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README.md
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# PaddlePaddle Speech toolkit
English | [简体中文](README_ch.md)
<p align="center">
<img src="./docs/images/PaddleSpeech_logo.png" />
</p>
<div align="center">
<h3>
<a href="#quick-start"> Quick Start </a>
| <a href="#tutorials"> Tutorials </a>
| <a href="#model-list"> Models List </a>
</div>
------------------------------------------------------------------------------------
![License](https://img.shields.io/badge/license-Apache%202-red.svg)
![python version](https://img.shields.io/badge/python-3.7+-orange.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.
<!---
from https://github.com/18F/open-source-guide/blob/18f-pages/pages/making-readmes-readable.md
1.What is this repo or project? (You can reuse the repo description you used earlier because this section doesn’t have to be long.)
2.How does it work?
3.Who will use this repo or project?
4.What is the goal of this project?
-->
**PaddleSpeech** is an open-source toolkit on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) platform for a variety of critical tasks in speech, with state-of-art and influential models.
##### Speech-To-Text
<div align = "center">
<table style="width:100%">
<thead>
<tr>
<th> Input Audio </th>
<th> Recognition Result </th>
</tr>
</thead>
<tbody>
<tr>
<td align = "center">
<a href="https://paddlehub.bj.bcebos.com/resources/fastspeech_ljspeech-0.wav" rel="nofollow">
<img align="center" src="./docs/images/audio_icon.png" width="200" style="max-width: 100%;"></a><br>
</td>
<td >Life was like a box of chocolates, you never know what you will get.</td>
</tr>
<tr>
<td align = "center">
<a href="https://paddlehub.bj.bcebos.com/resources/fastspeech_ljspeech-0.wav" rel="nofollow">
<img align="center" src="./docs/images/audio_icon.png" width="200" style="max-width: 100%;"></a><br>
</td>
<td>早上好，今天是2020/10/29，最低温度是-3°C。</td>
</tr>
</tbody>
</table>
</div>
##### Text-To-Speech
<div align = "center">
<table style="width:100%">
<thead>
<tr>
<th><img width="200" height="1"> Input Text <img width="200" height="1"> </th>
<th>Synthetic Audio</th>
</tr>
</thead>
<tbody>
<tr>
<td >Life was like a box of chocolates, you never know what you're gonna get.</td>
<td align = "center">
<a href="https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/transformer_tts_ljspeech_ckpt_0.4_waveflow_ljspeech_ckpt_0.3/001.wav" rel="nofollow">
<img align="center" src="./docs/images/audio_icon.png" width="200" style="max-width: 100%;"></a><br>
</td>
</tr>
<tr>
<td >早上好，今天是2020/10/29，最低温度是-3°C。</td>
<td align = "center">
<a href="https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/parakeet_espnet_fs2_pwg_demo/tn_g2p/parakeet/001.wav" rel="nofollow">
<img align="center" src="./docs/images/audio_icon.png" width="200" style="max-width: 100%;"></a><br>
</td>
</tr>
</tbody>
</table>
</div>
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 modules, and deployment process. To be more specific, this toolkit features at:
- **Fast and Light-weight**: we provide high-speed and ultra-lightweight models that are convenient for industrial deployment.
- **Rule-based Chinese frontend**: our frontend contains Text Normalization 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 both Industrial and Academia**:
- *Implementation of critical audio tasks*: this toolkit contains audio functions like Speech Translation, Automatic Speech Recognition, Text-To-Speech Synthesis, Voice Cloning, etc.
- *Integration of mainstream models and datasets*: the toolkit implements modules that participate in the whole pipeline of the speech tasks, and uses mainstream datasets like LibriSpeech, LJSpeech, AIShell, CSMSC, etc. See also [model lists](#models-list) for more details.
- *Cascaded models application*: as an extension of the application of traditional audio tasks, we combine the workflows of aforementioned tasks with other fields like Natural language processing (NLP), like Punctuation Restoration.
Please refer to [our PaddleSpeech demo page](https://paddlespeech.readthedocs.io/en/latest/tts/demo.html) for more examples.
# Community
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!
If you are from China, we strongly recommend you join our PaddleSpeech WeChat group. Scan the following WeChat QR code and get in touch with the other developers in this community!
<div align="center">
<img src="./docs/images/wechat-code-speech.png" width = "200">
</div>
# Alternative Installation
The base environment in this page is
- Ubuntu 16.04
- python>=3.7
- paddlepaddle==2.1.2
If you want to set up PaddleSpeech in other environment, please see the [installation](./docs/installation.md) documents for all the alternatives.
# Quick Start
Just a quick test of our functions: [English Speech-To-Text]() and [English Text-To-Speech]() by typing message or upload your own audio file.
Developers can have a try of our model with only a few lines of code.
A tiny DeepSpeech2 *Speech-To-Text* model training on toy set of LibriSpeech:
```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
```
For *Text-To-Speech*, try FastSpeech2 on LJSpeech:
- Download LJSpeech-1.1 from the [ljspeech official website](https://keithito.com/LJ-Speech-Dataset/), our prepared durations for fastspeech2 [ljspeech_alignment](https://paddlespeech.bj.bcebos.com/MFA/LJSpeech-1.1/ljspeech_alignment.tar.gz).
