars=False,
filetype="mat",
utt2spk=None,
spk2utt=None,
reverse=False,
std_floor=1.0e-20, ):
self.stats_file = stats
self.norm_means = norm_means
self.norm_vars = norm_vars
self.reverse = reverse
if isinstance(stats, dict):
stats_dict = dict(stats)
else:
# Use for global CMVN
if filetype == "mat":
stats_dict = {None: kaldiio.load_mat(stats)}
# Use for global CMVN
elif filetype == "npy":
stats_dict = {None: np.load(stats)}
# Use for speaker CMVN
elif filetype == "ark":
self.accept_uttid = True
stats_dict = dict(kaldiio.load_ark(stats))
# Use for speaker CMVN
elif filetype == "hdf5":
self.accept_uttid = True
stats_dict = h5py.File(stats)
else:
raise ValueError("Not supporting filetype={}".format(filetype))
if utt2spk is not None:
self.utt2spk = {}
with io.open(utt2spk, "r", encoding="utf-8") as f:
for line in f:
utt, spk = line.rstrip().split(None, 1)
self.utt2spk[utt] = spk
elif spk2utt is not None:
self.utt2spk = {}
with io.open(spk2utt, "r", encoding="utf-8") as f:
for line in f:
spk, utts = line.rstrip().split(None, 1)
for utt in utts.split():
self.utt2spk[utt] = spk
else:
self.utt2spk = None
# Kaldi makes a matrix for CMVN which has a shape of (2, feat_dim + 1),
# and the first vector contains the sum of feats and the second is
# the sum of squares. The last value of the first, i.e. stats[0,-1],
# is the number of samples for this statistics.
self.bias = {}
self.scale = {}
for spk, stats in stats_dict.items():
assert len(stats) == 2, stats.shape
count = stats[0, -1]
# If the feature has two or more dimensions
if not (np.isscalar(count) or isinstance(count, (int, float))):
# The first is only used
count = count.flatten()[0]
mean = stats[0, :-1] / count
# V(x) = E(x^2) - (E(x))^2
var = stats[1, :-1] / count - mean * mean
std = np.maximum(np.sqrt(var), std_floor)
self.bias[spk] = -mean
self.scale[spk] = 1 / std
def __repr__(self):
return ("{name}(stats_file={stats_file}, "
"norm_means={norm_means}, norm_vars={norm_vars}, "
"reverse={reverse})".format(
name=self.__class__.__name__,
stats_file=self.stats_file,
norm_means=self.norm_means,
norm_vars=self.norm_vars,
reverse=self.reverse, ))
def __call__(self, x, uttid=None):
if self.utt2spk is not None:
spk = self.utt2spk[uttid]
else:
spk = uttid
if not self.reverse:
# apply cmvn
if self.norm_means:
x = np.add(x, self.bias[spk])
if self.norm_vars:
x = np.multiply(x, self.scale[spk])
else:
# apply reverse cmvn
if self.norm_vars:
x = np.divide(x, self.scale[spk])
if self.norm_means:
x = np.subtract(x, self.bias[spk])
return x
class UtteranceCMVN():
"Apply Utterance CMVN"
def __init__(self, norm_means=True, norm_vars=False, std_floor=1.0e-20):
self.norm_means = norm_means
self.norm_vars = norm_vars
self.std_floor = std_floor
def __repr__(self):
return "{name}(norm_means={norm_means}, norm_vars={norm_vars})".format(
name=self.__class__.__name__,
norm_means=self.norm_means,
norm_vars=self.norm_vars, )
def __call__(self, x, uttid=None):
# x: [Time, Dim]
square_sums = (x**2).sum(axis=0)
mean = x.mean(axis=0)
if self.norm_means:
x = np.subtract(x, mean)
if self.norm_vars:
var = square_sums / x.shape[0] - mean**2
std = np.maximum(np.sqrt(var), self.std_floor)
x = np.divide(x, std)
return x
deepspeech/transform/functional.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from deepspeech.transform.transform_interface import TransformInterface
from deepspeech.utils.check_kwargs import check_kwargs
class FuncTrans(TransformInterface):
"""Functional Transformation
WARNING:
Builtin or C/C++ functions may not work properly
because this class heavily depends on the `inspect` module.
Usage:
>>> def foo_bar(x, a=1, b=2):
... '''Foo bar
... :param x: input
... :param int a: default 1
... :param int b: default 2
... '''
... return x + a - b
>>> class FooBar(FuncTrans):
... _func = foo_bar
... __doc__ = foo_bar.__doc__
"""
_func = None
def __init__(self, **kwargs):
self.kwargs = kwargs
check_kwargs(self.func, kwargs)
def __call__(self, x):
return self.func(x, **self.kwargs)
@classmethod
def add_arguments(cls, parser):
fname = cls._func.__name__.replace("_", "-")
group = parser.add_argument_group(fname + " transformation setting")
for k, v in cls.default_params().items():
# TODO(karita): get help and choices from docstring?
attr = k.replace("_", "-")
group.add_argument(f"--{fname}-{attr}", default=v, type=type(v))
return parser
@property
def func(self):
return type(self)._func
@classmethod
def default_params(cls):
try:
d = dict(inspect.signature(cls._func).parameters)
except ValueError:
d = dict()
return {
k: v.default
for k, v in d.items() if v.default != inspect.Parameter.empty
}
def __repr__(self):
params = self.default_params()
params.update(**self.kwargs)
ret = self.__class__.__name__ + "("
if len(params) == 0:
return ret + ")"
for k, v in params.items():
ret += "{}={}, ".format(k, v)
return ret[:-2] + ")"
deepspeech/transform/perturb.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import librosa
import numpy
import scipy
import soundfile
from deepspeech.io.reader import SoundHDF5File
class SpeedPerturbation():
"""SpeedPerturbation
The speed perturbation in kaldi uses sox-speed instead of sox-tempo,
and sox-speed just to resample the input,
i.e pitch and tempo are changed both.
"Why use speed option instead of tempo -s in SoX for speed perturbation"
https://groups.google.com/forum/#!topic/kaldi-help/8OOG7eE4sZ8
Warning:
This function is very slow because of resampling.
I recommmend to apply speed-perturb outside the training using sox.
"""
def __init__(
self,
lower=0.9,
upper=1.1,
utt2ratio=None,
keep_length=True,
res_type="kaiser_best",
seed=None, ):
self.res_type = res_type
self.keep_length = keep_length
self.state = numpy.random.RandomState(seed)
if utt2ratio is not None:
self.utt2ratio = {}
# Use the scheduled ratio for each utterances
self.utt2ratio_file = utt2ratio
self.lower = None
self.upper = None
self.accept_uttid = True
with open(utt2ratio, "r") as f:
for line in f:
utt, ratio = line.rstrip().split(None, 1)
ratio = float(ratio)
self.utt2ratio[utt] = ratio
else:
self.utt2ratio = None
# The ratio is given on runtime randomly
self.lower = lower
self.upper = upper
def __repr__(self):
if self.utt2ratio is None:
return "{}(lower={}, upper={}, " "keep_length={}, res_type={})".format(
self.__class__.__name__,
self.lower,
self.upper,
self.keep_length,
self.res_type, )
else:
return "{}({}, res_type={})".format(
self.__class__.__name__, self.utt2ratio_file, self.res_type)
def __call__(self, x, uttid=None, train=True):
if not train:
return x
x = x.astype(numpy.float32)
if self.accept_uttid:
ratio = self.utt2ratio[uttid]
else:
ratio = self.state.uniform(self.lower, self.upper)
# Note1: resample requires the sampling-rate of input and output,
# but actually only the ratio is used.
y = librosa.resample(x, ratio, 1, res_type=self.res_type)
if self.keep_length:
diff = abs(len(x) - len(y))
if len(y) > len(x):
# Truncate noise
y = y[diff // 2:-((diff + 1) // 2)]
elif len(y) < len(x):
# Assume the time-axis is the first: (Time, Channel)
pad_width = [(diff // 2, (diff + 1) // 2)] + [
(0, 0) for _ in range(y.ndim - 1)
]
y = numpy.pad(
y, pad_width=pad_width, constant_values=0, mode="constant")
return y
class BandpassPerturbation():
"""BandpassPerturbation
Randomly dropout along the frequency axis.
The original idea comes from the following:
"randomly-selected frequency band was cut off under the constraint of
leaving at least 1,000 Hz band within the range of less than 4,000Hz."
(The Hitachi/JHU CHiME-5 system: Advances in speech recognition for
everyday home environments using multiple microphone arrays;
http://spandh.dcs.shef.ac.uk/chime_workshop/papers/CHiME_2018_paper_kanda.pdf)
"""
def __init__(self, lower=0.0, upper=0.75, seed=None, axes=(-1, )):
self.lower = lower
self.upper = upper
self.state = numpy.random.RandomState(seed)
# x_stft: (Time, Channel, Freq)
self.axes = axes
def __repr__(self):
return "{}(lower={}, upper={})".format(self.__class__.__name__,
self.lower, self.upper)
def __call__(self, x_stft, uttid=None, train=True):
if not train:
return x_stft
if x_stft.ndim == 1:
raise RuntimeError("Input in time-freq domain: "
"(Time, Channel, Freq) or (Time, Freq)")
ratio = self.state.uniform(self.lower, self.upper)
axes = [i if i >= 0 else x_stft.ndim - i for i in self.axes]
shape = [s if i in axes else 1 for i, s in enumerate(x_stft.shape)]
mask = self.state.randn(*shape) > ratio
x_stft *= mask
return x_stft
class VolumePerturbation():
def __init__(self,
lower=-1.6,
upper=1.6,
utt2ratio=None,
dbunit=True,
seed=None):
self.dbunit = dbunit
self.utt2ratio_file = utt2ratio
self.lower = lower
self.upper = upper
self.state = numpy.random.RandomState(seed)
if utt2ratio is not None:
# Use the scheduled ratio for each utterances
self.utt2ratio = {}
self.lower = None
self.upper = None
self.accept_uttid = True
with open(utt2ratio, "r") as f:
for line in f:
utt, ratio = line.rstrip().split(None, 1)
ratio = float(ratio)
self.utt2ratio[utt] = ratio
else:
# The ratio is given on runtime randomly
self.utt2ratio = None
def __repr__(self):
if self.utt2ratio is None:
return "{}(lower={}, upper={}, dbunit={})".format(
self.__class__.__name__, self.lower, self.upper, self.dbunit)
else:
return '{}("{}", dbunit={})'.format(
self.__class__.__name__, self.utt2ratio_file, self.dbunit)
def __call__(self, x, uttid=None, train=True):
if not train:
return x
x = x.astype(numpy.float32)
if self.accept_uttid:
ratio = self.utt2ratio[uttid]
else:
ratio = self.state.uniform(self.lower, self.upper)
if self.dbunit:
ratio = 10**(ratio / 20)
return x * ratio
class NoiseInjection():
"""Add isotropic noise"""
def __init__(
self,
utt2noise=None,
lower=-20,
upper=-5,
utt2ratio=None,
filetype="list",
dbunit=True,
seed=None, ):
self.utt2noise_file = utt2noise
self.utt2ratio_file = utt2ratio
self.filetype = filetype
self.dbunit = dbunit
self.lower = lower
self.upper = upper
self.state = numpy.random.RandomState(seed)
if utt2ratio is not None:
# Use the scheduled ratio for each utterances
self.utt2ratio = {}
with open(utt2noise, "r") as f:
for line in f:
utt, snr = line.rstrip().split(None, 1)
snr = float(snr)
self.utt2ratio[utt] = snr
else:
# The ratio is given on runtime randomly
self.utt2ratio = None
if utt2noise is not None:
self.utt2noise = {}
if filetype == "list":
with open(utt2noise, "r") as f:
for line in f:
utt, filename = line.rstrip().split(None, 1)
signal, rate = soundfile.read(filename, dtype="int16")
# Load all files in memory
self.utt2noise[utt] = (signal, rate)
elif filetype == "sound.hdf5":
self.utt2noise = SoundHDF5File(utt2noise, "r")
else:
raise ValueError(filetype)
else:
self.utt2noise = None
if utt2noise is not None and utt2ratio is not None:
if set(self.utt2ratio) != set(self.utt2noise):
raise RuntimeError("The uttids mismatch between {} and {}".
format(utt2ratio, utt2noise))
def __repr__(self):
if self.utt2ratio is None:
return "{}(lower={}, upper={}, dbunit={})".format(
self.__class__.__name__, self.lower, self.upper, self.dbunit)
else:
return '{}("{}", dbunit={})'.format(
self.__class__.__name__, self.utt2ratio_file, self.dbunit)
def __call__(self, x, uttid=None, train=True):
if not train:
return x
x = x.astype(numpy.float32)
# 1. Get ratio of noise to signal in sound pressure level
if uttid is not None and self.utt2ratio is not None:
ratio = self.utt2ratio[uttid]
else:
ratio = self.state.uniform(self.lower, self.upper)
if self.dbunit:
ratio = 10**(ratio / 20)
scale = ratio * numpy.sqrt((x**2).mean())
# 2. Get noise
if self.utt2noise is not None:
# Get noise from the external source
if uttid is not None:
noise, rate = self.utt2noise[uttid]
else:
# Randomly select the noise source
noise = self.state.choice(list(self.utt2noise.values()))
# Normalize the level
noise /= numpy.sqrt((noise**2).mean())
# Adjust the noise length
diff = abs(len(x) - len(noise))
offset = self.state.randint(0, diff)
if len(noise) > len(x):
# Truncate noise
noise = noise[offset:-(diff - offset)]
else:
noise = numpy.pad(
noise, pad_width=[offset, diff - offset], mode="wrap")
else:
# Generate white noise
noise = self.state.normal(0, 1, x.shape)
# 3. Add noise to signal
return x + noise * scale
class RIRConvolve():
def __init__(self, utt2rir, filetype="list"):
self.utt2rir_file = utt2rir
self.filetype = filetype
self.utt2rir = {}
if filetype == "list":
with open(utt2rir, "r") as f:
for line in f:
utt, filename = line.rstrip().split(None, 1)
signal, rate = soundfile.read(filename, dtype="int16")
self.utt2rir[utt] = (signal, rate)
elif filetype == "sound.hdf5":
self.utt2rir = SoundHDF5File(utt2rir, "r")
else:
raise NotImplementedError(filetype)
def __repr__(self):
return '{}("{}")'.format(self.__class__.__name__, self.utt2rir_file)
def __call__(self, x, uttid=None, train=True):
if not train:
return x
x = x.astype(numpy.float32)
if x.ndim != 1:
# Must be single channel
raise RuntimeError(
"Input x must be one dimensional array, but got {}".format(
x.shape))
rir, rate = self.utt2rir[uttid]
if rir.ndim == 2:
# FIXME(kamo): Use chainer.convolution_1d?
# return [Time, Channel]
return numpy.stack(
[scipy.convolve(x, r, mode="same") for r in rir], axis=-1)
else:
return scipy.convolve(x, rir, mode="same")
deepspeech/transform/spec_augment.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Spec Augment module for preprocessing i.e., data augmentation"""
import random
import numpy
from PIL import Image
from PIL.Image import BICUBIC
from deepspeech.transform.functional import FuncTrans
def time_warp(x, max_time_warp=80, inplace=False, mode="PIL"):
"""time warp for spec augment
move random center frame by the random width ~ uniform(-window, window)
:param numpy.ndarray x: spectrogram (time, freq)
:param int max_time_warp: maximum time frames to warp
:param bool inplace: overwrite x with the result
:param str mode: "PIL" (default, fast, not differentiable) or "sparse_image_warp"
(slow, differentiable)
:returns numpy.ndarray: time warped spectrogram (time, freq)
"""
window = max_time_warp
if mode == "PIL":
t = x.shape[0]
if t - window <= window:
return x
# NOTE: randrange(a, b) emits a, a + 1, ..., b - 1
center = random.randrange(window, t - window)
warped = random.randrange(center - window, center +
window) + 1 # 1 ... t - 1
left = Image.fromarray(x[:center]).resize((x.shape[1], warped), BICUBIC)
right = Image.fromarray(x[center:]).resize((x.shape[1], t - warped),
BICUBIC)
if inplace:
x[:warped] = left
x[warped:] = right
return x
return numpy.concatenate((left, right), 0)
elif mode == "sparse_image_warp":
import paddle
from espnet.utils import spec_augment
# TODO(karita): make this differentiable again
return spec_augment.time_warp(paddle.to_tensor(x), window).numpy()
else:
raise NotImplementedError("unknown resize mode: " + mode +
", choose one from (PIL, sparse_image_warp).")
class TimeWarp(FuncTrans):
_func = time_warp
__doc__ = time_warp.__doc__
def __call__(self, x, train):
if not train:
return x
return super().__call__(x)
def freq_mask(x, F=30, n_mask=2, replace_with_zero=True, inplace=False):
"""freq mask for spec agument
:param numpy.ndarray x: (time, freq)
:param int n_mask: the number of masks
:param bool inplace: overwrite
:param bool replace_with_zero: pad zero on mask if true else use mean
"""
if inplace:
cloned = x
else:
cloned = x.copy()
num_mel_channels = cloned.shape[1]
fs = numpy.random.randint(0, F, size=(n_mask, 2))
for f, mask_end in fs:
f_zero = random.randrange(0, num_mel_channels - f)
mask_end += f_zero
# avoids randrange error if values are equal and range is empty
if f_zero == f_zero + f:
continue
if replace_with_zero:
cloned[:, f_zero:mask_end] = 0
else:
cloned[:, f_zero:mask_end] = cloned.mean()
return cloned
class FreqMask(FuncTrans):
_func = freq_mask
__doc__ = freq_mask.__doc__
def __call__(self, x, train):
if not train:
return x
return super().__call__(x)
def time_mask(spec, T=40, n_mask=2, replace_with_zero=True, inplace=False):
"""freq mask for spec agument
:param numpy.ndarray spec: (time, freq)
:param int n_mask: the number of masks
:param bool inplace: overwrite
:param bool replace_with_zero: pad zero on mask if true else use mean
"""
if inplace:
cloned = spec
else:
cloned = spec.copy()
len_spectro = cloned.shape[0]
ts = numpy.random.randint(0, T, size=(n_mask, 2))
for t, mask_end in ts:
# avoid randint range error
if len_spectro - t <= 0:
continue
t_zero = random.randrange(0, len_spectro - t)
# avoids randrange error if values are equal and range is empty
if t_zero == t_zero + t:
continue
mask_end += t_zero
if replace_with_zero:
cloned[t_zero:mask_end] = 0
else:
cloned[t_zero:mask_end] = cloned.mean()
return cloned
class TimeMask(FuncTrans):
_func = time_mask
__doc__ = time_mask.__doc__
def __call__(self, x, train):
if not train:
return x
return super().__call__(x)
def spec_augment(
x,
resize_mode="PIL",
max_time_warp=80,
max_freq_width=27,
n_freq_mask=2,
max_time_width=100,
n_time_mask=2,
inplace=True,
replace_with_zero=True, ):
"""spec agument
apply random time warping and time/freq masking
default setting is based on LD (Librispeech double) in Table 2
https://arxiv.org/pdf/1904.08779.pdf
:param numpy.ndarray x: (time, freq)
:param str resize_mode: "PIL" (fast, nondifferentiable) or "sparse_image_warp"
(slow, differentiable)
:param int max_time_warp: maximum frames to warp the center frame in spectrogram (W)
:param int freq_mask_width: maximum width of the random freq mask (F)
:param int n_freq_mask: the number of the random freq mask (m_F)
:param int time_mask_width: maximum width of the random time mask (T)
:param int n_time_mask: the number of the random time mask (m_T)
:param bool inplace: overwrite intermediate array
:param bool replace_with_zero: pad zero on mask if true else use mean
"""
assert isinstance(x, numpy.ndarray)
assert x.ndim == 2
x = time_warp(x, max_time_warp, inplace=inplace, mode=resize_mode)
x = freq_mask(
x,
max_freq_width,
n_freq_mask,
inplace=inplace,
replace_with_zero=replace_with_zero, )
x = time_mask(
x,
max_time_width,
n_time_mask,
inplace=inplace,
replace_with_zero=replace_with_zero, )
return x
class SpecAugment(FuncTrans):
_func = spec_augment
__doc__ = spec_augment.__doc__
def __call__(self, x, train):
if not train:
return x
return super().__call__(x)
deepspeech/transform/spectrogram.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import librosa
import numpy as np
def stft(x,
n_fft,
n_shift,
win_length=None,
window="hann",
center=True,
pad_mode="reflect"):
# x: [Time, Channel]
if x.ndim == 1:
single_channel = True
# x: [Time] -> [Time, Channel]
x = x[:, None]
else:
single_channel = False
x = x.astype(np.float32)
# FIXME(kamo): librosa.stft can't use multi-channel?
# x: [Time, Channel, Freq]
x = np.stack(
[
librosa.stft(
x[:, ch],
n_fft=n_fft,
hop_length=n_shift,
win_length=win_length,
window=window,
center=center,
pad_mode=pad_mode, ).T for ch in range(x.shape[1])
],
axis=1, )
if single_channel:
# x: [Time, Channel, Freq] -> [Time, Freq]
x = x[:, 0]
return x
def istft(x, n_shift, win_length=None, window="hann", center=True):
# x: [Time, Channel, Freq]
if x.ndim == 2:
single_channel = True
# x: [Time, Freq] -> [Time, Channel, Freq]
x = x[:, None, :]
else:
single_channel = False
# x: [Time, Channel]
x = np.stack(
[
librosa.istft(
x[:, ch].T, # [Time, Freq] -> [Freq, Time]
hop_length=n_shift,
win_length=win_length,
window=window,
center=center, ) for ch in range(x.shape[1])
],
axis=1, )
if single_channel:
# x: [Time, Channel] -> [Time]
x = x[:, 0]
return x
def stft2logmelspectrogram(x_stft,
fs,
n_mels,
n_fft,
fmin=None,
fmax=None,
eps=1e-10):
# x_stft: (Time, Channel, Freq) or (Time, Freq)
fmin = 0 if fmin is None else fmin
fmax = fs / 2 if fmax is None else fmax
# spc: (Time, Channel, Freq) or (Time, Freq)
spc = np.abs(x_stft)
# mel_basis: (Mel_freq, Freq)
mel_basis = librosa.filters.mel(fs, n_fft, n_mels, fmin, fmax)
# lmspc: (Time, Channel, Mel_freq) or (Time, Mel_freq)
lmspc = np.log10(np.maximum(eps, np.dot(spc, mel_basis.T)))
return lmspc
def spectrogram(x, n_fft, n_shift, win_length=None, window="hann"):
# x: (Time, Channel) -> spc: (Time, Channel, Freq)
spc = np.abs(stft(x, n_fft, n_shift, win_length, window=window))
return spc
def logmelspectrogram(
x,
fs,
n_mels,
n_fft,
n_shift,
win_length=None,
window="hann",
fmin=None,
fmax=None,
eps=1e-10,
pad_mode="reflect", ):
# stft: (Time, Channel, Freq) or (Time, Freq)
x_stft = stft(
x,
n_fft=n_fft,
n_shift=n_shift,
win_length=win_length,
window=window,
pad_mode=pad_mode, )
return stft2logmelspectrogram(
x_stft,
fs=fs,
n_mels=n_mels,
n_fft=n_fft,
fmin=fmin,
fmax=fmax,
eps=eps)
class Spectrogram():
def __init__(self, n_fft, n_shift, win_length=None, window="hann"):
self.n_fft = n_fft
self.n_shift = n_shift
self.win_length = win_length
self.window = window
def __repr__(self):
return ("{name}(n_fft={n_fft}, n_shift={n_shift}, "
"win_length={win_length}, window={window})".format(
name=self.__class__.__name__,
n_fft=self.n_fft,
n_shift=self.n_shift,
win_length=self.win_length,
window=self.window, ))
def __call__(self, x):
return spectrogram(
x,
n_fft=self.n_fft,
n_shift=self.n_shift,
win_length=self.win_length,
window=self.window, )
class LogMelSpectrogram():
def __init__(
self,
fs,
n_mels,
n_fft,
n_shift,
win_length=None,
window="hann",
fmin=None,
fmax=None,
eps=1e-10, ):
self.fs = fs
self.n_mels = n_mels
self.n_fft = n_fft
self.n_shift = n_shift
self.win_length = win_length
self.window = window
self.fmin = fmin
self.fmax = fmax
self.eps = eps
def __repr__(self):
return ("{name}(fs={fs}, n_mels={n_mels}, n_fft={n_fft}, "
"n_shift={n_shift}, win_length={win_length}, window={window}, "
"fmin={fmin}, fmax={fmax}, eps={eps}))".format(
name=self.__class__.__name__,
fs=self.fs,
n_mels=self.n_mels,
n_fft=self.n_fft,
n_shift=self.n_shift,
win_length=self.win_length,
window=self.window,
fmin=self.fmin,
fmax=self.fmax,
eps=self.eps, ))
def __call__(self, x):
return logmelspectrogram(
x,
fs=self.fs,
n_mels=self.n_mels,
n_fft=self.n_fft,
n_shift=self.n_shift,
win_length=self.win_length,
window=self.window, )
class Stft2LogMelSpectrogram():
def __init__(self, fs, n_mels, n_fft, fmin=None, fmax=None, eps=1e-10):
self.fs = fs
self.n_mels = n_mels
self.n_fft = n_fft
self.fmin = fmin
self.fmax = fmax
self.eps = eps
def __repr__(self):
return ("{name}(fs={fs}, n_mels={n_mels}, n_fft={n_fft}, "
"fmin={fmin}, fmax={fmax}, eps={eps}))".format(
name=self.__class__.__name__,
fs=self.fs,
n_mels=self.n_mels,
n_fft=self.n_fft,
fmin=self.fmin,
fmax=self.fmax,
eps=self.eps, ))
def __call__(self, x):
return stft2logmelspectrogram(
x,
fs=self.fs,
n_mels=self.n_mels,
n_fft=self.n_fft,
fmin=self.fmin,
fmax=self.fmax, )
class Stft():
def __init__(
self,
n_fft,
n_shift,
win_length=None,
window="hann",
center=True,
pad_mode="reflect", ):
self.n_fft = n_fft
self.n_shift = n_shift
self.win_length = win_length
self.window = window
self.center = center
self.pad_mode = pad_mode
def __repr__(self):
return ("{name}(n_fft={n_fft}, n_shift={n_shift}, "
"win_length={win_length}, window={window},"
"center={center}, pad_mode={pad_mode})".format(
name=self.__class__.__name__,
n_fft=self.n_fft,
n_shift=self.n_shift,
win_length=self.win_length,
window=self.window,
center=self.center,
pad_mode=self.pad_mode, ))
def __call__(self, x):
return stft(
x,
self.n_fft,
self.n_shift,
win_length=self.win_length,
window=self.window,
center=self.center,
pad_mode=self.pad_mode, )
class IStft():
def __init__(self, n_shift, win_length=None, window="hann", center=True):
self.n_shift = n_shift
self.win_length = win_length
self.window = window
self.center = center
def __repr__(self):
return ("{name}(n_shift={n_shift}, "
"win_length={win_length}, window={window},"
"center={center})".format(
name=self.__class__.__name__,
n_shift=self.n_shift,
win_length=self.win_length,
window=self.window,
center=self.center, ))
def __call__(self, x):
return istft(
x,
self.n_shift,
win_length=self.win_length,
window=self.window,
center=self.center, )
deepspeech/transform/transform_interface.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO(karita): add this to all the transform impl.
class TransformInterface:
"""Transform Interface"""
def __call__(self, x):
raise NotImplementedError("__call__ method is not implemented")
@classmethod
def add_arguments(cls, parser):
return parser
def __repr__(self):
return self.__class__.__name__ + "()"
class Identity(TransformInterface):
"""Identity Function"""
def __call__(self, x):
return x
deepspeech/transform/transformation.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Transformation module."""
import copy
import io
import logging
from collections import OrderedDict
from collections.abc import Sequence
from inspect import signature
import yaml
from deepspeech.utils.dynamic_import import dynamic_import
# TODO(karita): inherit TransformInterface
# TODO(karita): register cmd arguments in asr_train.py
import_alias = dict(
identity="deepspeech.transform.transform_interface:Identity",
time_warp="deepspeech.transform.spec_augment:TimeWarp",
time_mask="deepspeech.transform.spec_augment:TimeMask",
freq_mask="deepspeech.transform.spec_augment:FreqMask",
spec_augment="deepspeech.transform.spec_augment:SpecAugment",
speed_perturbation="deepspeech.transform.perturb:SpeedPerturbation",
volume_perturbation="deepspeech.transform.perturb:VolumePerturbation",
noise_injection="deepspeech.transform.perturb:NoiseInjection",
bandpass_perturbation="deepspeech.transform.perturb:BandpassPerturbation",
rir_convolve="deepspeech.transform.perturb:RIRConvolve",
delta="deepspeech.transform.add_deltas:AddDeltas",
cmvn="deepspeech.transform.cmvn:CMVN",
utterance_cmvn="deepspeech.transform.cmvn:UtteranceCMVN",
fbank="deepspeech.transform.spectrogram:LogMelSpectrogram",
spectrogram="deepspeech.transform.spectrogram:Spectrogram",
stft="deepspeech.transform.spectrogram:Stft",
istft="deepspeech.transform.spectrogram:IStft",
stft2fbank="deepspeech.transform.spectrogram:Stft2LogMelSpectrogram",
wpe="deepspeech.transform.wpe:WPE",
channel_selector="deepspeech.transform.channel_selector:ChannelSelector", )
class Transformation():
"""Apply some functions to the mini-batch
Examples:
>>> kwargs = {"process": [{"type": "fbank",
... "n_mels": 80,
... "fs": 16000},
... {"type": "cmvn",
... "stats": "data/train/cmvn.ark",
... "norm_vars": True},
... {"type": "delta", "window": 2, "order": 2}]}
>>> transform = Transformation(kwargs)
>>> bs = 10
>>> xs = [np.random.randn(100, 80).astype(np.float32)
... for _ in range(bs)]
>>> xs = transform(xs)
"""
def __init__(self, conffile=None):
if conffile is not None:
if isinstance(conffile, dict):
self.conf = copy.deepcopy(conffile)
else:
with io.open(conffile, encoding="utf-8") as f:
self.conf = yaml.safe_load(f)
assert isinstance(self.conf, dict), type(self.conf)
else:
self.conf = {"mode": "sequential", "process": []}
self.functions = OrderedDict()
if self.conf.get("mode", "sequential") == "sequential":
for idx, process in enumerate(self.conf["process"]):
assert isinstance(process, dict), type(process)
opts = dict(process)
process_type = opts.pop("type")
class_obj = dynamic_import(process_type, import_alias)
# TODO(karita): assert issubclass(class_obj, TransformInterface)
try:
self.functions[idx] = class_obj(**opts)
except TypeError:
try:
signa = signature(class_obj)
except ValueError:
# Some function, e.g. built-in function, are failed
pass
else:
logging.error("Expected signature: {}({})".format(
class_obj.__name__, signa))
raise
else:
raise NotImplementedError(
"Not supporting mode={}".format(self.conf["mode"]))
def __repr__(self):
rep = "\n" + "\n".join(" {}: {}".format(k, v)
for k, v in self.functions.items())
return "{}({})".format(self.__class__.__name__, rep)
def __call__(self, xs, uttid_list=None, **kwargs):
"""Return new mini-batch
:param Union[Sequence[np.ndarray], np.ndarray] xs:
:param Union[Sequence[str], str] uttid_list:
:return: batch:
:rtype: List[np.ndarray]
"""
if not isinstance(xs, Sequence):
is_batch = False
xs = [xs]
else:
is_batch = True
if isinstance(uttid_list, str):
uttid_list = [uttid_list for _ in range(len(xs))]
if self.conf.get("mode", "sequential") == "sequential":
for idx in range(len(self.conf["process"])):
func = self.functions[idx]
# TODO(karita): use TrainingTrans and UttTrans to check __call__ args
# Derive only the args which the func has
try:
param = signature(func).parameters
except ValueError:
# Some function, e.g. built-in function, are failed
param = {}
_kwargs = {k: v for k, v in kwargs.items() if k in param}
try:
if uttid_list is not None and "uttid" in param:
xs = [
func(x, u, **_kwargs)
for x, u in zip(xs, uttid_list)
]
else:
xs = [func(x, **_kwargs) for x in xs]
except Exception:
logging.fatal("Catch a exception from {}th func: {}".format(
idx, func))
raise
else:
raise NotImplementedError(
"Not supporting mode={}".format(self.conf["mode"]))
if is_batch:
return xs
else:
return xs[0]
deepspeech/transform/wpe.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from nara_wpe.wpe import wpe
class WPE(object):
def __init__(self,
taps=10,
delay=3,
iterations=3,
psd_context=0,
statistics_mode="full"):
self.taps = taps
self.delay = delay
self.iterations = iterations
self.psd_context = psd_context
self.statistics_mode = statistics_mode
def __repr__(self):
return ("{name}(taps={taps}, delay={delay}"
"iterations={iterations}, psd_context={psd_context}, "
"statistics_mode={statistics_mode})".format(
name=self.__class__.__name__,
taps=self.taps,
delay=self.delay,
iterations=self.iterations,
psd_context=self.psd_context,
statistics_mode=self.statistics_mode, ))
def __call__(self, xs):
"""Return enhanced
:param np.ndarray xs: (Time, Channel, Frequency)
:return: enhanced_xs
:rtype: np.ndarray
"""
# nara_wpe.wpe: (F, C, T)
xs = wpe(
xs.transpose((2, 1, 0)),
taps=self.taps,
delay=self.delay,
iterations=self.iterations,
psd_context=self.psd_context,
statistics_mode=self.statistics_mode, )
return xs.transpose(2, 1, 0)
deepspeech/utils/asr_utils.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import numpy as np
__all__ = ["label_smoothing_dist"]
# TODO(takaaki-hori): add different smoothing methods
def label_smoothing_dist(odim, lsm_type, transcript=None, blank=0):
"""Obtain label distribution for loss smoothing.
:param odim:
:param lsm_type:
:param blank:
:param transcript:
:return:
"""
if transcript is not None:
with open(transcript, "rb") as f:
trans_json = json.load(f)["utts"]
if lsm_type == "unigram":
assert transcript is not None, (
"transcript is required for %s label smoothing" % lsm_type)
labelcount = np.zeros(odim)
for k, v in trans_json.items():
ids = np.array([int(n) for n in v["output"][0]["tokenid"].split()])
# to avoid an error when there is no text in an uttrance
if len(ids) > 0:
labelcount[ids] += 1
labelcount[odim - 1] = len(transcript) # count <eos>
labelcount[labelcount == 0] = 1 # flooring
labelcount[blank] = 0 # remove counts for blank
labeldist = labelcount.astype(np.float32) / np.sum(labelcount)
else:
logging.error("Error: unexpected label smoothing type: %s" % lsm_type)
sys.exit()
return labeldist
deepspeech/utils/bleu_score.py
浏览文件 @ 950d17cb
...@@ -14,17 +14,17 @@ ...@@ -14,17 +14,17 @@
"""This module provides functions to calculate bleu score in different level. """This module provides functions to calculate bleu score in different level.
e.g. wer for word-level, cer for char-level. e.g. wer for word-level, cer for char-level.
""" """
import nltk
import numpy as np
import sacrebleu import sacrebleu
__all__ = ['bleu', 'char_bleu'] __all__ = ['bleu', 'char_bleu', "ErrorCalculator"]
def bleu(hypothesis, reference): def bleu(hypothesis, reference):
"""Calculate BLEU. BLEU compares reference text and """Calculate BLEU. BLEU compares reference text and
hypothesis text in word-level using scarebleu. hypothesis text in word-level using scarebleu.
:param reference: The reference sentences. :param reference: The reference sentences.
:type reference: list[list[str]] :type reference: list[list[str]]
:param hypothesis: The hypothesis sentence. :param hypothesis: The hypothesis sentence.
...@@ -39,8 +39,6 @@ def char_bleu(hypothesis, reference): ...@@ -39,8 +39,6 @@ def char_bleu(hypothesis, reference):
"""Calculate BLEU. BLEU compares reference text and """Calculate BLEU. BLEU compares reference text and
hypothesis text in char-level using scarebleu. hypothesis text in char-level using scarebleu.
:param reference: The reference sentences. :param reference: The reference sentences.
:type reference: list[list[str]] :type reference: list[list[str]]
:param hypothesis: The hypothesis sentence. :param hypothesis: The hypothesis sentence.
...@@ -52,3 +50,70 @@ def char_bleu(hypothesis, reference): ...@@ -52,3 +50,70 @@ def char_bleu(hypothesis, reference):
for ref in reference] for ref in reference]
return sacrebleu.corpus_bleu(hypothesis, reference) return sacrebleu.corpus_bleu(hypothesis, reference)
class ErrorCalculator():
"""Calculate BLEU for ST and MT models during training.
:param y_hats: numpy array with predicted text
:param y_pads: numpy array with true (target) text
:param char_list: vocabulary list
:param sym_space: space symbol
:param sym_pad: pad symbol
:param report_bleu: report BLUE score if True
"""
def __init__(self, char_list, sym_space, sym_pad, report_bleu=False):
"""Construct an ErrorCalculator object."""
super().__init__()
self.char_list = char_list
self.space = sym_space
self.pad = sym_pad
self.report_bleu = report_bleu
if self.space in self.char_list:
self.idx_space = self.char_list.index(self.space)
else:
self.idx_space = None
def __call__(self, ys_hat, ys_pad):
"""Calculate corpus-level BLEU score.
