Skip to content

M
models

PaddlePaddle / models
大约 1 年 前同步成功

通知 222
Star 6828
Fork 2962
M
models
提交 c8b0ae8f 编写于 作者: Y Yibing Liu
浏览文件
操作

adapt to the new data provider

上级 c02c6502 0a0fcade
展开全部 隐藏空白更改
内联 并排
Showing with 4823 addition and 1406 deletion
.travis.yml
浏览文件 @ c8b0ae8f
......@@ -2,6 +2,8 @@ language: cpp
cache: ccache
sudo: required
dist: trusty
services:
- docker
os:
- linux
env:
......@@ -16,8 +18,12 @@ addons:
- python2.7-dev
before_install:
- pip install -U virtualenv pre-commit pip
- docker pull paddlepaddle/paddle:latest
script:
- .travis/precommit.sh
- docker run -i --rm -v "$PWD:/py_unittest" paddlepaddle/paddle:latest /bin/bash -c
'cd /py_unittest; sh .travis/unittest.sh'
notifications:
email:
on_success: change
......
.travis/unittest.sh 0 → 100755
浏览文件 @ c8b0ae8f
#!/bin/bash
abort(){
echo "Run unittest failed" 1>&2
echo "Please check your code" 1>&2
exit 1
}
unittest(){
cd $1 > /dev/null
if [ -f "requirements.txt" ]; then
pip install -r requirements.txt
fi
if [ $? != 0 ]; then
exit 1
fi
find . -name 'tests' -type d -print0 | \
xargs -0 -I{} -n1 bash -c \
'python -m unittest discover -v -s {}'
cd - > /dev/null
}
trap 'abort' 0
set -e
for proj in */ ; do
if [ -d $proj ]; then
unittest $proj
if [ $? != 0 ]; then
exit 1
fi
fi
done
trap : 0
README.md
浏览文件 @ c8b0ae8f
......@@ -14,46 +14,57 @@ PaddlePaddle提供了丰富的运算单元，帮助大家以模块化的方式
在词向量的例子中，我们向大家展示如何使用Hierarchical-Sigmoid 和噪声对比估计（Noise Contrastive Estimation，NCE）来加速词向量的学习。
- 1.1 [Hsigmoid加速词向量训练](https://github.com/PaddlePaddle/models/tree/develop/word_embedding)
- 1.2 [噪声对比估计加速词向量训练](https://github.com/PaddlePaddle/models/tree/develop/nce_cost)
## 2. 点击率预估
## 2. 语言模型
语言模型是自然语言处理领域里一个重要的基础模型，它是一个概率分布模型，利用它可以确定哪个词序列的可能性更大，或者给定若干个词，可以预测下一个最可能出现的词。语言模型被应用在很多领域，如：自动写作、QA、机器翻译、拼写检查、语音识别、词性标注等。
在语言模型的例子中，我们以文本生成为例，提供了RNN LM（包括LSTM、GRU）和N-Gram LM，供大家学习和使用。用户可以通过文档中的 “使用说明” 快速上手：适配训练语料，以训练 “自动写诗”、“自动写散文” 等有趣的模型。
- 2.1 [基于LSTM、GRU、N-Gram的文本生成模型](https://github.com/PaddlePaddle/models/tree/develop/language_model)
## 3. 点击率预估
点击率预估模型预判用户对一条广告点击的概率，对每次广告的点击情况做出预测，是广告技术的核心算法之一。逻谛斯克回归对大规模稀疏特征有着很好的学习能力，在点击率预估任务发展的早期一统天下。近年来，DNN 模型由于其强大的学习能力逐渐接过点击率预估任务的大旗。
在点击率预估的例子中，我们给出谷歌提出的 Wide & Deep 模型。这一模型融合了适用于学习抽象特征的 DNN 和适用于大规模稀疏特征的逻谛斯克回归两者模型的优点，可以作为一种相对成熟的模型框架使用， 在工业界也有一定的应用。