- The pretrained models are seperated into two parts: [fastspeech2_nosil_ljspeech_ckpt](https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_ljspeech_ckpt_0.5.zip) and [pwg_ljspeech_ckpt](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_ljspeech_ckpt_0.5.zip). Please download then unzip to `./model/fastspeech2` and `./model/pwg` respectively.
- 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
cd examples/csmsc/tts3
# download the pretrained models and unaip them
wget https://paddlespeech.bj.bcebos.com/Parakeet/pwg_baker_ckpt_0.4.zip
unzip pwg_baker_ckpt_0.4.zip
wget https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_baker_ckpt_0.4.zip
unzip fastspeech2_nosil_baker_ckpt_0.4.zip
# source the environment
source path.sh
# run end-to-end synthesize
FLAGS_allocator_strategy=naive_best_fit \
FLAGS_fraction_of_gpu_memory_to_use=0.01 \
python3 ${BIN_DIR}/synthesize_e2e.py \
--fastspeech2-config=fastspeech2_nosil_baker_ckpt_0.4/default.yaml \
--fastspeech2-checkpoint=fastspeech2_nosil_baker_ckpt_0.4/snapshot_iter_76000.pdz \
--fastspeech2-stat=fastspeech2_nosil_baker_ckpt_0.4/speech_stats.npy \
--pwg-config=pwg_baker_ckpt_0.4/pwg_default.yaml \
--pwg-checkpoint=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \
--pwg-stat=pwg_baker_ckpt_0.4/pwg_stats.npy \
--text=${BIN_DIR}/../sentences.txt \
--output-dir=exp/default/test_e2e \
--inference-dir=exp/default/inference \
--device="gpu" \
--phones-dict=fastspeech2_nosil_baker_ckpt_0.4/phone_id_map.txt
```
## Features
If you want to try more functions like training and tuning, please see [Speech-To-Text getting started](./docs/source/asr/getting_started.md) and [Text-To-Speech Basic Use](./docs/source/tts/basic_usage.md).
See [feature list](docs/source/asr/feature_list.md) for more information.
# Models List
## Setup
PaddleSpeech supports a series of most popular models, summarized in released models [Speech-To-Text](./docs/source/asr/released_model.md)/[Text-To-Speech](./docs/source/tts/released_models.md) with available pretrained models.
All tested under:
* Ubuntu 16.04
* python>=3.7
* paddlepaddle==2.1.2
Speech-To-Text module contains *Acoustic Model* and *Language Model*, with the following details:
Please see [install](docs/source/asr/install.md).
<!---
The current hyperlinks redirect to [Previous Parakeet](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples).
-->
## Getting Started
> Note: The `Link` should be code path rather than download links.
Please see [Getting Started](docs/source/asr/getting_started.md) and [tiny egs](examples/tiny/s0/README.md).
<table style="width:100%">
<thead>
<tr>
<th>Speech-To-Text Module Type</th>
<th>Dataset</th>
<th>Model Type</th>
<th>Link</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="3">Acoustic Model</td>
<td rowspan="2" >Aishell</td>
<td >DeepSpeech2 RNN + Conv based Models</td>
<td>
<a href = "https://deepspeech.bj.bcebos.com/release2.1/aishell/s0/aishell.s0.ds_online.5rnn.debug.tar.gz">Online</a> / <a href = "https://deepspeech.bj.bcebos.com/release2.1/aishell/s0/aishell.s0.ds2.offline.cer6p65.release.tar.gz">Offline </a>
</td>
</tr>
<tr>
<td>Transformer based Attention Models </td>
<td>
<a href = "https://deepspeech.bj.bcebos.com/release2.1/librispeech/s1/conformer.release.tar.gz">Non-CTC Loss</a> / <a href = "https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.release.tar.gz">CTC Loss </a>
</td>
</tr>
<tr>
<td> Librispeech</td>
<td>Transformer based Attention Models </td>
<td>
<a href = "https://deepspeech.bj.bcebos.com/release2.1/librispeech/s1/conformer.release.tar.gz">Conformer</a> / <a href = "https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.release.tar.gz"> Transformer </a> Decoder
</td>
</td>
</tr>
<tr>
<td rowspan="2">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>Baidu Internal Corpus</td>
<td>Mandarin Language Model Small</td>
<td>
Mandarin Language Model <a href = "https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm"> Small</a> / <a href = "https://deepspeech.bj.bcebos.com/zh_lm/zhidao_giga.klm">Large</a>
</td>
</tr>
</tbody>
</table>
## More Information
* [Data Prepration](docs/source/asr/data_preparation.md)
* [Data Augmentation](docs/source/asr/augmentation.md)
* [Ngram LM](docs/source/asr/ngram_lm.md)
* [Benchmark](docs/source/asr/benchmark.md)
* [Relased Model](docs/source/asr/released_model.md)