:param torch.Tensor ys_hat: prediction (batch, seqlen)
:param torch.Tensor ys_pad: reference (batch, seqlen)
:return: corpus-level BLEU score in a mini-batch
:rtype float
"""
bleu = None
if not self.report_bleu:
return bleu
bleu = self.calculate_corpus_bleu(ys_hat, ys_pad)
return bleu
def calculate_corpus_bleu(self, ys_hat, ys_pad):
"""Calculate corpus-level BLEU score in a mini-batch.
:param torch.Tensor seqs_hat: prediction (batch, seqlen)
:param torch.Tensor seqs_true: reference (batch, seqlen)
:return: corpus-level BLEU score
:rtype float
"""
seqs_hat, seqs_true = [], []
for i, y_hat in enumerate(ys_hat):
y_true = ys_pad[i]
eos_true = np.where(y_true == -1)[0]
ymax = eos_true[0] if len(eos_true) > 0 else len(y_true)
# NOTE: padding index (-1) in y_true is used to pad y_hat
# because y_hats is not padded with -1
seq_hat = [self.char_list[int(idx)] for idx in y_hat[:ymax]]
seq_true = [
self.char_list[int(idx)] for idx in y_true if int(idx) != -1
]
seq_hat_text = "".join(seq_hat).replace(self.space, " ")
seq_hat_text = seq_hat_text.replace(self.pad, "")
seq_true_text = "".join(seq_true).replace(self.space, " ")
seqs_hat.append(seq_hat_text)
seqs_true.append(seq_true_text)
bleu = nltk.bleu_score.corpus_bleu([[ref] for ref in seqs_true],
seqs_hat)
return bleu * 100
deepspeech/utils/check_kwargs.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
def check_kwargs(func, kwargs, name=None):
"""check kwargs are valid for func
If kwargs are invalid, raise TypeError as same as python default
:param function func: function to be validated
:param dict kwargs: keyword arguments for func
:param str name: name used in TypeError (default is func name)
"""
try:
params = inspect.signature(func).parameters
except ValueError:
return
if name is None:
name = func.__name__
for k in kwargs.keys():
if k not in params:
raise TypeError(
f"{name}() got an unexpected keyword argument '{k}'")
deepspeech/utils/cli_readers.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import io
import logging
import sys
import h5py
import kaldiio
import soundfile
from deepspeech.io.reader import SoundHDF5File
def file_reader_helper(
rspecifier: str,
filetype: str="mat",
return_shape: bool=False,
segments: str=None, ):
"""Read uttid and array in kaldi style
This function might be a bit confusing as "ark" is used
for HDF5 to imitate "kaldi-rspecifier".
Args:
rspecifier: Give as "ark:feats.ark" or "scp:feats.scp"
filetype: "mat" is kaldi-martix, "hdf5": HDF5
return_shape: Return the shape of the matrix,
instead of the matrix. This can reduce IO cost for HDF5.
segments (str): The file format is
"<segment-id> <recording-id> <start-time> <end-time>\n"
"e.g. call-861225-A-0050-0065 call-861225-A 5.0 6.5\n"
Returns:
Generator[Tuple[str, np.ndarray], None, None]:
Examples:
Read from kaldi-matrix ark file:
>>> for u, array in file_reader_helper('ark:feats.ark', 'mat'):
... array
Read from HDF5 file:
>>> for u, array in file_reader_helper('ark:feats.h5', 'hdf5'):
... array
"""
if filetype == "mat":
return KaldiReader(
rspecifier, return_shape=return_shape, segments=segments)
elif filetype == "hdf5":
return HDF5Reader(rspecifier, return_shape=return_shape)
elif filetype == "sound.hdf5":
return SoundHDF5Reader(rspecifier, return_shape=return_shape)
elif filetype == "sound":
return SoundReader(rspecifier, return_shape=return_shape)
else:
raise NotImplementedError(f"filetype={filetype}")
class KaldiReader:
def __init__(self, rspecifier, return_shape=False, segments=None):
self.rspecifier = rspecifier
self.return_shape = return_shape
self.segments = segments
def __iter__(self):
with kaldiio.ReadHelper(
self.rspecifier, segments=self.segments) as reader:
for key, array in reader:
if self.return_shape:
array = array.shape
yield key, array
class HDF5Reader:
def __init__(self, rspecifier, return_shape=False):
if ":" not in rspecifier:
raise ValueError('Give "rspecifier" such as "ark:some.ark: {}"'.
format(self.rspecifier))
self.rspecifier = rspecifier
self.ark_or_scp, self.filepath = self.rspecifier.split(":", 1)
if self.ark_or_scp not in ["ark", "scp"]:
raise ValueError(f"Must be scp or ark: {self.ark_or_scp}")
self.return_shape = return_shape
def __iter__(self):
if self.ark_or_scp == "scp":
hdf5_dict = {}
with open(self.filepath, "r", encoding="utf-8") as f:
for line in f:
key, value = line.rstrip().split(None, 1)
if ":" not in value:
raise RuntimeError(
"scp file for hdf5 should be like: "
'"uttid filepath.h5:key": {}({})'.format(
line, self.filepath))
path, h5_key = value.split(":", 1)
hdf5_file = hdf5_dict.get(path)
if hdf5_file is None:
try:
hdf5_file = h5py.File(path, "r")
except Exception:
logging.error("Error when loading {}".format(path))
raise
hdf5_dict[path] = hdf5_file
try:
data = hdf5_file[h5_key]
except Exception:
logging.error("Error when loading {} with key={}".
format(path, h5_key))
raise
if self.return_shape:
yield key, data.shape
else:
yield key, data[()]
# Closing all files
for k in hdf5_dict:
try:
hdf5_dict[k].close()
except Exception:
pass
else:
if self.filepath == "-":
# Required h5py>=2.9
filepath = io.BytesIO(sys.stdin.buffer.read())
else:
filepath = self.filepath
with h5py.File(filepath, "r") as f:
for key in f:
if self.return_shape:
yield key, f[key].shape
else:
yield key, f[key][()]
class SoundHDF5Reader:
def __init__(self, rspecifier, return_shape=False):
if ":" not in rspecifier:
raise ValueError('Give "rspecifier" such as "ark:some.ark: {}"'.
format(rspecifier))
self.ark_or_scp, self.filepath = rspecifier.split(":", 1)
if self.ark_or_scp not in ["ark", "scp"]:
raise ValueError(f"Must be scp or ark: {self.ark_or_scp}")
self.return_shape = return_shape
def __iter__(self):
if self.ark_or_scp == "scp":
hdf5_dict = {}
with open(self.filepath, "r", encoding="utf-8") as f:
for line in f:
key, value = line.rstrip().split(None, 1)
if ":" not in value:
raise RuntimeError(
"scp file for hdf5 should be like: "
'"uttid filepath.h5:key": {}({})'.format(
line, self.filepath))
path, h5_key = value.split(":", 1)
hdf5_file = hdf5_dict.get(path)
if hdf5_file is None:
try:
hdf5_file = SoundHDF5File(path, "r")
except Exception:
logging.error("Error when loading {}".format(path))
raise
hdf5_dict[path] = hdf5_file
try:
data = hdf5_file[h5_key]
except Exception:
logging.error("Error when loading {} with key={}".
format(path, h5_key))
raise
# Change Tuple[ndarray, int] -> Tuple[int, ndarray]
# (soundfile style -> scipy style)
array, rate = data
if self.return_shape:
array = array.shape
yield key, (rate, array)
# Closing all files
for k in hdf5_dict:
try:
hdf5_dict[k].close()
except Exception:
pass
else:
if self.filepath == "-":
# Required h5py>=2.9
filepath = io.BytesIO(sys.stdin.buffer.read())
else:
filepath = self.filepath
for key, (a, r) in SoundHDF5File(filepath, "r").items():
if self.return_shape:
a = a.shape
yield key, (r, a)
class SoundReader:
def __init__(self, rspecifier, return_shape=False):
if ":" not in rspecifier:
raise ValueError('Give "rspecifier" such as "scp:some.scp: {}"'.
format(rspecifier))
self.ark_or_scp, self.filepath = rspecifier.split(":", 1)
if self.ark_or_scp != "scp":
raise ValueError('Only supporting "scp" for sound file: {}'.format(
self.ark_or_scp))
self.return_shape = return_shape
def __iter__(self):
with open(self.filepath, "r", encoding="utf-8") as f:
for line in f:
key, sound_file_path = line.rstrip().split(None, 1)
# Assume PCM16
array, rate = soundfile.read(sound_file_path, dtype="int16")
# Change Tuple[ndarray, int] -> Tuple[int, ndarray]
# (soundfile style -> scipy style)
if self.return_shape:
array = array.shape
yield key, (rate, array)
deepspeech/utils/cli_utils.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
from collections.abc import Sequence
from distutils.util import strtobool as dist_strtobool
import numpy
def strtobool(x):
# distutils.util.strtobool returns integer, but it's confusing,
return bool(dist_strtobool(x))
def get_commandline_args():
extra_chars = [
" ",
";",
"&",
"(",
")",
"|",
"^",
"<",
">",
"?",
"*",
"[",
"]",
"$",
"`",
'"',
"\\",
"!",
"{",
"}",
]
# Escape the extra characters for shell
argv = [
arg.replace("'", "'\\''") if all(char not in arg
for char in extra_chars) else
"'" + arg.replace("'", "'\\''") + "'" for arg in sys.argv
]
return sys.executable + " " + " ".join(argv)
def is_scipy_wav_style(value):
# If Tuple[int, numpy.ndarray] or not
return (isinstance(value, Sequence) and len(value) == 2 and
isinstance(value[0], int) and isinstance(value[1], numpy.ndarray))
def assert_scipy_wav_style(value):
assert is_scipy_wav_style(
value), "Must be Tuple[int, numpy.ndarray], but got {}".format(
type(value) if not isinstance(value, Sequence) else "{}[{}]".format(
type(value), ", ".join(str(type(v)) for v in value)))
deepspeech/utils/cli_writers.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pathlib import Path
from typing import Dict
import h5py
import kaldiio
import numpy
import soundfile
from deepspeech.io.reader import SoundHDF5File
from deepspeech.utils.cli_utils import assert_scipy_wav_style
def file_writer_helper(
wspecifier: str,
filetype: str="mat",
write_num_frames: str=None,
compress: bool=False,
compression_method: int=2,
pcm_format: str="wav", ):
"""Write matrices in kaldi style
Args:
wspecifier: e.g. ark,scp:out.ark,out.scp
filetype: "mat" is kaldi-martix, "hdf5": HDF5
write_num_frames: e.g. 'ark,t:num_frames.txt'
compress: Compress or not
compression_method: Specify compression level
Write in kaldi-matrix-ark with "kaldi-scp" file:
>>> with file_writer_helper('ark,scp:out.ark,out.scp') as f:
>>> f['uttid'] = array
This "scp" has the following format:
uttidA out.ark:1234
uttidB out.ark:2222
where, 1234 and 2222 points the strating byte address of the matrix.
(For detail, see official documentation of Kaldi)
Write in HDF5 with "scp" file:
>>> with file_writer_helper('ark,scp:out.h5,out.scp', 'hdf5') as f:
>>> f['uttid'] = array
This "scp" file is created as:
uttidA out.h5:uttidA
uttidB out.h5:uttidB
HDF5 can be, unlike "kaldi-ark", accessed to any keys,
so originally "scp" is not required for random-reading.
Nevertheless we create "scp" for HDF5 because it is useful
for some use-case. e.g. Concatenation, Splitting.
"""
if filetype == "mat":
return KaldiWriter(
wspecifier,
write_num_frames=write_num_frames,
compress=compress,
compression_method=compression_method, )
elif filetype == "hdf5":
return HDF5Writer(
wspecifier, write_num_frames=write_num_frames, compress=compress)
elif filetype == "sound.hdf5":
return SoundHDF5Writer(
wspecifier,
write_num_frames=write_num_frames,
pcm_format=pcm_format)
elif filetype == "sound":
return SoundWriter(
wspecifier,
write_num_frames=write_num_frames,
pcm_format=pcm_format)
else:
raise NotImplementedError(f"filetype={filetype}")
class BaseWriter:
def __setitem__(self, key, value):
raise NotImplementedError
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.close()
def close(self):
try:
self.writer.close()
except Exception:
pass
if self.writer_scp is not None:
try:
self.writer_scp.close()
except Exception:
pass
if self.writer_nframe is not None:
try:
self.writer_nframe.close()
except Exception:
pass
def get_num_frames_writer(write_num_frames: str):
"""get_num_frames_writer
Examples:
>>> get_num_frames_writer('ark,t:num_frames.txt')
"""
if write_num_frames is not None:
if ":" not in write_num_frames:
raise ValueError('Must include ":", write_num_frames={}'.format(
write_num_frames))
nframes_type, nframes_file = write_num_frames.split(":", 1)
if nframes_type != "ark,t":
raise ValueError("Only supporting text mode. "
"e.g. --write-num-frames=ark,t:foo.txt :"
"{}".format(nframes_type))
return open(nframes_file, "w", encoding="utf-8")
class KaldiWriter(BaseWriter):
def __init__(self,
wspecifier,
write_num_frames=None,
compress=False,
compression_method=2):
if compress:
self.writer = kaldiio.WriteHelper(
wspecifier, compression_method=compression_method)
else:
self.writer = kaldiio.WriteHelper(wspecifier)
self.writer_scp = None
if write_num_frames is not None:
self.writer_nframe = get_num_frames_writer(write_num_frames)
else:
self.writer_nframe = None
def __setitem__(self, key, value):
self.writer[key] = value
if self.writer_nframe is not None:
self.writer_nframe.write(f"{key} {len(value)}\n")
def parse_wspecifier(wspecifier: str) -> Dict[str, str]:
"""Parse wspecifier to dict
Examples:
>>> parse_wspecifier('ark,scp:out.ark,out.scp')
{'ark': 'out.ark', 'scp': 'out.scp'}
"""
ark_scp, filepath = wspecifier.split(":", 1)
if ark_scp not in ["ark", "scp,ark", "ark,scp"]:
raise ValueError("{} is not allowed: {}".format(ark_scp, wspecifier))
ark_scps = ark_scp.split(",")
filepaths = filepath.split(",")
if len(ark_scps) != len(filepaths):
raise ValueError("Mismatch: {} and {}".format(ark_scp, filepath))
spec_dict = dict(zip(ark_scps, filepaths))
return spec_dict
class HDF5Writer(BaseWriter):
"""HDF5Writer
Examples:
>>> with HDF5Writer('ark:out.h5', compress=True) as f:
... f['key'] = array
"""
def __init__(self, wspecifier, write_num_frames=None, compress=False):
spec_dict = parse_wspecifier(wspecifier)
self.filename = spec_dict["ark"]
if compress:
self.kwargs = {"compression": "gzip"}
else:
self.kwargs = {}
self.writer = h5py.File(spec_dict["ark"], "w")
if "scp" in spec_dict:
self.writer_scp = open(spec_dict["scp"], "w", encoding="utf-8")
else:
self.writer_scp = None
if write_num_frames is not None:
self.writer_nframe = get_num_frames_writer(write_num_frames)
else:
self.writer_nframe = None
def __setitem__(self, key, value):
self.writer.create_dataset(key, data=value, **self.kwargs)
if self.writer_scp is not None:
self.writer_scp.write(f"{key} {self.filename}:{key}\n")
if self.writer_nframe is not None:
self.writer_nframe.write(f"{key} {len(value)}\n")
class SoundHDF5Writer(BaseWriter):
"""SoundHDF5Writer
Examples:
>>> fs = 16000
>>> with SoundHDF5Writer('ark:out.h5') as f:
... f['key'] = fs, array
"""
def __init__(self, wspecifier, write_num_frames=None, pcm_format="wav"):
self.pcm_format = pcm_format
spec_dict = parse_wspecifier(wspecifier)
self.filename = spec_dict["ark"]
self.writer = SoundHDF5File(
spec_dict["ark"], "w", format=self.pcm_format)
if "scp" in spec_dict:
self.writer_scp = open(spec_dict["scp"], "w", encoding="utf-8")
else:
self.writer_scp = None
if write_num_frames is not None:
self.writer_nframe = get_num_frames_writer(write_num_frames)
else:
self.writer_nframe = None
def __setitem__(self, key, value):
assert_scipy_wav_style(value)
# Change Tuple[int, ndarray] -> Tuple[ndarray, int]
# (scipy style -> soundfile style)
value = (value[1], value[0])
self.writer.create_dataset(key, data=value)
if self.writer_scp is not None:
self.writer_scp.write(f"{key} {self.filename}:{key}\n")
if self.writer_nframe is not None:
self.writer_nframe.write(f"{key} {len(value[0])}\n")
class SoundWriter(BaseWriter):
"""SoundWriter
Examples:
>>> fs = 16000
>>> with SoundWriter('ark,scp:outdir,out.scp') as f:
... f['key'] = fs, array
"""
def __init__(self, wspecifier, write_num_frames=None, pcm_format="wav"):
self.pcm_format = pcm_format
spec_dict = parse_wspecifier(wspecifier)
# e.g. ark,scp:dirname,wav.scp
# -> The wave files are found in dirname/*.wav
self.dirname = spec_dict["ark"]
Path(self.dirname).mkdir(parents=True, exist_ok=True)
self.writer = None
if "scp" in spec_dict:
self.writer_scp = open(spec_dict["scp"], "w", encoding="utf-8")
else:
self.writer_scp = None
if write_num_frames is not None:
self.writer_nframe = get_num_frames_writer(write_num_frames)
else:
self.writer_nframe = None
def __setitem__(self, key, value):
assert_scipy_wav_style(value)
rate, signal = value
wavfile = Path(self.dirname) / (key + "." + self.pcm_format)
soundfile.write(wavfile, signal.astype(numpy.int16), rate)
if self.writer_scp is not None:
self.writer_scp.write(f"{key} {wavfile}\n")
if self.writer_nframe is not None:
self.writer_nframe.write(f"{key} {len(signal)}\n")
deepspeech/utils/error_rate.py
浏览文件 @ 950d17cb
...@@ -14,12 +14,12 @@ ...@@ -14,12 +14,12 @@
"""This module provides functions to calculate error rate in different level. """This module provides functions to calculate error rate in different level.
e.g. wer for word-level, cer for char-level. e.g. wer for word-level, cer for char-level.
""" """
from itertools import groupby
import editdistance import editdistance
import numpy as np import numpy as np
__all__ = ['word_errors', 'char_errors', 'wer', 'cer'] __all__ = ['word_errors', 'char_errors', 'wer', 'cer', "ErrorCalculator"]
editdistance.eval("a", "b")
def _levenshtein_distance(ref, hyp): def _levenshtein_distance(ref, hyp):
...@@ -211,3 +211,154 @@ def cer(reference, hypothesis, ignore_case=False, remove_space=False): ...@@ -211,3 +211,154 @@ def cer(reference, hypothesis, ignore_case=False, remove_space=False):
cer = float(edit_distance) / ref_len cer = float(edit_distance) / ref_len
return cer return cer
class ErrorCalculator():
"""Calculate CER and WER for E2E_ASR and CTC models during training.
:param y_hats: numpy array with predicted text
:param y_pads: numpy array with true (target) text
:param char_list: List[str]
:param sym_space: <space>
:param sym_blank: <blank>
:return:
"""
def __init__(self,
char_list,
sym_space,
sym_blank,
report_cer=False,
report_wer=False):
"""Construct an ErrorCalculator object."""
super().__init__()
self.report_cer = report_cer
self.report_wer = report_wer
self.char_list = char_list
self.space = sym_space
self.blank = sym_blank
self.idx_blank = self.char_list.index(self.blank)
if self.space in self.char_list:
self.idx_space = self.char_list.index(self.space)
else:
self.idx_space = None
def __call__(self, ys_hat, ys_pad, is_ctc=False):
"""Calculate sentence-level WER/CER score.
:param paddle.Tensor ys_hat: prediction (batch, seqlen)
:param paddle.Tensor ys_pad: reference (batch, seqlen)
:param bool is_ctc: calculate CER score for CTC
:return: sentence-level WER score
:rtype float
:return: sentence-level CER score
:rtype float
"""
cer, wer = None, None
if is_ctc:
return self.calculate_cer_ctc(ys_hat, ys_pad)
elif not self.report_cer and not self.report_wer:
return cer, wer
seqs_hat, seqs_true = self.convert_to_char(ys_hat, ys_pad)
if self.report_cer:
cer = self.calculate_cer(seqs_hat, seqs_true)
if self.report_wer:
wer = self.calculate_wer(seqs_hat, seqs_true)
return cer, wer
def calculate_cer_ctc(self, ys_hat, ys_pad):
"""Calculate sentence-level CER score for CTC.
:param paddle.Tensor ys_hat: prediction (batch, seqlen)
:param paddle.Tensor ys_pad: reference (batch, seqlen)
:return: average sentence-level CER score
:rtype float
"""
cers, char_ref_lens = [], []
for i, y in enumerate(ys_hat):
y_hat = [x[0] for x in groupby(y)]
y_true = ys_pad[i]
seq_hat, seq_true = [], []
for idx in y_hat:
idx = int(idx)
if idx != -1 and idx != self.idx_blank and idx != self.idx_space:
seq_hat.append(self.char_list[int(idx)])
for idx in y_true:
idx = int(idx)
if idx != -1 and idx != self.idx_blank and idx != self.idx_space:
seq_true.append(self.char_list[int(idx)])
hyp_chars = "".join(seq_hat)
ref_chars = "".join(seq_true)
if len(ref_chars) > 0:
cers.append(editdistance.eval(hyp_chars, ref_chars))
char_ref_lens.append(len(ref_chars))
cer_ctc = float(sum(cers)) / sum(char_ref_lens) if cers else None
return cer_ctc
def convert_to_char(self, ys_hat, ys_pad):
"""Convert index to character.
:param paddle.Tensor seqs_hat: prediction (batch, seqlen)
:param paddle.Tensor seqs_true: reference (batch, seqlen)
:return: token list of prediction
:rtype list
:return: token list of reference
:rtype list
"""
seqs_hat, seqs_true = [], []
for i, y_hat in enumerate(ys_hat):
y_true = ys_pad[i]
eos_true = np.where(y_true == -1)[0]
ymax = eos_true[0] if len(eos_true) > 0 else len(y_true)
# NOTE: padding index (-1) in y_true is used to pad y_hat
seq_hat = [self.char_list[int(idx)] for idx in y_hat[:ymax]]
seq_true = [
self.char_list[int(idx)] for idx in y_true if int(idx) != -1
]
seq_hat_text = "".join(seq_hat).replace(self.space, " ")
seq_hat_text = seq_hat_text.replace(self.blank, "")
seq_true_text = "".join(seq_true).replace(self.space, " ")
seqs_hat.append(seq_hat_text)
seqs_true.append(seq_true_text)
return seqs_hat, seqs_true
def calculate_cer(self, seqs_hat, seqs_true):
"""Calculate sentence-level CER score.
:param list seqs_hat: prediction
:param list seqs_true: reference
:return: average sentence-level CER score
:rtype float
"""
char_eds, char_ref_lens = [], []
for i, seq_hat_text in enumerate(seqs_hat):
seq_true_text = seqs_true[i]
hyp_chars = seq_hat_text.replace(" ", "")
ref_chars = seq_true_text.replace(" ", "")
char_eds.append(editdistance.eval(hyp_chars, ref_chars))
char_ref_lens.append(len(ref_chars))
return float(sum(char_eds)) / sum(char_ref_lens)
def calculate_wer(self, seqs_hat, seqs_true):
"""Calculate sentence-level WER score.
:param list seqs_hat: prediction
:param list seqs_true: reference
:return: average sentence-level WER score
:rtype float
"""
word_eds, word_ref_lens = [], []
for i, seq_hat_text in enumerate(seqs_hat):
seq_true_text = seqs_true[i]
hyp_words = seq_hat_text.split()
ref_words = seq_true_text.split()
word_eds.append(editdistance.eval(hyp_words, ref_words))
word_ref_lens.append(len(ref_words))
return float(sum(word_eds)) / sum(word_ref_lens)
deepspeech/utils/spec_augment.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
docs/source/asr/released_model.md
浏览文件 @ 950d17cb
...@@ -8,7 +8,7 @@ Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER | ...@@ -8,7 +8,7 @@ Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER |
[Conformer Online Aishell Model](https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.chunk.release.tar.gz) | Aishell Dataset | Char-based | 283 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention + CTC | 0.0594 |-| 151 h [Conformer Online Aishell Model](https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.chunk.release.tar.gz) | Aishell Dataset | Char-based | 283 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention + CTC | 0.0594 |-| 151 h
[Conformer Offline Aishell Model](https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.release.tar.gz) | Aishell Dataset | Char-based | 284 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention | 0.0547 |-| 151 h [Conformer Offline Aishell Model](https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.release.tar.gz) | Aishell Dataset | Char-based | 284 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention | 0.0547 |-| 151 h
[Conformer Librispeech Model](https://deepspeech.bj.bcebos.com/release2.1/librispeech/s1/conformer.release.tar.gz) | Librispeech Dataset | Word-based | 287 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention |-| 0.0325 | 960 h [Conformer Librispeech Model](https://deepspeech.bj.bcebos.com/release2.1/librispeech/s1/conformer.release.tar.gz) | Librispeech Dataset | Word-based | 287 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention |-| 0.0325 | 960 h
[Transformer Librispeech Model](https://deepspeech.bj.bcebos.com/release2.1/librispeech/s1/transformer.release.tar.gz) | Librispeech Dataset | Word-based | 195 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention |-| 0.0544 | 960 h [Transformer Librispeech Model](https://deepspeech.bj.bcebos.com/release2.1/librispeech/s1/transformer.release.tar.gz) | Librispeech Dataset | Word-based | 195 MB | Encoder:Transformer, Decoder:Transformer, Decoding method: Attention |-| 0.0544 | 960 h
## Acoustic Model Transformed from paddle 1.8 ## Acoustic Model Transformed from paddle 1.8
Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER | Hours of speech Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER | Hours of speech
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docs/source/tts/install.md
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...@@ -10,13 +10,13 @@ Example instruction to install paddlepaddle via pip is listed below. ...@@ -10,13 +10,13 @@ Example instruction to install paddlepaddle via pip is listed below.
### PaddlePaddle with GPU ### PaddlePaddle with GPU
```python ```python
# CUDA10.1 的 PaddlePaddle # PaddlePaddle for CUDA10.1
python -m pip install paddlepaddle-gpu==2.1.2.post101 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html python -m pip install paddlepaddle-gpu==2.1.2.post101 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html
# CUDA10.2 的 PaddlePaddle # PaddlePaddle for CUDA10.2
python -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple python -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
# CUDA11.0 的 PaddlePaddle # PaddlePaddle for CUDA11.0
python -m pip install paddlepaddle-gpu==2.1.2.post110 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html python -m pip install paddlepaddle-gpu==2.1.2.post110 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html
# CUDA11.2 的 PaddlePaddle # PaddlePaddle for CUDA11.2
python -m pip install paddlepaddle-gpu==2.1.2.post112 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html python -m pip install paddlepaddle-gpu==2.1.2.post112 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html
``` ```
### PaddlePaddle with CPU ### PaddlePaddle with CPU
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examples/aishell3/tts3/README.md
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...@@ -17,7 +17,7 @@ tar zxvf data_aishell3.tgz -C data_aishell3 ...@@ -17,7 +17,7 @@ tar zxvf data_aishell3.tgz -C data_aishell3
``` ```
### Get MFA result of AISHELL-3 and Extract it ### Get MFA result of AISHELL-3 and Extract it
We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for aishell3_fastspeech2. We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for aishell3_fastspeech2.
You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/aishell3_alignment_tone.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/use_mfa) (use MFA1.x now) of our repo. You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/aishell3_alignment_tone.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/other/use_mfa) (use MFA1.x now) of our repo.
## Get Started ## Get Started
Assume the path to the dataset is `~/datasets/data_aishell3`. Assume the path to the dataset is `~/datasets/data_aishell3`.
...@@ -98,7 +98,7 @@ optional arguments: ...@@ -98,7 +98,7 @@ optional arguments:
7. `--speaker-dict`is the path of the speaker id map file when training a multi-speaker FastSpeech2. 7. `--speaker-dict`is the path of the speaker id map file when training a multi-speaker FastSpeech2.
### Synthesize ### Synthesize
We use [parallel wavegan](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/parallelwave_gan/baker) as the neural vocoder. We use [parallel wavegan](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/csmsc/voc1) as the neural vocoder.
Download pretrained parallel wavegan model from [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip) and unzip it. Download pretrained parallel wavegan model from [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip) and unzip it.
```bash ```bash
unzip pwg_baker_ckpt_0.4.zip unzip pwg_baker_ckpt_0.4.zip
......
examples/aishell3/vc0/README.md
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# Tacotron2 + AISHELL-3 Voice Cloning # Tacotron2 + AISHELL-3 Voice Cloning
This example contains code used to train a [Tacotron2 ](https://arxiv.org/abs/1712.05884) model with [AISHELL-3](http://www.aishelltech.com/aishell_3). The trained model can be used in Voice Cloning Task, We refer to the model structure of [Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis](https://arxiv.org/pdf/1806.04558.pdf) . The general steps are as follows: This example contains code used to train a [Tacotron2 ](https://arxiv.org/abs/1712.05884) model with [AISHELL-3](http://www.aishelltech.com/aishell_3). The trained model can be used in Voice Cloning Task, We refer to the model structure of [Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis](https://arxiv.org/pdf/1806.04558.pdf) . The general steps are as follows:
1. Speaker Encoder: We use a Speaker Verification to train a speaker encoder. Datasets used in this task are different from those used in Tacotron2, because the transcriptions are not needed, we use more datasets, refer to [ge2e](../../other/ge2e). 1. Speaker Encoder: We use a Speaker Verification to train a speaker encoder. Datasets used in this task are different from those used in Tacotron2, because the transcriptions are not needed, we use more datasets, refer to [ge2e](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/other/ge2e).
2. Synthesizer: Then, we use the trained speaker encoder to generate utterance embedding for each sentence in AISHELL-3. This embedding is a extra input of Tacotron2 which will be concated with encoder outputs. 2. Synthesizer: Then, we use the trained speaker encoder to generate utterance embedding for each sentence in AISHELL-3. This embedding is a extra input of Tacotron2 which will be concated with encoder outputs.
3. Vocoder: We use WaveFlow as the neural Vocoder, refer to [waveflow](../../ljspeech/voc0). 3. Vocoder: We use WaveFlow as the neural Vocoder, refer to [waveflow](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/ljspeech/voc0).
## Get Started ## Get Started
Assume the path to the dataset is `~/datasets/data_aishell3`. Assume the path to the dataset is `~/datasets/data_aishell3`.
...@@ -39,9 +39,9 @@ There are silence in the edge of AISHELL-3's wavs, and the audio amplitude is ve ...@@ -39,9 +39,9 @@ There are silence in the edge of AISHELL-3's wavs, and the audio amplitude is ve
We use Montreal Force Aligner 1.0. The label in aishell3 include pinyin，so the lexicon we provided to MFA is pinyin rather than Chinese characters. And the prosody marks(`$` and `%`) need to be removed. You shoud preprocess the dataset into the format which MFA needs, the texts have the same name with wavs and have the suffix `.lab`. We use Montreal Force Aligner 1.0. The label in aishell3 include pinyin，so the lexicon we provided to MFA is pinyin rather than Chinese characters. And the prosody marks(`$` and `%`) need to be removed. You shoud preprocess the dataset into the format which MFA needs, the texts have the same name with wavs and have the suffix `.lab`.
We use [lexicon.txt](./lexicon.txt) as the lexicon. We use [lexicon.txt](https://github.com/PaddlePaddle/DeepSpeech/blob/develop/parakeet/exps/voice_cloning/tacotron2_ge2e/lexicon.txt) as the lexicon.
You can download the alignment results from here [alignment_aishell3.tar.gz](https://paddlespeech.bj.bcebos.com/Parakeet/alignment_aishell3.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/use_mfa) (use MFA1.x now) of our repo. You can download the alignment results from here [alignment_aishell3.tar.gz](https://paddlespeech.bj.bcebos.com/Parakeet/alignment_aishell3.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/other/use_mfa) (use MFA1.x now) of our repo.
```bash ```bash
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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examples/csmsc/tts2/README.md
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...@@ -7,7 +7,7 @@ Download CSMSC from it's [Official Website](https://test.data-baker.com/data/ind ...@@ -7,7 +7,7 @@ Download CSMSC from it's [Official Website](https://test.data-baker.com/data/ind
### Get MFA result of CSMSC and Extract it ### Get MFA result of CSMSC and Extract it
We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for SPEEDYSPEECH. We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for SPEEDYSPEECH.
You can download from here [baker_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/BZNSYP/with_tone/baker_alignment_tone.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/use_mfa) of our repo. You can download from here [baker_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/BZNSYP/with_tone/baker_alignment_tone.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/other/use_mfa) of our repo.
## Get Started ## Get Started
Assume the path to the dataset is `~/datasets/BZNSYP`. Assume the path to the dataset is `~/datasets/BZNSYP`.
...@@ -91,7 +91,7 @@ optional arguments: ...@@ -91,7 +91,7 @@ optional arguments:
7. `--tones-dict` is the path of the tone vocabulary file. 7. `--tones-dict` is the path of the tone vocabulary file.
### Synthesize ### Synthesize
We use [parallel wavegan](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/parallelwave_gan/baker) as the neural vocoder. We use [parallel wavegan](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/csmsc/voc1) as the neural vocoder.
Download pretrained parallel wavegan model from [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip) and unzip it. Download pretrained parallel wavegan model from [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip) and unzip it.
```bash ```bash
unzip pwg_baker_ckpt_0.4.zip unzip pwg_baker_ckpt_0.4.zip
......
examples/csmsc/tts3/README.md
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...@@ -7,7 +7,7 @@ Download CSMSC from it's [Official Website](https://test.data-baker.com/data/ind ...@@ -7,7 +7,7 @@ Download CSMSC from it's [Official Website](https://test.data-baker.com/data/ind
### Get MFA result of CSMSC and Extract it ### Get MFA result of CSMSC and Extract it
We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for fastspeech2. We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for fastspeech2.
You can download from here [baker_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/BZNSYP/with_tone/baker_alignment_tone.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/use_mfa) of our repo. You can download from here [baker_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/BZNSYP/with_tone/baker_alignment_tone.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/other/use_mfa) of our repo.
## Get Started ## Get Started
Assume the path to the dataset is `~/datasets/BZNSYP`. Assume the path to the dataset is `~/datasets/BZNSYP`.
...@@ -88,7 +88,7 @@ optional arguments: ...@@ -88,7 +88,7 @@ optional arguments:
6. `--phones-dict` is the path of the phone vocabulary file. 6. `--phones-dict` is the path of the phone vocabulary file.
### Synthesize ### Synthesize
We use [parallel wavegan](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/parallelwave_gan/baker) as the neural vocoder. We use [parallel wavegan](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/csmsc/voc1) as the neural vocoder.
Download pretrained parallel wavegan model from [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip) and unzip it. Download pretrained parallel wavegan model from [pwg_baker_ckpt_0.4.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip) and unzip it.
```bash ```bash
unzip pwg_baker_ckpt_0.4.zip unzip pwg_baker_ckpt_0.4.zip
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examples/csmsc/voc1/README.md
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...@@ -6,7 +6,7 @@ Download CSMSC from the [official website](https://www.data-baker.com/data/index ...@@ -6,7 +6,7 @@ Download CSMSC from the [official website](https://www.data-baker.com/data/index
### Get MFA results for silence trim ### Get MFA results for silence trim
We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to cut silence in the edge of audio. We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to cut silence in the edge of audio.
You can download from here [baker_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/BZNSYP/with_tone/baker_alignment_tone.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/use_mfa) of our repo. You can download from here [baker_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/BZNSYP/with_tone/baker_alignment_tone.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/other/use_mfa) of our repo.
## Get Started ## Get Started
Assume the path to the dataset is `~/datasets/BZNSYP`. Assume the path to the dataset is `~/datasets/BZNSYP`.
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examples/librispeech/s2/README.md
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# LibriSpeech # LibriSpeech
| Model | Params | Config | Augmentation| Loss |
| --- | --- | --- | --- | ## Transformer
| transformer | 32.52 M | conf/transformer.yaml | spec_aug | 6.3197922706604 |
| Model | Params | GPUS | Averaged Model | Config | Augmentation| Loss |
| --- | --- | --- | --- | --- | --- |
| transformer | 32.52 M | 8 Tesla V100-SXM2-32GB | 10-best val_loss | conf/transformer.yaml | spec_aug | 6.3197922706604 |
| Test Set | Decode Method | #Snt | #Wrd | Corr | Sub | Del | Ins | Err | S.Err | | Test Set | Decode Method | #Snt | #Wrd | Corr | Sub | Del | Ins | Err | S.Err |
...@@ -11,4 +14,14 @@ ...@@ -11,4 +14,14 @@
| test-clean | ctc_greedy_search | 2620 | 52576 | 95.9 | 3.7 | 0.4 | 0.5 | 4.6 | 48.0 | | test-clean | ctc_greedy_search | 2620 | 52576 | 95.9 | 3.7 | 0.4 | 0.5 | 4.6 | 48.0 |
| test-clean | ctc_prefix_beamsearch | 2620 | 52576 | 95.9 | 3.7 | 0.4 | 0.5 | 4.6 | 47.6 | | test-clean | ctc_prefix_beamsearch | 2620 | 52576 | 95.9 | 3.7 | 0.4 | 0.5 | 4.6 | 47.6 |
| test-clean | attention_rescore | 2620 | 52576 | 96.8 | 2.9 | 0.3 | 0.4 | 3.7 | 38.0 | | test-clean | attention_rescore | 2620 | 52576 | 96.8 | 2.9 | 0.3 | 0.4 | 3.7 | 38.0 |
### JoinCTC
| Test Set | Decode Method | #Snt | #Wrd | Corr | Sub | Del | Ins | Err | S.Err |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| test-clean | join_ctc_only_att | 2620 | 52576 | 96.1 | 2.5 | 1.4 | 0.4 | 4.4 | 34.7 |
| test-clean | join_ctc_w/o_lm | 2620 | 52576 | 97.2 | 2.6 | 0.3 | 0.4 | 3.2 | 34.9 | | test-clean | join_ctc_w/o_lm | 2620 | 52576 | 97.2 | 2.6 | 0.3 | 0.4 | 3.2 | 34.9 |
| test-clean | join_ctc_w_lm | 2620 | 52576 | 97.9 | 1.8 | 0.2 | 0.3 | 2.4 | 27.8 |
Compare with [ESPNET](https://github.com/espnet/espnet/blob/master/egs/librispeech/asr1/RESULTS.md#pytorch-large-transformer-with-specaug-4-gpus--transformer-lm-4-gpus)
we using 8gpu, but model size (aheads4-adim256) small than it.