- 2.1 [Wide & deep 点击率预估模型](https://github.com/PaddlePaddle/models/tree/develop/ctr)
- 3.1 [Wide & deep 点击率预估模型](https://github.com/PaddlePaddle/models/tree/develop/ctr)
## 3. 文本分类
## 4. 文本分类
文本分类是自然语言处理领域最基础的任务之一，深度学习方法能够免除复杂的特征工程，直接使用原始文本作为输入，数据驱动地最优化分类准确率。
在文本分类的例子中，我们以情感分类任务为例，提供了基于DNN的非序列文本分类模型，以及基于CNN的序列模型供大家学习和使用（基于LSTM的模型见PaddleBook中[情感分类](https://github.com/PaddlePaddle/book/blob/develop/06.understand_sentiment/README.cn.md)一课）。
- 3.1 [基于 DNN / CNN 的情感分类](https://github.com/PaddlePaddle/models/tree/develop/text_classification)
- 4.1 [基于 DNN / CNN 的情感分类](https://github.com/PaddlePaddle/models/tree/develop/text_classification)
## 4. 排序学习
## 5. 排序学习
排序学习(Learning to Rank， LTR)是信息检索和搜索引擎研究的核心问题之一，通过机器学习方法学习一个分值函数对待排序的候选进行打分，再根据分值的高低确定序关系。深度神经网络可以用来建模分值函数，构成各类基于深度学习的LTR模型。
在排序学习的例子中，我们介绍基于 RankLoss 损失函数的 Pairwise 排序模型和基于LambdaRank损失函数的Listwise排序模型(Pointwise学习策略见PaddleBook中[推荐系统](https://github.com/PaddlePaddle/book/blob/develop/05.recommender_system/README.cn.md)一课）。
- 4.1 [基于 Pairwise 和 Listwise 的排序学习](https://github.com/PaddlePaddle/models/tree/develop/ltr)
- 5.1 [基于 Pairwise 和 Listwise 的排序学习](https://github.com/PaddlePaddle/models/tree/develop/ltr)
## 5. 序列标注
## 6. 序列标注
给定输入序列，序列标注模型为序列中每一个元素贴上一个类别标签，是自然语言处理领域最基础的任务之一。随着深度学习的不断探索和发展，利用循环神经网络学习输入序列的特征表示，条件随机场（Conditional Random Field, CRF）在特征基础上完成序列标注任务，逐渐成为解决序列标注问题的标配解决方案。
在序列标注的例子中，我们以命名实体识别（Named Entity Recognition，NER）任务为例，介绍如何训练一个端到端的序列标注模型。
- 5.1 [命名实体识别](https://github.com/PaddlePaddle/models/tree/develop/sequence_tagging_for_ner)
- 6.1 [命名实体识别](https://github.com/PaddlePaddle/models/tree/develop/sequence_tagging_for_ner)
## 6. 序列到序列学习
## 7. 序列到序列学习
序列到序列学习实现两个甚至是多个不定长模型之间的映射，有着广泛的应用，包括：机器翻译、智能对话与问答、广告创意语料生成、自动编码（如金融画像编码）、判断多个文本串之间的语义相关性等。
在序列到序列学习的例子中，我们以机器翻译任务为例，提供了多种改进模型，供大家学习和使用。包括：不带注意力机制的序列到序列映射模型，这一模型是所有序列到序列学习模型的基础；使用 scheduled sampling 改善 RNN 模型在生成任务中的错误累积问题；带外部记忆机制的神经机器翻译，通过增强神经网络的记忆能力，来完成复杂的序列到序列学习任务。
- 6.1 [无注意力机制的编码器解码器模型](https://github.com/PaddlePaddle/models/tree/develop/nmt_without_attention)
- 7.1 [无注意力机制的编码器解码器模型](https://github.com/PaddlePaddle/models/tree/develop/nmt_without_attention)
## Copyright and License
PaddlePaddle is provided under the [Apache-2.0 license](LICENSE).
deep_speech_2/README.md
浏览文件 @ c8b0ae8f
......@@ -16,34 +16,48 @@ For some machines, we also need to install libsndfile1. Details to be added.