PaddleSpeech Text-To-Speech mainly contains three modules: *Text Frontend*, *Acoustic Model* and *Vocoder*. Acoustic Model and Vocoder models are listed as follow:
<table>
<thead>
<tr>
<th> Text-To-Speech Module Type <img width="110" height="1"> </th>
<th> Model Type </th>
<th> <img width="50" height="1"> Dataset <img width="50" height="1"> </th>
<th> <img width="101" height="1"> Link <img width="105" height="1"> </th>
</tr>
</thead>
<tbody>
<tr>
<td> Text Frontend</td>
<td colspan="2"> &emsp; </td>
<td>
<a href = "./examples/other/text_frontend">chinese-fronted</a>
</td>
</tr>
<tr>
<td rowspan="4">Acoustic Model</td>
<td >Tacotron2</td>
<td rowspan="2" >LJSpeech</td>
<td>
<a href = "./examples/ljspeech/tts0">tacotron2-ljspeech</a>
</td>
</tr>
<tr>
<td>TransformerTTS</td>
<td>
<a href = "./examples/ljspeech/tts1">transformer-ljspeech</a>
</td>
</tr>
<tr>
<td>SpeedySpeech</td>
<td>CSMSC</td>
<td >
<a href = "./examples/csmsc/tts2">speedyspeech-csmsc</a>
</td>
</tr>
<tr>
<td>FastSpeech2</td>
<td>AISHELL-3 / VCTK / LJSpeech / CSMSC</td>
<td>
<a href = "./examples/aishell3/tts3">fastspeech2-aishell3</a> / <a href = "./examples/vctk/tts3">fastspeech2-vctk</a> / <a href = "./examples/ljspeech/tts3">fastspeech2-ljspeech</a> / <a href = "./examples/csmsc/tts3">fastspeech2-csmsc</a>
</td>
</tr>
<tr>
<td rowspan="2">Vocoder</td>
<td >WaveFlow</td>
<td >LJSpeech</td>
<td>
<a href = "./examples/ljspeech/voc0">waveflow-ljspeech</a>
</td>
</tr>
<tr>
<td >Parallel WaveGAN</td>
<td >LJSpeech / VCTK / CSMSC</td>
<td>
<a href = "./examples/ljspeech/voc1">PWGAN-ljspeech</a> / <a href = "./examples/vctk/voc1">PWGAN-vctk</a> / <a href = "./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 = "./examples/other/ge2e">ge2e</a>
</td>
</tr>
<tr>
<td>GE2E + Tactron2</td>
<td>AISHELL-3</td>
<td>
<a href = "./examples/aishell3/vc0">ge2e-tactron2-aishell3</a>
</td>
</td>
</tr>
</tbody>
</table>
## 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.
# Tutorials
Normally, [Speech SoTA](https://paperswithcode.com/area/speech) gives you an overview of the hot academic topics in speech. To focus on the tasks in PaddleSpeech, you will find the following guidelines are helpful to grasp the core ideas.
- [Overview](./docs/source/introduction.md)
- Quick Start
- [Dependencies](./docs/source/dependencies.md) and [Installation](./docs/source/install.md)
- [Quick Start of Speech-To-Text](./docs/source/asr/quick_start.md)
- [Quick Start of Text-To-Speech](./docs/source/tts/quick_start.md)
- Speech-To-Text
- [Models Introduction](./docs/source/asr/models_introduction.md)
- [Data Preparation](./docs/source/asr/data_preparation.md)
- [Data Augmentation Pipeline](./docs/source/asr/augmentation.md)
- [Features](./docs/source/asr/feature_list.md)
- [Ngram LM](./docs/source/asr/ngram_lm.md)
- Text-To-Speech
- [Introduction](./docs/source/tts/models_introduction.md)
- [Advanced Usage](./docs/source/tts/advanced_usage.md)
- [Chinese Rule Based Text Frontend](./docs/source/tts/zh_text_frontend.md)
- [Test Audio Samples](https://paddlespeech.readthedocs.io/en/latest/tts/demo.html) and [PaddleSpeech VS. Espnet](https://paddlespeech.readthedocs.io/en/latest/tts/demo_2.html)
- [Released Models](./docs/source/released_model.md)
## License
# License and Acknowledgement
DeepSpeech is provided under the [Apache-2.0 License](./LICENSE).
PaddleSpeech is provided under the [Apache-2.0 License](./LICENSE).
## Acknowledgement
PaddleSpeech depends on a lot of open source repositories. See [references](./docs/source/asr/reference.md) for more information.
We depends on many open source repos. See [References](docs/source/asr/reference.md) for more information.
# Citation
To cite PaddleSpeech for research, please use the following format.
```tex
@misc{ppspeech2021,
title={PaddleSpeech, a toolkit for audio processing based on PaddlePaddle.},
author={PaddlePaddle Authors},
howpublished = {\url{https://github.com/PaddlePaddle/DeepSpeech}},
year={2021}
}
```
docs/source/tts/install.md
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......@@ -10,13 +10,13 @@ Example instruction to install paddlepaddle via pip is listed below.
### PaddlePaddle with GPU
```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
# CUDA10.2 的 PaddlePaddle
# PaddlePaddle for CUDA10.2
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
# 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
```
### PaddlePaddle with CPU
......
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