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# https://yaml.org/type/float.html
data:
train_manifest: data/manifest.train
dev_manifest: data/manifest.dev
test_manifest: data/manifest.test
min_input_len: 0.5
max_input_len: 20.0
min_output_len: 0.0
max_output_len: 400.0
min_output_input_ratio: 0.05
max_output_input_ratio: 10.0
collator:
vocab_filepath: data/vocab.txt
unit_type: 'spm'
spm_model_prefix: 'data/bpe_unigram_5000'
mean_std_filepath: ""
augmentation_config: conf/augmentation.json
batch_size: 16
raw_wav: True # use raw_wav or kaldi feature
spectrum_type: fbank #linear, mfcc, fbank
feat_dim: 80
delta_delta: False
dither: 1.0
target_sample_rate: 16000
max_freq: None
n_fft: None
stride_ms: 10.0
window_ms: 25.0
use_dB_normalization: True
target_dB: -20
random_seed: 0
keep_transcription_text: False
sortagrad: True
shuffle_method: batch_shuffle
num_workers: 2
# network architecture
model:
cmvn_file: "data/mean_std.json"
cmvn_file_type: "json"
# encoder related
encoder: conformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: True
use_cnn_module: True
cnn_module_kernel: 15
activation_type: 'swish'
pos_enc_layer_type: 'rel_pos'
selfattention_layer_type: 'rel_selfattn'
causal: True
use_dynamic_chunk: true
cnn_module_norm: 'layer_norm' # using nn.LayerNorm makes model converge faster
use_dynamic_left_chunk: false
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
ctc_dropoutrate: 0.0
ctc_grad_norm_type: null
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
training:
n_epoch: 240
accum_grad: 8
global_grad_clip: 5.0
optim: adam
optim_conf:
lr: 0.001
weight_decay: 1e-06
scheduler: warmuplr # pytorch v1.1.0+ required
scheduler_conf:
warmup_steps: 25000
lr_decay: 1.0
log_interval: 100
checkpoint:
kbest_n: 50
latest_n: 5
decoding:
batch_size: 128
error_rate_type: wer
decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
alpha: 2.5
beta: 0.3
beam_size: 10
cutoff_prob: 1.0
cutoff_top_n: 0
num_proc_bsearch: 8
ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
# <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here.
num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
simulate_streaming: true # simulate streaming inference. Defaults to False.
examples/librispeech/s2/conf/chunk_transformer.yaml 已删除 100644 → 0
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# https://yaml.org/type/float.html
data:
train_manifest: data/manifest.train
dev_manifest: data/manifest.dev
test_manifest: data/manifest.test
min_input_len: 0.5 # second
max_input_len: 20.0 # second
min_output_len: 0.0 # tokens
max_output_len: 400.0 # tokens
min_output_input_ratio: 0.05
max_output_input_ratio: 10.0
collator:
vocab_filepath: data/vocab.txt
unit_type: 'spm'
spm_model_prefix: 'data/bpe_unigram_5000'
mean_std_filepath: ""
augmentation_config: conf/augmentation.json
batch_size: 64
raw_wav: True # use raw_wav or kaldi feature
spectrum_type: fbank #linear, mfcc, fbank
feat_dim: 80
delta_delta: False
dither: 1.0
target_sample_rate: 16000
max_freq: None
n_fft: None
stride_ms: 10.0
window_ms: 25.0
use_dB_normalization: True
target_dB: -20
random_seed: 0
keep_transcription_text: False
sortagrad: True
shuffle_method: batch_shuffle
num_workers: 2
# network architecture
model:
cmvn_file: "data/mean_std.json"
cmvn_file_type: "json"
# encoder related
encoder: transformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: true
use_dynamic_chunk: true
use_dynamic_left_chunk: false
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
ctc_dropoutrate: 0.0
ctc_grad_norm_type: null
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
training:
n_epoch: 120
accum_grad: 1
global_grad_clip: 5.0
optim: adam
optim_conf:
lr: 0.001
weight_decay: 1e-06
scheduler: warmuplr # pytorch v1.1.0+ required
scheduler_conf:
warmup_steps: 25000
lr_decay: 1.0
log_interval: 100
checkpoint:
kbest_n: 50
latest_n: 5
decoding:
batch_size: 64
error_rate_type: wer
decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
alpha: 2.5
beta: 0.3
beam_size: 10
cutoff_prob: 1.0
cutoff_top_n: 0
num_proc_bsearch: 8
ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
# <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here.
num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
simulate_streaming: true # simulate streaming inference. Defaults to False.
examples/librispeech/s2/conf/conformer.yaml 已删除 100644 → 0
浏览文件 @ 04bcb6a1
# https://yaml.org/type/float.html
data:
train_manifest: data/manifest.train
dev_manifest: data/manifest.dev
test_manifest: data/manifest.test-clean
min_input_len: 0.5 # seconds
max_input_len: 20.0 # seconds
min_output_len: 0.0 # tokens
max_output_len: 400.0 # tokens
min_output_input_ratio: 0.05
max_output_input_ratio: 10.0
collator:
vocab_filepath: data/vocab.txt
unit_type: 'spm'
spm_model_prefix: 'data/bpe_unigram_5000'
mean_std_filepath: ""
augmentation_config: conf/augmentation.json
batch_size: 16
raw_wav: True # use raw_wav or kaldi feature
spectrum_type: fbank #linear, mfcc, fbank
feat_dim: 80
delta_delta: False
dither: 1.0
target_sample_rate: 16000
max_freq: None
n_fft: None
stride_ms: 10.0
window_ms: 25.0
use_dB_normalization: True
target_dB: -20
random_seed: 0
keep_transcription_text: False
sortagrad: True
shuffle_method: batch_shuffle
num_workers: 2
# network architecture
model:
cmvn_file: "data/mean_std.json"
cmvn_file_type: "json"
# encoder related
encoder: conformer
encoder_conf:
output_size: 256 # dimension of attention
attention_heads: 4
linear_units: 2048 # the number of units of position-wise feed forward
num_blocks: 12 # the number of encoder blocks
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
normalize_before: True
use_cnn_module: True
cnn_module_kernel: 15
activation_type: 'swish'
pos_enc_layer_type: 'rel_pos'
selfattention_layer_type: 'rel_selfattn'
# decoder related
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
ctc_dropoutrate: 0.0
ctc_grad_norm_type: null
lsm_weight: 0.1 # label smoothing option
length_normalized_loss: false
training:
n_epoch: 120
accum_grad: 8
global_grad_clip: 3.0
optim: adam
optim_conf:
lr: 0.004
weight_decay: 1e-06
scheduler: warmuplr # pytorch v1.1.0+ required
scheduler_conf:
warmup_steps: 25000
lr_decay: 1.0
log_interval: 100
checkpoint:
kbest_n: 50
latest_n: 5
decoding:
batch_size: 64
error_rate_type: wer
decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
alpha: 2.5
beta: 0.3
beam_size: 10
cutoff_prob: 1.0
cutoff_top_n: 0
num_proc_bsearch: 8
ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
# <0: for decoding, use full chunk.
# >0: for decoding, use fixed chunk size as set.
# 0: used for training, it's prohibited here.
num_decoding_left_chunks: -1 # number of left chunks for decoding. Defaults to -1.
simulate_streaming: False # simulate streaming inference. Defaults to False.
examples/librispeech/s2/conf/decode/decode.yaml
浏览文件 @ 950d17cb
batchsize: 0 batchsize: 0
beam-size: 60 beam-size: 60
ctc-weight: 0.0 ctc-weight: 0.4
lm-weight: 0.0 lm-weight: 0.6
maxlenratio: 0.0 maxlenratio: 0.0
minlenratio: 0.0 minlenratio: 0.0
penalty: 0.0 penalty: 0.0
examples/librispeech/s2/conf/decode/decode_att.yaml 0 → 100644
浏览文件 @ 950d17cb
batchsize: 0
beam-size: 60
ctc-weight: 0.0
lm-weight: 0.0
maxlenratio: 0.0
minlenratio: 0.0
penalty: 0.0
examples/librispeech/s2/conf/decode/decode_all.yaml → examples/librispeech/s2/conf/decode/decode_ctc.yaml
浏览文件 @ 950d17cb
batchsize: 0 batchsize: 0
beam-size: 60 beam-size: 60
ctc-weight: 0.4 ctc-weight: 0.4
lm-weight: 0.6 lm-weight: 0.0
maxlenratio: 0.0 maxlenratio: 0.0
minlenratio: 0.0 minlenratio: 0.0
penalty: 0.0 penalty: 0.0
examples/librispeech/s2/conf/fbank.conf 0 → 100644
浏览文件 @ 950d17cb
--sample-frequency=16000
--num-mel-bins=80
examples/librispeech/s2/conf/lm/transformer.yaml 0 → 100644
浏览文件 @ 950d17cb
model_module: transformer
model:
n_vocab: 5002
pos_enc: null
embed_unit: 128
att_unit: 512
head: 8
unit: 2048
layer: 16
dropout_rate: 0.5
emb_dropout_rate: 0.0
att_dropout_rate: 0.0
tie_weights: False
examples/librispeech/s2/conf/transformer.yaml
浏览文件 @ 950d17cb
...@@ -5,9 +5,9 @@ data: ...@@ -5,9 +5,9 @@ data:
test_manifest: data/manifest.test-clean test_manifest: data/manifest.test-clean
collator: collator:
vocab_filepath: data/bpe_unigram_5000_units.txt vocab_filepath: data/lang_char/train_960_unigram5000_units.txt
unit_type: spm unit_type: spm
spm_model_prefix: data/bpe_unigram_5000 spm_model_prefix: data/lang_char/train_960_unigram5000
feat_dim: 83 feat_dim: 83
stride_ms: 10.0 stride_ms: 10.0
window_ms: 25.0 window_ms: 25.0
......
examples/librispeech/s2/local/data.sh
浏览文件 @ 950d17cb
...@@ -2,19 +2,42 @@ ...@@ -2,19 +2,42 @@
stage=-1 stage=-1
stop_stage=100 stop_stage=100
nj=32
debugmode=1
dumpdir=dump # directory to dump full features
N=0 # number of minibatches to be used (mainly for debugging). "0" uses all minibatches.
verbose=0 # verbose option
resume= # Resume the training from snapshot
# feature configuration
do_delta=false
# Set this to somewhere where you want to put your data, or where
# someone else has already put it. You'll want to change this
# if you're not on the CLSP grid.
datadir=${MAIN_ROOT}/examples/dataset/
# bpemode (unigram or bpe) # bpemode (unigram or bpe)
nbpe=5000 nbpe=5000
bpemode=unigram bpemode=unigram
bpeprefix="data/bpe_${bpemode}_${nbpe}"
source ${MAIN_ROOT}/utils/parse_options.sh source ${MAIN_ROOT}/utils/parse_options.sh
# Set bash to 'debug' mode, it will exit on :
# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
set -e
set -u
set -o pipefail
train_set=train_960
train_sp=train_sp
train_dev=dev
recog_set="test_clean test_other dev_clean dev_other"
mkdir -p data mkdir -p data
TARGET_DIR=${MAIN_ROOT}/examples/dataset TARGET_DIR=${MAIN_ROOT}/examples/dataset
mkdir -p ${TARGET_DIR} mkdir -p ${TARGET_DIR}
if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
# download data, generate manifests # download data, generate manifests
python3 ${TARGET_DIR}/librispeech/librispeech.py \ python3 ${TARGET_DIR}/librispeech/librispeech.py \
...@@ -46,63 +69,98 @@ if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then ...@@ -46,63 +69,98 @@ if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
fi fi
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
# compute mean and stddev for normalizer ### Task dependent. You have to make data the following preparation part by yourself.
num_workers=$(nproc) ### But you can utilize Kaldi recipes in most cases
python3 ${MAIN_ROOT}/utils/compute_mean_std.py \ echo "stage 0: Data preparation"
--manifest_path="data/manifest.train.raw" \ for part in dev-clean test-clean dev-other test-other train-clean-100 train-clean-360 train-other-500; do
--num_samples=-1 \ # use underscore-separated names in data directories.
--spectrum_type="fbank" \ local/data_prep.sh ${datadir}/librispeech/${part}/LibriSpeech/${part} data/${part//-/_}
--feat_dim=80 \ done
--delta_delta=false \
--sample_rate=16000 \
--stride_ms=10.0 \
--window_ms=25.0 \
--use_dB_normalization=False \
--num_workers=${num_workers} \
--output_path="data/mean_std.json"
if [ $? -ne 0 ]; then
echo "Compute mean and stddev failed. Terminated."
exit 1
fi
fi fi
feat_tr_dir=${dumpdir}/${train_set}/delta${do_delta}; mkdir -p ${feat_tr_dir}
feat_sp_dir=${dumpdir}/${train_sp}/delta${do_delta}; mkdir -p ${feat_sp_dir}
feat_dt_dir=${dumpdir}/${train_dev}/delta${do_delta}; mkdir -p ${feat_dt_dir}
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
# build vocabulary ### Task dependent. You have to design training and dev sets by yourself.
python3 ${MAIN_ROOT}/utils/build_vocab.py \ ### But you can utilize Kaldi recipes in most cases
--unit_type "spm" \ echo "stage 1: Feature Generation"
--spm_vocab_size=${nbpe} \ fbankdir=fbank
--spm_mode ${bpemode} \ # Generate the fbank features; by default 80-dimensional fbanks with pitch on each frame
--spm_model_prefix ${bpeprefix} \ for x in dev_clean test_clean dev_other test_other train_clean_100 train_clean_360 train_other_500; do
--vocab_path="data/vocab.txt" \ steps/make_fbank_pitch.sh --cmd "$train_cmd" --nj ${nj} --write_utt2num_frames true \
--manifest_paths="data/manifest.train.raw" data/${x} exp/make_fbank/${x} ${fbankdir}
utils/fix_data_dir.sh data/${x}
done
if [ $? -ne 0 ]; then utils/combine_data.sh --extra_files utt2num_frames data/${train_set}_org data/train_clean_100 data/train_clean_360 data/train_other_500
echo "Build vocabulary failed. Terminated." utils/combine_data.sh --extra_files utt2num_frames data/${train_dev}_org data/dev_clean data/dev_other
exit 1 utils/perturb_data_dir_speed.sh 0.9 data/${train_set}_org data/temp1
fi utils/perturb_data_dir_speed.sh 1.0 data/${train_set}_org data/temp2
utils/perturb_data_dir_speed.sh 1.1 data/${train_set}_org data/temp3
utils/combine_data.sh --extra-files utt2uniq data/${train_sp}_org data/temp1 data/temp2 data/temp3
# remove utt having more than 3000 frames
# remove utt having more than 400 characters
remove_longshortdata.sh --maxframes 3000 --maxchars 400 data/${train_set}_org data/${train_set}
remove_longshortdata.sh --maxframes 3000 --maxchars 400 data/${train_sp}_org data/${train_sp}
remove_longshortdata.sh --maxframes 3000 --maxchars 400 data/${train_dev}_org data/${train_dev}
steps/make_fbank_pitch.sh --cmd "$train_cmd" --nj $nj --write_utt2num_frames true \
data/train_sp exp/make_fbank/train_sp ${fbankdir}
utils/fix_data_dir.sh data/train_sp
# compute global CMVN
compute-cmvn-stats scp:data/${train_sp}/feats.scp data/${train_sp}/cmvn.ark
# dump features for training
dump.sh --cmd "$train_cmd" --nj ${nj} --do_delta ${do_delta} \
data/${train_sp}/feats.scp data/${train_sp}/cmvn.ark exp/dump_feats/train ${feat_sp_dir}
dump.sh --cmd "$train_cmd" --nj ${nj} --do_delta ${do_delta} \
data/${train_dev}/feats.scp data/${train_sp}/cmvn.ark exp/dump_feats/dev ${feat_dt_dir}
for rtask in ${recog_set}; do
feat_recog_dir=${dumpdir}/${rtask}/delta${do_delta}; mkdir -p ${feat_recog_dir}
dump.sh --cmd "$train_cmd" --nj ${nj} --do_delta ${do_delta} \
data/${rtask}/feats.scp data/${train_sp}/cmvn.ark exp/dump_feats/recog/${rtask} \
${feat_recog_dir}
done
fi fi
dict=data/lang_char/${train_set}_${bpemode}${nbpe}_units.txt
bpemodel=data/lang_char/${train_set}_${bpemode}${nbpe}
echo "dictionary: ${dict}"
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
# format manifest with tokenids, vocab size ### Task dependent. You have to check non-linguistic symbols used in the corpus.
for set in train dev test dev-clean dev-other test-clean test-other; do echo "stage 2: Dictionary and Json Data Preparation"
{ mkdir -p data/lang_char/
python3 ${MAIN_ROOT}/utils/format_data.py \ echo "<unk> 1" > ${dict} # <unk> must be 1, 0 will be used for "blank" in CTC
--feat_type "raw" \ cut -f 2- -d" " data/${train_set}/text > data/lang_char/input.txt
--cmvn_path "data/mean_std.json" \ spm_train --input=data/lang_char/input.txt --vocab_size=${nbpe} --model_type=${bpemode} --model_prefix=${bpemodel} --input_sentence_size=100000000
--unit_type "spm" \ spm_encode --model=${bpemodel}.model --output_format=piece < data/lang_char/input.txt | tr ' ' '\n' | sort | uniq | awk '{print $0 " " NR+1}' >> ${dict}
--spm_model_prefix ${bpeprefix} \ wc -l ${dict}
--vocab_path="data/vocab.txt" \
--manifest_path="data/manifest.${set}.raw" \ # make json labels
--output_path="data/manifest.${set}" data2json.sh --nj ${nj} --feat ${feat_sp_dir}/feats.scp --bpecode ${bpemodel}.model \
data/${train_sp} ${dict} > ${feat_sp_dir}/data_${bpemode}${nbpe}.json
data2json.sh --nj ${nj} --feat ${feat_dt_dir}/feats.scp --bpecode ${bpemodel}.model \
data/${train_dev} ${dict} > ${feat_dt_dir}/data_${bpemode}${nbpe}.json
for rtask in ${recog_set}; do
feat_recog_dir=${dumpdir}/${rtask}/delta${do_delta}
data2json.sh --nj ${nj} --feat ${feat_recog_dir}/feats.scp --bpecode ${bpemodel}.model \
data/${rtask} ${dict} > ${feat_recog_dir}/data_${bpemode}${nbpe}.json
done
fi
if [ $? -ne 0 ]; then
echo "Formt mnaifest failed. Terminated." if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
exit 1 # make json labels
fi python3 local/espnet_json_to_manifest.py --json-file ${feat_sp_dir}/data_${bpemode}${nbpe}.json --manifest-file data/manifest.train
}& python3 local/espnet_json_to_manifest.py --json-file ${feat_dt_dir}/data_${bpemode}${nbpe}.json --manifest-file data/manifest.dev
for rtask in ${recog_set}; do
feat_recog_dir=${dumpdir}/${rtask}/delta${do_delta}
python3 local/espnet_json_to_manifest.py --json-file ${feat_recog_dir}/data_${bpemode}${nbpe}.json --manifest-file data/manifest.${rtask//_/-}
done done
wait
fi fi
echo "LibriSpeech Data preparation done." echo "LibriSpeech Data preparation done."
......
examples/librispeech/s2/local/data_prep.sh 0 → 100755
浏览文件 @ 950d17cb
#!/usr/bin/env bash
# Copyright 2014 Vassil Panayotov
# 2014 Johns Hopkins University (author: Daniel Povey)
# Apache 2.0
if [ "$#" -ne 2 ]; then
echo "Usage: $0 <src-dir> <dst-dir>"
echo "e.g.: $0 /export/a15/vpanayotov/data/LibriSpeech/dev-clean data/dev-clean"
exit 1
fi
src=$1
dst=$2
# all utterances are FLAC compressed
if ! which flac >&/dev/null; then
echo "Please install 'flac' on ALL worker nodes!"
exit 1
fi
spk_file=$src/../SPEAKERS.TXT
mkdir -p $dst || exit 1
[ ! -d $src ] && echo "$0: no such directory $src" && exit 1
[ ! -f $spk_file ] && echo "$0: expected file $spk_file to exist" && exit 1
wav_scp=$dst/wav.scp; [[ -f "$wav_scp" ]] && rm $wav_scp
trans=$dst/text; [[ -f "$trans" ]] && rm $trans
utt2spk=$dst/utt2spk; [[ -f "$utt2spk" ]] && rm $utt2spk
spk2gender=$dst/spk2gender; [[ -f $spk2gender ]] && rm $spk2gender
for reader_dir in $(find -L $src -mindepth 1 -maxdepth 1 -type d | sort); do
reader=$(basename $reader_dir)
if ! [ $reader -eq $reader ]; then # not integer.
echo "$0: unexpected subdirectory name $reader"
exit 1
fi
reader_gender=$(egrep "^$reader[ ]+\|" $spk_file | awk -F'|' '{gsub(/[ ]+/, ""); print tolower($2)}')
if [ "$reader_gender" != 'm' ] && [ "$reader_gender" != 'f' ]; then
echo "Unexpected gender: '$reader_gender'"
exit 1
fi
for chapter_dir in $(find -L $reader_dir/ -mindepth 1 -maxdepth 1 -type d | sort); do
chapter=$(basename $chapter_dir)
if ! [ "$chapter" -eq "$chapter" ]; then
echo "$0: unexpected chapter-subdirectory name $chapter"
exit 1
fi
find -L $chapter_dir/ -iname "*.flac" | sort | xargs -I% basename % .flac | \
awk -v "dir=$chapter_dir" '{printf "%s flac -c -d -s %s/%s.flac |\n", $0, dir, $0}' >>$wav_scp|| exit 1
chapter_trans=$chapter_dir/${reader}-${chapter}.trans.txt
[ ! -f $chapter_trans ] && echo "$0: expected file $chapter_trans to exist" && exit 1
cat $chapter_trans >>$trans
# NOTE: For now we are using per-chapter utt2spk. That is each chapter is considered
# to be a different speaker. This is done for simplicity and because we want
# e.g. the CMVN to be calculated per-chapter
awk -v "reader=$reader" -v "chapter=$chapter" '{printf "%s %s-%s\n", $1, reader, chapter}' \
<$chapter_trans >>$utt2spk || exit 1
# reader -> gender map (again using per-chapter granularity)
echo "${reader}-${chapter} $reader_gender" >>$spk2gender
done
done
spk2utt=$dst/spk2utt
utils/utt2spk_to_spk2utt.pl <$utt2spk >$spk2utt || exit 1
ntrans=$(wc -l <$trans)
nutt2spk=$(wc -l <$utt2spk)
! [ "$ntrans" -eq "$nutt2spk" ] && \
echo "Inconsistent #transcripts($ntrans) and #utt2spk($nutt2spk)" && exit 1
utils/validate_data_dir.sh --no-feats $dst || exit 1
echo "$0: successfully prepared data in $dst"
exit 0
examples/librispeech/s2/local/recog.sh
浏览文件 @ 950d17cb
...@@ -11,22 +11,24 @@ tag= ...@@ -11,22 +11,24 @@ tag=
decode_config=conf/decode/decode.yaml decode_config=conf/decode/decode.yaml
# lm params # lm params
lang_model=rnnlm.model.best rnnlm_config_path=conf/lm/transformer.yaml
lmexpdir=exp/train_rnnlm_pytorch_lm_transformer_cosine_batchsize32_lr1e-4_layer16_unigram5000_ngpu4/ lmexpdir=exp/lm
lmtag='nolm' lang_model=rnnlm.pdparams
lmtag='transformer'
train_set=train_960
recog_set="test-clean test-other dev-clean dev-other" recog_set="test-clean test-other dev-clean dev-other"
recog_set="test-clean" recog_set="test-clean"
# bpemode (unigram or bpe) # bpemode (unigram or bpe)
nbpe=5000 nbpe=5000
bpemode=unigram bpemode=unigram
bpeprefix="data/bpe_${bpemode}_${nbpe}" bpeprefix=data/lang_char/${train_set}_${bpemode}${nbpe}
bpemodel=${bpeprefix}.model bpemodel=${bpeprefix}.model
# bin params # bin params
config_path=conf/transformer.yaml config_path=conf/transformer.yaml
dict=data/bpe_unigram_5000_units.txt dict=data/lang_char/${train_set}_${bpemode}${nbpe}_units.txt
ckpt_prefix= ckpt_prefix=
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1; source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
...@@ -51,6 +53,9 @@ if [[ ${config_path} =~ ^.*chunk_.*yaml$ ]];then ...@@ -51,6 +53,9 @@ if [[ ${config_path} =~ ^.*chunk_.*yaml$ ]];then
fi fi
echo "chunk mode: ${chunk_mode}" echo "chunk mode: ${chunk_mode}"
echo "decode conf: ${decode_config}" echo "decode conf: ${decode_config}"
echo "lm conf: ${rnnlm_config_path}"
echo "lm model: ${lmexpdir}/${lang_model}"
# download language model # download language model
#bash local/download_lm_en.sh #bash local/download_lm_en.sh
...@@ -59,6 +64,13 @@ echo "decode conf: ${decode_config}" ...@@ -59,6 +64,13 @@ echo "decode conf: ${decode_config}"
#fi #fi
# download rnnlm
mkdir -p ${lmexpdir}
if [ ! -f ${lmexpdir}/${lang_model} ]; then
wget -c -O ${lmexpdir}/${lang_model} https://deepspeech.bj.bcebos.com/transformer_lm/transformerLM.pdparams
fi
pids=() # initialize pids pids=() # initialize pids
for dmethd in join_ctc; do for dmethd in join_ctc; do
...@@ -90,9 +102,9 @@ for dmethd in join_ctc; do ...@@ -90,9 +102,9 @@ for dmethd in join_ctc; do
--recog-json ${feat_recog_dir}/split${nj}/JOB/manifest.${rtask} \ --recog-json ${feat_recog_dir}/split${nj}/JOB/manifest.${rtask} \
--result-label ${decode_dir}/data.JOB.json \ --result-label ${decode_dir}/data.JOB.json \
--model-conf ${config_path} \ --model-conf ${config_path} \
--model ${ckpt_prefix}.pdparams --model ${ckpt_prefix}.pdparams \
--rnnlm-conf ${rnnlm_config_path} \
#--rnnlm ${lmexpdir}/${lang_model} \ --rnnlm ${lmexpdir}/${lang_model}
score_sclite.sh --bpe ${nbpe} --bpemodel ${bpemodel} --wer false ${decode_dir} ${dict} score_sclite.sh --bpe ${nbpe} --bpemodel ${bpemodel} --wer false ${decode_dir} ${dict}
......
examples/librispeech/s2/local/test.sh
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...@@ -8,17 +8,18 @@ nj=32 ...@@ -8,17 +8,18 @@ nj=32
lmtag='nolm' lmtag='nolm'
train_set=train_960
recog_set="test-clean test-other dev-clean dev-other" recog_set="test-clean test-other dev-clean dev-other"
recog_set="test-clean" recog_set="test-clean"
# bpemode (unigram or bpe) # bpemode (unigram or bpe)
nbpe=5000 nbpe=5000
bpemode=unigram bpemode=unigram
bpeprefix="data/bpe_${bpemode}_${nbpe}" bpeprefix=data/lang_char/${train_set}_${bpemode}${nbpe}
bpemodel=${bpeprefix}.model bpemodel=${bpeprefix}.model
config_path=conf/transformer.yaml config_path=conf/transformer.yaml
dict=data/bpe_unigram_5000_units.txt dict=data/lang_char/${train_set}_${bpemode}${nbpe}_units.txt
ckpt_prefix= ckpt_prefix=
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1; source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
......
examples/librispeech/s2/path.sh
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export MAIN_ROOT=`realpath ${PWD}/../../../` export MAIN_ROOT=`realpath ${PWD}/../../../`
export PATH=${MAIN_ROOT}:${MAIN_ROOT}/tools/sctk/bin:${PWD}/utils:${PATH} export PATH=${MAIN_ROOT}:${MAIN_ROOT}/tools/sctk/bin:${MAIN_ROOT}/utils:${PWD}/utils:${PATH}
export LC_ALL=C export LC_ALL=C
export PYTHONDONTWRITEBYTECODE=1 export PYTHONDONTWRITEBYTECODE=1
...@@ -13,3 +13,16 @@ export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/ ...@@ -13,3 +13,16 @@ export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
MODEL=u2_kaldi MODEL=u2_kaldi
export BIN_DIR=${MAIN_ROOT}/deepspeech/exps/${MODEL}/bin export BIN_DIR=${MAIN_ROOT}/deepspeech/exps/${MODEL}/bin
# srilm
export LIBLBFGS=${MAIN_ROOT}/tools/liblbfgs-1.10
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH:-}:${LIBLBFGS}/lib/.libs
export SRILM=${MAIN_ROOT}/tools/srilm
export PATH=${PATH}:${SRILM}/bin:${SRILM}/bin/i686-m64
# Kaldi
export KALDI_ROOT=${MAIN_ROOT}/tools/kaldi
[ -f $KALDI_ROOT/tools/env.sh ] && . $KALDI_ROOT/tools/env.sh
export PATH=$PWD/utils/:$KALDI_ROOT/tools/openfst/bin:$PWD:$PATH
[ ! -f $KALDI_ROOT/tools/config/common_path.sh ] && echo >&2 "The standard file $KALDI_ROOT/tools/config/common_path.sh is not present, can not using Kaldi!"
[ -f $KALDI_ROOT/tools/config/common_path.sh ] && . $KALDI_ROOT/tools/config/common_path.sh
\ No newline at end of file
examples/librispeech/s2/run.sh
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...@@ -33,16 +33,21 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then ...@@ -33,16 +33,21 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
fi fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# test ckpt avg_n # attetion resocre decoder
./local/test.sh ${conf_path} ${dict_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1 ./local/test.sh ${conf_path} ${dict_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
fi fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
# join ctc decoder, use transformerlm to score
./local/recog.sh --ckpt_prefix exp/${ckpt}/checkpoints/${avg_ckpt}
fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
# ctc alignment of test data # ctc alignment of test data
CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} ${dict_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1 CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} ${dict_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
fi fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
# export ckpt avg_n # export ckpt avg_n
CUDA_VISIBLE_DEVICES= ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit CUDA_VISIBLE_DEVICES= ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
fi fi
examples/librispeech/s2/steps 0 → 120000
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../../../tools/kaldi/egs/wsj/s5/steps/
\ No newline at end of file
examples/librispeech/s2/utils
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../../../utils/ ../../../tools/kaldi/egs/wsj/s5/utils
\ No newline at end of file \ No newline at end of file
examples/ljspeech/tts0/README.md
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...@@ -77,7 +77,7 @@ optional arguments: ...@@ -77,7 +77,7 @@ optional arguments:
config, passing in KEY VALUE pairs config, passing in KEY VALUE pairs
-v, --verbose print msg -v, --verbose print msg
``` ```
**Ps.** You can use [waveflow](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/waveflow) as the neural vocoder to synthesize mels to wavs. (Please refer to `synthesize.sh` in our LJSpeech waveflow example) **Ps.** You can use [waveflow](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/ljspeech/voc0) as the neural vocoder to synthesize mels to wavs. (Please refer to `synthesize.sh` in our LJSpeech waveflow example)
## Pretrained Models ## Pretrained Models
Pretrained Models can be downloaded from links below. We provide 2 models with different configurations. Pretrained Models can be downloaded from links below. We provide 2 models with different configurations.
......
examples/ljspeech/tts1/README.md
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...@@ -81,7 +81,7 @@ optional arguments: ...@@ -81,7 +81,7 @@ optional arguments:
6. `--phones-dict` is the path of the phone vocabulary file. 6. `--phones-dict` is the path of the phone vocabulary file.
## Synthesize ## Synthesize
We use [waveflow](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/waveflow) as the neural vocoder. We use [waveflow](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/ljspeech/voc0) as the neural vocoder.
Download Pretrained WaveFlow Model with residual channel equals 128 from [waveflow_ljspeech_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/waveflow_ljspeech_ckpt_0.3.zip) and unzip it. Download Pretrained WaveFlow Model with residual channel equals 128 from [waveflow_ljspeech_ckpt_0.3.zip](https://paddlespeech.bj.bcebos.com/Parakeet/waveflow_ljspeech_ckpt_0.3.zip) and unzip it.
```bash ```bash
unzip waveflow_ljspeech_ckpt_0.3.zip unzip waveflow_ljspeech_ckpt_0.3.zip
......
examples/ljspeech/tts3/README.md
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...@@ -7,7 +7,7 @@ Download LJSpeech-1.1 from the [official website](https://keithito.com/LJ-Speech ...@@ -7,7 +7,7 @@ Download LJSpeech-1.1 from the [official website](https://keithito.com/LJ-Speech
### Get MFA result of LJSpeech-1.1 and Extract it ### Get MFA result of LJSpeech-1.1 and Extract it
We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for fastspeech2. We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for fastspeech2.
You can download from here [ljspeech_alignment.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/LJSpeech-1.1/ljspeech_alignment.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/use_mfa) of our repo. You can download from here [ljspeech_alignment.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/LJSpeech-1.1/ljspeech_alignment.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/other/use_mfa) of our repo.
## Get Started ## Get Started
Assume the path to the dataset is `~/datasets/LJSpeech-1.1`. Assume the path to the dataset is `~/datasets/LJSpeech-1.1`.
...@@ -88,7 +88,7 @@ optional arguments: ...@@ -88,7 +88,7 @@ optional arguments:
6. `--phones-dict` is the path of the phone vocabulary file. 6. `--phones-dict` is the path of the phone vocabulary file.
### Synthesize ### Synthesize
We use [parallel wavegan](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/parallelwave_gan/ljspeech/) as the neural vocoder. We use [parallel wavegan](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/ljspeech/voc1) as the neural vocoder.
Download pretrained parallel wavegan model from [pwg_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_ljspeech_ckpt_0.5.zip) and unzip it. Download pretrained parallel wavegan model from [pwg_ljspeech_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_ljspeech_ckpt_0.5.zip) and unzip it.
```bash ```bash
unzip pwg_ljspeech_ckpt_0.5.zip unzip pwg_ljspeech_ckpt_0.5.zip
......
examples/ljspeech/voc1/README.md
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...@@ -5,7 +5,7 @@ This example contains code used to train a [parallel wavegan](http://arxiv.org/a ...@@ -5,7 +5,7 @@ This example contains code used to train a [parallel wavegan](http://arxiv.org/a
Download LJSpeech-1.1 from the [official website](https://keithito.com/LJ-Speech-Dataset/). Download LJSpeech-1.1 from the [official website](https://keithito.com/LJ-Speech-Dataset/).
### Get MFA results for silence trim ### Get MFA results for silence trim
We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to cut silence in the edge of audio. We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to cut silence in the edge of audio.
You can download from here [ljspeech_alignment.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/LJSpeech-1.1/ljspeech_alignment.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/use_mfa) of our repo. You can download from here [ljspeech_alignment.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/LJSpeech-1.1/ljspeech_alignment.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/other/use_mfa) of our repo.
## Get Started ## Get Started
Assume the path to the dataset is `~/datasets/LJSpeech-1.1`. Assume the path to the dataset is `~/datasets/LJSpeech-1.1`.
......
examples/other/ge2e/README.md
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# Speaker Encoder # Speaker Encoder
This experiment trains a speaker encoder with speaker verification as its task. It is done as a part of the experiment of transfer learning from speaker verification to multispeaker text-to-speech synthesis, which can be found at [tacotron2_aishell3](../tacotron2_shell3). The trained speaker encoder is used to extract utterance embeddings from utterances. This experiment trains a speaker encoder with speaker verification as its task. It is done as a part of the experiment of transfer learning from speaker verification to multispeaker text-to-speech synthesis, which can be found at [examples/aishell3/vc0](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/aishell3/vc0). The trained speaker encoder is used to extract utterance embeddings from utterances.
## Model ## Model
The model used in this experiment is the speaker encoder with text independent speaker verification task in [GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION](https://arxiv.org/pdf/1710.10467.pdf). GE2E-softmax loss is used. The model used in this experiment is the speaker encoder with text independent speaker verification task in [GENERALIZED END-TO-END LOSS FOR SPEAKER VERIFICATION](https://arxiv.org/pdf/1710.10467.pdf). GE2E-softmax loss is used.