### Preparing Data
```
cd data
python librispeech.py
cat manifest.libri.train-* > manifest.libri.train-all
cd datasets
sh run_all.sh
cd ..
```
After running librispeech.py, we have several "manifest" json files named with a prefix `manifest.libri.`. A manifest file summarizes a speech data set, with each line containing the meta data (i.e. audio filepath, transcription text, audio duration) of each audio file within the data set, in json format.
`sh run_all.sh` prepares all ASR datasets (currently, only LibriSpeech available). After running, we have several summarization manifest files in json-format.
By `cat manifest.libri.train-* > manifest.libri.train-all`, we simply merge the three seperate sample sets of LibriSpeech (train-clean-100, train-clean-360, train-other-500) into one training set. This is a simple way for merging different data sets.
A manifest file summarizes a speech data set, with each line containing the meta data (i.e. audio filepath, transcript text, audio duration) of each audio file within the data set, in json format. Manifest file serves as an interface informing our system of where and what to read the speech samples.
More help for arguments:
```
python datasets/librispeech/librispeech.py --help
```
### Preparing for Training
```
python compute_mean_std.py
```
`python compute_mean_std.py` computes mean and stdandard deviation for audio features, and save them to a file with a default name `./mean_std.npz`. This file will be used in both training and inferencing.
More help for arguments:
```
python librispeech.py --help
python compute_mean_std.py --help
```
### Traininig
### Training
For GPU Training:
```
CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --trainer_count 4 --train_manifest_path ./data/manifest.libri.train-all
CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --trainer_count 4
```
For CPU Training:
```
python train.py --trainer_count 8 --use_gpu False -- train_manifest_path ./data/manifest.libri.train-all
python train.py --trainer_count 8 --use_gpu False
```
More help for arguments:
......@@ -55,7 +69,7 @@ python train.py --help
### Inferencing
```
python infer.py
CUDA_VISIBLE_DEVICES=0 python infer.py
```
More help for arguments:
......
deep_speech_2/audio_data_utils.py 已删除 100644 → 0
浏览文件 @ c02c6502
"""
Providing basic audio data preprocessing pipeline, and offering
both instance-level and batch-level data reader interfaces.
"""
import paddle.v2 as paddle
import logging
import json
import random
import soundfile
import numpy as np
import os
RANDOM_SEED = 0
logger = logging.getLogger(__name__)
class DataGenerator(object):
"""
DataGenerator provides basic audio data preprocessing pipeline, and offers
both instance-level and batch-level data reader interfaces.
Normalized FFT are used as audio features here.
:param vocab_filepath: Vocabulary file path for indexing tokenized
transcriptions.
:type vocab_filepath: basestring
:param normalizer_manifest_path: Manifest filepath for collecting feature
normalization statistics, e.g. mean, std.
:type normalizer_manifest_path: basestring
:param normalizer_num_samples: Number of instances sampled for collecting
feature normalization statistics.
Default is 100.
:type normalizer_num_samples: int
:param max_duration: Audio clips with duration (in seconds) greater than
this will be discarded. Default is 20.0.
:type max_duration: float
:param min_duration: Audio clips with duration (in seconds) smaller than
this will be discarded. Default is 0.0.
:type min_duration: float
:param stride_ms: Striding size (in milliseconds) for generating frames.
Default is 10.0.
:type stride_ms: float
:param window_ms: Window size (in milliseconds) for frames. Default is 20.0.
:type window_ms: float
:param max_frequency: Maximun frequency for FFT features. FFT features of
frequency larger than this will be discarded.
If set None, all features will be kept.
Default is None.
:type max_frequency: float
"""
def __init__(self,
vocab_filepath,
normalizer_manifest_path,
normalizer_num_samples=100,
max_duration=20.0,
min_duration=0.0,
stride_ms=10.0,
window_ms=20.0,
max_frequency=None):
self.__max_duration__ = max_duration
self.__min_duration__ = min_duration
self.__stride_ms__ = stride_ms
self.__window_ms__ = window_ms
self.__max_frequency__ = max_frequency
self.__random__ = random.Random(RANDOM_SEED)
# load vocabulary (dictionary)
self.__vocab_dict__, self.__vocab_list__ = \
self.__load_vocabulary_from_file__(vocab_filepath)
# collect normalizer statistics
self.__mean__, self.__std__ = self.__collect_normalizer_statistics__(
manifest_path=normalizer_manifest_path,
num_samples=normalizer_num_samples)
def __audio_featurize__(self, audio_filename):
"""
Preprocess audio data, including feature extraction, normalization etc..
"""
features = self.__audio_basic_featurize__(audio_filename)
return self.__normalize__(features)
def __text_featurize__(self, text):
"""
Preprocess text data, including tokenizing and token indexing etc..
"""
return self.__convert_text_to_char_index__(
text=text, vocabulary=self.__vocab_dict__)
def __audio_basic_featurize__(self, audio_filename):
"""
Compute basic (without normalization etc.) features for audio data.
"""
return self.__spectrogram_from_file__(
filename=audio_filename,
stride_ms=self.__stride_ms__,
window_ms=self.__window_ms__,
max_freq=self.__max_frequency__)
def __collect_normalizer_statistics__(self, manifest_path, num_samples=100):
"""
Compute feature normalization statistics, i.e. mean and stddev.
"""
# read manifest
manifest = self.__read_manifest__(
manifest_path=manifest_path,
max_duration=self.__max_duration__,
min_duration=self.__min_duration__)
# sample for statistics
sampled_manifest = self.__random__.sample(manifest, num_samples)
# extract spectrogram feature
features = []
for instance in sampled_manifest:
spectrogram = self.__audio_basic_featurize__(
instance["audio_filepath"])
features.append(spectrogram)
features = np.hstack(features)
mean = np.mean(features, axis=1).reshape([-1, 1])
std = np.std(features, axis=1).reshape([-1, 1])
return mean, std
def __normalize__(self, features, eps=1e-14):
"""
Normalize features to be of zero mean and unit stddev.
"""
return (features - self.__mean__) / (self.__std__ + eps)
def __spectrogram_from_file__(self,
filename,
stride_ms=10.0,
window_ms=20.0,
max_freq=None,
eps=1e-14):
"""
Laod audio data and calculate the log of spectrogram by FFT.
Refer to utils.py in https://github.com/baidu-research/ba-dls-deepspeech
"""
audio, sample_rate = soundfile.read(filename)
if audio.ndim >= 2:
audio = np.mean(audio, 1)
if max_freq is None:
max_freq = sample_rate / 2
if max_freq > sample_rate / 2:
raise ValueError("max_freq must be greater than half of "
"sample rate.")
if stride_ms > window_ms:
raise ValueError("Stride size must not be greater than "
"window size.")
stride_size = int(0.001 * sample_rate * stride_ms)
window_size = int(0.001 * sample_rate * window_ms)
spectrogram, freqs = self.__extract_spectrogram__(
audio,
window_size=window_size,
stride_size=stride_size,
sample_rate=sample_rate)
ind = np.where(freqs <= max_freq)[0][-1] + 1
return np.log(spectrogram[:ind, :] + eps)
def __extract_spectrogram__(self, samples, window_size, stride_size,
sample_rate):
"""
Compute the spectrogram by FFT for a discrete real signal.
Refer to utils.py in https://github.com/baidu-research/ba-dls-deepspeech
"""
# extract strided windows
truncate_size = (len(samples) - window_size) % stride_size
samples = samples[:len(samples) - truncate_size]
nshape = (window_size, (len(samples) - window_size) // stride_size + 1)
nstrides = (samples.strides[0], samples.strides[0] * stride_size)
windows = np.lib.stride_tricks.as_strided(
samples, shape=nshape, strides=nstrides)
assert np.all(
windows[:, 1] == samples[stride_size:(stride_size + window_size)])
# window weighting, squared Fast Fourier Transform (fft), scaling
weighting = np.hanning(window_size)[:, None]
fft = np.fft.rfft(windows * weighting, axis=0)
fft = np.absolute(fft)**2
scale = np.sum(weighting**2) * sample_rate
fft[1:-1, :] *= (2.0 / scale)
fft[(0, -1), :] /= scale
# prepare fft frequency list
freqs = float(sample_rate) / window_size * np.arange(fft.shape[0])
return fft, freqs
def __load_vocabulary_from_file__(self, vocabulary_path):
"""
Load vocabulary from file.