......
examples/tiny/s0/README.md
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...@@ -23,12 +23,6 @@ ...@@ -23,12 +23,6 @@
- Case inference with an existing model - Case inference with an existing model
```bash
bash local/infer.sh
```
`infer.sh` will show us some speech-to-text decoding results for several (default: 10) samples with the trained model. The performance might not be good now as the current model is only trained with a toy subset of LibriSpeech. To see the results with a better model, you can download a well-trained (trained for several days, with the complete LibriSpeech) model and do the inference.
- Evaluate an existing model - Evaluate an existing model
```bash ```bash
...@@ -44,8 +38,3 @@ ...@@ -44,8 +38,3 @@
bash local/export.sh ckpt_path saved_jit_model_path bash local/export.sh ckpt_path saved_jit_model_path
``` ```
- Tune hyper paerameter
```bash
bash local/tune.sh
```
examples/vctk/tts3/README.md
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...@@ -7,8 +7,8 @@ Download VCTK-0.92 from the [official website](https://datashare.ed.ac.uk/handle ...@@ -7,8 +7,8 @@ Download VCTK-0.92 from the [official website](https://datashare.ed.ac.uk/handle
### Get MFA result of VCTK and Extract it ### Get MFA result of VCTK and Extract it
We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for fastspeech2. We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get durations for fastspeech2.
You can download from here [vctk_alignment.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/VCTK-Corpus-0.92/vctk_alignment.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/use_mfa) of our repo. You can download from here [vctk_alignment.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/VCTK-Corpus-0.92/vctk_alignment.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/other/use_mfa) of our repo.
ps: we remove three speakers in VCTK-0.92 (see [reorganize_vctk.py](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/use_mfa/local/reorganize_vctk.py)): ps: we remove three speakers in VCTK-0.92 (see [reorganize_vctk.py](https://github.com/PaddlePaddle/DeepSpeech/blob/develop/examples/other/use_mfa/local/reorganize_vctk.py)):
1. `p315`, because no txt for it. 1. `p315`, because no txt for it.
2. `p280` and `p362`, because no *_mic2.flac (which is better than *_mic1.flac) for them. 2. `p280` and `p362`, because no *_mic2.flac (which is better than *_mic1.flac) for them.
...@@ -91,7 +91,7 @@ optional arguments: ...@@ -91,7 +91,7 @@ optional arguments:
6. `--phones-dict` is the path of the phone vocabulary file. 6. `--phones-dict` is the path of the phone vocabulary file.
### Synthesize ### Synthesize
We use [parallel wavegan](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/parallelwave_gan/baker) as the neural vocoder. We use [parallel wavegan](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/vctk/voc1) as the neural vocoder.
Download pretrained parallel wavegan model from [pwg_vctk_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_vctk_ckpt_0.5.zip)and unzip it. Download pretrained parallel wavegan model from [pwg_vctk_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_vctk_ckpt_0.5.zip)and unzip it.
```bash ```bash
......
examples/vctk/voc1/README.md
浏览文件 @ 950d17cb
...@@ -7,8 +7,8 @@ Download VCTK-0.92 from the [official website](https://datashare.ed.ac.uk/handl ...@@ -7,8 +7,8 @@ Download VCTK-0.92 from the [official website](https://datashare.ed.ac.uk/handl
### Get MFA results for silence trim ### Get MFA results for silence trim
We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to cut silence in the edge of audio. We use [MFA](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) results to cut silence in the edge of audio.
You can download from here [vctk_alignment.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/VCTK-Corpus-0.92/vctk_alignment.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/use_mfa) of our repo. You can download from here [vctk_alignment.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/VCTK-Corpus-0.92/vctk_alignment.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/other/use_mfa) of our repo.
ps: we remove three speakers in VCTK-0.92 (see [reorganize_vctk.py](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples/use_mfa/local/reorganize_vctk.py)): ps: we remove three speakers in VCTK-0.92 (see [reorganize_vctk.py](https://github.com/PaddlePaddle/DeepSpeech/blob/develop/examples/other/use_mfa/local/reorganize_vctk.py)):
1. `p315`, because no txt for it. 1. `p315`, because no txt for it.
2. `p280` and `p362`, because no *_mic2.flac (which is better than *_mic1.flac) for them. 2. `p280` and `p362`, because no *_mic2.flac (which is better than *_mic1.flac) for them.
......
paddleaudio/.gitignore 0 → 100644
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.ipynb_checkpoints/**
*.ipynb
nohup.out
__pycache__/
*.wav
*.m4a
obsolete/**
paddleaudio/.pre-commit-config.yaml 0 → 100644
浏览文件 @ 950d17cb
repos:
- repo: local
hooks:
- id: yapf
name: yapf
entry: yapf
language: system
args: [-i, --style .style.yapf]
files: \.py$
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: a11d9314b22d8f8c7556443875b731ef05965464
hooks:
- id: check-merge-conflict
- id: check-symlinks
- id: end-of-file-fixer
- id: trailing-whitespace
- id: detect-private-key
- id: check-symlinks
- id: check-added-large-files
- repo: https://github.com/pycqa/isort
rev: 5.8.0
hooks:
- id: isort
name: isort (python)
- id: isort
name: isort (cython)
types: [cython]
- id: isort
name: isort (pyi)
types: [pyi]
- repo: local
hooks:
- id: flake8
name: flake8
entry: flake8
language: system
args:
- --count
- --select=E9,F63,F7,F82
- --show-source
- --statistics
files: \.py$
paddleaudio/.style.yapf 0 → 100644
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[style]
based_on_style = pep8
column_limit = 80
paddleaudio/LICENSE 0 → 100644
浏览文件 @ 950d17cb
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# PaddleAudio: The audio library for PaddlePaddle
## Introduction
PaddleAudio is the audio toolkit to speed up your audio research and development loop in PaddlePaddle. It currently provides a collection of audio datasets, feature-extraction functions, audio transforms,state-of-the-art pre-trained models in sound tagging/classification and anomaly sound detection. More models and features are on the roadmap.
## Features
- Spectrogram and related features are compatible with librosa.
- State-of-the-art models in sound tagging on Audioset, sound classification on esc50, and more to come.
- Ready-to-use audio embedding with a line of code, includes sound embedding and more on the roadmap.
- Data loading supports for common open source audio in multiple languages including English, Mandarin and so on.
## Install
```
git clone https://github.com/PaddlePaddle/models
cd models/PaddleAudio
pip install .
```
## Quick start
### Audio loading and feature extraction
```
import paddleaudio as pa
s,r = pa.load(f)
mel_spect = pa.melspectrogram(s,sr=r)
```
### Examples
We provide a set of examples to help you get started in using PaddleAudio quickly.
- [PANNs: acoustic scene and events analysis using pre-trained models](./examples/panns)
- [Environmental Sound classification on ESC-50 dataset](./examples/sound_classification)
- [Training a audio-tagging network on Audioset](./examples/audioset_training)
Please refer to [example directory](./examples) for more details.
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# Audio Tagging
声音分类的任务是单标签的分类任务，但是对于一段音频来说，它可以是多标签的。譬如在一般的室内办公环境进行录音，这段音频里可能包含人们说话的声音、键盘敲打的声音、鼠标点击的声音，还有室内的一些其他背景声音。对于通用的声音识别和声音检测场景而言，对一段音频预测多个标签是具有很强的实用性的。
在IEEE ICASSP 2017 大会上，谷歌开放了一个大规模的音频数据集[Audioset](https://research.google.com/audioset/)。该数据集包含了 632 类的音频类别以及 2,084,320 条人工标记的每段 10 秒长度的声音剪辑片段（来源于YouTube视频）。目前该数据集已经有210万个已标注的视频数据，5800小时的音频数据，经过标记的声音样本的标签类别为527。
`PANNs`([PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://arxiv.org/pdf/1912.10211.pdf))是基于Audioset数据集训练的声音分类/识别的模型。其预训练的任务是多标签的声音识别，因此可用于声音的实时tagging。
本示例采用`PANNs`预训练模型，基于Audioset的标签类别对输入音频实时tagging，并最终以文本形式输出对应时刻的top k类别和对应的得分。
## 模型简介
PaddleAudio提供了PANNs的CNN14、CNN10和CNN6的预训练模型，可供用户选择使用：
- CNN14: 该模型主要包含12个卷积层和2个全连接层，模型参数的数量为79.6M，embbedding维度是2048。
- CNN10: 该模型主要包含8个卷积层和2个全连接层，模型参数的数量为4.9M，embbedding维度是512。
- CNN6: 该模型主要包含4个卷积层和2个全连接层，模型参数的数量为4.5M，embbedding维度是512。
## 快速开始
### 模型预测
```shell
export CUDA_VISIBLE_DEVICES=0
python audio_tag.py --device gpu --wav ./cat.wav --sample_duration 2 --hop_duration 0.3 --output_dir ./output_dir
```
可支持配置的参数：
- `device`: 选用什么设备进行训练，可选cpu或gpu，默认为gpu。如使用gpu训练则参数gpus指定GPU卡号。
- `wav`: 指定预测的音频文件。
- `sample_duration`: 模型每次预测的音频时间长度，单位为秒，默认为2s。
- `hop_duration`: 每两个预测音频的时间间隔，单位为秒，默认为0.3s。
- `output_dir`: 模型预测结果存放的路径，默认为`./output_dir`。
示例代码中使用的预训练模型为`CNN14`，如果想更换为其他预训练模型，可通过以下方式执行：
```python
from paddleaudio.models.panns import cnn14, cnn10, cnn6
# CNN14
model = cnn14(pretrained=True, extract_embedding=False)
# CNN10
model = cnn10(pretrained=True, extract_embedding=False)
# CNN6
model = cnn6(pretrained=True, extract_embedding=False)
```
执行结果：
```
[2021-04-30 19:15:41,025] [ INFO] - Saved tagging results to ./output_dir/audioset_tagging_sr_44100.npz
```
执行后得分结果保存在`output_dir`的`.npz`文件中。
### 生成tagging标签文本
```shell
python parse_result.py --tagging_file ./output_dir/audioset_tagging_sr_44100.npz --top_k 10 --smooth True --smooth_size 5 --label_file ./assets/audioset_labels.txt --output_dir ./output_dir
```
可支持配置的参数：
- `tagging_file`: 模型预测结果文件。
- `top_k`: 获取预测结果中，得分最高的前top_k个标签，默认为10。
- `smooth`: 预测结果的后验概率平滑，默认为True，表示应用平滑。
- `smooth_size`: 平滑计算过程中的样本数量，默认为5。
- `label_file`: 模型预测结果对应的Audioset类别的文本文件。
- `output_dir`: 标签文本存放的路径，默认为`./output_dir`。
执行结果：
```
[2021-04-30 19:26:58,743] [ INFO] - Posterior smoothing...
[2021-04-30 19:26:58,746] [ INFO] - Saved tagging labels to ./output_dir/audioset_tagging_sr_44100.txt
```
执行后文本结果保存在`output_dir`的`.txt`文件中。
## Tagging标签文本
最终输出的文本结果如下所示。
样本每个时间范围的top k结果用空行分隔。在每一个结果中，第一行是时间信息，数字表示tagging结果在时间起点信息，比例值代表当前时刻`t`与音频总长度`T`的比值；紧接的k行是对应的标签和得分。
```
0.0
Cat: 0.9144676923751831
Animal: 0.8855036497116089
Domestic animals, pets: 0.804577112197876
Meow: 0.7422927021980286
Music: 0.19959309697151184
Inside, small room: 0.12550437450408936
Caterwaul: 0.021584441885352135
Purr: 0.020247288048267365
Speech: 0.018197158351540565
Vehicle: 0.007446660194545984
0.059197544398158296
Cat: 0.9250872135162354
Animal: 0.8957151174545288
Domestic animals, pets: 0.8228275775909424
Meow: 0.7650775909423828
Music: 0.20210561156272888
Inside, small room: 0.12290887534618378
Caterwaul: 0.029371455311775208
Purr: 0.018731823191046715
Speech: 0.017130598425865173
Vehicle: 0.007748497650027275
0.11839508879631659
Cat: 0.9336574673652649
Animal: 0.9111202359199524
Domestic animals, pets: 0.8349071145057678
Meow: 0.7761964797973633
Music: 0.20467285811901093
Inside, small room: 0.10709915310144424
Caterwaul: 0.05370649695396423
Purr: 0.018830426037311554
Speech: 0.017361722886562347
Vehicle: 0.006929398979991674
...
...
```
以下[Demo](https://bj.bcebos.com/paddleaudio/media/audio_tagging_demo.mp4)展示了一个将tagging标签输出到视频的例子，可以实时地对音频进行多标签预测。
![](https://bj.bcebos.com/paddleaudio/media/audio_tagging_demo.gif)
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Speech
Male speech, man speaking
Female speech, woman speaking
Child speech, kid speaking
Conversation
Narration, monologue
Babbling
Speech synthesizer
Shout
Bellow
Whoop
Yell
Battle cry
Children shouting
Screaming
Whispering
Laughter
Baby laughter
Giggle
Snicker
Belly laugh
Chuckle, chortle
Crying, sobbing
Baby cry, infant cry
Whimper
Wail, moan
Sigh
Singing
Choir
Yodeling
Chant
Mantra
Male singing
Female singing
Child singing
Synthetic singing
Rapping
Humming
Groan
Grunt
Whistling
Breathing
Wheeze
Snoring
Gasp
Pant
Snort
Cough
Throat clearing
Sneeze
Sniff
Run
Shuffle
Walk, footsteps
Chewing, mastication
Biting
Gargling
Stomach rumble
Burping, eructation
Hiccup
Fart
Hands
Finger snapping
Clapping
Heart sounds, heartbeat
Heart murmur
Cheering
Applause
Chatter
Crowd
Hubbub, speech noise, speech babble
Children playing
Animal
Domestic animals, pets
Dog
Bark
Yip
Howl
Bow-wow
Growling
Whimper (dog)
Cat
Purr
Meow
Hiss
Caterwaul
Livestock, farm animals, working animals
Horse
Clip-clop
Neigh, whinny
Cattle, bovinae
Moo
Cowbell
Pig
Oink
Goat
Bleat
Sheep
Fowl
Chicken, rooster
Cluck
Crowing, cock-a-doodle-doo
Turkey
Gobble
Duck
Quack
Goose
Honk
Wild animals
Roaring cats (lions, tigers)
Roar
Bird
Bird vocalization, bird call, bird song
Chirp, tweet
Squawk
Pigeon, dove
Coo
Crow
Caw
Owl
Hoot
Bird flight, flapping wings
Canidae, dogs, wolves
Rodents, rats, mice
Mouse
Patter
Insect
Cricket
Mosquito
Fly, housefly
Buzz
Bee, wasp, etc.
Frog
Croak
Snake
Rattle
Whale vocalization
Music
Musical instrument
Plucked string instrument
Guitar
Electric guitar
Bass guitar
Acoustic guitar
Steel guitar, slide guitar
Tapping (guitar technique)
Strum
Banjo
Sitar
Mandolin
Zither
Ukulele
Keyboard (musical)
Piano
Electric piano
Organ
Electronic organ
Hammond organ
Synthesizer
Sampler
Harpsichord
Percussion
Drum kit
Drum machine
Drum
Snare drum
Rimshot
Drum roll
Bass drum
Timpani
Tabla
Cymbal
Hi-hat
Wood block
Tambourine
Rattle (instrument)
Maraca
Gong
Tubular bells
Mallet percussion
Marimba, xylophone
Glockenspiel
Vibraphone
Steelpan
Orchestra
Brass instrument
French horn
Trumpet
Trombone
Bowed string instrument
String section
Violin, fiddle
Pizzicato
Cello
Double bass
Wind instrument, woodwind instrument
Flute
Saxophone
Clarinet
Harp
Bell
Church bell
Jingle bell
Bicycle bell
Tuning fork
Chime
Wind chime
Change ringing (campanology)
Harmonica
Accordion
Bagpipes
Didgeridoo
Shofar
Theremin
Singing bowl
Scratching (performance technique)
Pop music
Hip hop music
Beatboxing
Rock music
Heavy metal
Punk rock
Grunge
Progressive rock
Rock and roll
Psychedelic rock
Rhythm and blues
Soul music
Reggae
Country
Swing music
Bluegrass
Funk
Folk music
Middle Eastern music
Jazz
Disco
Classical music
Opera
Electronic music
House music
Techno
Dubstep
Drum and bass
Electronica
Electronic dance music
Ambient music
Trance music
Music of Latin America
Salsa music
Flamenco
Blues
Music for children
New-age music
Vocal music
A capella
Music of Africa
Afrobeat
Christian music
Gospel music
Music of Asia
Carnatic music
Music of Bollywood
Ska
Traditional music
Independent music
Song
Background music
Theme music
Jingle (music)
Soundtrack music
Lullaby
Video game music
Christmas music
Dance music
Wedding music
Happy music
Funny music
Sad music
Tender music
Exciting music
Angry music
Scary music
Wind
Rustling leaves
Wind noise (microphone)
Thunderstorm
Thunder
Water
Rain
Raindrop
Rain on surface
Stream
Waterfall
Ocean
Waves, surf
Steam
Gurgling
Fire
Crackle
Vehicle
Boat, Water vehicle
Sailboat, sailing ship
Rowboat, canoe, kayak
Motorboat, speedboat
Ship
Motor vehicle (road)
Car
Vehicle horn, car horn, honking
Toot
Car alarm
Power windows, electric windows
Skidding
Tire squeal
Car passing by
Race car, auto racing
Truck
Air brake
Air horn, truck horn
Reversing beeps
Ice cream truck, ice cream van
Bus
Emergency vehicle
Police car (siren)
Ambulance (siren)
Fire engine, fire truck (siren)
Motorcycle
Traffic noise, roadway noise
Rail transport
Train
Train whistle
Train horn
Railroad car, train wagon
Train wheels squealing
Subway, metro, underground
Aircraft
Aircraft engine
Jet engine
Propeller, airscrew
Helicopter
Fixed-wing aircraft, airplane
Bicycle
Skateboard
Engine
Light engine (high frequency)
Dental drill, dentist's drill
Lawn mower
Chainsaw
Medium engine (mid frequency)
Heavy engine (low frequency)
Engine knocking
Engine starting
Idling
Accelerating, revving, vroom
Door
Doorbell
Ding-dong
Sliding door
Slam
Knock
Tap
Squeak
Cupboard open or close
Drawer open or close
Dishes, pots, and pans
Cutlery, silverware
Chopping (food)
Frying (food)
Microwave oven
Blender
Water tap, faucet
Sink (filling or washing)
Bathtub (filling or washing)
Hair dryer
Toilet flush
Toothbrush
Electric toothbrush
Vacuum cleaner
Zipper (clothing)
Keys jangling
Coin (dropping)
Scissors
Electric shaver, electric razor
Shuffling cards
Typing
Typewriter
Computer keyboard
Writing
Alarm
Telephone
Telephone bell ringing
Ringtone
Telephone dialing, DTMF
Dial tone
Busy signal
Alarm clock
Siren
Civil defense siren
Buzzer
Smoke detector, smoke alarm
Fire alarm
Foghorn
Whistle
Steam whistle
Mechanisms
Ratchet, pawl
Clock
Tick
Tick-tock
Gears
Pulleys
Sewing machine
Mechanical fan
Air conditioning
Cash register
Printer
Camera
Single-lens reflex camera
Tools
Hammer
Jackhammer
Sawing
Filing (rasp)
Sanding
Power tool
Drill
Explosion
Gunshot, gunfire
Machine gun
Fusillade
Artillery fire
Cap gun
Fireworks
Firecracker
Burst, pop
Eruption
Boom
Wood
Chop
Splinter
Crack
Glass
Chink, clink
Shatter
Liquid
Splash, splatter
Slosh
Squish
Drip
Pour
Trickle, dribble
Gush
Fill (with liquid)
Spray
Pump (liquid)
Stir
Boiling
Sonar
Arrow
Whoosh, swoosh, swish
Thump, thud
Thunk
Electronic tuner
Effects unit
Chorus effect
Basketball bounce
Bang
Slap, smack
Whack, thwack
Smash, crash
Breaking
Bouncing
Whip
Flap
Scratch
Scrape
Rub
Roll
Crushing
Crumpling, crinkling
Tearing
Beep, bleep
Ping
Ding
Clang
Squeal
Creak
Rustle
Whir
Clatter
Sizzle
Clicking
Clickety-clack
Rumble
Plop
Jingle, tinkle
Hum
Zing
Boing
Crunch
Silence
Sine wave
Harmonic
Chirp tone
Sound effect
Pulse
Inside, small room
Inside, large room or hall
Inside, public space
Outside, urban or manmade
Outside, rural or natural
Reverberation
Echo
Noise
Environmental noise
Static
Mains hum
Distortion
Sidetone
Cacophony
White noise
Pink noise
Throbbing
Vibration
Television
Radio
Field recording
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from typing import List
import numpy as np
import paddle
from paddleaudio.backends import load as load_audio
from paddleaudio.features import melspectrogram
from paddleaudio.models.panns import cnn14
from paddleaudio.utils import logger
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument('--device', choices=['cpu', 'gpu'], default='gpu', help='Select which device to predict, defaults to gpu.')
parser.add_argument('--wav', type=str, required=True, help='Audio file to infer.')
parser.add_argument('--sample_duration', type=float, default=2.0, help='Duration(in seconds) of tagging samples to predict.')
parser.add_argument('--hop_duration', type=float, default=0.3, help='Duration(in seconds) between two samples.')
parser.add_argument('--output_dir', type=str, default='./output_dir', help='Directory to save tagging result.')
args = parser.parse_args()
# yapf: enable
def split(waveform: np.ndarray, win_size: int, hop_size: int):
"""
Split into N waveforms.
N is decided by win_size and hop_size.
"""
assert isinstance(waveform, np.ndarray)
time = []
data = []
for i in range(0, len(waveform), hop_size):
segment = waveform[i:i + win_size]
if len(segment) < win_size:
segment = np.pad(segment, (0, win_size - len(segment)))
data.append(segment)
time.append(i / len(waveform))
return time, data
def batchify(data: List[List[float]],
sample_rate: int,
batch_size: int,
**kwargs):
"""
Extract features from waveforms and create batches.
"""
examples = []
for waveform in data:
feats = melspectrogram(waveform, sample_rate, **kwargs).transpose()
examples.append(feats)
# Seperates data into some batches.
one_batch = []
for example in examples:
one_batch.append(example)
if len(one_batch) == batch_size:
yield one_batch
one_batch = []
if one_batch:
yield one_batch
def predict(model, data: List[List[float]], sample_rate: int,
batch_size: int=1):
"""
Use pretrained model to make predictions.
"""
batches = batchify(data, sample_rate, batch_size)
results = None
model.eval()
for batch in batches:
feats = paddle.to_tensor(batch).unsqueeze(1) \
# (batch_size, num_frames, num_melbins) -> (batch_size, 1, num_frames, num_melbins)
audioset_scores = model(feats)
if results is None:
results = audioset_scores.numpy()
else:
results = np.concatenate((results, audioset_scores.numpy()))
return results
if __name__ == '__main__':
paddle.set_device(args.device)
model = cnn14(pretrained=True, extract_embedding=False)
waveform, sr = load_audio(args.wav, sr=None)
time, data = split(waveform,
int(args.sample_duration * sr),
int(args.hop_duration * sr))
results = predict(model, data, sr, batch_size=8)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
time = np.arange(0, 1, int(args.hop_duration * sr) / len(waveform))
output_file = os.path.join(args.output_dir, f'audioset_tagging_sr_{sr}.npz')
np.savez(output_file, time=time, scores=results)
logger.info(f'Saved tagging results to {output_file}')
paddleaudio/examples/panns/parse_result.py 0 → 100644
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import ast
import os
from typing import Dict
import numpy as np
from paddleaudio.utils import logger
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument('--tagging_file', type=str, required=True, help='')
parser.add_argument('--top_k', type=int, default=10, help='Get top k predicted results of audioset labels.')
parser.add_argument('--smooth', type=ast.literal_eval, default=True, help='Set "True" to apply posterior smoothing.')
parser.add_argument('--smooth_size', type=int, default=5, help='Window size of posterior smoothing.')
parser.add_argument('--label_file', type=str, default='./assets/audioset_labels.txt', help='File of audioset labels.')
parser.add_argument('--output_dir', type=str, default='./output_dir', help='Directory to save tagging labels.')
args = parser.parse_args()
# yapf: enable
def smooth(results: np.ndarray, win_size: int):
"""
Execute posterior smoothing in-place.
"""
for i in range(len(results) - 1, -1, -1):
if i < win_size - 1:
left = 0
else:
left = i + 1 - win_size
results[i] = np.sum(results[left:i + 1], axis=0) / (i - left + 1)
def generate_topk_label(k: int, label_map: Dict, result: np.ndarray):
"""
Return top k result.
"""
result = np.asarray(result)
topk_idx = (-result).argsort()[:k]
ret = ''
for idx in topk_idx:
label, score = label_map[idx], result[idx]
ret += f'{label}: {score}\n'
return ret
if __name__ == "__main__":
label_map = {}
with open(args.label_file, 'r') as f:
for i, l in enumerate(f.readlines()):
label_map[i] = l.strip()
results = np.load(args.tagging_file, allow_pickle=True)
times, scores = results['time'], results['scores']
if args.smooth:
logger.info('Posterior smoothing...')
smooth(scores, win_size=args.smooth_size)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
output_file = os.path.join(
args.output_dir,
os.path.basename(args.tagging_file).split('.')[0] + '.txt')
with open(output_file, 'w') as f:
for time, score in zip(times, scores):
f.write(f'{time}\n')
f.write(generate_topk_label(args.top_k, label_map, score) + '\n')
logger.info(f'Saved tagging labels to {output_file}')
paddleaudio/examples/sound_classification/README.md 0 → 100644
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# 声音分类
声音分类和检测是声音算法的一个热门研究方向。
对于声音分类任务，传统机器学习的一个常用做法是首先人工提取音频的时域和频域的多种特征并做特征选择、组合、变换等，然后基于SVM或决策树进行分类。而端到端的深度学习则通常利用深度网络如RNN，CNN等直接对声间波形(waveform)或时频特征(time-frequency)进行特征学习(representation learning)和分类预测。
在IEEE ICASSP 2017 大会上，谷歌开放了一个大规模的音频数据集[Audioset](https://research.google.com/audioset/)。该数据集包含了 632 类的音频类别以及 2,084,320 条人工标记的每段 10 秒长度的声音剪辑片段（来源于YouTube视频）。目前该数据集已经有210万个已标注的视频数据，5800小时的音频数据，经过标记的声音样本的标签类别为527。
`PANNs`([PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://arxiv.org/pdf/1912.10211.pdf))是基于Audioset数据集训练的声音分类/识别的模型。经过预训练后，模型可以用于提取音频的embbedding。本示例将使用`PANNs`的预训练模型Finetune完成声音分类的任务。
## 模型简介
PaddleAudio提供了PANNs的CNN14、CNN10和CNN6的预训练模型，可供用户选择使用：
- CNN14: 该模型主要包含12个卷积层和2个全连接层，模型参数的数量为79.6M，embbedding维度是2048。
- CNN10: 该模型主要包含8个卷积层和2个全连接层，模型参数的数量为4.9M，embbedding维度是512。
- CNN6: 该模型主要包含4个卷积层和2个全连接层，模型参数的数量为4.5M，embbedding维度是512。
## 快速开始
### 模型训练
以环境声音分类数据集`ESC50`为示例，运行下面的命令，可在训练集上进行模型的finetune，支持单机的单卡训练和多卡训练。关于如何使用`paddle.distributed.launch`启动多卡训练，请查看[单机多卡训练](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/02_paddle2.0_develop/06_device_cn.html)。
单卡训练:
```shell
$ python train.py --epochs 50 --batch_size 16 --checkpoint_dir ./checkpoint --save_freq 10
```
多卡训练:
```shell
$ unset CUDA_VISIBLE_DEVICES
$ python -m paddle.distributed.launch --gpus "0,1" train.py --epochs 50 --batch_size 16 --num_worker 4 --checkpoint_dir ./checkpoint --save_freq 10
```
可支持配置的参数：
- `device`: 选用什么设备进行训练，可选cpu或gpu，默认为gpu。如使用gpu训练则参数gpus指定GPU卡号。
- `epochs`: 训练轮次，默认为50。
- `learning_rate`: Fine-tune的学习率；默认为5e-5。
- `batch_size`: 批处理大小，请结合显存情况进行调整，若出现显存不足，请适当调低这一参数；默认为16。
- `num_workers`: Dataloader获取数据的子进程数。默认为0，加载数据的流程在主进程执行。
- `checkpoint_dir`: 模型参数文件和optimizer参数文件的保存目录，默认为`./checkpoint`。
- `save_freq`: 训练过程中的模型保存频率，默认为10。
- `log_freq`: 训练过程中的信息打印频率，默认为10。
示例代码中使用的预训练模型为`CNN14`，如果想更换为其他预训练模型，可通过以下方式执行：
```python
from model import SoundClassifier
from paddleaudio.datasets import ESC50
from paddleaudio.models.panns import cnn14, cnn10, cnn6
# CNN14
backbone = cnn14(pretrained=True, extract_embedding=True)
model = SoundClassifier(backbone, num_class=len(ESC50.label_list))
# CNN10
backbone = cnn10(pretrained=True, extract_embedding=True)
model = SoundClassifier(backbone, num_class=len(ESC50.label_list))
# CNN6
backbone = cnn6(pretrained=True, extract_embedding=True)
model = SoundClassifier(backbone, num_class=len(ESC50.label_list))
```
### 模型预测
```shell
python -u predict.py --wav ./dog.wav --top_k 3 --checkpoint ./checkpoint/epoch_50/model.pdparams
```
可支持配置的参数：
- `device`: 选用什么设备进行训练，可选cpu或gpu，默认为gpu。如使用gpu训练则参数gpus指定GPU卡号。
- `wav`: 指定预测的音频文件。
- `top_k`: 预测显示的top k标签的得分，默认为1。
- `checkpoint`: 模型参数checkpoint文件。
输出的预测结果如下：
```
[/audio/dog.wav]
Dog: 0.9999538660049438
Clock tick: 1.3341237718123011e-05
Cat: 6.579841738130199e-06
```
### 模型部署
#### 1. 动转静
模型训练结束后，可以将已保存的动态图参数导出成静态图的模型和参数，然后实施静态图的部署。
```shell
python -u export_model.py --checkpoint ./checkpoint/epoch_50/model.pdparams --output_dir ./export
```
可支持配置的参数：
- `checkpoint`: 模型参数checkpoint文件。
- `output_dir`: 导出静态图模型和参数文件的保存目录。
导出的静态图模型和参数文件如下：
```sh
$ tree export
export
├── inference.pdiparams
├── inference.pdiparams.info
└── inference.pdmodel
```
#### 2. 模型部署和预测
`deploy/python/predict.py` 脚本使用了`paddle.inference`模块下的api，提供了python端部署的示例：
```sh
python deploy/python/predict.py --model_dir ./export --device gpu
```
paddleaudio/examples/sound_classification/deploy/python/predict.py 0 → 100644
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import numpy as np
from paddle import inference
from scipy.special import softmax
from paddleaudio.backends import load as load_audio
from paddleaudio.datasets import ESC50
from paddleaudio.features import melspectrogram
# yapf: disable
parser = argparse.ArgumentParser()
parser.add_argument("--model_dir", type=str, required=True, default="./export", help="The directory to static model.")
parser.add_argument("--batch_size", type=int, default=2, help="Batch size per GPU/CPU for training.")
parser.add_argument('--device', choices=['cpu', 'gpu', 'xpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
parser.add_argument('--use_tensorrt', type=eval, default=False, choices=[True, False], help='Enable to use tensorrt to speed up.')
parser.add_argument("--precision", type=str, default="fp32", choices=["fp32", "fp16"], help='The tensorrt precision.')
parser.add_argument('--cpu_threads', type=int, default=10, help='Number of threads to predict when using cpu.')
parser.add_argument('--enable_mkldnn', type=eval, default=False, choices=[True, False], help='Enable to use mkldnn to speed up when using cpu.')
parser.add_argument("--log_dir", type=str, default="./log", help="The path to save log.")
args = parser.parse_args()
# yapf: enable
def extract_features(files: str, **kwargs):
waveforms = []
srs = []
max_length = float('-inf')
for file in files:
waveform, sr = load_audio(file, sr=None)
max_length = max(max_length, len(waveform))
waveforms.append(waveform)
srs.append(sr)
feats = []
for i in range(len(waveforms)):
# padding
if len(waveforms[i]) < max_length:
pad_width = max_length - len(waveforms[i])
waveforms[i] = np.pad(waveforms[i], pad_width=(0, pad_width))
feat = melspectrogram(waveforms[i], sr, **kwargs).transpose()
feats.append(feat)
return np.stack(feats, axis=0)
class Predictor(object):
def __init__(self,
model_dir,
device="gpu",
batch_size=1,
use_tensorrt=False,
precision="fp32",
cpu_threads=10,
enable_mkldnn=False):
self.batch_size = batch_size
model_file = os.path.join(model_dir, "inference.pdmodel")
params_file = os.path.join(model_dir, "inference.pdiparams")
assert os.path.isfile(model_file) and os.path.isfile(
params_file), 'Please check model and parameter files.'
config = inference.Config(model_file, params_file)
if device == "gpu":
# set GPU configs accordingly
# such as intialize the gpu memory, enable tensorrt
config.enable_use_gpu(100, 0)
precision_map = {
"fp16": inference.PrecisionType.Half,
"fp32": inference.PrecisionType.Float32,
}
precision_mode = precision_map[precision]
if use_tensorrt:
config.enable_tensorrt_engine(
max_batch_size=batch_size,
min_subgraph_size=30,
precision_mode=precision_mode)
elif device == "cpu":
# set CPU configs accordingly,
# such as enable_mkldnn, set_cpu_math_library_num_threads
config.disable_gpu()
if enable_mkldnn:
# cache 10 different shapes for mkldnn to avoid memory leak
config.set_mkldnn_cache_capacity(10)
config.enable_mkldnn()
config.set_cpu_math_library_num_threads(cpu_threads)
elif device == "xpu":
# set XPU configs accordingly
config.enable_xpu(100)
config.switch_use_feed_fetch_ops(False)
self.predictor = inference.create_predictor(config)
self.input_handles = [
self.predictor.get_input_handle(name)
for name in self.predictor.get_input_names()
]
self.output_handle = self.predictor.get_output_handle(
self.predictor.get_output_names()[0])
def predict(self, wavs):
feats = extract_features(wavs)
self.input_handles[0].copy_from_cpu(feats)
self.predictor.run()
logits = self.output_handle.copy_to_cpu()
probs = softmax(logits, axis=1)
indices = np.argmax(probs, axis=1)
return indices
if __name__ == "__main__":
# Define predictor to do prediction.
predictor = Predictor(args.model_dir, args.device, args.batch_size,
args.use_tensorrt, args.precision, args.cpu_threads,
args.enable_mkldnn)
wavs = [
'~/audio_demo_resource/cat.wav',
'~/audio_demo_resource/dog.wav',
]
for i in range(len(wavs)):
wavs[i] = os.path.abspath(os.path.expanduser(wavs[i]))
assert os.path.isfile(
wavs[i]), f'Please check input wave file: {wavs[i]}'
results = predictor.predict(wavs)
for idx, wav in enumerate(wavs):
print(f'Wav: {wav} \t Label: {ESC50.label_list[results[idx]]}')
paddleaudio/examples/sound_classification/export_model.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import paddle
from model import SoundClassifier
from paddleaudio.datasets import ESC50
from paddleaudio.models.panns import cnn14
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument("--checkpoint", type=str, required=True, help="Checkpoint of model.")
parser.add_argument("--output_dir", type=str, default='./export', help="Path to save static model and its parameters.")
args = parser.parse_args()
# yapf: enable
if __name__ == '__main__':
model = SoundClassifier(
backbone=cnn14(pretrained=False, extract_embedding=True),
num_class=len(ESC50.label_list))
model.set_state_dict(paddle.load(args.checkpoint))
model.eval()
model = paddle.jit.to_static(
model,
input_spec=[
paddle.static.InputSpec(
shape=[None, None, 64], dtype=paddle.float32)
])
# Save in static graph model.
paddle.jit.save(model, os.path.join(args.output_dir, "inference"))
paddleaudio/examples/sound_classification/model.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.nn as nn
class SoundClassifier(nn.Layer):
"""
Model for sound classification which uses panns pretrained models to extract
embeddings from audio files.
"""
def __init__(self, backbone, num_class, dropout=0.1):
super(SoundClassifier, self).__init__()
self.backbone = backbone
self.dropout = nn.Dropout(dropout)
self.fc = nn.Linear(self.backbone.emb_size, num_class)
def forward(self, x):
# x: (batch_size, num_frames, num_melbins) -> (batch_size, 1, num_frames, num_melbins)
x = x.unsqueeze(1)
x = self.backbone(x)
x = self.dropout(x)
logits = self.fc(x)
return logits
paddleaudio/examples/sound_classification/predict.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import numpy as np
import paddle
import paddle.nn.functional as F
from model import SoundClassifier
from paddleaudio.backends import load as load_audio
from paddleaudio.datasets import ESC50
from paddleaudio.features import melspectrogram
from paddleaudio.models.panns import cnn14
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to predict, defaults to gpu.")
parser.add_argument("--wav", type=str, required=True, help="Audio file to infer.")
parser.add_argument("--top_k", type=int, default=1, help="Show top k predicted results")
parser.add_argument("--checkpoint", type=str, required=True, help="Checkpoint of model.")
args = parser.parse_args()
# yapf: enable
def extract_features(file: str, **kwargs):
waveform, sr = load_audio(file, sr=None)
feat = melspectrogram(waveform, sr, **kwargs).transpose()
return feat
if __name__ == '__main__':
paddle.set_device(args.device)
model = SoundClassifier(
backbone=cnn14(pretrained=False, extract_embedding=True),
num_class=len(ESC50.label_list))
model.set_state_dict(paddle.load(args.checkpoint))
model.eval()
feat = np.expand_dims(extract_features(args.wav), 0)
feat = paddle.to_tensor(feat)
logits = model(feat)
probs = F.softmax(logits, axis=1).numpy()
sorted_indices = (-probs[0]).argsort()
msg = f'[{args.wav}]\n'
for idx in sorted_indices[:args.top_k]:
msg += f'{ESC50.label_list[idx]}: {probs[0][idx]}\n'
print(msg)
paddleaudio/examples/sound_classification/train.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import paddle
from model import SoundClassifier
from paddleaudio.datasets import ESC50
from paddleaudio.models.panns import cnn14
from paddleaudio.utils import logger
from paddleaudio.utils import Timer
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
parser.add_argument("--epochs", type=int, default=50, help="Number of epoches for fine-tuning.")
parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
parser.add_argument("--batch_size", type=int, default=16, help="Total examples' number in batch for training.")
parser.add_argument("--num_workers", type=int, default=0, help="Number of workers in dataloader.")
parser.add_argument("--checkpoint_dir", type=str, default='./checkpoint', help="Directory to save model checkpoints.")
parser.add_argument("--save_freq", type=int, default=10, help="Save checkpoint every n epoch.")
parser.add_argument("--log_freq", type=int, default=10, help="Log the training infomation every n steps.")
args = parser.parse_args()
# yapf: enable
if __name__ == "__main__":
paddle.set_device(args.device)
nranks = paddle.distributed.get_world_size()
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
local_rank = paddle.distributed.get_rank()
backbone = cnn14(pretrained=True, extract_embedding=True)
model = SoundClassifier(backbone, num_class=len(ESC50.label_list))
model = paddle.DataParallel(model)
optimizer = paddle.optimizer.Adam(
learning_rate=args.learning_rate, parameters=model.parameters())
criterion = paddle.nn.loss.CrossEntropyLoss()
train_ds = ESC50(mode='train', feat_type='melspectrogram')
dev_ds = ESC50(mode='dev', feat_type='melspectrogram')
train_sampler = paddle.io.DistributedBatchSampler(
train_ds, batch_size=args.batch_size, shuffle=True, drop_last=False)
train_loader = paddle.io.DataLoader(
train_ds,
batch_sampler=train_sampler,
num_workers=args.num_workers,
return_list=True,
use_buffer_reader=True, )
steps_per_epoch = len(train_sampler)
timer = Timer(steps_per_epoch * args.epochs)
timer.start()
for epoch in range(1, args.epochs + 1):
model.train()
avg_loss = 0
num_corrects = 0
num_samples = 0
for batch_idx, batch in enumerate(train_loader):
feats, labels = batch
logits = model(feats)
loss = criterion(logits, labels)
loss.backward()
optimizer.step()
if isinstance(optimizer._learning_rate,
paddle.optimizer.lr.LRScheduler):
optimizer._learning_rate.step()
optimizer.clear_grad()
# Calculate loss
avg_loss += loss.numpy()[0]
# Calculate metrics
preds = paddle.argmax(logits, axis=1)
num_corrects += (preds == labels).numpy().sum()
num_samples += feats.shape[0]
timer.count()
if (batch_idx + 1) % args.log_freq == 0 and local_rank == 0:
lr = optimizer.get_lr()
avg_loss /= args.log_freq
avg_acc = num_corrects / num_samples
print_msg = 'Epoch={}/{}, Step={}/{}'.format(
epoch, args.epochs, batch_idx + 1, steps_per_epoch)
print_msg += ' loss={:.4f}'.format(avg_loss)
print_msg += ' acc={:.4f}'.format(avg_acc)
print_msg += ' lr={:.6f} step/sec={:.2f} | ETA {}'.format(
lr, timer.timing, timer.eta)
logger.train(print_msg)
avg_loss = 0
num_corrects = 0
num_samples = 0
if epoch % args.save_freq == 0 and batch_idx + 1 == steps_per_epoch and local_rank == 0:
dev_sampler = paddle.io.BatchSampler(
dev_ds,
batch_size=args.batch_size,
shuffle=False,
drop_last=False)
dev_loader = paddle.io.DataLoader(
dev_ds,
batch_sampler=dev_sampler,
num_workers=args.num_workers,
return_list=True, )
model.eval()
num_corrects = 0
num_samples = 0
with logger.processing('Evaluation on validation dataset'):
for batch_idx, batch in enumerate(dev_loader):
feats, labels = batch
logits = model(feats)
preds = paddle.argmax(logits, axis=1)
num_corrects += (preds == labels).numpy().sum()
num_samples += feats.shape[0]
print_msg = '[Evaluation result]'
print_msg += ' dev_acc={:.4f}'.format(num_corrects / num_samples)
logger.eval(print_msg)
# Save model
save_dir = os.path.join(args.checkpoint_dir,
'epoch_{}'.format(epoch))
logger.info('Saving model checkpoint to {}'.format(save_dir))
paddle.save(model.state_dict(),
os.path.join(save_dir, 'model.pdparams'))
paddle.save(optimizer.state_dict(),
os.path.join(save_dir, 'model.pdopt'))
paddleaudio/paddleaudio/__init__.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .backends import *
from .features import *
paddleaudio/paddleaudio/backends/__init__.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .audio import *
paddleaudio/paddleaudio/backends/audio.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from typing import Optional
from typing import Tuple
from typing import Union
import numpy as np
import resampy
import soundfile as sf
from numpy import ndarray as array
from scipy.io import wavfile
from ..utils import ParameterError
__all__ = [
'resample',
'to_mono',
'depth_convert',
'normalize',
'save_wav',
'load',
]
NORMALMIZE_TYPES = ['linear', 'gaussian']
MERGE_TYPES = ['ch0', 'ch1', 'random', 'average']
RESAMPLE_MODES = ['kaiser_best', 'kaiser_fast']
EPS = 1e-8
def resample(y: array, src_sr: int, target_sr: int,
mode: str='kaiser_fast') -> array:
""" Audio resampling
This function is the same as using resampy.resample().
Notes:
The default mode is kaiser_fast. For better audio quality, use mode = 'kaiser_fast'
"""
if mode == 'kaiser_best':
warnings.warn(
f'Using resampy in kaiser_best to {src_sr}=>{target_sr}. This function is pretty slow, \
we recommend the mode kaiser_fast in large scale audio trainning')
if not isinstance(y, np.ndarray):
raise ParameterError(
'Only support numpy array, but received y in {type(y)}')
if mode not in RESAMPLE_MODES:
raise ParameterError(f'resample mode must in {RESAMPLE_MODES}')
return resampy.resample(y, src_sr, target_sr, filter=mode)
def to_mono(y: array, merge_type: str='average') -> array:
""" convert sterior audio to mono
"""
if merge_type not in MERGE_TYPES:
raise ParameterError(
f'Unsupported merge type {merge_type}, available types are {MERGE_TYPES}'
)
if y.ndim > 2:
raise ParameterError(
f'Unsupported audio array, y.ndim > 2, the shape is {y.shape}')
if y.ndim == 1: # nothing to merge
return y
if merge_type == 'ch0':
return y[0]
if merge_type == 'ch1':
return y[1]
if merge_type == 'random':
return y[np.random.randint(0, 2)]
# need to do averaging according to dtype
if y.dtype == 'float32':
y_out = (y[0] + y[1]) * 0.5
elif y.dtype == 'int16':
y_out = y.astype('int32')
y_out = (y_out[0] + y_out[1]) // 2
y_out = np.clip(y_out, np.iinfo(y.dtype).min,
np.iinfo(y.dtype).max).astype(y.dtype)
elif y.dtype == 'int8':
y_out = y.astype('int16')
y_out = (y_out[0] + y_out[1]) // 2
y_out = np.clip(y_out, np.iinfo(y.dtype).min,
np.iinfo(y.dtype).max).astype(y.dtype)
else:
raise ParameterError(f'Unsupported dtype: {y.dtype}')
return y_out
def _safe_cast(y: array, dtype: Union[type, str]) -> array:
""" data type casting in a safe way, i.e., prevent overflow or underflow
This function is used internally.
"""
return np.clip(y, np.iinfo(dtype).min, np.iinfo(dtype).max).astype(dtype)
def depth_convert(y: array, dtype: Union[type, str],
dithering: bool=True) -> array:
"""Convert audio array to target dtype safely
This function convert audio waveform to a target dtype, with addition steps of
preventing overflow/underflow and preserving audio range.
"""
SUPPORT_DTYPE = ['int16', 'int8', 'float32', 'float64']
if y.dtype not in SUPPORT_DTYPE:
raise ParameterError(
'Unsupported audio dtype, '
f'y.dtype is {y.dtype}, supported dtypes are {SUPPORT_DTYPE}')
if dtype not in SUPPORT_DTYPE:
raise ParameterError(
'Unsupported audio dtype, '
f'target dtype is {dtype}, supported dtypes are {SUPPORT_DTYPE}')
if dtype == y.dtype:
return y
if dtype == 'float64' and y.dtype == 'float32':
return _safe_cast(y, dtype)
if dtype == 'float32' and y.dtype == 'float64':
return _safe_cast(y, dtype)
if dtype == 'int16' or dtype == 'int8':
if y.dtype in ['float64', 'float32']:
factor = np.iinfo(dtype).max
y = np.clip(y * factor, np.iinfo(dtype).min,
np.iinfo(dtype).max).astype(dtype)
y = y.astype(dtype)
else:
if dtype == 'int16' and y.dtype == 'int8':
factor = np.iinfo('int16').max / np.iinfo('int8').max - EPS
y = y.astype('float32') * factor
y = y.astype('int16')
else: # dtype == 'int8' and y.dtype=='int16':
y = y.astype('int32') * np.iinfo('int8').max / \
np.iinfo('int16').max
y = y.astype('int8')
if dtype in ['float32', 'float64']:
org_dtype = y.dtype
y = y.astype(dtype) / np.iinfo(org_dtype).max
return y
def sound_file_load(file: str,
offset: Optional[float]=None,
dtype: str='int16',
duration: Optional[int]=None) -> Tuple[array, int]:
"""Load audio using soundfile library
This function load audio file using libsndfile.
Reference:
http://www.mega-nerd.com/libsndfile/#Features
"""
with sf.SoundFile(file) as sf_desc:
sr_native = sf_desc.samplerate
if offset:
sf_desc.seek(int(offset * sr_native))
if duration is not None:
frame_duration = int(duration * sr_native)
else:
frame_duration = -1
y = sf_desc.read(frames=frame_duration, dtype=dtype, always_2d=False).T
return y, sf_desc.samplerate
def audio_file_load():
"""Load audio using audiofile library
This function load audio file using audiofile.
Reference:
https://audiofile.68k.org/
"""
raise NotImplementedError()
def sox_file_load():
"""Load audio using sox library
This function load audio file using sox.
Reference:
http://sox.sourceforge.net/
"""
raise NotImplementedError()
def normalize(y: array, norm_type: str='linear',
mul_factor: float=1.0) -> array:
""" normalize an input audio with additional multiplier.
"""
if norm_type == 'linear':
amax = np.max(np.abs(y))
factor = 1.0 / (amax + EPS)
y = y * factor * mul_factor
elif norm_type == 'gaussian':
amean = np.mean(y)
astd = np.std(y)
astd = max(astd, EPS)
y = mul_factor * (y - amean) / astd
else:
raise NotImplementedError(f'norm_type should be in {NORMALMIZE_TYPES}')
return y
def save_wav(y: array, sr: int, file: str) -> None:
"""Save audio file to disk.
This function saves audio to disk using scipy.io.wavfile, with additional step
to convert input waveform to int16 unless it already is int16
Notes:
It only support raw wav format.
"""
if not file.endswith('.wav'):
raise ParameterError(
f'only .wav file supported, but dst file name is: {file}')
if sr <= 0:
raise ParameterError(
f'Sample rate should be larger than 0, recieved sr = {sr}')
if y.dtype not in ['int16', 'int8']:
warnings.warn(
f'input data type is {y.dtype}, will convert data to int16 format before saving'
)
y_out = depth_convert(y, 'int16')
else:
y_out = y
wavfile.write(file, sr, y_out)
def load(
file: str,
sr: Optional[int]=None,
mono: bool=True,
merge_type: str='average', # ch0,ch1,random,average
normal: bool=True,
norm_type: str='linear',
norm_mul_factor: float=1.0,
offset: float=0.0,
duration: Optional[int]=None,
dtype: str='float32',
resample_mode: str='kaiser_fast') -> Tuple[array, int]:
"""Load audio file from disk.
This function loads audio from disk using using audio beackend.
Parameters:
Notes:
"""
y, r = sound_file_load(file, offset=offset, dtype=dtype, duration=duration)
if not ((y.ndim == 1 and len(y) > 0) or (y.ndim == 2 and len(y[0]) > 0)):
raise ParameterError(f'audio file {file} looks empty')
if mono:
y = to_mono(y, merge_type)
if sr is not None and sr != r:
y = resample(y, r, sr, mode=resample_mode)
r = sr
if normal:
y = normalize(y, norm_type, norm_mul_factor)
elif dtype in ['int8', 'int16']:
# still need to do normalization, before depth convertion
y = normalize(y, 'linear', 1.0)
y = depth_convert(y, dtype)
return y, r
paddleaudio/paddleaudio/datasets/__init__.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .aishell import AISHELL1
from .dcase import UrbanAcousticScenes
from .dcase import UrbanAudioVisualScenes
from .esc50 import ESC50
from .gtzan import GTZAN
from .librispeech import LIBRISPEECH
from .ravdess import RAVDESS
from .tess import TESS
from .urban_sound import UrbanSound8K
__all__ = [
'AISHELL1',
'LIBRISPEECH',
'ESC50',
'UrbanSound8K',
'GTZAN',
'UrbanAcousticScenes',
'UrbanAudioVisualScenes',
'RAVDESS',
'TESS',
]
paddleaudio/paddleaudio/datasets/aishell.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import codecs
import collections
import json
import os
from typing import Dict
from paddle.io import Dataset
from tqdm import tqdm
from ..backends import load as load_audio
from ..utils.download import decompress
from ..utils.download import download_and_decompress
from ..utils.env import DATA_HOME
from ..utils.log import logger
from .dataset import feat_funcs
__all__ = ['AISHELL1']
class AISHELL1(Dataset):
"""
This Open Source Mandarin Speech Corpus, AISHELL-ASR0009-OS1, is 178 hours long.
It is a part of AISHELL-ASR0009, of which utterance contains 11 domains, including
smart home, autonomous driving, and industrial production. The whole recording was
put in quiet indoor environment, using 3 different devices at the same time: high
fidelity microphone (44.1kHz, 16-bit,); Android-system mobile phone (16kHz, 16-bit),
iOS-system mobile phone (16kHz, 16-bit). Audios in high fidelity were re-sampled
to 16kHz to build AISHELL- ASR0009-OS1. 400 speakers from different accent areas
in China were invited to participate in the recording. The manual transcription
accuracy rate is above 95%, through professional speech annotation and strict
quality inspection. The corpus is divided into training, development and testing
sets.
Reference:
AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline
https://arxiv.org/abs/1709.05522
"""
archieves = [
{
'url': 'http://www.openslr.org/resources/33/data_aishell.tgz',
'md5': '2f494334227864a8a8fec932999db9d8',
},
]
text_meta = os.path.join('data_aishell', 'transcript',
'aishell_transcript_v0.8.txt')
utt_info = collections.namedtuple('META_INFO',
('file_path', 'utt_id', 'text'))
audio_path = os.path.join('data_aishell', 'wav')
manifest_path = os.path.join('data_aishell', 'manifest')
subset = ['train', 'dev', 'test']
def __init__(self, subset: str='train', feat_type: str='raw', **kwargs):
assert subset in self.subset, 'Dataset subset must be one in {}, but got {}'.format(
self.subset, subset)
self.subset = subset
self.feat_type = feat_type
self.feat_config = kwargs
self._data = self._get_data()
super(AISHELL1, self).__init__()
def _get_text_info(self) -> Dict[str, str]:
ret = {}
with open(os.path.join(DATA_HOME, self.text_meta), 'r') as rf:
for line in rf.readlines()[1:]:
utt_id, text = map(str.strip, line.split(' ',
1)) # utt_id, text
ret.update({utt_id: ''.join(text.split())})
return ret
def _get_data(self):
if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)) or \
not os.path.isfile(os.path.join(DATA_HOME, self.text_meta)):
download_and_decompress(self.archieves, DATA_HOME)
# Extract *wav from *.tar.gz.
for root, _, files in os.walk(
os.path.join(DATA_HOME, self.audio_path)):
for file in files:
if file.endswith('.tar.gz'):
decompress(os.path.join(root, file))
os.remove(os.path.join(root, file))
text_info = self._get_text_info()
data = []
for root, _, files in os.walk(
os.path.join(DATA_HOME, self.audio_path, self.subset)):
for file in files:
if file.endswith('.wav'):
utt_id = os.path.splitext(file)[0]
if utt_id not in text_info: # There are some utt_id that without label
continue
text = text_info[utt_id]
file_path = os.path.join(root, file)
data.append(self.utt_info(file_path, utt_id, text))
return data
def _convert_to_record(self, idx: int):
sample = self._data[idx]
record = {}
# To show all fields in a namedtuple: `type(sample)._fields`
for field in type(sample)._fields:
record[field] = getattr(sample, field)
waveform, sr = load_audio(
sample[0]) # The first element of sample is file path
feat_func = feat_funcs[self.feat_type]
feat = feat_func(
waveform, sample_rate=sr,
**self.feat_config) if feat_func else waveform
record.update({'feat': feat, 'duration': len(waveform) / sr})
return record
def create_manifest(self, prefix='manifest'):
if not os.path.isdir(os.path.join(DATA_HOME, self.manifest_path)):
os.makedirs(os.path.join(DATA_HOME, self.manifest_path))
manifest_file = os.path.join(DATA_HOME, self.manifest_path,
f'{prefix}.{self.subset}')
with codecs.open(manifest_file, 'w', 'utf-8') as f:
for idx in tqdm(range(len(self))):
record = self._convert_to_record(idx)
record_line = json.dumps(
{
'utt': record['utt_id'],
'feat': record['file_path'],
'feat_shape': (record['duration'], ),
'text': record['text']
},
ensure_ascii=False)
f.write(record_line + '\n')
logger.info(f'Manifest file {manifest_file} created.')
def __getitem__(self, idx):
record = self._convert_to_record(idx)
return tuple(record.values())
def __len__(self):
return len(self._data)
paddleaudio/paddleaudio/datasets/dataset.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List
import numpy as np
import paddle
from ..backends import load as load_audio
from ..features import melspectrogram
from ..features import mfcc
feat_funcs = {
'raw': None,
'melspectrogram': melspectrogram,
'mfcc': mfcc,
}
class AudioClassificationDataset(paddle.io.Dataset):
"""
Base class of audio classification dataset.
"""
def __init__(self,
files: List[str],
labels: List[int],
feat_type: str='raw',
**kwargs):
"""
Ags:
files (:obj:`List[str]`): A list of absolute path of audio files.
labels (:obj:`List[int]`): Labels of audio files.
feat_type (:obj:`str`, `optional`, defaults to `raw`):
It identifies the feature type that user wants to extrace of an audio file.
"""
super(AudioClassificationDataset, self).__init__()
if feat_type not in feat_funcs.keys():
raise RuntimeError(
f"Unknown feat_type: {feat_type}, it must be one in {list(feat_funcs.keys())}"
)
self.files = files
self.labels = labels
self.feat_type = feat_type
self.feat_config = kwargs # Pass keyword arguments to customize feature config
def _get_data(self, input_file: str):
raise NotImplementedError
def _convert_to_record(self, idx):
file, label = self.files[idx], self.labels[idx]
waveform, sample_rate = load_audio(file)
feat_func = feat_funcs[self.feat_type]
record = {}
record['feat'] = feat_func(
waveform, sample_rate,
**self.feat_config) if feat_func else waveform
record['label'] = label
return record
def __getitem__(self, idx):
record = self._convert_to_record(idx)
return np.array(record['feat']).transpose(), np.array(
record['label'], dtype=np.int64)
def __len__(self):
return len(self.files)
paddleaudio/paddleaudio/datasets/dcase.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import os
from typing import List
from typing import Tuple
from ..utils.download import download_and_decompress
from ..utils.env import DATA_HOME
from .dataset import AudioClassificationDataset
__all__ = ['UrbanAcousticScenes', 'UrbanAudioVisualScenes']
class UrbanAcousticScenes(AudioClassificationDataset):
"""
TAU Urban Acoustic Scenes 2020 Mobile Development dataset contains recordings from
12 European cities in 10 different acoustic scenes using 4 different devices.
Additionally, synthetic data for 11 mobile devices was created based on the original
recordings. Of the 12 cities, two are present only in the evaluation set.
Reference:
A multi-device dataset for urban acoustic scene classification
https://arxiv.org/abs/1807.09840
"""
source_url = 'https://zenodo.org/record/3819968/files/'
base_name = 'TAU-urban-acoustic-scenes-2020-mobile-development'
archieves = [
{
'url': source_url + base_name + '.meta.zip',
'md5': '6eae9db553ce48e4ea246e34e50a3cf5',
},
{
'url': source_url + base_name + '.audio.1.zip',
'md5': 'b1e85b8a908d3d6a6ab73268f385d5c8',
},
{
'url': source_url + base_name + '.audio.2.zip',
'md5': '4310a13cc2943d6ce3f70eba7ba4c784',
},
{
'url': source_url + base_name + '.audio.3.zip',
'md5': 'ed38956c4246abb56190c1e9b602b7b8',
},
{
'url': source_url + base_name + '.audio.4.zip',
'md5': '97ab8560056b6816808dedc044dcc023',
},
{
'url': source_url + base_name + '.audio.5.zip',
'md5': 'b50f5e0bfed33cd8e52cb3e7f815c6cb',
},
{
'url': source_url + base_name + '.audio.6.zip',
'md5': 'fbf856a3a86fff7520549c899dc94372',
},
{
'url': source_url + base_name + '.audio.7.zip',
'md5': '0dbffe7b6e45564da649378723284062',
},
{
'url': source_url + base_name + '.audio.8.zip',
'md5': 'bb6f77832bf0bd9f786f965beb251b2e',
},
{
'url': source_url + base_name + '.audio.9.zip',
'md5': 'a65596a5372eab10c78e08a0de797c9e',
},
{
'url': source_url + base_name + '.audio.10.zip',
'md5': '2ad595819ffa1d56d2de4c7ed43205a6',
},
{
'url': source_url + base_name + '.audio.11.zip',
'md5': '0ad29f7040a4e6a22cfd639b3a6738e5',
},
{
'url': source_url + base_name + '.audio.12.zip',
'md5': 'e5f4400c6b9697295fab4cf507155a2f',
},
{
'url': source_url + base_name + '.audio.13.zip',
'md5': '8855ab9f9896422746ab4c5d89d8da2f',
},
{
'url': source_url + base_name + '.audio.14.zip',
'md5': '092ad744452cd3e7de78f988a3d13020',
},
{
'url': source_url + base_name + '.audio.15.zip',
'md5': '4b5eb85f6592aebf846088d9df76b420',
},
{
'url': source_url + base_name + '.audio.16.zip',
'md5': '2e0a89723e58a3836be019e6996ae460',
},
]
label_list = [
'airport', 'shopping_mall', 'metro_station', 'street_pedestrian',
'public_square', 'street_traffic', 'tram', 'bus', 'metro', 'park'
]
meta = os.path.join(base_name, 'meta.csv')
meta_info = collections.namedtuple('META_INFO', (
'filename', 'scene_label', 'identifier', 'source_label'))
subset_meta = {
'train': os.path.join(base_name, 'evaluation_setup', 'fold1_train.csv'),
'dev':
os.path.join(base_name, 'evaluation_setup', 'fold1_evaluate.csv'),
'test': os.path.join(base_name, 'evaluation_setup', 'fold1_test.csv'),
}
subset_meta_info = collections.namedtuple('SUBSET_META_INFO',
('filename', 'scene_label'))
audio_path = os.path.join(base_name, 'audio')
def __init__(self, mode: str='train', feat_type: str='raw', **kwargs):
"""
Ags:
mode (:obj:`str`, `optional`, defaults to `train`):
It identifies the dataset mode (train or dev).
feat_type (:obj:`str`, `optional`, defaults to `raw`):
It identifies the feature type that user wants to extrace of an audio file.
"""
files, labels = self._get_data(mode)
super(UrbanAcousticScenes, self).__init__(
files=files, labels=labels, feat_type=feat_type, **kwargs)
def _get_meta_info(self, subset: str=None,
skip_header: bool=True) -> List[collections.namedtuple]:
if subset is None:
meta_file = self.meta
meta_info = self.meta_info
else:
assert subset in self.subset_meta, f'Subset must be one in {list(self.subset_meta.keys())}, but got {subset}.'
meta_file = self.subset_meta[subset]
meta_info = self.subset_meta_info
ret = []
with open(os.path.join(DATA_HOME, meta_file), 'r') as rf:
lines = rf.readlines()[1:] if skip_header else rf.readlines()
for line in lines:
ret.append(meta_info(*line.strip().split('\t')))
return ret
def _get_data(self, mode: str) -> Tuple[List[str], List[int]]:
if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)) or \
not os.path.isfile(os.path.join(DATA_HOME, self.meta)):
download_and_decompress(self.archieves, DATA_HOME)
meta_info = self._get_meta_info(subset=mode, skip_header=True)
files = []
labels = []
for sample in meta_info:
filename, label = sample[:2]
filename = os.path.basename(filename)
target = self.label_list.index(label)
files.append(os.path.join(DATA_HOME, self.audio_path, filename))
labels.append(int(target))
return files, labels
class UrbanAudioVisualScenes(AudioClassificationDataset):
"""
TAU Urban Audio Visual Scenes 2021 Development dataset contains synchronized audio
and video recordings from 12 European cities in 10 different scenes.
This dataset consists of 10-seconds audio and video segments from 10
acoustic scenes. The total amount of audio in the development set is 34 hours.
Reference:
A Curated Dataset of Urban Scenes for Audio-Visual Scene Analysis
https://arxiv.org/abs/2011.00030
"""
source_url = 'https://zenodo.org/record/4477542/files/'
base_name = 'TAU-urban-audio-visual-scenes-2021-development'
archieves = [
{
'url': source_url + base_name + '.meta.zip',
'md5': '76e3d7ed5291b118372e06379cb2b490',
},
{
'url': source_url + base_name + '.audio.1.zip',
'md5': '186f6273f8f69ed9dbdc18ad65ac234f',
},
{
'url': source_url + base_name + '.audio.2.zip',
'md5': '7fd6bb63127f5785874a55aba4e77aa5',
},
{
'url': source_url + base_name + '.audio.3.zip',
'md5': '61396bede29d7c8c89729a01a6f6b2e2',
},
{
'url': source_url + base_name + '.audio.4.zip',
'md5': '6ddac89717fcf9c92c451868eed77fe1',
},
{
'url': source_url + base_name + '.audio.5.zip',
'md5': 'af4820756cdf1a7d4bd6037dc034d384',
},
{
'url': source_url + base_name + '.audio.6.zip',
'md5': 'ebd11ec24411f2a17a64723bd4aa7fff',
},
{
'url': source_url + base_name + '.audio.7.zip',
'md5': '2be39a76aeed704d5929d020a2909efd',
},
{
'url': source_url + base_name + '.audio.8.zip',
'md5': '972d8afe0874720fc2f28086e7cb22a9',
},
]
label_list = [
'airport', 'shopping_mall', 'metro_station', 'street_pedestrian',
'public_square', 'street_traffic', 'tram', 'bus', 'metro', 'park'
]
meta_base_path = os.path.join(base_name, base_name + '.meta')
meta = os.path.join(meta_base_path, 'meta.csv')
meta_info = collections.namedtuple('META_INFO', (
'filename_audio', 'filename_video', 'scene_label', 'identifier'))
subset_meta = {
'train':
os.path.join(meta_base_path, 'evaluation_setup', 'fold1_train.csv'),
'dev':
os.path.join(meta_base_path, 'evaluation_setup', 'fold1_evaluate.csv'),
'test':
os.path.join(meta_base_path, 'evaluation_setup', 'fold1_test.csv'),
}
subset_meta_info = collections.namedtuple('SUBSET_META_INFO', (
'filename_audio', 'filename_video', 'scene_label'))
audio_path = os.path.join(base_name, 'audio')
def __init__(self, mode: str='train', feat_type: str='raw', **kwargs):
"""
Ags:
mode (:obj:`str`, `optional`, defaults to `train`):
It identifies the dataset mode (train or dev).
feat_type (:obj:`str`, `optional`, defaults to `raw`):
It identifies the feature type that user wants to extrace of an audio file.
"""
files, labels = self._get_data(mode)
super(UrbanAudioVisualScenes, self).__init__(
files=files, labels=labels, feat_type=feat_type, **kwargs)
def _get_meta_info(self, subset: str=None,
skip_header: bool=True) -> List[collections.namedtuple]:
if subset is None:
meta_file = self.meta
meta_info = self.meta_info
else:
assert subset in self.subset_meta, f'Subset must be one in {list(self.subset_meta.keys())}, but got {subset}.'
meta_file = self.subset_meta[subset]
meta_info = self.subset_meta_info
ret = []
with open(os.path.join(DATA_HOME, meta_file), 'r') as rf:
lines = rf.readlines()[1:] if skip_header else rf.readlines()
for line in lines:
ret.append(meta_info(*line.strip().split('\t')))
return ret
def _get_data(self, mode: str) -> Tuple[List[str], List[int]]:
if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)) or \
not os.path.isfile(os.path.join(DATA_HOME, self.meta)):
download_and_decompress(self.archieves,
os.path.join(DATA_HOME, self.base_name))
meta_info = self._get_meta_info(subset=mode, skip_header=True)
files = []
labels = []
for sample in meta_info:
filename, _, label = sample[:3]
filename = os.path.basename(filename)
target = self.label_list.index(label)
files.append(os.path.join(DATA_HOME, self.audio_path, filename))
labels.append(int(target))
return files, labels
paddleaudio/paddleaudio/datasets/esc50.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import os
from typing import List
from typing import Tuple
from ..utils.download import download_and_decompress
from ..utils.env import DATA_HOME
from .dataset import AudioClassificationDataset
__all__ = ['ESC50']
class ESC50(AudioClassificationDataset):
"""
The ESC-50 dataset is a labeled collection of 2000 environmental audio recordings
suitable for benchmarking methods of environmental sound classification. The dataset
consists of 5-second-long recordings organized into 50 semantical classes (with
40 examples per class)
Reference:
ESC: Dataset for Environmental Sound Classification
http://dx.doi.org/10.1145/2733373.2806390
"""
archieves = [
{
'url':
'https://paddleaudio.bj.bcebos.com/datasets/ESC-50-master.zip',
'md5': '7771e4b9d86d0945acce719c7a59305a',
},
]
label_list = [
# Animals
'Dog',
'Rooster',
'Pig',
'Cow',
'Frog',
'Cat',
'Hen',
'Insects (flying)',
'Sheep',
'Crow',
# Natural soundscapes & water sounds
'Rain',
'Sea waves',
'Crackling fire',
'Crickets',
'Chirping birds',
'Water drops',
'Wind',
'Pouring water',
'Toilet flush',
'Thunderstorm',
# Human, non-speech sounds
'Crying baby',
'Sneezing',
'Clapping',
'Breathing',
'Coughing',
'Footsteps',
'Laughing',
'Brushing teeth',
'Snoring',
'Drinking, sipping',
# Interior/domestic sounds
'Door knock',
'Mouse click',
'Keyboard typing',
'Door, wood creaks',
'Can opening',
'Washing machine',
'Vacuum cleaner',
'Clock alarm',
'Clock tick',
'Glass breaking',
# Exterior/urban noises
'Helicopter',
'Chainsaw',
'Siren',
'Car horn',
'Engine',
'Train',
'Church bells',
'Airplane',
'Fireworks',
'Hand saw',
]
meta = os.path.join('ESC-50-master', 'meta', 'esc50.csv')
meta_info = collections.namedtuple(
'META_INFO',
('filename', 'fold', 'target', 'category', 'esc10', 'src_file', 'take'))
audio_path = os.path.join('ESC-50-master', 'audio')
def __init__(self,
mode: str='train',
split: int=1,
feat_type: str='raw',
**kwargs):
"""
Ags:
mode (:obj:`str`, `optional`, defaults to `train`):
It identifies the dataset mode (train or dev).
split (:obj:`int`, `optional`, defaults to 1):
It specify the fold of dev dataset.
feat_type (:obj:`str`, `optional`, defaults to `raw`):
It identifies the feature type that user wants to extrace of an audio file.
"""
files, labels = self._get_data(mode, split)
super(ESC50, self).__init__(
files=files, labels=labels, feat_type=feat_type, **kwargs)
def _get_meta_info(self) -> List[collections.namedtuple]:
ret = []
with open(os.path.join(DATA_HOME, self.meta), 'r') as rf:
for line in rf.readlines()[1:]:
ret.append(self.meta_info(*line.strip().split(',')))
return ret
def _get_data(self, mode: str, split: int) -> Tuple[List[str], List[int]]:
if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)) or \
not os.path.isfile(os.path.join(DATA_HOME, self.meta)):
download_and_decompress(self.archieves, DATA_HOME)
meta_info = self._get_meta_info()
files = []
labels = []
for sample in meta_info:
filename, fold, target, _, _, _, _ = sample
if mode == 'train' and int(fold) != split:
files.append(os.path.join(DATA_HOME, self.audio_path, filename))
labels.append(int(target))
if mode != 'train' and int(fold) == split:
files.append(os.path.join(DATA_HOME, self.audio_path, filename))
labels.append(int(target))
return files, labels
paddleaudio/paddleaudio/datasets/gtzan.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import os
import random
from typing import List
from typing import Tuple
from ..utils.download import download_and_decompress
from ..utils.env import DATA_HOME
from .dataset import AudioClassificationDataset
__all__ = ['GTZAN']
class GTZAN(AudioClassificationDataset):
"""
The GTZAN dataset consists of 1000 audio tracks each 30 seconds long. It contains 10 genres,
each represented by 100 tracks. The dataset is the most-used public dataset for evaluation
in machine listening research for music genre recognition (MGR).
Reference:
Musical genre classification of audio signals
https://ieeexplore.ieee.org/document/1021072/
"""
archieves = [
{
'url': 'http://opihi.cs.uvic.ca/sound/genres.tar.gz',
'md5': '5b3d6dddb579ab49814ab86dba69e7c7',
},
]
label_list = [
'blues', 'classical', 'country', 'disco', 'hiphop', 'jazz', 'metal',
'pop', 'reggae', 'rock'
]
meta = os.path.join('genres', 'input.mf')
meta_info = collections.namedtuple('META_INFO', ('file_path', 'label'))
audio_path = 'genres'
def __init__(self,
mode='train',
seed=0,
n_folds=5,
split=1,
feat_type='raw',
**kwargs):
"""
Ags:
mode (:obj:`str`, `optional`, defaults to `train`):
It identifies the dataset mode (train or dev).
seed (:obj:`int`, `optional`, defaults to 0):
Set the random seed to shuffle samples.
n_folds (:obj:`int`, `optional`, defaults to 5):
Split the dataset into n folds. 1 fold for dev dataset and n-1 for train dataset.
split (:obj:`int`, `optional`, defaults to 1):
It specify the fold of dev dataset.
feat_type (:obj:`str`, `optional`, defaults to `raw`):
It identifies the feature type that user wants to extrace of an audio file.
"""
assert split <= n_folds, f'The selected split should not be larger than n_fold, but got {split} > {n_folds}'
files, labels = self._get_data(mode, seed, n_folds, split)
super(GTZAN, self).__init__(
files=files, labels=labels, feat_type=feat_type, **kwargs)
def _get_meta_info(self) -> List[collections.namedtuple]:
ret = []
with open(os.path.join(DATA_HOME, self.meta), 'r') as rf:
for line in rf.readlines():
ret.append(self.meta_info(*line.strip().split('\t')))
return ret
def _get_data(self, mode, seed, n_folds,
split) -> Tuple[List[str], List[int]]:
if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)) or \
not os.path.isfile(os.path.join(DATA_HOME, self.meta)):
download_and_decompress(self.archieves, DATA_HOME)
meta_info = self._get_meta_info()
random.seed(seed) # shuffle samples to split data
random.shuffle(
meta_info
) # make sure using the same seed to create train and dev dataset
files = []
labels = []
n_samples_per_fold = len(meta_info) // n_folds
for idx, sample in enumerate(meta_info):
file_path, label = sample
filename = os.path.basename(file_path)
target = self.label_list.index(label)
fold = idx // n_samples_per_fold + 1
if mode == 'train' and int(fold) != split:
files.append(
os.path.join(DATA_HOME, self.audio_path, label, filename))
labels.append(target)
if mode != 'train' and int(fold) == split:
files.append(
os.path.join(DATA_HOME, self.audio_path, label, filename))
labels.append(target)
return files, labels
paddleaudio/paddleaudio/datasets/librispeech.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import codecs
import collections
import json
import os
from typing import Dict
from paddle.io import Dataset
from tqdm import tqdm
from ..backends import load as load_audio
from ..utils.download import download_and_decompress
from ..utils.env import DATA_HOME
from ..utils.log import logger
from .dataset import feat_funcs
__all__ = ['LIBRISPEECH']
class LIBRISPEECH(Dataset):
"""
LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech,
prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is
derived from read audiobooks from the LibriVox project, and has been carefully
segmented and aligned.