"""
if not os.path.exists(vocabulary_path):
raise ValueError("Vocabulary file %s not found.", vocabulary_path)
vocab_lines = []
with open(vocabulary_path, 'r') as file:
vocab_lines.extend(file.readlines())
vocab_list = [line[:-1] for line in vocab_lines]
vocab_dict = dict(
[(token, id) for (id, token) in enumerate(vocab_list)])
return vocab_dict, vocab_list
def __convert_text_to_char_index__(self, text, vocabulary):
"""
Convert text string to a list of character index integers.
"""
return [vocabulary[w] for w in text]
def __read_manifest__(self, manifest_path, max_duration, min_duration):
"""
Load and parse manifest file.
"""
manifest = []
for json_line in open(manifest_path):
try:
json_data = json.loads(json_line)
except Exception as e:
raise ValueError("Error reading manifest: %s" % str(e))
if (json_data["duration"] <= max_duration and
json_data["duration"] >= min_duration):
manifest.append(json_data)
return manifest
def __padding_batch__(self, batch, padding_to=-1, flatten=False):
"""
Padding audio part of features (only in the time axis -- column axis)
with zeros, to make each instance in the batch share the same
audio feature shape.
If `padding_to` is set -1, the maximun column numbers in the batch will
be used as the target size. Otherwise, `padding_to` will be the target
size. Default is -1.
If `flatten` is set True, audio data will be flatten to be a 1-dim
ndarray. Default is False.
"""
new_batch = []
# get target shape
max_length = max([audio.shape[1] for audio, text in batch])
if padding_to != -1:
if padding_to < max_length:
raise ValueError("If padding_to is not -1, it should be greater"
" or equal to the original instance length.")
max_length = padding_to
# padding
for audio, text in batch:
padded_audio = np.zeros([audio.shape[0], max_length])
padded_audio[:, :audio.shape[1]] = audio
if flatten:
padded_audio = padded_audio.flatten()
new_batch.append((padded_audio, text))
return new_batch
def instance_reader_creator(self,
manifest_path,
sort_by_duration=True,
shuffle=False):
"""
Instance reader creator for audio data. Creat a callable function to
produce instances of data.
Instance: a tuple of a numpy ndarray of audio spectrogram and a list of
tokenized and indexed transcription text.
:param manifest_path: Filepath of manifest for audio clip files.
:type manifest_path: basestring
:param sort_by_duration: Sort the audio clips by duration if set True
(for SortaGrad).
:type sort_by_duration: bool
:param shuffle: Shuffle the audio clips if set True.
:type shuffle: bool
:return: Data reader function.
:rtype: callable
"""
if sort_by_duration and shuffle:
sort_by_duration = False
logger.warn("When shuffle set to true, "
"sort_by_duration is forced to set False.")
def reader():
# read manifest
manifest = self.__read_manifest__(
manifest_path=manifest_path,
max_duration=self.__max_duration__,
min_duration=self.__min_duration__)
# sort (by duration) or shuffle manifest
if sort_by_duration:
manifest.sort(key=lambda x: x["duration"])
if shuffle:
self.__random__.shuffle(manifest)
# extract spectrogram feature
for instance in manifest:
spectrogram = self.__audio_featurize__(
instance["audio_filepath"])
transcript = self.__text_featurize__(instance["text"])
yield (spectrogram, transcript)
return reader
def batch_reader_creator(self,
manifest_path,
batch_size,
padding_to=-1,
flatten=False,
sort_by_duration=True,
shuffle=False):
"""
Batch data reader creator for audio data. Creat a callable function to
produce batches of data.