Reference:
LIBRISPEECH: AN ASR CORPUS BASED ON PUBLIC DOMAIN AUDIO BOOKS
http://www.danielpovey.com/files/2015_icassp_librispeech.pdf
https://arxiv.org/abs/1709.05522
"""
source_url = 'http://www.openslr.org/resources/12/'
archieves = [
{
'url': source_url + 'train-clean-100.tar.gz',
'md5': '2a93770f6d5c6c964bc36631d331a522',
},
{
'url': source_url + 'train-clean-360.tar.gz',
'md5': 'c0e676e450a7ff2f54aeade5171606fa',
},
{
'url': source_url + 'train-other-500.tar.gz',
'md5': 'd1a0fd59409feb2c614ce4d30c387708',
},
{
'url': source_url + 'dev-clean.tar.gz',
'md5': '42e2234ba48799c1f50f24a7926300a1',
},
{
'url': source_url + 'dev-other.tar.gz',
'md5': 'c8d0bcc9cca99d4f8b62fcc847357931',
},
{
'url': source_url + 'test-clean.tar.gz',
'md5': '32fa31d27d2e1cad72775fee3f4849a9',
},
{
'url': source_url + 'test-other.tar.gz',
'md5': 'fb5a50374b501bb3bac4815ee91d3135',
},
]
speaker_meta = os.path.join('LibriSpeech', 'SPEAKERS.TXT')
utt_info = collections.namedtuple('META_INFO', (
'file_path', 'utt_id', 'text', 'spk_id', 'spk_gender'))
audio_path = 'LibriSpeech'
manifest_path = os.path.join('LibriSpeech', 'manifest')
subset = [
'train-clean-100', 'train-clean-360', 'train-clean-500', 'dev-clean',
'dev-other', 'test-clean', 'test-other'
]
def __init__(self,
subset: str='train-clean-100',
feat_type: str='raw',
**kwargs):
assert subset in self.subset, 'Dataset subset must be one in {}, but got {}'.format(
self.subset, subset)
self.subset = subset
self.feat_type = feat_type
self.feat_config = kwargs
self._data = self._get_data()
super(LIBRISPEECH, self).__init__()
def _get_speaker_info(self) -> Dict[str, str]:
ret = {}
with open(os.path.join(DATA_HOME, self.speaker_meta), 'r') as rf:
for line in rf.readlines():
if ';' in line: # Skip dataset abstract
continue
spk_id, gender = map(str.strip,
line.split('|')[:2]) # spk_id, gender
ret.update({spk_id: gender})
return ret
def _get_text_info(self, trans_file) -> Dict[str, str]:
ret = {}
with open(trans_file, 'r') as rf:
for line in rf.readlines():
utt_id, text = map(str.strip, line.split(' ',
1)) # utt_id, text
ret.update({utt_id: text})
return ret
def _get_data(self):
if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)) or \
not os.path.isfile(os.path.join(DATA_HOME, self.speaker_meta)):
download_and_decompress(self.archieves, DATA_HOME,
len(self.archieves))
# Speaker info
speaker_info = self._get_speaker_info()
# Text info
text_info = {}
for root, _, files in os.walk(
os.path.join(DATA_HOME, self.audio_path, self.subset)):
for file in files:
if file.endswith('.trans.txt'):
text_info.update(
self._get_text_info(os.path.join(root, file)))
data = []
for root, _, files in os.walk(
os.path.join(DATA_HOME, self.audio_path, self.subset)):
for file in files:
if file.endswith('.flac'):
utt_id = os.path.splitext(file)[0]
spk_id = utt_id.split('-')[0]
if utt_id not in text_info \
or spk_id not in speaker_info : # Skip samples with incomplete data
continue
file_path = os.path.join(root, file)
text = text_info[utt_id]
spk_gender = speaker_info[spk_id]
data.append(
self.utt_info(file_path, utt_id, text, spk_id,
spk_gender))
return data
def _convert_to_record(self, idx: int):
sample = self._data[idx]
record = {}
# To show all fields in a namedtuple: `type(sample)._fields`
for field in type(sample)._fields:
record[field] = getattr(sample, field)
waveform, sr = load_audio(
sample[0]) # The first element of sample is file path
feat_func = feat_funcs[self.feat_type]
feat = feat_func(
waveform, sample_rate=sr,
**self.feat_config) if feat_func else waveform
record.update({'feat': feat, 'duration': len(waveform) / sr})
return record
def create_manifest(self, prefix='manifest'):
if not os.path.isdir(os.path.join(DATA_HOME, self.manifest_path)):
os.makedirs(os.path.join(DATA_HOME, self.manifest_path))
manifest_file = os.path.join(DATA_HOME, self.manifest_path,
f'{prefix}.{self.subset}')
with codecs.open(manifest_file, 'w', 'utf-8') as f:
for idx in tqdm(range(len(self))):
record = self._convert_to_record(idx)
record_line = json.dumps(
{
'utt': record['utt_id'],
'feat': record['file_path'],
'feat_shape': (record['duration'], ),
'text': record['text'],
'spk': record['spk_id'],
'gender': record['spk_gender'],
},
ensure_ascii=False)
f.write(record_line + '\n')
logger.info(f'Manifest file {manifest_file} created.')
def __getitem__(self, idx):
record = self._convert_to_record(idx)
return tuple(record.values())
def __len__(self):
return len(self._data)
paddleaudio/paddleaudio/datasets/ravdess.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import os
import random
from typing import List
from typing import Tuple
from ..utils.download import download_and_decompress
from ..utils.env import DATA_HOME
from .dataset import AudioClassificationDataset
__all__ = ['RAVDESS']
class RAVDESS(AudioClassificationDataset):
"""
The RAVDESS contains 24 professional actors (12 female, 12 male), vocalizing two
lexically-matched statements in a neutral North American accent. Speech emotions
includes calm, happy, sad, angry, fearful, surprise, and disgust expressions.
Each expression is produced at two levels of emotional intensity (normal, strong),
with an additional neutral expression.
Reference:
The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS):
A dynamic, multimodal set of facial and vocal expressions in North American English
https://doi.org/10.1371/journal.pone.0196391
"""
archieves = [
{
'url':
'https://zenodo.org/record/1188976/files/Audio_Song_Actors_01-24.zip',
'md5':
'5411230427d67a21e18aa4d466e6d1b9',
},
{
'url':
'https://zenodo.org/record/1188976/files/Audio_Speech_Actors_01-24.zip',
'md5':
'bc696df654c87fed845eb13823edef8a',
},
]
label_list = [
'neutral', 'calm', 'happy', 'sad', 'angry', 'fearful', 'disgust',
'surprised'
]
meta_info = collections.namedtuple(
'META_INFO', ('modality', 'vocal_channel', 'emotion',
'emotion_intensity', 'statement', 'repitition', 'actor'))
speech_path = os.path.join(DATA_HOME, 'Audio_Speech_Actors_01-24')
song_path = os.path.join(DATA_HOME, 'Audio_Song_Actors_01-24')
def __init__(self,
mode='train',
seed=0,
n_folds=5,
split=1,
feat_type='raw',
**kwargs):
"""
Ags:
mode (:obj:`str`, `optional`, defaults to `train`):
It identifies the dataset mode (train or dev).
seed (:obj:`int`, `optional`, defaults to 0):
Set the random seed to shuffle samples.
n_folds (:obj:`int`, `optional`, defaults to 5):
Split the dataset into n folds. 1 fold for dev dataset and n-1 for train dataset.
split (:obj:`int`, `optional`, defaults to 1):
It specify the fold of dev dataset.
feat_type (:obj:`str`, `optional`, defaults to `raw`):
It identifies the feature type that user wants to extrace of an audio file.
"""
assert split <= n_folds, f'The selected split should not be larger than n_fold, but got {split} > {n_folds}'
files, labels = self._get_data(mode, seed, n_folds, split)
super(RAVDESS, self).__init__(
files=files, labels=labels, feat_type=feat_type, **kwargs)
def _get_meta_info(self, files) -> List[collections.namedtuple]:
ret = []
for file in files:
basename_without_extend = os.path.basename(file)[:-4]
ret.append(self.meta_info(*basename_without_extend.split('-')))
return ret
def _get_data(self, mode, seed, n_folds,
split) -> Tuple[List[str], List[int]]:
if not os.path.isdir(self.speech_path) and not os.path.isdir(
self.song_path):
download_and_decompress(self.archieves, DATA_HOME)
wav_files = []
for root, _, files in os.walk(self.speech_path):
for file in files:
if file.endswith('.wav'):
wav_files.append(os.path.join(root, file))
for root, _, files in os.walk(self.song_path):
for file in files:
if file.endswith('.wav'):
wav_files.append(os.path.join(root, file))
random.seed(seed) # shuffle samples to split data
random.shuffle(
wav_files
) # make sure using the same seed to create train and dev dataset
meta_info = self._get_meta_info(wav_files)
files = []
labels = []
n_samples_per_fold = len(meta_info) // n_folds
for idx, sample in enumerate(meta_info):
_, _, emotion, _, _, _, _ = sample
target = int(emotion) - 1
fold = idx // n_samples_per_fold + 1
if mode == 'train' and int(fold) != split:
files.append(wav_files[idx])
labels.append(target)
if mode != 'train' and int(fold) == split:
files.append(wav_files[idx])
labels.append(target)
return files, labels
paddleaudio/paddleaudio/datasets/tess.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import os
import random
from typing import List
from typing import Tuple
from ..utils.download import download_and_decompress
from ..utils.env import DATA_HOME
from .dataset import AudioClassificationDataset
__all__ = ['TESS']
class TESS(AudioClassificationDataset):
"""
TESS is a set of 200 target words were spoken in the carrier phrase
"Say the word _____' by two actresses (aged 26 and 64 years) and
recordings were made of the set portraying each of seven emotions(anger,
disgust, fear, happiness, pleasant surprise, sadness, and neutral).
There are 2800 stimuli in total.
Reference:
Toronto emotional speech set (TESS)
https://doi.org/10.5683/SP2/E8H2MF
"""
archieves = [
{
'url':
'https://bj.bcebos.com/paddleaudio/datasets/TESS_Toronto_emotional_speech_set.zip',
'md5':
'1465311b24d1de704c4c63e4ccc470c7',
},
]
label_list = [
'angry',
'disgust',
'fear',
'happy',
'neutral',
'ps', # pleasant surprise
'sad',
]
meta_info = collections.namedtuple('META_INFO',
('speaker', 'word', 'emotion'))
audio_path = 'TESS_Toronto_emotional_speech_set'
def __init__(self,
mode='train',
seed=0,
n_folds=5,
split=1,
feat_type='raw',
**kwargs):
"""
Ags:
mode (:obj:`str`, `optional`, defaults to `train`):
It identifies the dataset mode (train or dev).
seed (:obj:`int`, `optional`, defaults to 0):
Set the random seed to shuffle samples.
n_folds (:obj:`int`, `optional`, defaults to 5):
Split the dataset into n folds. 1 fold for dev dataset and n-1 for train dataset.
split (:obj:`int`, `optional`, defaults to 1):
It specify the fold of dev dataset.
feat_type (:obj:`str`, `optional`, defaults to `raw`):
It identifies the feature type that user wants to extrace of an audio file.
"""
assert split <= n_folds, f'The selected split should not be larger than n_fold, but got {split} > {n_folds}'
files, labels = self._get_data(mode, seed, n_folds, split)
super(TESS, self).__init__(
files=files, labels=labels, feat_type=feat_type, **kwargs)
def _get_meta_info(self, files) -> List[collections.namedtuple]:
ret = []
for file in files:
basename_without_extend = os.path.basename(file)[:-4]
ret.append(self.meta_info(*basename_without_extend.split('_')))
return ret
def _get_data(self, mode, seed, n_folds,
split) -> Tuple[List[str], List[int]]:
if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)):
download_and_decompress(self.archieves, DATA_HOME)
wav_files = []
for root, _, files in os.walk(os.path.join(DATA_HOME, self.audio_path)):
for file in files:
if file.endswith('.wav'):
wav_files.append(os.path.join(root, file))
random.seed(seed) # shuffle samples to split data
random.shuffle(
wav_files
) # make sure using the same seed to create train and dev dataset
meta_info = self._get_meta_info(wav_files)
files = []
labels = []
n_samples_per_fold = len(meta_info) // n_folds
for idx, sample in enumerate(meta_info):
_, _, emotion = sample
target = self.label_list.index(emotion)
fold = idx // n_samples_per_fold + 1
if mode == 'train' and int(fold) != split:
files.append(wav_files[idx])
labels.append(target)
if mode != 'train' and int(fold) == split:
files.append(wav_files[idx])
labels.append(target)
return files, labels
paddleaudio/paddleaudio/datasets/urban_sound.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import os
from typing import List
from typing import Tuple
from ..utils.download import download_and_decompress
from ..utils.env import DATA_HOME
from .dataset import AudioClassificationDataset
__all__ = ['UrbanSound8K']
class UrbanSound8K(AudioClassificationDataset):
"""
UrbanSound8K dataset contains 8732 labeled sound excerpts (<=4s) of urban
sounds from 10 classes: air_conditioner, car_horn, children_playing, dog_bark,
drilling, enginge_idling, gun_shot, jackhammer, siren, and street_music. The
classes are drawn from the urban sound taxonomy.
Reference:
A Dataset and Taxonomy for Urban Sound Research
https://dl.acm.org/doi/10.1145/2647868.2655045
"""
archieves = [
{
'url':
'https://zenodo.org/record/1203745/files/UrbanSound8K.tar.gz',
'md5': '9aa69802bbf37fb986f71ec1483a196e',
},
]
label_list = [
"air_conditioner", "car_horn", "children_playing", "dog_bark",
"drilling", "engine_idling", "gun_shot", "jackhammer", "siren",
"street_music"
]
meta = os.path.join('UrbanSound8K', 'metadata', 'UrbanSound8K.csv')
meta_info = collections.namedtuple(
'META_INFO', ('filename', 'fsid', 'start', 'end', 'salience', 'fold',
'class_id', 'label'))
audio_path = os.path.join('UrbanSound8K', 'audio')
def __init__(self,
mode: str='train',
split: int=1,
feat_type: str='raw',
**kwargs):
files, labels = self._get_data(mode, split)
super(UrbanSound8K, self).__init__(
files=files, labels=labels, feat_type=feat_type, **kwargs)
"""
Ags:
mode (:obj:`str`, `optional`, defaults to `train`):
It identifies the dataset mode (train or dev).
split (:obj:`int`, `optional`, defaults to 1):
It specify the fold of dev dataset.
feat_type (:obj:`str`, `optional`, defaults to `raw`):
It identifies the feature type that user wants to extrace of an audio file.
"""
def _get_meta_info(self):
ret = []
with open(os.path.join(DATA_HOME, self.meta), 'r') as rf:
for line in rf.readlines()[1:]:
ret.append(self.meta_info(*line.strip().split(',')))
return ret
def _get_data(self, mode: str, split: int) -> Tuple[List[str], List[int]]:
if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)) or \
not os.path.isfile(os.path.join(DATA_HOME, self.meta)):
download_and_decompress(self.archieves, DATA_HOME)
meta_info = self._get_meta_info()
files = []
labels = []
for sample in meta_info:
filename, _, _, _, _, fold, target, _ = sample
if mode == 'train' and int(fold) != split:
files.append(
os.path.join(DATA_HOME, self.audio_path, f'fold{fold}',
filename))
labels.append(int(target))
if mode != 'train' and int(fold) == split:
files.append(
os.path.join(DATA_HOME, self.audio_path, f'fold{fold}',
filename))
labels.append(int(target))
return files, labels
paddleaudio/paddleaudio/features/__init__.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .augment import *
from .core import *
paddleaudio/paddleaudio/features/augment.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List
import numpy as np
from numpy import ndarray as array
from paddleaudio.backends import depth_convert
from paddleaudio.utils import ParameterError
__all__ = [
'depth_augment',
'spect_augment',
'random_crop1d',
'random_crop2d',
'adaptive_spect_augment',
]
def randint(high: int) -> int:
"""Generate one random integer in range [0 high)
This is a helper function for random data augmentaiton
"""
return int(np.random.randint(0, high=high))
def rand() -> float:
"""Generate one floating-point number in range [0 1)
This is a helper function for random data augmentaiton
"""
return float(np.random.rand(1))
def depth_augment(y: array,
choices: List=['int8', 'int16'],
probs: List[float]=[0.5, 0.5]) -> array:
""" Audio depth augmentation
Do audio depth augmentation to simulate the distortion brought by quantization.
"""
assert len(probs) == len(
choices
), 'number of choices {} must be equal to size of probs {}'.format(
len(choices), len(probs))
depth = np.random.choice(choices, p=probs)
src_depth = y.dtype
y1 = depth_convert(y, depth)
y2 = depth_convert(y1, src_depth)
return y2
def adaptive_spect_augment(spect: array, tempo_axis: int=0,
level: float=0.1) -> array:
"""Do adpative spectrogram augmentation
The level of the augmentation is gowern by the paramter level,
ranging from 0 to 1, with 0 represents no augmentation。
"""
assert spect.ndim == 2., 'only supports 2d tensor or numpy array'
if tempo_axis == 0:
nt, nf = spect.shape
else:
nf, nt = spect.shape
time_mask_width = int(nt * level * 0.5)
freq_mask_width = int(nf * level * 0.5)
num_time_mask = int(10 * level)
num_freq_mask = int(10 * level)
if tempo_axis == 0:
for _ in range(num_time_mask):
start = randint(nt - time_mask_width)
spect[start:start + time_mask_width, :] = 0
for _ in range(num_freq_mask):
start = randint(nf - freq_mask_width)
spect[:, start:start + freq_mask_width] = 0
else:
for _ in range(num_time_mask):
start = randint(nt - time_mask_width)
spect[:, start:start + time_mask_width] = 0
for _ in range(num_freq_mask):
start = randint(nf - freq_mask_width)
spect[start:start + freq_mask_width, :] = 0
return spect
def spect_augment(spect: array,
tempo_axis: int=0,
max_time_mask: int=3,
max_freq_mask: int=3,
max_time_mask_width: int=30,
max_freq_mask_width: int=20) -> array:
"""Do spectrogram augmentation in both time and freq axis
Reference:
"""
assert spect.ndim == 2., 'only supports 2d tensor or numpy array'
if tempo_axis == 0:
nt, nf = spect.shape
else:
nf, nt = spect.shape
num_time_mask = randint(max_time_mask)
num_freq_mask = randint(max_freq_mask)
time_mask_width = randint(max_time_mask_width)
freq_mask_width = randint(max_freq_mask_width)
if tempo_axis == 0:
for _ in range(num_time_mask):
start = randint(nt - time_mask_width)
spect[start:start + time_mask_width, :] = 0
for _ in range(num_freq_mask):
start = randint(nf - freq_mask_width)
spect[:, start:start + freq_mask_width] = 0
else:
for _ in range(num_time_mask):
start = randint(nt - time_mask_width)
spect[:, start:start + time_mask_width] = 0
for _ in range(num_freq_mask):
start = randint(nf - freq_mask_width)
spect[start:start + freq_mask_width, :] = 0
return spect
def random_crop1d(y: array, crop_len: int) -> array:
""" Do random cropping on 1d input signal
The input is a 1d signal, typically a sound waveform
"""
if y.ndim != 1:
'only accept 1d tensor or numpy array'
n = len(y)
idx = randint(n - crop_len)
return y[idx:idx + crop_len]
def random_crop2d(s: array, crop_len: int, tempo_axis: int=0) -> array:
""" Do random cropping for 2D array, typically a spectrogram.
The cropping is done in temporal direction on the time-freq input signal.
"""
if tempo_axis >= s.ndim:
raise ParameterError('axis out of range')
n = s.shape[tempo_axis]
idx = randint(high=n - crop_len)
sli = [slice(None) for i in range(s.ndim)]
sli[tempo_axis] = slice(idx, idx + crop_len)
out = s[tuple(sli)]
return out
paddleaudio/paddleaudio/features/core.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from typing import List
from typing import Optional
from typing import Union
import numpy as np
import scipy
from numpy import ndarray as array
from numpy.lib.stride_tricks import as_strided
from scipy.signal import get_window
from paddleaudio.utils import ParameterError
__all__ = [
'stft',
'mfcc',
'hz_to_mel',
'mel_to_hz',
'split_frames',
'mel_frequencies',
'power_to_db',
'compute_fbank_matrix',
'melspectrogram',
'spectrogram',
'mu_encode',
'mu_decode',
]
def pad_center(data: array, size: int, axis: int=-1, **kwargs) -> array:
"""Pad an array to a target length along a target axis.
This differs from `np.pad` by centering the data prior to padding,
analogous to `str.center`
"""
kwargs.setdefault("mode", "constant")
n = data.shape[axis]
lpad = int((size - n) // 2)
lengths = [(0, 0)] * data.ndim
lengths[axis] = (lpad, int(size - n - lpad))
if lpad < 0:
raise ParameterError(("Target size ({size:d}) must be "
"at least input size ({n:d})"))
return np.pad(data, lengths, **kwargs)
def split_frames(x: array, frame_length: int, hop_length: int,
axis: int=-1) -> array:
"""Slice a data array into (overlapping) frames.
This function is aligned with librosa.frame
"""
if not isinstance(x, np.ndarray):
raise ParameterError(
f"Input must be of type numpy.ndarray, given type(x)={type(x)}")
if x.shape[axis] < frame_length:
raise ParameterError(f"Input is too short (n={x.shape[axis]:d})"
f" for frame_length={frame_length:d}")
if hop_length < 1:
raise ParameterError(f"Invalid hop_length: {hop_length:d}")
if axis == -1 and not x.flags["F_CONTIGUOUS"]:
warnings.warn(f"librosa.util.frame called with axis={axis} "
"on a non-contiguous input. This will result in a copy.")
x = np.asfortranarray(x)
elif axis == 0 and not x.flags["C_CONTIGUOUS"]:
warnings.warn(f"librosa.util.frame called with axis={axis} "
"on a non-contiguous input. This will result in a copy.")
x = np.ascontiguousarray(x)
n_frames = 1 + (x.shape[axis] - frame_length) // hop_length
strides = np.asarray(x.strides)
new_stride = np.prod(strides[strides > 0] // x.itemsize) * x.itemsize
if axis == -1:
shape = list(x.shape)[:-1] + [frame_length, n_frames]
strides = list(strides) + [hop_length * new_stride]
elif axis == 0:
shape = [n_frames, frame_length] + list(x.shape)[1:]
strides = [hop_length * new_stride] + list(strides)
else:
raise ParameterError(f"Frame axis={axis} must be either 0 or -1")
return as_strided(x, shape=shape, strides=strides)
def _check_audio(y, mono=True) -> bool:
"""Determine whether a variable contains valid audio data.
The audio y must be a np.ndarray, ether 1-channel or two channel
"""
if not isinstance(y, np.ndarray):
raise ParameterError("Audio data must be of type numpy.ndarray")
if y.ndim > 2:
raise ParameterError(
f"Invalid shape for audio ndim={y.ndim:d}, shape={y.shape}")
if mono and y.ndim == 2:
raise ParameterError(
f"Invalid shape for mono audio ndim={y.ndim:d}, shape={y.shape}")
if (mono and len(y) == 0) or (not mono and y.shape[1] < 0):
raise ParameterError(f"Audio is empty ndim={y.ndim:d}, shape={y.shape}")
if not np.issubdtype(y.dtype, np.floating):
raise ParameterError("Audio data must be floating-point")
if not np.isfinite(y).all():
raise ParameterError("Audio buffer is not finite everywhere")
return True
def hz_to_mel(frequencies: Union[float, List[float], array],
htk: bool=False) -> array:
"""Convert Hz to Mels
This function is aligned with librosa.
"""
freq = np.asanyarray(frequencies)
if htk:
return 2595.0 * np.log10(1.0 + freq / 700.0)
# Fill in the linear part
f_min = 0.0
f_sp = 200.0 / 3
mels = (freq - f_min) / f_sp
# Fill in the log-scale part
min_log_hz = 1000.0 # beginning of log region (Hz)
min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels)
logstep = np.log(6.4) / 27.0 # step size for log region
if freq.ndim:
# If we have array data, vectorize
log_t = freq >= min_log_hz
mels[log_t] = min_log_mel + \
np.log(freq[log_t] / min_log_hz) / logstep
elif freq >= min_log_hz:
# If we have scalar data, heck directly
mels = min_log_mel + np.log(freq / min_log_hz) / logstep
return mels
def mel_to_hz(mels: Union[float, List[float], array], htk: int=False) -> array:
"""Convert mel bin numbers to frequencies.
This function is aligned with librosa.
"""
mel_array = np.asanyarray(mels)
if htk:
return 700.0 * (10.0**(mel_array / 2595.0) - 1.0)
# Fill in the linear scale
f_min = 0.0
f_sp = 200.0 / 3
freqs = f_min + f_sp * mel_array
# And now the nonlinear scale
min_log_hz = 1000.0 # beginning of log region (Hz)
min_log_mel = (min_log_hz - f_min) / f_sp # same (Mels)
logstep = np.log(6.4) / 27.0 # step size for log region
if mel_array.ndim:
# If we have vector data, vectorize
log_t = mel_array >= min_log_mel
freqs[log_t] = min_log_hz * \
np.exp(logstep * (mel_array[log_t] - min_log_mel))
elif mel_array >= min_log_mel:
# If we have scalar data, check directly
freqs = min_log_hz * np.exp(logstep * (mel_array - min_log_mel))
return freqs
def mel_frequencies(n_mels: int=128,
fmin: float=0.0,
fmax: float=11025.0,
htk: bool=False) -> array:
"""Compute mel frequencies
This function is aligned with librosa.
"""
# 'Center freqs' of mel bands - uniformly spaced between limits
min_mel = hz_to_mel(fmin, htk=htk)
max_mel = hz_to_mel(fmax, htk=htk)
mels = np.linspace(min_mel, max_mel, n_mels)
return mel_to_hz(mels, htk=htk)
def fft_frequencies(sr: int, n_fft: int) -> array:
"""Compute fourier frequencies.
This function is aligned with librosa.
"""
return np.linspace(0, float(sr) / 2, int(1 + n_fft // 2), endpoint=True)
def compute_fbank_matrix(sr: int,
n_fft: int,
n_mels: int=128,
fmin: float=0.0,
fmax: Optional[float]=None,
htk: bool=False,
norm: str="slaney",
dtype: type=np.float32):
"""Compute fbank matrix.
This funciton is aligned with librosa.
"""
if norm != "slaney":
raise ParameterError('norm must set to slaney')
if fmax is None:
fmax = float(sr) / 2
# Initialize the weights
n_mels = int(n_mels)
weights = np.zeros((n_mels, int(1 + n_fft // 2)), dtype=dtype)
# Center freqs of each FFT bin
fftfreqs = fft_frequencies(sr=sr, n_fft=n_fft)
# 'Center freqs' of mel bands - uniformly spaced between limits
mel_f = mel_frequencies(n_mels + 2, fmin=fmin, fmax=fmax, htk=htk)
fdiff = np.diff(mel_f)
ramps = np.subtract.outer(mel_f, fftfreqs)
for i in range(n_mels):
# lower and upper slopes for all bins
lower = -ramps[i] / fdiff[i]
upper = ramps[i + 2] / fdiff[i + 1]
# .. then intersect them with each other and zero
weights[i] = np.maximum(0, np.minimum(lower, upper))
if norm == "slaney":
# Slaney-style mel is scaled to be approx constant energy per channel
enorm = 2.0 / (mel_f[2:n_mels + 2] - mel_f[:n_mels])
weights *= enorm[:, np.newaxis]
# Only check weights if f_mel[0] is positive
if not np.all((mel_f[:-2] == 0) | (weights.max(axis=1) > 0)):
# This means we have an empty channel somewhere
warnings.warn("Empty filters detected in mel frequency basis. "
"Some channels will produce empty responses. "
"Try increasing your sampling rate (and fmax) or "
"reducing n_mels.")
return weights
def stft(x: array,
n_fft: int=2048,
hop_length: Optional[int]=None,
win_length: Optional[int]=None,
window: str="hann",
center: bool=True,
dtype: type=np.complex64,
pad_mode: str="reflect") -> array:
"""Short-time Fourier transform (STFT).
This function is aligned with librosa.
"""
_check_audio(x)
# By default, use the entire frame
if win_length is None:
win_length = n_fft
# Set the default hop, if it's not already specified
if hop_length is None:
hop_length = int(win_length // 4)
fft_window = get_window(window, win_length, fftbins=True)
# Pad the window out to n_fft size
fft_window = pad_center(fft_window, n_fft)
# Reshape so that the window can be broadcast
fft_window = fft_window.reshape((-1, 1))
# Pad the time series so that frames are centered
if center:
if n_fft > x.shape[-1]:
warnings.warn(
f"n_fft={n_fft} is too small for input signal of length={x.shape[-1]}"
)
x = np.pad(x, int(n_fft // 2), mode=pad_mode)
elif n_fft > x.shape[-1]:
raise ParameterError(
f"n_fft={n_fft} is too small for input signal of length={x.shape[-1]}"
)
# Window the time series.
x_frames = split_frames(x, frame_length=n_fft, hop_length=hop_length)
# Pre-allocate the STFT matrix
stft_matrix = np.empty(
(int(1 + n_fft // 2), x_frames.shape[1]), dtype=dtype, order="F")
fft = np.fft # use numpy fft as default
# Constrain STFT block sizes to 256 KB
MAX_MEM_BLOCK = 2**8 * 2**10
# how many columns can we fit within MAX_MEM_BLOCK?
n_columns = MAX_MEM_BLOCK // (stft_matrix.shape[0] * stft_matrix.itemsize)
n_columns = max(n_columns, 1)
for bl_s in range(0, stft_matrix.shape[1], n_columns):
bl_t = min(bl_s + n_columns, stft_matrix.shape[1])
stft_matrix[:, bl_s:bl_t] = fft.rfft(
fft_window * x_frames[:, bl_s:bl_t], axis=0)
return stft_matrix
def power_to_db(spect: array,
ref: float=1.0,
amin: float=1e-10,
top_db: Optional[float]=80.0) -> array:
"""Convert a power spectrogram (amplitude squared) to decibel (dB) units
This computes the scaling ``10 * log10(spect / ref)`` in a numerically
stable way.
This function is aligned with librosa.
"""
spect = np.asarray(spect)
if amin <= 0:
raise ParameterError("amin must be strictly positive")
if np.issubdtype(spect.dtype, np.complexfloating):
warnings.warn(
"power_to_db was called on complex input so phase "
"information will be discarded. To suppress this warning, "
"call power_to_db(np.abs(D)**2) instead.")
magnitude = np.abs(spect)
else:
magnitude = spect
if callable(ref):
# User supplied a function to calculate reference power
ref_value = ref(magnitude)
else:
ref_value = np.abs(ref)
log_spec = 10.0 * np.log10(np.maximum(amin, magnitude))
log_spec -= 10.0 * np.log10(np.maximum(amin, ref_value))
if top_db is not None:
if top_db < 0:
raise ParameterError("top_db must be non-negative")
log_spec = np.maximum(log_spec, log_spec.max() - top_db)
return log_spec
def mfcc(x,
sr: int=16000,
spect: Optional[array]=None,
n_mfcc: int=20,
dct_type: int=2,
norm: str="ortho",
lifter: int=0,
**kwargs) -> array:
"""Mel-frequency cepstral coefficients (MFCCs)
This function is NOT strictly aligned with librosa. The following example shows how to get the
same result with librosa:
# paddleaudioe mfcc:
kwargs = {
'window_size':512,
'hop_length':320,
'mel_bins':64,
'fmin':50,
'to_db':False}
a = mfcc(x,
spect=None,
n_mfcc=20,
dct_type=2,
norm='ortho',
lifter=0,
**kwargs)
# librosa mfcc:
spect = librosa.feature.melspectrogram(x,sr=16000,n_fft=512,
win_length=512,
hop_length=320,
n_mels=64, fmin=50)
b = librosa.feature.mfcc(x,
sr=16000,
S=spect,
n_mfcc=20,
dct_type=2,
norm='ortho',
lifter=0)
assert np.mean( (a-b)**2) < 1e-8
"""
if spect is None:
spect = melspectrogram(x, sr=sr, **kwargs)
M = scipy.fftpack.dct(spect, axis=0, type=dct_type, norm=norm)[:n_mfcc]
if lifter > 0:
factor = np.sin(np.pi * np.arange(1, 1 + n_mfcc, dtype=M.dtype) /
lifter)
return M * factor[:, np.newaxis]
elif lifter == 0:
return M
else:
raise ParameterError(
f"MFCC lifter={lifter} must be a non-negative number")
def melspectrogram(x: array,
sr: int=16000,
window_size: int=512,
hop_length: int=320,
n_mels: int=64,
fmin: int=50,
fmax: Optional[float]=None,
window: str='hann',
center: bool=True,
pad_mode: str='reflect',
power: float=2.0,
to_db: bool=True,
ref: float=1.0,
amin: float=1e-10,
top_db: Optional[float]=None) -> array:
"""Compute mel-spectrogram.
Parameters:
x: numpy.ndarray
The input wavform is a numpy array [shape=(n,)]
window_size: int, typically 512, 1024, 2048, etc.
The window size for framing, also used as n_fft for stft
Returns:
The mel-spectrogram in power scale or db scale(default)
Notes:
1. sr is default to 16000, which is commonly used in speech/speaker processing.
2. when fmax is None, it is set to sr//2.
3. this function will convert mel spectgrum to db scale by default. This is different
that of librosa.
"""
_check_audio(x, mono=True)
if len(x) <= 0:
raise ParameterError('The input waveform is empty')
if fmax is None:
fmax = sr // 2
if fmin < 0 or fmin >= fmax:
raise ParameterError('fmin and fmax must statisfy 0<fmin<fmax')
s = stft(
x,
n_fft=window_size,
hop_length=hop_length,
win_length=window_size,
window=window,
center=center,
pad_mode=pad_mode)
spect_power = np.abs(s)**power
fb_matrix = compute_fbank_matrix(
sr=sr, n_fft=window_size, n_mels=n_mels, fmin=fmin, fmax=fmax)
mel_spect = np.matmul(fb_matrix, spect_power)
if to_db:
return power_to_db(mel_spect, ref=ref, amin=amin, top_db=top_db)
else:
return mel_spect
def spectrogram(x: array,
sr: int=16000,
window_size: int=512,
hop_length: int=320,
window: str='hann',
center: bool=True,
pad_mode: str='reflect',
power: float=2.0) -> array:
"""Compute spectrogram from an input waveform.
This function is a wrapper for librosa.feature.stft, with addition step to
compute the magnitude of the complex spectrogram.
"""
s = stft(
x,
n_fft=window_size,
hop_length=hop_length,
win_length=window_size,
window=window,
center=center,
pad_mode=pad_mode)
return np.abs(s)**power
def mu_encode(x: array, mu: int=255, quantized: bool=True) -> array:
"""Mu-law encoding.
Compute the mu-law decoding given an input code.
When quantized is True, the result will be converted to
integer in range [0,mu-1]. Otherwise, the resulting signal
is in range [-1,1]
Reference:
https://en.wikipedia.org/wiki/%CE%9C-law_algorithm
"""
mu = 255
y = np.sign(x) * np.log1p(mu * np.abs(x)) / np.log1p(mu)
if quantized:
y = np.floor((y + 1) / 2 * mu + 0.5) # convert to [0 , mu-1]
return y
def mu_decode(y: array, mu: int=255, quantized: bool=True) -> array:
"""Mu-law decoding.
Compute the mu-law decoding given an input code.
it assumes that the input y is in
range [0,mu-1] when quantize is True and [-1,1] otherwise
Reference:
https://en.wikipedia.org/wiki/%CE%9C-law_algorithm
"""
if mu < 1:
raise ParameterError('mu is typically set as 2**k-1, k=1, 2, 3,...')
mu = mu - 1
if quantized: # undo the quantization
y = y * 2 / mu - 1
x = np.sign(y) / mu * ((1 + mu)**np.abs(y) - 1)
return x
paddleaudio/paddleaudio/models/__init__.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
paddleaudio/paddleaudio/models/panns.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import paddle.nn as nn
import paddle.nn.functional as F
from ..utils.download import load_state_dict_from_url
from ..utils.env import MODEL_HOME
__all__ = ['CNN14', 'CNN10', 'CNN6', 'cnn14', 'cnn10', 'cnn6']
pretrained_model_urls = {
'cnn14': 'https://bj.bcebos.com/paddleaudio/models/panns_cnn14.pdparams',
'cnn10': 'https://bj.bcebos.com/paddleaudio/models/panns_cnn10.pdparams',
'cnn6': 'https://bj.bcebos.com/paddleaudio/models/panns_cnn6.pdparams',
}
class ConvBlock(nn.Layer):
def __init__(self, in_channels, out_channels):
super(ConvBlock, self).__init__()
self.conv1 = nn.Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
bias_attr=False)
self.conv2 = nn.Conv2D(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
bias_attr=False)
self.bn1 = nn.BatchNorm2D(out_channels)
self.bn2 = nn.BatchNorm2D(out_channels)
def forward(self, x, pool_size=(2, 2), pool_type='avg'):
x = self.conv1(x)
x = self.bn1(x)
x = F.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = F.relu(x)
if pool_type == 'max':
x = F.max_pool2d(x, kernel_size=pool_size)
elif pool_type == 'avg':
x = F.avg_pool2d(x, kernel_size=pool_size)
elif pool_type == 'avg+max':
x = F.avg_pool2d(
x, kernel_size=pool_size) + F.max_pool2d(
x, kernel_size=pool_size)
else:
raise Exception(
f'Pooling type of {pool_type} is not supported. It must be one of "max", "avg" and "avg+max".'
)
return x
class ConvBlock5x5(nn.Layer):
def __init__(self, in_channels, out_channels):
super(ConvBlock5x5, self).__init__()
self.conv1 = nn.Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=(5, 5),
stride=(1, 1),
padding=(2, 2),
bias_attr=False)
self.bn1 = nn.BatchNorm2D(out_channels)
def forward(self, x, pool_size=(2, 2), pool_type='avg'):
x = self.conv1(x)
x = self.bn1(x)
x = F.relu(x)
if pool_type == 'max':
x = F.max_pool2d(x, kernel_size=pool_size)
elif pool_type == 'avg':
x = F.avg_pool2d(x, kernel_size=pool_size)
elif pool_type == 'avg+max':
x = F.avg_pool2d(
x, kernel_size=pool_size) + F.max_pool2d(
x, kernel_size=pool_size)
else:
raise Exception(
f'Pooling type of {pool_type} is not supported. It must be one of "max", "avg" and "avg+max".'
)
return x
class CNN14(nn.Layer):
"""
The CNN14(14-layer CNNs) mainly consist of 6 convolutional blocks while each convolutional
block consists of 2 convolutional layers with a kernel size of 3 × 3.
Reference:
PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
https://arxiv.org/pdf/1912.10211.pdf
"""
emb_size = 2048
def __init__(self, extract_embedding: bool=True):
super(CNN14, self).__init__()
self.bn0 = nn.BatchNorm2D(64)
self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024)
self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048)
self.fc1 = nn.Linear(2048, self.emb_size)
self.fc_audioset = nn.Linear(self.emb_size, 527)
self.extract_embedding = extract_embedding
def forward(self, x):
x.stop_gradient = False
x = x.transpose([0, 3, 2, 1])
x = self.bn0(x)
x = x.transpose([0, 3, 2, 1])
x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = x.mean(axis=3)
x = x.max(axis=2) + x.mean(axis=2)
x = F.dropout(x, p=0.5, training=self.training)
x = F.relu(self.fc1(x))
if self.extract_embedding:
output = F.dropout(x, p=0.5, training=self.training)
else:
output = F.sigmoid(self.fc_audioset(x))
return output
class CNN10(nn.Layer):
"""
The CNN10(14-layer CNNs) mainly consist of 4 convolutional blocks while each convolutional
block consists of 2 convolutional layers with a kernel size of 3 × 3.
Reference:
PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
https://arxiv.org/pdf/1912.10211.pdf
"""
emb_size = 512
def __init__(self, extract_embedding: bool=True):
super(CNN10, self).__init__()
self.bn0 = nn.BatchNorm2D(64)
self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
self.fc1 = nn.Linear(512, self.emb_size)
self.fc_audioset = nn.Linear(self.emb_size, 527)
self.extract_embedding = extract_embedding
def forward(self, x):
x.stop_gradient = False
x = x.transpose([0, 3, 2, 1])
x = self.bn0(x)
x = x.transpose([0, 3, 2, 1])
x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = x.mean(axis=3)
x = x.max(axis=2) + x.mean(axis=2)
x = F.dropout(x, p=0.5, training=self.training)
x = F.relu(self.fc1(x))
if self.extract_embedding:
output = F.dropout(x, p=0.5, training=self.training)
else:
output = F.sigmoid(self.fc_audioset(x))
return output
class CNN6(nn.Layer):
"""
The CNN14(14-layer CNNs) mainly consist of 4 convolutional blocks while each convolutional
block consists of 1 convolutional layers with a kernel size of 5 × 5.
Reference:
PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
https://arxiv.org/pdf/1912.10211.pdf
"""
emb_size = 512
def __init__(self, extract_embedding: bool=True):
super(CNN6, self).__init__()
self.bn0 = nn.BatchNorm2D(64)
self.conv_block1 = ConvBlock5x5(in_channels=1, out_channels=64)
self.conv_block2 = ConvBlock5x5(in_channels=64, out_channels=128)
self.conv_block3 = ConvBlock5x5(in_channels=128, out_channels=256)
self.conv_block4 = ConvBlock5x5(in_channels=256, out_channels=512)
self.fc1 = nn.Linear(512, self.emb_size)
self.fc_audioset = nn.Linear(self.emb_size, 527)
self.extract_embedding = extract_embedding
def forward(self, x):
x.stop_gradient = False
x = x.transpose([0, 3, 2, 1])
x = self.bn0(x)
x = x.transpose([0, 3, 2, 1])
x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg')
x = F.dropout(x, p=0.2, training=self.training)
x = x.mean(axis=3)
x = x.max(axis=2) + x.mean(axis=2)
x = F.dropout(x, p=0.5, training=self.training)
x = F.relu(self.fc1(x))
if self.extract_embedding:
output = F.dropout(x, p=0.5, training=self.training)
else:
output = F.sigmoid(self.fc_audioset(x))
return output
def cnn14(pretrained: bool=False, extract_embedding: bool=True) -> CNN14:
model = CNN14(extract_embedding=extract_embedding)
if pretrained:
state_dict = load_state_dict_from_url(
url=pretrained_model_urls['cnn14'],
path=os.path.join(MODEL_HOME, 'panns'))
model.set_state_dict(state_dict)
return model
def cnn10(pretrained: bool=False, extract_embedding: bool=True) -> CNN10:
model = CNN10(extract_embedding=extract_embedding)
if pretrained:
state_dict = load_state_dict_from_url(
url=pretrained_model_urls['cnn10'],
path=os.path.join(MODEL_HOME, 'panns'))
model.set_state_dict(state_dict)
return model
def cnn6(pretrained: bool=False, extract_embedding: bool=True) -> CNN6:
model = CNN6(extract_embedding=extract_embedding)
if pretrained:
state_dict = load_state_dict_from_url(
url=pretrained_model_urls['cnn6'],
path=os.path.join(MODEL_HOME, 'panns'))
model.set_state_dict(state_dict)
return model
paddleaudio/paddleaudio/utils/__init__.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .download import *
from .env import *
from .error import *
from .log import *
from .time import *
paddleaudio/paddleaudio/utils/download.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import Dict
from typing import List
from paddle.framework import load as load_state_dict
from paddle.utils import download
from pathos.multiprocessing import ProcessPool
from .log import logger
download.logger = logger
def decompress(file: str):
"""
Extracts all files from a compressed file.
"""
assert os.path.isfile(file), "File: {} not exists.".format(file)
download._decompress(file)
def download_and_decompress(archives: List[Dict[str, str]],
path: str,
n_workers: int=0):
"""
Download archieves and decompress to specific path.
"""
if not os.path.isdir(path):
os.makedirs(path)
if n_workers <= 0:
for archive in archives:
assert 'url' in archive and 'md5' in archive, \
'Dictionary keys of "url" and "md5" are required in the archive, but got: {list(archieve.keys())}'
download.get_path_from_url(archive['url'], path, archive['md5'])
else:
pool = ProcessPool(nodes=n_workers)
pool.imap(download.get_path_from_url, [_['url'] for _ in archives],
[path] * len(archives), [_['md5'] for _ in archives])
pool.close()
pool.join()
def load_state_dict_from_url(url: str, path: str, md5: str=None):
"""
Download and load a state dict from url
"""
if not os.path.isdir(path):
os.makedirs(path)
download.get_path_from_url(url, path, md5)
return load_state_dict(os.path.join(path, os.path.basename(url)))
paddleaudio/paddleaudio/utils/env.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
'''
This module is used to store environmental variables in PaddleAudio.
PPAUDIO_HOME --> the root directory for storing PaddleAudio related data. Default to ~/.paddleaudio. Users can change the
├ default value through the PPAUDIO_HOME environment variable.
├─ MODEL_HOME --> Store model files.
└─ DATA_HOME --> Store automatically downloaded datasets.
'''
import os
def _get_user_home():
return os.path.expanduser('~')
def _get_ppaudio_home():
if 'PPAUDIO_HOME' in os.environ:
home_path = os.environ['PPAUDIO_HOME']
if os.path.exists(home_path):
if os.path.isdir(home_path):
return home_path
else:
raise RuntimeError(
'The environment variable PPAUDIO_HOME {} is not a directory.'.
format(home_path))
else:
return home_path
return os.path.join(_get_user_home(), '.paddleaudio')
def _get_sub_home(directory):
home = os.path.join(_get_ppaudio_home(), directory)
if not os.path.exists(home):
os.makedirs(home)
return home
USER_HOME = _get_user_home()
PPAUDIO_HOME = _get_ppaudio_home()
MODEL_HOME = _get_sub_home('models')
DATA_HOME = _get_sub_home('datasets')
paddleaudio/paddleaudio/utils/error.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = ['ParameterError']
class ParameterError(Exception):
"""Exception class for Parameter checking"""
pass
paddleaudio/paddleaudio/utils/log.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import contextlib
import functools
import logging
import threading
import time
import colorlog
loggers = {}
log_config = {
'DEBUG': {
'level': 10,
'color': 'purple'
},
'INFO': {
'level': 20,
'color': 'green'
},
'TRAIN': {
'level': 21,
'color': 'cyan'
},
'EVAL': {
'level': 22,
'color': 'blue'
},
'WARNING': {
'level': 30,
'color': 'yellow'
},
'ERROR': {
'level': 40,
'color': 'red'
},
'CRITICAL': {
'level': 50,
'color': 'bold_red'
}
}
class Logger(object):
'''
Deafult logger in PaddleAudio
Args:
name(str) : Logger name, default is 'PaddleAudio'
'''
def __init__(self, name: str=None):
name = 'PaddleAudio' if not name else name
self.logger = logging.getLogger(name)
for key, conf in log_config.items():
logging.addLevelName(conf['level'], key)
self.__dict__[key] = functools.partial(self.__call__, conf['level'])
self.__dict__[key.lower()] = functools.partial(self.__call__,
conf['level'])
self.format = colorlog.ColoredFormatter(
'%(log_color)s[%(asctime)-15s] [%(levelname)8s]%(reset)s - %(message)s',
log_colors={key: conf['color']
for key, conf in log_config.items()})
self.handler = logging.StreamHandler()
self.handler.setFormatter(self.format)
self.logger.addHandler(self.handler)
self.logLevel = 'DEBUG'
self.logger.setLevel(logging.DEBUG)
self.logger.propagate = False
self._is_enable = True
def disable(self):
self._is_enable = False
def enable(self):
self._is_enable = True
@property
def is_enable(self) -> bool:
return self._is_enable
def __call__(self, log_level: str, msg: str):
if not self.is_enable:
return
self.logger.log(log_level, msg)
@contextlib.contextmanager
def use_terminator(self, terminator: str):
old_terminator = self.handler.terminator
self.handler.terminator = terminator
yield
self.handler.terminator = old_terminator
@contextlib.contextmanager
def processing(self, msg: str, interval: float=0.1):
'''
Continuously print a progress bar with rotating special effects.
Args:
msg(str): Message to be printed.
interval(float): Rotation interval. Default to 0.1.
'''
end = False
def _printer():
index = 0
flags = ['\\', '|', '/', '-']
while not end:
flag = flags[index % len(flags)]
with self.use_terminator('\r'):
self.info('{}: {}'.format(msg, flag))
time.sleep(interval)
index += 1
t = threading.Thread(target=_printer)
t.start()
yield
end = True
logger = Logger()
paddleaudio/paddleaudio/utils/time.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import time
class Timer(object):
'''Calculate runing speed and estimated time of arrival(ETA)'''
def __init__(self, total_step: int):
self.total_step = total_step
self.last_start_step = 0
self.current_step = 0
self._is_running = True
def start(self):
self.last_time = time.time()
self.start_time = time.time()
def stop(self):
self._is_running = False
self.end_time = time.time()
def count(self) -> int:
if not self.current_step >= self.total_step:
self.current_step += 1
return self.current_step
@property
def timing(self) -> float:
run_steps = self.current_step - self.last_start_step
self.last_start_step = self.current_step
time_used = time.time() - self.last_time
self.last_time = time.time()
return run_steps / time_used
@property
def is_running(self) -> bool:
return self._is_running
@property
def eta(self) -> str:
if not self.is_running:
return '00:00:00'
scale = self.total_step / self.current_step
remaining_time = (time.time() - self.start_time) * scale
return seconds_to_hms(remaining_time)
def seconds_to_hms(seconds: int) -> str:
'''Convert the number of seconds to hh:mm:ss'''
h = math.floor(seconds / 3600)
m = math.floor((seconds - h * 3600) / 60)
s = int(seconds - h * 3600 - m * 60)
hms_str = '{:0>2}:{:0>2}:{:0>2}'.format(h, m, s)
return hms_str
paddleaudio/requirements.txt 0 → 100644
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numpy >= 1.15.0
resampy >= 0.2.2
scipy >= 1.0.0
soundfile >= 0.9.0
\ No newline at end of file
paddleaudio/setup.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import setuptools
# set the version here
version = '0.1.0a'
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="paddleaudio",
version=version,
author="",
author_email="",
description="PaddleAudio, in development",
long_description=long_description,
long_description_content_type="text/markdown",
url="",
packages=setuptools.find_packages(exclude=["build*", "test*", "examples*"]),
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
python_requires='>=3.6',
install_requires=[
'numpy >= 1.15.0', 'scipy >= 1.0.0', 'resampy >= 0.2.2',
'soundfile >= 0.9.0'
],
extras_require={'dev': ['pytest>=3.7', 'librosa>=0.7.2']
} # for dev only, install: pip install -e .[dev]
)
paddleaudio/test/README.md 0 → 100644
浏览文件 @ 950d17cb
# PaddleAudio Testing Guide
# Testing
First clone a version of the project by
```
git clone https://github.com/PaddlePaddle/models.git
```
Then install the project in your virtual environment.
```
cd models/PaddleAudio
python setup.py bdist_wheel
pip install -e .[dev]
```
The requirements for testing will be installed along with PaddleAudio.
Now run
```
pytest test
```
If it goes well, you will see outputs like these:
```
platform linux -- Python 3.7.10, pytest-6.2.4, py-1.10.0, pluggy-0.13.1
rootdir: ./models/PaddleAudio
plugins: hydra-core-1.0.6
collected 16 items
test/unit_test/test_backend.py ........... [ 68%]
test/unit_test/test_features.py ..... [100%]
==================================================== warnings summary ====================================================
.
.
.
-- Docs: https://docs.pytest.org/en/stable/warnings.html
============================================ 16 passed, 11 warnings in 6.76s =============================================
```
paddleaudio/test/unit_test/test_backend.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import librosa
import numpy as np
import pytest
import paddleaudio
TEST_FILE = './test/data/test_audio.wav'
def relative_err(a, b, real=True):
"""compute relative error of two matrices or vectors"""
if real:
return np.sum((a - b)**2) / (EPS + np.sum(a**2) + np.sum(b**2))
else:
err = np.sum((a.real - b.real)**2) / \
(EPS + np.sum(a.real**2) + np.sum(b.real**2))
err += np.sum((a.imag - b.imag)**2) / \
(EPS + np.sum(a.imag**2) + np.sum(b.imag**2))
return err
@pytest.mark.filterwarnings("ignore::DeprecationWarning")
def load_audio():
x, r = librosa.load(TEST_FILE, sr=16000)
print(f'librosa: mean: {np.mean(x)}, std:{np.std(x)}')
return x, r
# start testing
x, r = load_audio()
EPS = 1e-8
def test_load():
s, r = paddleaudio.load(TEST_FILE, sr=16000)
assert r == 16000
assert s.dtype == 'float32'
s, r = paddleaudio.load(
TEST_FILE, sr=16000, offset=1, duration=2, dtype='int16')
assert len(s) / r == 2.0
assert r == 16000
assert s.dtype == 'int16'
def test_depth_convert():
y = paddleaudio.depth_convert(x, 'int16')
assert len(y) == len(x)
assert y.dtype == 'int16'
assert np.max(y) <= 32767
assert np.min(y) >= -32768
assert np.std(y) > EPS
y = paddleaudio.depth_convert(x, 'int8')
assert len(y) == len(x)
assert y.dtype == 'int8'
assert np.max(y) <= 127
assert np.min(y) >= -128
assert np.std(y) > EPS
# test case for resample
rs_test_data = [
(32000, 'kaiser_fast'),
(16000, 'kaiser_fast'),
(8000, 'kaiser_fast'),
(32000, 'kaiser_best'),
(16000, 'kaiser_best'),
(8000, 'kaiser_best'),
(22050, 'kaiser_best'),
(44100, 'kaiser_best'),
]
@pytest.mark.parametrize('sr,mode', rs_test_data)
def test_resample(sr, mode):
y = paddleaudio.resample(x, 16000, sr, mode=mode)
factor = sr / 16000
err = relative_err(len(y), len(x) * factor)
print('err:', err)
assert err < EPS
def test_normalize():
y = paddleaudio.normalize(x, norm_type='linear', mul_factor=0.5)
assert np.max(y) < 0.5 + EPS
y = paddleaudio.normalize(x, norm_type='linear', mul_factor=2.0)
assert np.max(y) <= 2.0 + EPS
y = paddleaudio.normalize(x, norm_type='gaussian', mul_factor=1.0)
print('np.std(y):', np.std(y))
assert np.abs(np.std(y) - 1.0) < EPS
if __name__ == '__main__':
test_load()
test_depth_convert()
test_resample(22050, 'kaiser_fast')
test_normalize()
paddleaudio/test/unit_test/test_features.py 0 → 100644
浏览文件 @ 950d17cb
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import librosa
import numpy as np
import pytest
import paddleaudio as pa
@pytest.mark.filterwarnings("ignore::DeprecationWarning")
def load_audio():
x, r = librosa.load('./test/data/test_audio.wav')
#x,r = librosa.load('../data/test_audio.wav',sr=16000)
return x, r
## start testing
x, r = load_audio()
EPS = 1e-8
def relative_err(a, b, real=True):
"""compute relative error of two matrices or vectors"""
if real:
return np.sum((a - b)**2) / (EPS + np.sum(a**2) + np.sum(b**2))
else:
err = np.sum((a.real - b.real)**2) / (
EPS + np.sum(a.real**2) + np.sum(b.real**2))
err += np.sum((a.imag - b.imag)**2) / (
EPS + np.sum(a.imag**2) + np.sum(b.imag**2))
return err
@pytest.mark.filterwarnings("ignore::DeprecationWarning")
def test_melspectrogram():
a = pa.melspectrogram(
x,
window_size=512,
sr=16000,
hop_length=320,
n_mels=64,
fmin=50,
to_db=False, )
b = librosa.feature.melspectrogram(
x,
sr=16000,
n_fft=512,
win_length=512,
hop_length=320,
n_mels=64,
fmin=50)
assert relative_err(a, b) < EPS
@pytest.mark.filterwarnings("ignore::DeprecationWarning")
def test_melspectrogram_db():
a = pa.melspectrogram(
x,
window_size=512,
sr=16000,
hop_length=320,
n_mels=64,
fmin=50,
to_db=True,
ref=1.0,
amin=1e-10,
top_db=None)
b = librosa.feature.melspectrogram(
x,
sr=16000,
n_fft=512,
win_length=512,
hop_length=320,
n_mels=64,
fmin=50)
b = pa.power_to_db(b, ref=1.0, amin=1e-10, top_db=None)
assert relative_err(a, b) < EPS
@pytest.mark.filterwarnings("ignore::DeprecationWarning")
def test_stft():
a = pa.stft(x, n_fft=1024, hop_length=320, win_length=512)
b = librosa.stft(x, n_fft=1024, hop_length=320, win_length=512)
assert a.shape == b.shape
assert relative_err(a, b, real=False) < EPS
@pytest.mark.filterwarnings("ignore::DeprecationWarning")
def test_split_frames():
a = librosa.util.frame(x, frame_length=512, hop_length=320)
b = pa.split_frames(x, frame_length=512, hop_length=320)
assert relative_err(a, b) < EPS
@pytest.mark.filterwarnings("ignore::DeprecationWarning")
def test_mfcc():
kwargs = {
'window_size': 512,
'hop_length': 320,
'n_mels': 64,
'fmin': 50,
'to_db': False
}
a = pa.mfcc(
x,
#sample_rate=16000,
spect=None,
n_mfcc=20,
dct_type=2,
norm='ortho',
lifter=0,
**kwargs)
S = librosa.feature.melspectrogram(
x,
sr=16000,
n_fft=512,
win_length=512,
hop_length=320,
n_mels=64,
fmin=50)
b = librosa.feature.mfcc(
x, sr=16000, S=S, n_mfcc=20, dct_type=2, norm='ortho', lifter=0)
assert relative_err(a, b) < EPS
if __name__ == '__main__':
test_melspectrogram()
test_melspectrogram_db()
test_stft()
test_split_frames()
test_mfcc()
parakeet/data/batch.py
浏览文件 @ 950d17cb
...@@ -53,8 +53,8 @@ def batch_text_id(minibatch, pad_id=0, dtype=np.int64): ...@@ -53,8 +53,8 @@ def batch_text_id(minibatch, pad_id=0, dtype=np.int64):
peek_example = minibatch[0] peek_example = minibatch[0]
assert len(peek_example.shape) == 1, "text example is an 1D tensor" assert len(peek_example.shape) == 1, "text example is an 1D tensor"
lengths = [example.shape[0] for example in minibatch lengths = [example.shape[0] for example in
] # assume (channel, n_samples) or (n_samples, ) minibatch] # assume (channel, n_samples) or (n_samples, )
max_len = np.max(lengths) max_len = np.max(lengths)
batch = [] batch = []
......
parakeet/exps/tacotron2/ljspeech.py
浏览文件 @ 950d17cb
...@@ -67,19 +67,16 @@ class LJSpeechCollector(object): ...@@ -67,19 +67,16 @@ class LJSpeechCollector(object):
# Sort by text_len in descending order # Sort by text_len in descending order
texts = [ texts = [
i i for i, _ in sorted(
for i, _ in sorted(
zip(texts, text_lens), key=lambda x: x[1], reverse=True) zip(texts, text_lens), key=lambda x: x[1], reverse=True)
] ]
mels = [ mels = [
i i for i, _ in sorted(
for i, _ in sorted(
zip(mels, text_lens), key=lambda x: x[1], reverse=True) zip(mels, text_lens), key=lambda x: x[1], reverse=True)
] ]
mel_lens = [ mel_lens = [
i i for i, _ in sorted(
for i, _ in sorted(
zip(mel_lens, text_lens), key=lambda x: x[1], reverse=True) zip(mel_lens, text_lens), key=lambda x: x[1], reverse=True)
] ]
......
requirements.txt
浏览文件 @ 950d17cb
...@@ -13,6 +13,7 @@ librosa ...@@ -13,6 +13,7 @@ librosa
llvmlite llvmlite
loguru loguru
matplotlib matplotlib
nara_wpe
nltk nltk
numba numba
numpy==1.20.0 numpy==1.20.0
...@@ -23,6 +24,7 @@ praatio~=4.1 ...@@ -23,6 +24,7 @@ praatio~=4.1
pre-commit pre-commit
pybind11 pybind11
pypinyin pypinyin
python-dateutil
pyworld pyworld
resampy==0.2.2 resampy==0.2.2
sacrebleu sacrebleu
......
setup.py
浏览文件 @ 950d17cb
...@@ -65,13 +65,6 @@ def _remove(files: str): ...@@ -65,13 +65,6 @@ def _remove(files: str):
def _post_install(install_lib_dir): def _post_install(install_lib_dir):
# apt
check_call("apt-get update -y")
check_call("apt-get install -y " + 'vim tig tree sox pkg-config ' +
'libsndfile1 libflac-dev libogg-dev ' +
'libvorbis-dev libboost-dev swig python3-dev ')
print("apt install.")
# tools/make # tools/make
tool_dir = HERE / "tools" tool_dir = HERE / "tools"
_remove(tool_dir.glob("*.done")) _remove(tool_dir.glob("*.done"))
......
setup.sh
浏览文件 @ 950d17cb
...@@ -10,7 +10,7 @@ fi ...@@ -10,7 +10,7 @@ fi
if [ -e /etc/lsb-release ];then if [ -e /etc/lsb-release ];then
${SUDO} apt-get update -y ${SUDO} apt-get update -y
${SUDO} apt-get install -y jq vim tig tree sox pkg-config libflac-dev libogg-dev libvorbis-dev libboost-dev swig python3-dev ${SUDO} apt-get install -y bc jq vim tig tree sox pkg-config libflac-dev libogg-dev libvorbis-dev libboost-dev swig python3-dev
if [ $? != 0 ]; then if [ $? != 0 ]; then
error_msg "Please using Ubuntu or install pkg-config libflac-dev libogg-dev libvorbis-dev libboost-dev swig python3-dev by user." error_msg "Please using Ubuntu or install pkg-config libflac-dev libogg-dev libvorbis-dev libboost-dev swig python3-dev by user."
exit -1 exit -1
......
tools/Makefile
浏览文件 @ 950d17cb
...@@ -10,7 +10,7 @@ WGET ?= wget --no-check-certificate ...@@ -10,7 +10,7 @@ WGET ?= wget --no-check-certificate
.PHONY: all clean .PHONY: all clean
all: virtualenv.done kenlm.done sox.done soxbindings.done mfa.done sclite.done all: virtualenv.done apt.done kenlm.done sox.done soxbindings.done mfa.done sclite.done
virtualenv.done: virtualenv.done:
test -d venv || virtualenv -p $(PYTHON) venv test -d venv || virtualenv -p $(PYTHON) venv
...@@ -21,6 +21,13 @@ clean: ...@@ -21,6 +21,13 @@ clean:
find -iname "*.pyc" -delete find -iname "*.pyc" -delete
rm -rf kenlm rm -rf kenlm
apt.done:
apt update -y
apt install -y bc flac jq vim tig tree pkg-config libflac-dev libogg-dev libvorbis-dev libboost-dev swig python3-dev
echo "check_certificate = off" >> ~/.wgetrc
touch apt.done
kenlm.done: kenlm.done:
# Ubuntu 16.04 透過 apt 會安裝 boost 1.58.0 # Ubuntu 16.04 透過 apt 會安裝 boost 1.58.0
# it seems that boost (1.54.0) requires higher version. After I switched to g++-5 it compiles normally. # it seems that boost (1.54.0) requires higher version. After I switched to g++-5 it compiles normally.
...@@ -48,6 +55,13 @@ mfa.done: ...@@ -48,6 +55,13 @@ mfa.done:
tar xvf montreal-forced-aligner_linux.tar.gz tar xvf montreal-forced-aligner_linux.tar.gz
touch mfa.done touch mfa.done
openblas.done:
bash extras/install_openblas.sh
touch openblas.done
kaldi.done: openblas.done
bash extras/install_kaldi.sh
touch kaldi.done
#== SCTK =============================================================================== #== SCTK ===============================================================================
# SCTK official repo does not have version tags. Here's the mapping: # SCTK official repo does not have version tags. Here's the mapping:
......
tools/extras/install_kaldi.sh
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...@@ -16,7 +16,7 @@ else ...@@ -16,7 +16,7 @@ else
echo "$KALDI_DIR already exists!" echo "$KALDI_DIR already exists!"
fi fi
cd "$KALDI_DIR/tools" pushd "$KALDI_DIR/tools"
git pull git pull
# Prevent kaldi from switching default python version # Prevent kaldi from switching default python version
...@@ -28,8 +28,12 @@ touch "python/.use_default_python" ...@@ -28,8 +28,12 @@ touch "python/.use_default_python"
make -j4 make -j4
pushd ../src pushd ../src
./configure --shared --use-cuda=no --static-math --mathlib=OPENBLAS --openblas-root=${KALDI_DIR}/../OpenBLAS/install OPENBLAS_DIR=${KALDI_DIR}/../OpenBLAS
mkdir -p ${OPENBLAS_DIR}/install
./configure --shared --use-cuda=no --static-math --mathlib=OPENBLAS --openblas-root=${OPENBLAS_DIR}/install
make clean -j && make depend -j && make -j4 make clean -j && make depend -j && make -j4
popd popd
popd
echo "Done installing Kaldi." echo "Done installing Kaldi."
utils/__init__.py 100644 → 100755
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文件模式从 100644 更改为 100755
utils/apply-cmvn.py 0 → 100755
浏览文件 @ 950d17cb
#!/usr/bin/env python3
import argparse
import logging
from distutils.util import strtobool
import kaldiio
import numpy
from deepspeech.transform.cmvn import CMVN
from deepspeech.utils.cli_readers import file_reader_helper
from deepspeech.utils.cli_utils import get_commandline_args
from deepspeech.utils.cli_utils import is_scipy_wav_style
from deepspeech.utils.cli_writers import file_writer_helper
def get_parser():
parser = argparse.ArgumentParser(
description="apply mean-variance normalization to files",
formatter_class=argparse.ArgumentDefaultsHelpFormatter, )
parser.add_argument(
"--verbose", "-V", default=0, type=int, help="Verbose option")
parser.add_argument(
"--in-filetype",
type=str,
default="mat",
choices=["mat", "hdf5", "sound.hdf5", "sound"],
help="Specify the file format for the rspecifier. "
'"mat" is the matrix format in kaldi', )
parser.add_argument(
"--stats-filetype",
type=str,
default="mat",
choices=["mat", "hdf5", "npy"],
help="Specify the file format for the rspecifier. "
'"mat" is the matrix format in kaldi', )
parser.add_argument(
"--out-filetype",
type=str,
default="mat",
choices=["mat", "hdf5"],
help="Specify the file format for the wspecifier. "
'"mat" is the matrix format in kaldi', )
parser.add_argument(
"--norm-means",
type=strtobool,
default=True,
help="Do variance normalization or not.", )
parser.add_argument(
"--norm-vars",
type=strtobool,
default=False,
help="Do variance normalization or not.", )
parser.add_argument(
"--reverse",
type=strtobool,
default=False,
help="Do reverse mode or not")
parser.add_argument(
"--spk2utt",
type=str,
help="A text file of speaker to utterance-list map. "
"(Don't give rspecifier format, such as "
'"ark:spk2utt")', )
parser.add_argument(
"--utt2spk",
type=str,
help="A text file of utterance to speaker map. "
"(Don't give rspecifier format, such as "
'"ark:utt2spk")', )
parser.add_argument(
"--write-num-frames",
type=str,
help="Specify wspecifer for utt2num_frames")
parser.add_argument(
"--compress",
type=strtobool,
default=False,
help="Save in compressed format")
parser.add_argument(
"--compression-method",
type=int,
default=2,
help="Specify the method(if mat) or "
"gzip-level(if hdf5)", )
parser.add_argument(
"stats_rspecifier_or_rxfilename",
help="Input stats. e.g. ark:stats.ark or stats.mat", )
parser.add_argument(
"rspecifier", type=str, help="Read specifier id. e.g. ark:some.ark")
parser.add_argument(
"wspecifier", type=str, help="Write specifier id. e.g. ark:some.ark")
return parser
def main():
args = get_parser().parse_args()
# logging info
logfmt = "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
if args.verbose > 0:
logging.basicConfig(level=logging.INFO, format=logfmt)
else:
logging.basicConfig(level=logging.WARN, format=logfmt)
logging.info(get_commandline_args())
if ":" in args.stats_rspecifier_or_rxfilename:
is_rspcifier = True
if args.stats_filetype == "npy":
stats_filetype = "hdf5"
else:
stats_filetype = args.stats_filetype
stats_dict = dict(
file_reader_helper(args.stats_rspecifier_or_rxfilename,
stats_filetype))
else:
is_rspcifier = False
if args.stats_filetype == "mat":
stats = kaldiio.load_mat(args.stats_rspecifier_or_rxfilename)
else:
stats = numpy.load(args.stats_rspecifier_or_rxfilename)
stats_dict = {None: stats}
cmvn = CMVN(
stats=stats_dict,
norm_means=args.norm_means,
norm_vars=args.norm_vars,
utt2spk=args.utt2spk,
spk2utt=args.spk2utt,
reverse=args.reverse, )
with file_writer_helper(
args.wspecifier,
filetype=args.out_filetype,
write_num_frames=args.write_num_frames,
compress=args.compress,
compression_method=args.compression_method, ) as writer:
for utt, mat in file_reader_helper(args.rspecifier, args.in_filetype):
if is_scipy_wav_style(mat):
# If data is sound file, then got as Tuple[int, ndarray]
rate, mat = mat
mat = cmvn(mat, utt if is_rspcifier else None)
writer[utt] = mat
if __name__ == "__main__":
main()
utils/avg_model.py
浏览文件 @ 950d17cb
...@@ -47,8 +47,10 @@ def main(args): ...@@ -47,8 +47,10 @@ def main(args):
beat_val_scores = sorted_val_scores[:args.num, 1] beat_val_scores = sorted_val_scores[:args.num, 1]
selected_epochs = sorted_val_scores[:args.num, 0].astype(np.int64) selected_epochs = sorted_val_scores[:args.num, 0].astype(np.int64)
avg_val_score = np.mean(beat_val_scores)
print("selected val scores = " + str(beat_val_scores)) print("selected val scores = " + str(beat_val_scores))
print("selected epochs = " + str(selected_epochs)) print("selected epochs = " + str(selected_epochs))
print("averaged val score = " + str(avg_val_score))
path_list = [ path_list = [
args.ckpt_dir + '/{}.pdparams'.format(int(epoch)) args.ckpt_dir + '/{}.pdparams'.format(int(epoch))
...@@ -80,7 +82,7 @@ def main(args): ...@@ -80,7 +82,7 @@ def main(args):
data = json.dumps({ data = json.dumps({
"mode": 'val_best' if args.val_best else 'latest', "mode": 'val_best' if args.val_best else 'latest',
"avg_ckpt": args.dst_model, "avg_ckpt": args.dst_model,
"val_loss_mean": np.mean(beat_val_scores), "val_loss_mean": avg_val_score,
"ckpts": path_list, "ckpts": path_list,
"epochs": selected_epochs.tolist(), "epochs": selected_epochs.tolist(),
"val_losses": beat_val_scores.tolist(), "val_losses": beat_val_scores.tolist(),
......