Audio features will be padded with zeros to make each instance in the
batch to share the same audio feature shape.
:param manifest_path: Filepath of manifest for audio clip files.
:type manifest_path: basestring
:param batch_size: Instance number in a batch.
:type batch_size: int
:param padding_to: If set -1, the maximun column numbers in the batch
will be used as the target size for padding.
Otherwise, `padding_to` will be the target size.
Default is -1.
:type padding_to: int
:param flatten: If set True, audio data will be flatten to be a 1-dim
ndarray. Otherwise, 2-dim ndarray. Default is False.
:type flatten: bool
:param sort_by_duration: Sort the audio clips by duration if set True
(for SortaGrad).
:type sort_by_duration: bool
:param shuffle: Shuffle the audio clips if set True.
:type shuffle: bool
:return: Batch reader function, producing batches of data when called.
:rtype: callable
"""
def batch_reader():
instance_reader = self.instance_reader_creator(
manifest_path=manifest_path,
sort_by_duration=sort_by_duration,
shuffle=shuffle)
batch = []
for instance in instance_reader():
batch.append(instance)
if len(batch) == batch_size:
yield self.__padding_batch__(batch, padding_to, flatten)
batch = []
if len(batch) > 0:
yield self.__padding_batch__(batch, padding_to, flatten)
return batch_reader
def vocabulary_size(self):
"""
Get vocabulary size.
:return: Vocabulary size.
:rtype: int
"""
return len(self.__vocab_list__)
def vocabulary_dict(self):
"""
Get vocabulary in dict.
:return: Vocabulary in dict.
:rtype: dict
"""
return self.__vocab_dict__
def vocabulary_list(self):
"""
Get vocabulary in list.
:return: Vocabulary in list
:rtype: list
"""
return self.__vocab_list__
def data_name_feeding(self):
"""
Get feeddings (data field name and corresponding field id).
:return: Feeding dict.
:rtype: dict
"""
feeding = {
"audio_spectrogram": 0,
"transcript_text": 1,
}
return feeding
deep_speech_2/compute_mean_std.py 0 → 100644
浏览文件 @ c8b0ae8f
"""Compute mean and std for feature normalizer, and save to file."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
from data_utils.normalizer import FeatureNormalizer
from data_utils.augmentor.augmentation import AugmentationPipeline
from data_utils.featurizer.audio_featurizer import AudioFeaturizer
parser = argparse.ArgumentParser(
description='Computing mean and stddev for feature normalizer.')
parser.add_argument(
"--manifest_path",
default='datasets/manifest.train',
type=str,
help="Manifest path for computing normalizer's mean and stddev."
"(default: %(default)s)")
parser.add_argument(
"--num_samples",
default=2000,
type=int,
help="Number of samples for computing mean and stddev. "
"(default: %(default)s)")
parser.add_argument(
"--augmentation_config",
default='{}',
type=str,
help="Augmentation configuration in json-format. "
"(default: %(default)s)")
parser.add_argument(
"--output_file",
default='mean_std.npz',
type=str,
help="Filepath to write mean and std to (.npz)."
"(default: %(default)s)")
args = parser.parse_args()
def main():
augmentation_pipeline = AugmentationPipeline(args.augmentation_config)
audio_featurizer = AudioFeaturizer()
def augment_and_featurize(audio_segment):
augmentation_pipeline.transform_audio(audio_segment)
return audio_featurizer.featurize(audio_segment)
normalizer = FeatureNormalizer(
mean_std_filepath=None,
manifest_path=args.manifest_path,
featurize_func=augment_and_featurize,
num_samples=args.num_samples)
normalizer.write_to_file(args.output_file)
if __name__ == '__main__':
main()
deep_speech_2/data_utils/audio.py 0 → 100644
浏览文件 @ c8b0ae8f
"""Contains the audio segment class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import io
import soundfile
class AudioSegment(object):
"""Monaural audio segment abstraction.
:param samples: Audio samples [num_samples x num_channels].