utils/caculate_rtf.py 0 → 100755
浏览文件 @ 950d17cb
#!/usr/bin/env python3
# encoding: utf-8
# Copyright 2021 Kyoto University (Hirofumi Inaguma)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import argparse
import codecs
import glob
import os
from dateutil import parser
def get_parser():
parser = argparse.ArgumentParser(
description="calculate real time factor (RTF)")
parser.add_argument(
"--log-dir",
type=str,
default=None,
help="path to logging directory", )
return parser
def main():
args = get_parser().parse_args()
audio_sec = 0
decode_sec = 0
n_utt = 0
audio_durations = []
start_times = []
end_times = []
for x in glob.glob(os.path.join(args.log_dir, "decode.*.log")):
with codecs.open(x, "r", "utf-8") as f:
for line in f:
x = line.strip()
# 2021-10-25 08:22:04.052 | INFO | xxx:recog_v2:188 - feat: (1570, 83)
if "feat:" in x:
dur = int(x.split("(")[1].split(',')[0])
audio_durations += [dur]
start_times += [parser.parse(x.split("|")[0])]
elif "total log probability:" in x:
end_times += [parser.parse(x.split("|")[0])]
assert len(audio_durations) == len(end_times), (len(audio_durations),
len(end_times), )
assert len(start_times) == len(end_times), (len(start_times),
len(end_times))
audio_sec += sum(audio_durations) / 100 # [sec]
decode_sec += sum([(end - start).total_seconds()
for start, end in zip(start_times, end_times)])
n_utt += len(audio_durations)
print("Total audio duration: %.3f [sec]" % audio_sec)
print("Total decoding time: %.3f [sec]" % decode_sec)
rtf = decode_sec / audio_sec if audio_sec > 0 else 0
print("RTF: %.3f" % rtf)
latency = decode_sec * 1000 / n_utt if n_utt > 0 else 0
print("Latency: %.3f [ms/sentence]" % latency)
if __name__ == "__main__":
main()
utils/compute-cmvn-stats.py 0 → 100755
浏览文件 @ 950d17cb
#!/usr/bin/env python3
import argparse
import logging
import kaldiio
import numpy as np
from deepspeech.transform.transformation import Transformation
from deepspeech.utils.cli_readers import file_reader_helper
from deepspeech.utils.cli_utils import get_commandline_args
from deepspeech.utils.cli_utils import is_scipy_wav_style
from deepspeech.utils.cli_writers import file_writer_helper
def get_parser():
parser = argparse.ArgumentParser(
description="Compute cepstral mean and "
"variance normalization statistics"
"If wspecifier provided: per-utterance by default, "
"or per-speaker if"
"spk2utt option provided; if wxfilename: global",
formatter_class=argparse.ArgumentDefaultsHelpFormatter, )
parser.add_argument(
"--spk2utt",
type=str,
help="A text file of speaker to utterance-list map. "
"(Don't give rspecifier format, such as "
'"ark:utt2spk")', )
parser.add_argument(
"--verbose", "-V", default=0, type=int, help="Verbose option")
parser.add_argument(
"--in-filetype",
type=str,
default="mat",
choices=["mat", "hdf5", "sound.hdf5", "sound"],
help="Specify the file format for the rspecifier. "
'"mat" is the matrix format in kaldi', )
parser.add_argument(
"--out-filetype",
type=str,
default="mat",
choices=["mat", "hdf5", "npy"],
help="Specify the file format for the wspecifier. "
'"mat" is the matrix format in kaldi', )
parser.add_argument(
"--preprocess-conf",
type=str,
default=None,
help="The configuration file for the pre-processing", )
parser.add_argument(
"rspecifier",
type=str,
help="Read specifier for feats. e.g. ark:some.ark")
parser.add_argument(
"wspecifier_or_wxfilename",
type=str,
help="Write specifier. e.g. ark:some.ark")
return parser
def main():
args = get_parser().parse_args()
logfmt = "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
if args.verbose > 0:
logging.basicConfig(level=logging.INFO, format=logfmt)
else:
logging.basicConfig(level=logging.WARN, format=logfmt)
logging.info(get_commandline_args())
is_wspecifier = ":" in args.wspecifier_or_wxfilename
if is_wspecifier:
if args.spk2utt is not None:
logging.info("Performing as speaker CMVN mode")
utt2spk_dict = {}
with open(args.spk2utt) as f:
for line in f:
spk, utts = line.rstrip().split(None, 1)
for utt in utts.split():
utt2spk_dict[utt] = spk
def utt2spk(x):
return utt2spk_dict[x]
else:
logging.info("Performing as utterance CMVN mode")
def utt2spk(x):
return x
if args.out_filetype == "npy":
logging.warning("--out-filetype npy is allowed only for "
"Global CMVN mode, changing to hdf5")
args.out_filetype = "hdf5"
else:
logging.info("Performing as global CMVN mode")
if args.spk2utt is not None:
logging.warning("spk2utt is not used for global CMVN mode")
def utt2spk(x):
return None
if args.out_filetype == "hdf5":
logging.warning("--out-filetype hdf5 is not allowed for "
"Global CMVN mode, changing to npy")
args.out_filetype = "npy"
if args.preprocess_conf is not None:
preprocessing = Transformation(args.preprocess_conf)
logging.info("Apply preprocessing: {}".format(preprocessing))
else:
preprocessing = None
# Calculate stats for each speaker
counts = {}
sum_feats = {}
square_sum_feats = {}
idx = 0
for idx, (utt, matrix) in enumerate(
file_reader_helper(args.rspecifier, args.in_filetype), 1):
if is_scipy_wav_style(matrix):
# If data is sound file, then got as Tuple[int, ndarray]
rate, matrix = matrix
if preprocessing is not None:
matrix = preprocessing(matrix, uttid_list=utt)
spk = utt2spk(utt)
# Init at the first seen of the spk
if spk not in counts:
counts[spk] = 0
feat_shape = matrix.shape[1:]
# Accumulate in double precision
sum_feats[spk] = np.zeros(feat_shape, dtype=np.float64)
square_sum_feats[spk] = np.zeros(feat_shape, dtype=np.float64)
counts[spk] += matrix.shape[0]
sum_feats[spk] += matrix.sum(axis=0)
square_sum_feats[spk] += (matrix**2).sum(axis=0)
logging.info("Processed {} utterances".format(idx))
assert idx > 0, idx
cmvn_stats = {}
for spk in counts:
feat_shape = sum_feats[spk].shape
cmvn_shape = (2, feat_shape[0] + 1) + feat_shape[1:]
_cmvn_stats = np.empty(cmvn_shape, dtype=np.float64)
_cmvn_stats[0, :-1] = sum_feats[spk]
_cmvn_stats[1, :-1] = square_sum_feats[spk]
_cmvn_stats[0, -1] = counts[spk]
_cmvn_stats[1, -1] = 0.0
# You can get the mean and std as following,
# >>> N = _cmvn_stats[0, -1]
# >>> mean = _cmvn_stats[0, :-1] / N
# >>> std = np.sqrt(_cmvn_stats[1, :-1] / N - mean ** 2)
cmvn_stats[spk] = _cmvn_stats
# Per utterance or speaker CMVN
if is_wspecifier:
with file_writer_helper(
args.wspecifier_or_wxfilename,
filetype=args.out_filetype) as writer:
for spk, mat in cmvn_stats.items():
writer[spk] = mat
# Global CMVN
else:
matrix = cmvn_stats[None]
if args.out_filetype == "npy":
np.save(args.wspecifier_or_wxfilename, matrix)
elif args.out_filetype == "mat":
# Kaldi supports only matrix or vector
kaldiio.save_mat(args.wspecifier_or_wxfilename, matrix)
else:
raise RuntimeError(
"Not supporting: --out-filetype {}".format(args.out_filetype))
if __name__ == "__main__":
main()
utils/compute_statistics.py 100644 → 100755
浏览文件 @ 950d17cb
文件模式从 100644 更改为 100755
utils/copy-feats.py 0 → 100755
浏览文件 @ 950d17cb
#!/usr/bin/env python3
import argparse
import logging
from distutils.util import strtobool
from deepspeech.transform.transformation import Transformation
from deepspeech.utils.cli_readers import file_reader_helper
from deepspeech.utils.cli_utils import get_commandline_args
from deepspeech.utils.cli_utils import is_scipy_wav_style
from deepspeech.utils.cli_writers import file_writer_helper
def get_parser():
parser = argparse.ArgumentParser(
description="copy feature with preprocessing",
formatter_class=argparse.ArgumentDefaultsHelpFormatter, )
parser.add_argument(
"--verbose", "-V", default=0, type=int, help="Verbose option")
parser.add_argument(
"--in-filetype",
type=str,
default="mat",
choices=["mat", "hdf5", "sound.hdf5", "sound"],
help="Specify the file format for the rspecifier. "
'"mat" is the matrix format in kaldi', )
parser.add_argument(
"--out-filetype",
type=str,
default="mat",
choices=["mat", "hdf5", "sound.hdf5", "sound"],
help="Specify the file format for the wspecifier. "
'"mat" is the matrix format in kaldi', )
parser.add_argument(
"--write-num-frames",
type=str,
help="Specify wspecifer for utt2num_frames")
parser.add_argument(
"--compress",
type=strtobool,
default=False,
help="Save in compressed format")
parser.add_argument(
"--compression-method",
type=int,
default=2,
help="Specify the method(if mat) or "
"gzip-level(if hdf5)", )
parser.add_argument(
"--preprocess-conf",
type=str,
default=None,
help="The configuration file for the pre-processing", )
parser.add_argument(
"rspecifier",
type=str,
help="Read specifier for feats. e.g. ark:some.ark")
parser.add_argument(
"wspecifier", type=str, help="Write specifier. e.g. ark:some.ark")
return parser
def main():
parser = get_parser()
args = parser.parse_args()
# logging info
logfmt = "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
if args.verbose > 0:
logging.basicConfig(level=logging.INFO, format=logfmt)
else:
logging.basicConfig(level=logging.WARN, format=logfmt)
logging.info(get_commandline_args())
if args.preprocess_conf is not None:
preprocessing = Transformation(args.preprocess_conf)
logging.info("Apply preprocessing: {}".format(preprocessing))
else:
preprocessing = None
with file_writer_helper(
args.wspecifier,
filetype=args.out_filetype,
write_num_frames=args.write_num_frames,
compress=args.compress,
compression_method=args.compression_method, ) as writer:
for utt, mat in file_reader_helper(args.rspecifier, args.in_filetype):
if is_scipy_wav_style(mat):
# If data is sound file, then got as Tuple[int, ndarray]
rate, mat = mat
if preprocessing is not None:
mat = preprocessing(mat, uttid_list=utt)
# shape = (Time, Channel)
if args.out_filetype in ["sound.hdf5", "sound"]:
# Write Tuple[int, numpy.ndarray] (scipy style)
writer[utt] = (rate, mat)
else:
writer[utt] = mat
if __name__ == "__main__":
main()
utils/data2json.sh 0 → 100755
浏览文件 @ 950d17cb
#!/usr/bin/env bash
# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
echo "$0 $*" >&2 # Print the command line for logging
. ./path.sh
nj=1
cmd=run.pl
nlsyms=""
lang=""
feat="" # feat.scp
oov="<unk>"
bpecode=""
allow_one_column=false
verbose=0
trans_type=char
filetype=""
preprocess_conf=""
category=""
out="" # If omitted, write in stdout
text=""
multilingual=false
help_message=$(cat << EOF
Usage: $0 <data-dir> <dict>
e.g. $0 data/train data/lang_1char/train_units.txt
Options:
--nj <nj> # number of parallel jobs
--cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs.
--feat <feat-scp> # feat.scp or feat1.scp,feat2.scp,...
--oov <oov-word> # Default: <unk>
--out <outputfile> # If omitted, write in stdout
--filetype <mat|hdf5|sound.hdf5> # Specify the format of feats file
--preprocess-conf <json> # Apply preprocess to feats when creating shape.scp
--verbose <num> # Default: 0
EOF
)
. utils/parse_options.sh
if [ $# != 2 ]; then
echo "${help_message}" 1>&2
exit 1;
fi
set -euo pipefail
dir=$1
dic=$2
tmpdir=$(mktemp -d ${dir}/tmp-XXXXX)
trap 'rm -rf ${tmpdir}' EXIT
if [ -z ${text} ]; then
text=${dir}/text
fi
# 1. Create scp files for inputs
# These are not necessary for decoding mode, and make it as an option
input=
if [ -n "${feat}" ]; then
_feat_scps=$(echo "${feat}" | tr ',' ' ' )
read -r -a feat_scps <<< $_feat_scps
num_feats=${#feat_scps[@]}
for (( i=1; i<=num_feats; i++ )); do
feat=${feat_scps[$((i-1))]}
mkdir -p ${tmpdir}/input_${i}
input+="input_${i} "
cat ${feat} > ${tmpdir}/input_${i}/feat.scp
# Dump in the "legacy" style JSON format
if [ -n "${filetype}" ]; then
awk -v filetype=${filetype} '{print $1 " " filetype}' ${feat} \
> ${tmpdir}/input_${i}/filetype.scp
fi
feat_to_shape.sh --cmd "${cmd}" --nj ${nj} \
--filetype "${filetype}" \
--preprocess-conf "${preprocess_conf}" \
--verbose ${verbose} ${feat} ${tmpdir}/input_${i}/shape.scp
done
fi
# 2. Create scp files for outputs
mkdir -p ${tmpdir}/output
if [ -n "${bpecode}" ]; then
if [ ${multilingual} = true ]; then
# remove a space before the language ID
paste -d " " <(awk '{print $1}' ${text}) <(cut -f 2- -d" " ${text} \
| spm_encode --model=${bpecode} --output_format=piece | cut -f 2- -d" ") \
> ${tmpdir}/output/token.scp
else
paste -d " " <(awk '{print $1}' ${text}) <(cut -f 2- -d" " ${text} \
| spm_encode --model=${bpecode} --output_format=piece) \
> ${tmpdir}/output/token.scp
fi
elif [ -n "${nlsyms}" ]; then
text2token.py -s 1 -n 1 -l ${nlsyms} ${text} --trans_type ${trans_type} > ${tmpdir}/output/token.scp
else
text2token.py -s 1 -n 1 ${text} --trans_type ${trans_type} > ${tmpdir}/output/token.scp
fi
< ${tmpdir}/output/token.scp utils/sym2int.pl --map-oov ${oov} -f 2- ${dic} > ${tmpdir}/output/tokenid.scp
# +2 comes from CTC blank and EOS
vocsize=$(tail -n 1 ${dic} | awk '{print $2}')
odim=$(echo "$vocsize + 2" | bc)
< ${tmpdir}/output/tokenid.scp awk -v odim=${odim} '{print $1 " " NF-1 "," odim}' > ${tmpdir}/output/shape.scp
cat ${text} > ${tmpdir}/output/text.scp
# 3. Create scp files for the others
mkdir -p ${tmpdir}/other
if [ ${multilingual} == true ]; then
awk '{
n = split($1,S,"[-]");
lang=S[n];
print $1 " " lang
}' ${text} > ${tmpdir}/other/lang.scp
elif [ -n "${lang}" ]; then
awk -v lang=${lang} '{print $1 " " lang}' ${text} > ${tmpdir}/other/lang.scp
fi
if [ -n "${category}" ]; then
awk -v category=${category} '{print $1 " " category}' ${dir}/text \
> ${tmpdir}/other/category.scp
fi
cat ${dir}/utt2spk > ${tmpdir}/other/utt2spk.scp
# 4. Merge scp files into a JSON file
opts=""
if [ -n "${feat}" ]; then
intypes="${input} output other"
else
intypes="output other"
fi
for intype in ${intypes}; do
if [ -z "$(find "${tmpdir}/${intype}" -name "*.scp")" ]; then
continue
fi
if [ ${intype} != other ]; then
opts+="--${intype%_*}-scps "
else
opts+="--scps "
fi
for x in "${tmpdir}/${intype}"/*.scp; do
k=$(basename ${x} .scp)
if [ ${k} = shape ]; then
opts+="shape:${x}:shape "
else
opts+="${k}:${x} "
fi
done
done
if ${allow_one_column}; then
opts+="--allow-one-column true "
else
opts+="--allow-one-column false "
fi
if [ -n "${out}" ]; then
opts+="-O ${out}"
fi
merge_scp2json.py --verbose ${verbose} ${opts}
rm -fr ${tmpdir}
utils/dump.sh 0 → 100755
浏览文件 @ 950d17cb
#!/usr/bin/env bash
# Copyright 2017 Nagoya University (Tomoki Hayashi)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
echo "$0 $*" # Print the command line for logging
. ./path.sh
cmd=run.pl
do_delta=false
nj=1
verbose=0
compress=true
write_utt2num_frames=true
filetype='mat' # mat or hdf5
help_message="Usage: $0 <scp> <cmvnark> <logdir> <dumpdir>"
. utils/parse_options.sh
scp=$1
cvmnark=$2
logdir=$3
dumpdir=$4
if [ $# != 4 ]; then
echo "${help_message}"
exit 1;
fi
set -euo pipefail
mkdir -p ${logdir}
mkdir -p ${dumpdir}
dumpdir=$(perl -e '($dir,$pwd)= @ARGV; if($dir!~m:^/:) { $dir = "$pwd/$dir"; } print $dir; ' ${dumpdir} ${PWD})
for n in $(seq ${nj}); do
# the next command does nothing unless $dumpdir/storage/ exists, see
# utils/create_data_link.pl for more info.
utils/create_data_link.pl ${dumpdir}/feats.${n}.ark
done
if ${write_utt2num_frames}; then
write_num_frames_opt="--write-num-frames=ark,t:$dumpdir/utt2num_frames.JOB"
else
write_num_frames_opt=
fi
# split scp file
split_scps=""
for n in $(seq ${nj}); do
split_scps="$split_scps $logdir/feats.$n.scp"
done
utils/split_scp.pl ${scp} ${split_scps} || exit 1;
# dump features
if ${do_delta}; then
${cmd} JOB=1:${nj} ${logdir}/dump_feature.JOB.log \
apply-cmvn --norm-vars=true ${cvmnark} scp:${logdir}/feats.JOB.scp ark:- \| \
add-deltas ark:- ark:- \| \
copy-feats.py --verbose ${verbose} --out-filetype ${filetype} \
--compress=${compress} --compression-method=2 ${write_num_frames_opt} \
ark:- ark,scp:${dumpdir}/feats.JOB.ark,${dumpdir}/feats.JOB.scp \
|| exit 1
else
${cmd} JOB=1:${nj} ${logdir}/dump_feature.JOB.log \
apply-cmvn --norm-vars=true ${cvmnark} scp:${logdir}/feats.JOB.scp ark:- \| \
copy-feats.py --verbose ${verbose} --out-filetype ${filetype} \
--compress=${compress} --compression-method=2 ${write_num_frames_opt} \
ark:- ark,scp:${dumpdir}/feats.JOB.ark,${dumpdir}/feats.JOB.scp \
|| exit 1
fi
# concatenate scp files
for n in $(seq ${nj}); do
cat ${dumpdir}/feats.${n}.scp || exit 1;
done > ${dumpdir}/feats.scp || exit 1
if ${write_utt2num_frames}; then
for n in $(seq ${nj}); do
cat ${dumpdir}/utt2num_frames.${n} || exit 1;
done > ${dumpdir}/utt2num_frames || exit 1
rm ${dumpdir}/utt2num_frames.* 2>/dev/null
fi
# Write the filetype, this will be used for data2json.sh
echo ${filetype} > ${dumpdir}/filetype
# remove temp scps
rm ${logdir}/feats.*.scp 2>/dev/null
if [ ${verbose} -eq 1 ]; then
echo "Succeeded dumping features for training"
fi
utils/feat-to-shape.py 0 → 100755
浏览文件 @ 950d17cb
#!/usr/bin/env python3
import argparse
import logging
import sys
from deepspeech.transform.transformation import Transformation
from deepspeech.utils.cli_readers import file_reader_helper
from deepspeech.utils.cli_utils import get_commandline_args
from deepspeech.utils.cli_utils import is_scipy_wav_style
def get_parser():
parser = argparse.ArgumentParser(
description="convert feature to its shape",
formatter_class=argparse.ArgumentDefaultsHelpFormatter, )
parser.add_argument(
"--verbose", "-V", default=0, type=int, help="Verbose option")
parser.add_argument(
"--filetype",
type=str,
default="mat",
choices=["mat", "hdf5", "sound.hdf5", "sound"],
help="Specify the file format for the rspecifier. "
'"mat" is the matrix format in kaldi', )
parser.add_argument(
"--preprocess-conf",
type=str,
default=None,
help="The configuration file for the pre-processing", )
parser.add_argument(
"rspecifier",
type=str,
help="Read specifier for feats. e.g. ark:some.ark")
parser.add_argument(
"out",
nargs="?",
type=argparse.FileType("w"),
default=sys.stdout,
help="The output filename. "
"If omitted, then output to sys.stdout", )
return parser
def main():
parser = get_parser()
args = parser.parse_args()
# logging info
logfmt = "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
if args.verbose > 0:
logging.basicConfig(level=logging.INFO, format=logfmt)
else:
logging.basicConfig(level=logging.WARN, format=logfmt)
logging.info(get_commandline_args())
if args.preprocess_conf is not None:
preprocessing = Transformation(args.preprocess_conf)
logging.info("Apply preprocessing: {}".format(preprocessing))
else:
preprocessing = None
# There are no necessary for matrix without preprocessing,
# so change to file_reader_helper to return shape.
# This make sense only with filetype="hdf5".
for utt, mat in file_reader_helper(
args.rspecifier, args.filetype, return_shape=preprocessing is None):
if preprocessing is not None:
if is_scipy_wav_style(mat):
# If data is sound file, then got as Tuple[int, ndarray]
rate, mat = mat
mat = preprocessing(mat, uttid_list=utt)
shape_str = ",".join(map(str, mat.shape))
else:
if len(mat) == 2 and isinstance(mat[1], tuple):
# If data is sound file, Tuple[int, Tuple[int, ...]]
rate, mat = mat
shape_str = ",".join(map(str, mat))
args.out.write("{} {}\n".format(utt, shape_str))
if __name__ == "__main__":
main()
utils/feat_to_shape.sh 0 → 100755
浏览文件 @ 950d17cb
#!/usr/bin/env bash
# Begin configuration section.
nj=4
cmd=run.pl
verbose=0
filetype=""
preprocess_conf=""
# End configuration section.
help_message=$(cat << EOF
Usage: $0 [options] <input-scp> <output-scp> [<log-dir>]
e.g.: $0 data/train/feats.scp data/train/shape.scp data/train/log
Options:
--nj <nj> # number of parallel jobs
--cmd (utils/run.pl|utils/queue.pl <queue opts>) # how to run jobs.
--filetype <mat|hdf5|sound.hdf5> # Specify the format of feats file
--preprocess-conf <json> # Apply preprocess to feats when creating shape.scp
--verbose <num> # Default: 0
EOF
)
echo "$0 $*" 1>&2 # Print the command line for logging
. parse_options.sh || exit 1;
if [ $# -lt 2 ] || [ $# -gt 3 ]; then
echo "${help_message}" 1>&2
exit 1;
fi
set -euo pipefail
scp=$1
outscp=$2
data=$(dirname ${scp})
if [ $# -eq 3 ]; then
logdir=$3
else
logdir=${data}/log
fi
mkdir -p ${logdir}
nj=$((nj<$(<"${scp}" wc -l)?nj:$(<"${scp}" wc -l)))
split_scps=""
for n in $(seq ${nj}); do
split_scps="${split_scps} ${logdir}/feats.${n}.scp"
done
utils/split_scp.pl ${scp} ${split_scps}
if [ -n "${preprocess_conf}" ]; then
preprocess_opt="--preprocess-conf ${preprocess_conf}"
else
preprocess_opt=""
fi
if [ -n "${filetype}" ]; then
filetype_opt="--filetype ${filetype}"
else
filetype_opt=""
fi
${cmd} JOB=1:${nj} ${logdir}/feat_to_shape.JOB.log \
feat-to-shape.py --verbose ${verbose} ${preprocess_opt} ${filetype_opt} \
scp:${logdir}/feats.JOB.scp ${logdir}/shape.JOB.scp
# concatenate the .scp files together.
for n in $(seq ${nj}); do
cat ${logdir}/shape.${n}.scp
done > ${outscp}
rm -f ${logdir}/feats.*.scp 2>/dev/null
utils/gen_duration_from_textgrid.py 100644 → 100755
浏览文件 @ 950d17cb
文件模式从 100644 更改为 100755
utils/merge_scp2json.py 0 → 100755
浏览文件 @ 950d17cb
#!/usr/bin/env python3
# encoding: utf-8
import argparse
import codecs
import json
import logging
import sys
from distutils.util import strtobool
from io import open
from deepspeech.utils.cli_utils import get_commandline_args
PY2 = sys.version_info[0] == 2
sys.stdin = codecs.getreader("utf-8")(sys.stdin if PY2 else sys.stdin.buffer)
sys.stdout = codecs.getwriter("utf-8")(sys.stdout if PY2 else sys.stdout.buffer)
# Special types:
def shape(x):
"""Change str to List[int]
>>> shape('3,5')
[3, 5]
>>> shape(' [3, 5] ')
[3, 5]
"""
# x: ' [3, 5] ' -> '3, 5'
x = x.strip()
if x[0] == "[":
x = x[1:]
if x[-1] == "]":
x = x[:-1]
return list(map(int, x.split(",")))
def get_parser():
parser = argparse.ArgumentParser(
description="Given each file paths with such format as "
"<key>:<file>:<type>. type> can be omitted and the default "
'is "str". e.g. {} '
"--input-scps feat:data/feats.scp shape:data/utt2feat_shape:shape "
"--input-scps feat:data/feats2.scp shape:data/utt2feat2_shape:shape "
"--output-scps text:data/text shape:data/utt2text_shape:shape "
"--scps utt2spk:data/utt2spk".format(sys.argv[0]),
formatter_class=argparse.ArgumentDefaultsHelpFormatter, )
parser.add_argument(
"--input-scps",
type=str,
nargs="*",
action="append",
default=[],
help="Json files for the inputs", )
parser.add_argument(
"--output-scps",
type=str,
nargs="*",
action="append",
default=[],
help="Json files for the outputs", )
parser.add_argument(
"--scps",
type=str,
nargs="+",
default=[],
help="The json files except for the input and outputs", )
parser.add_argument(
"--verbose", "-V", default=1, type=int, help="Verbose option")
parser.add_argument(
"--allow-one-column",
type=strtobool,
default=False,
help="Allow one column in input scp files. "
"In this case, the value will be empty string.", )
parser.add_argument(
"--out",
"-O",
type=str,
help="The output filename. "
"If omitted, then output to sys.stdout", )
return parser
if __name__ == "__main__":
parser = get_parser()
args = parser.parse_args()
args.scps = [args.scps]
# logging info
logfmt = "%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s"
if args.verbose > 0:
logging.basicConfig(level=logging.INFO, format=logfmt)
else:
logging.basicConfig(level=logging.WARN, format=logfmt)
logging.info(get_commandline_args())
# List[List[Tuple[str, str, Callable[[str], Any], str, str]]]
input_infos = []
output_infos = []
infos = []
for lis_list, key_scps_list in [
(input_infos, args.input_scps),
(output_infos, args.output_scps),
(infos, args.scps),
]:
for key_scps in key_scps_list:
lis = []
for key_scp in key_scps:
sps = key_scp.split(":")
if len(sps) == 2:
key, scp = sps
type_func = None
type_func_str = "none"
elif len(sps) == 3:
key, scp, type_func_str = sps
fail = False
try:
# type_func: Callable[[str], Any]
# e.g. type_func_str = "int" -> type_func = int
type_func = eval(type_func_str)
except Exception:
raise RuntimeError(
"Unknown type: {}".format(type_func_str))
if not callable(type_func):
raise RuntimeError(
"Unknown type: {}".format(type_func_str))
else:
raise RuntimeError(
"Format <key>:<filepath> "
"or <key>:<filepath>:<type> "
"e.g. feat:data/feat.scp "
"or shape:data/feat.scp:shape: {}".format(key_scp))
for item in lis:
if key == item[0]:
raise RuntimeError('The key "{}" is duplicated: {} {}'.
format(key, item[3], key_scp))
lis.append((key, scp, type_func, key_scp, type_func_str))
lis_list.append(lis)
# Open scp files
input_fscps = [[open(i[1], "r", encoding="utf-8") for i in il]
for il in input_infos]
output_fscps = [[open(i[1], "r", encoding="utf-8") for i in il]
for il in output_infos]
fscps = [[open(i[1], "r", encoding="utf-8") for i in il] for il in infos]
# Note(kamo): What is done here?
# The final goal is creating a JSON file such as.
# {
# "utts": {
# "sample_id1": {(omitted)},
# "sample_id2": {(omitted)},
# ....
# }
# }
#
# To reduce memory usage, reading the input text files for each lines
# and writing JSON elements per samples.
if args.out is None:
out = sys.stdout
else:
out = open(args.out, "w", encoding="utf-8")
out.write('{\n "utts": {\n')
nutt = 0
while True:
nutt += 1
# List[List[str]]
input_lines = [[f.readline() for f in fl] for fl in input_fscps]
output_lines = [[f.readline() for f in fl] for fl in output_fscps]
lines = [[f.readline() for f in fl] for fl in fscps]
# Get the first line
concat = sum(input_lines + output_lines + lines, [])
if len(concat) == 0:
break
first = concat[0]
# Sanity check: Must be sorted by the first column and have same keys
count = 0
for ls_list in (input_lines, output_lines, lines):
for ls in ls_list:
for line in ls:
if line == "" or first == "":
if line != first:
concat = sum(input_infos + output_infos + infos, [])
raise RuntimeError("The number of lines mismatch "
'between: "{}" and "{}"'.format(
concat[0][1],
concat[count][1]))
elif line.split()[0] != first.split()[0]:
concat = sum(input_infos + output_infos + infos, [])
raise RuntimeError(
"The keys are mismatch at {}th line "
'between "{}" and "{}":\n>>> {}\n>>> {}'.format(
nutt,
concat[0][1],
concat[count][1],
first.rstrip(),
line.rstrip(), ))
count += 1
# The end of file
if first == "":
if nutt != 1:
out.write("\n")
break
if nutt != 1:
out.write(",\n")
entry = {}
for inout, _lines, _infos in [
("input", input_lines, input_infos),
("output", output_lines, output_infos),
("other", lines, infos),
]:
lis = []
for idx, (line_list, info_list) in enumerate(
zip(_lines, _infos), 1):
if inout == "input":
d = {"name": "input{}".format(idx)}
elif inout == "output":
d = {"name": "target{}".format(idx)}
else:
d = {}
# info_list: List[Tuple[str, str, Callable]]
# line_list: List[str]
for line, info in zip(line_list, info_list):
sps = line.split(None, 1)
if len(sps) < 2:
if not args.allow_one_column:
raise RuntimeError(
"Format error {}th line in {}: "
' Expecting "<key> <value>":\n>>> {}'.format(
nutt, info[1], line))
uttid = sps[0]
value = ""
else:
uttid, value = sps
key = info[0]
type_func = info[2]
value = value.rstrip()
if type_func is not None:
try:
# type_func: Callable[[str], Any]
value = type_func(value)
except Exception:
logging.error(
'"{}" is an invalid function '
"for the {} th line in {}: \n>>> {}".format(
info[4], nutt, info[1], line))
raise
d[key] = value
lis.append(d)
if inout != "other":
entry[inout] = lis
else:
# If key == 'other'. only has the first item
entry.update(lis[0])
entry = json.dumps(
entry,
indent=4,
ensure_ascii=False,
sort_keys=True,
separators=(",", ": "))
# Add indent
indent = " " * 2
entry = ("\n" + indent).join(entry.split("\n"))
uttid = first.split()[0]
out.write(' "{}": {}'.format(uttid, entry))
out.write(" }\n}\n")
logging.info("{} entries in {}".format(nutt, out.name))
utils/reduce_data_dir.sh 0 → 100755
浏览文件 @ 950d17cb
#!/usr/bin/env bash
# koried, 10/29/2012
# Reduce a data set based on a list of turn-ids
help_message="usage: $0 srcdir turnlist destdir"
if [ $1 == "--help" ]; then
echo "${help_message}"
exit 0;
fi
if [ $# != 3 ]; then
echo "${help_message}"
exit 1;
fi
srcdir=$1
reclist=$2
destdir=$3
if [ ! -f ${srcdir}/utt2spk ]; then
echo "$0: no such file $srcdir/utt2spk"
exit 1;
fi
function do_filtering {
# assumes the utt2spk and spk2utt files already exist.
[ -f ${srcdir}/feats.scp ] && utils/filter_scp.pl ${destdir}/utt2spk <${srcdir}/feats.scp >${destdir}/feats.scp
[ -f ${srcdir}/wav.scp ] && utils/filter_scp.pl ${destdir}/utt2spk <${srcdir}/wav.scp >${destdir}/wav.scp
[ -f ${srcdir}/text ] && utils/filter_scp.pl ${destdir}/utt2spk <${srcdir}/text >${destdir}/text
[ -f ${srcdir}/utt2num_frames ] && utils/filter_scp.pl ${destdir}/utt2spk <${srcdir}/utt2num_frames >${destdir}/utt2num_frames
[ -f ${srcdir}/spk2gender ] && utils/filter_scp.pl ${destdir}/spk2utt <${srcdir}/spk2gender >${destdir}/spk2gender
[ -f ${srcdir}/cmvn.scp ] && utils/filter_scp.pl ${destdir}/spk2utt <${srcdir}/cmvn.scp >${destdir}/cmvn.scp
if [ -f ${srcdir}/segments ]; then
utils/filter_scp.pl ${destdir}/utt2spk <${srcdir}/segments >${destdir}/segments
awk '{print $2;}' ${destdir}/segments | sort | uniq > ${destdir}/reco # recordings.
# The next line would override the command above for wav.scp, which would be incorrect.
[ -f ${srcdir}/wav.scp ] && utils/filter_scp.pl ${destdir}/reco <${srcdir}/wav.scp >${destdir}/wav.scp
[ -f ${srcdir}/reco2file_and_channel ] && \
utils/filter_scp.pl ${destdir}/reco <${srcdir}/reco2file_and_channel >${destdir}/reco2file_and_channel
# Filter the STM file for proper sclite scoring (this will also remove the comments lines)
[ -f ${srcdir}/stm ] && utils/filter_scp.pl ${destdir}/reco < ${srcdir}/stm > ${destdir}/stm
rm ${destdir}/reco
fi
srcutts=$(wc -l < ${srcdir}/utt2spk)
destutts=$(wc -l < ${destdir}/utt2spk)
echo "Reduced #utt from $srcutts to $destutts"
}
mkdir -p ${destdir}
# filter the utt2spk based on the set of recordings
utils/filter_scp.pl ${reclist} < ${srcdir}/utt2spk > ${destdir}/utt2spk
utils/utt2spk_to_spk2utt.pl < ${destdir}/utt2spk > ${destdir}/spk2utt
do_filtering;
utils/remove_longshortdata.sh 0 → 100755
浏览文件 @ 950d17cb
#!/usr/bin/env bash
# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
. ./path.sh
maxframes=2000
minframes=10
maxchars=200
minchars=0
nlsyms=""
no_feat=false
trans_type=char
help_message="usage: $0 olddatadir newdatadir"
. utils/parse_options.sh || exit 1;
if [ $# != 2 ]; then
echo "${help_message}"
exit 1;
fi
sdir=$1
odir=$2
mkdir -p ${odir}/tmp
if [ ${no_feat} = true ]; then
# for machine translation
cut -d' ' -f 1 ${sdir}/text > ${odir}/tmp/reclist1
else
echo "extract utterances having less than $maxframes or more than $minframes frames"
utils/data/get_utt2num_frames.sh ${sdir}
< ${sdir}/utt2num_frames awk -v maxframes="$maxframes" '{ if ($2 < maxframes) print }' \
| awk -v minframes="$minframes" '{ if ($2 > minframes) print }' \
| awk '{print $1}' > ${odir}/tmp/reclist1
fi
echo "extract utterances having less than $maxchars or more than $minchars characters"
# counting number of chars. Use (NF - 1) instead of NF to exclude the utterance ID column
if [ -z ${nlsyms} ]; then
text2token.py -s 1 -n 1 ${sdir}/text --trans_type ${trans_type} \
| awk -v maxchars="$maxchars" '{ if (NF - 1 < maxchars) print }' \
| awk -v minchars="$minchars" '{ if (NF - 1 > minchars) print }' \
| awk '{print $1}' > ${odir}/tmp/reclist2
else
text2token.py -l ${nlsyms} -s 1 -n 1 ${sdir}/text --trans_type ${trans_type} \
| awk -v maxchars="$maxchars" '{ if (NF - 1 < maxchars) print }' \
| awk -v minchars="$minchars" '{ if (NF - 1 > minchars) print }' \
| awk '{print $1}' > ${odir}/tmp/reclist2
fi
# extract common lines
comm -12 <(sort ${odir}/tmp/reclist1) <(sort ${odir}/tmp/reclist2) > ${odir}/tmp/reclist
reduce_data_dir.sh ${sdir} ${odir}/tmp/reclist ${odir}
utils/fix_data_dir.sh ${odir}
oldnum=$(wc -l ${sdir}/feats.scp | awk '{print $1}')
newnum=$(wc -l ${odir}/feats.scp | awk '{print $1}')
echo "change from $oldnum to $newnum"
utils/text2token.py 0 → 100755
浏览文件 @ 950d17cb
#!/usr/bin/env python3
# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import argparse
import codecs
import re
import sys
is_python2 = sys.version_info[0] == 2
def exist_or_not(i, match_pos):
start_pos = None
end_pos = None
for pos in match_pos:
if pos[0] <= i < pos[1]:
start_pos = pos[0]
end_pos = pos[1]
break
return start_pos, end_pos
def get_parser():
parser = argparse.ArgumentParser(
description="convert raw text to tokenized text",
formatter_class=argparse.ArgumentDefaultsHelpFormatter, )
parser.add_argument(
"--nchar",
"-n",
default=1,
type=int,
help="number of characters to split, i.e., \
aabb -> a a b b with -n 1 and aa bb with -n 2", )
parser.add_argument(
"--skip-ncols", "-s", default=0, type=int, help="skip first n columns")
parser.add_argument(
"--space", default="<space>", type=str, help="space symbol")
parser.add_argument(
"--non-lang-syms",
"-l",
default=None,
type=str,
help="list of non-linguistic symobles, e.g., <NOISE> etc.", )
parser.add_argument(
"text", type=str, default=False, nargs="?", help="input text")
parser.add_argument(
"--trans_type",
"-t",
type=str,
default="char",
choices=["char", "phn"],
help="""Transcript type. char/phn. e.g., for TIMIT FADG0_SI1279 -
If trans_type is char,
read from SI1279.WRD file -> "bricks are an alternative"
Else if trans_type is phn,
read from SI1279.PHN file -> "sil b r ih sil k s aa r er n aa l
sil t er n ih sil t ih v sil" """, )
return parser
def main():
parser = get_parser()
args = parser.parse_args()
rs = []
if args.non_lang_syms is not None:
with codecs.open(args.non_lang_syms, "r", encoding="utf-8") as f:
nls = [x.rstrip() for x in f.readlines()]
rs = [re.compile(re.escape(x)) for x in nls]
if args.text:
f = codecs.open(args.text, encoding="utf-8")
else:
f = codecs.getreader("utf-8")(sys.stdin
if is_python2 else sys.stdin.buffer)
sys.stdout = codecs.getwriter("utf-8")(sys.stdout
if is_python2 else sys.stdout.buffer)
line = f.readline()
n = args.nchar
while line:
x = line.split()
print(" ".join(x[:args.skip_ncols]), end=" ")
a = " ".join(x[args.skip_ncols:])
# get all matched positions
match_pos = []
for r in rs:
i = 0
while i >= 0:
m = r.search(a, i)
if m:
match_pos.append([m.start(), m.end()])
i = m.end()
else:
break
if args.trans_type == "phn":
a = a.split(" ")
else:
if len(match_pos) > 0:
chars = []
i = 0
while i < len(a):
start_pos, end_pos = exist_or_not(i, match_pos)
if start_pos is not None:
chars.append(a[start_pos:end_pos])
i = end_pos
else:
chars.append(a[i])
i += 1
a = chars
a = [a[j:j + n] for j in range(0, len(a), n)]
a_flat = []
for z in a:
a_flat.append("".join(z))
a_chars = [z.replace(" ", args.space) for z in a_flat]
if args.trans_type == "phn":
a_chars = [z.replace("sil", args.space) for z in a_chars]
print(" ".join(a_chars))
line = f.readline()
if __name__ == "__main__":
main()
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