:type samples: ndarray.float32
:param sample_rate: Audio sample rate.
:type sample_rate: int
:raises TypeError: If the sample data type is not float or int.
"""
def __init__(self, samples, sample_rate):
"""Create audio segment from samples.
Samples are convert float32 internally, with int scaled to [-1, 1].
"""
self._samples = self._convert_samples_to_float32(samples)
self._sample_rate = sample_rate
if self._samples.ndim >= 2:
self._samples = np.mean(self._samples, 1)
def __eq__(self, other):
"""Return whether two objects are equal."""
if type(other) is not type(self):
return False
if self._sample_rate != other._sample_rate:
return False
if self._samples.shape != other._samples.shape:
return False
if np.any(self.samples != other._samples):
return False
return True
def __ne__(self, other):
"""Return whether two objects are unequal."""
return not self.__eq__(other)
def __str__(self):
"""Return human-readable representation of segment."""
return ("%s: num_samples=%d, sample_rate=%d, duration=%.2fsec, "
"rms=%.2fdB" % (type(self), self.num_samples, self.sample_rate,
self.duration, self.rms_db))
@classmethod
def from_file(cls, file):
"""Create audio segment from audio file.
:param filepath: Filepath or file object to audio file.
:type filepath: basestring|file
:return: Audio segment instance.
:rtype: AudioSegment
"""
samples, sample_rate = soundfile.read(file, dtype='float32')
return cls(samples, sample_rate)
@classmethod
def from_bytes(cls, bytes):
"""Create audio segment from a byte string containing audio samples.
:param bytes: Byte string containing audio samples.
:type bytes: str
:return: Audio segment instance.
:rtype: AudioSegment
"""
samples, sample_rate = soundfile.read(
io.BytesIO(bytes), dtype='float32')
return cls(samples, sample_rate)
def to_wav_file(self, filepath, dtype='float32'):
"""Save audio segment to disk as wav file.
:param filepath: WAV filepath or file object to save the
audio segment.
:type filepath: basestring|file
:param dtype: Subtype for audio file. Options: 'int16', 'int32',
'float32', 'float64'. Default is 'float32'.
:type dtype: str
:raises TypeError: If dtype is not supported.
"""
samples = self._convert_samples_from_float32(self._samples, dtype)
subtype_map = {
'int16': 'PCM_16',
'int32': 'PCM_32',
'float32': 'FLOAT',
'float64': 'DOUBLE'
}
soundfile.write(
filepath,
samples,
self._sample_rate,
format='WAV',
subtype=subtype_map[dtype])
def to_bytes(self, dtype='float32'):
"""Create a byte string containing the audio content.
:param dtype: Data type for export samples. Options: 'int16', 'int32',
'float32', 'float64'. Default is 'float32'.
:type dtype: str
:return: Byte string containing audio content.
:rtype: str
"""
samples = self._convert_samples_from_float32(self._samples, dtype)
return samples.tostring()
def apply_gain(self, gain):
"""Apply gain in decibels to samples.
Note that this is an in-place transformation.
:param gain: Gain in decibels to apply to samples.
:type gain: float
"""
self._samples *= 10.**(gain / 20.)
def change_speed(self, speed_rate):
"""Change the audio speed by linear interpolation.
Note that this is an in-place transformation.
:param speed_rate: Rate of speed change:
speed_rate > 1.0, speed up the audio;
speed_rate = 1.0, unchanged;
speed_rate < 1.0, slow down the audio;
speed_rate <= 0.0, not allowed, raise ValueError.
:type speed_rate: float
:raises ValueError: If speed_rate <= 0.0.
"""
if speed_rate <= 0:
raise ValueError("speed_rate should be greater than zero.")
old_length = self._samples.shape[0]
new_length = int(old_length / speed_rate)
old_indices = np.arange(old_length)
new_indices = np.linspace(start=0, stop=old_length, num=new_length)
self._samples = np.interp(new_indices, old_indices, self._samples)
def normalize(self, target_sample_rate):
raise NotImplementedError()
def resample(self, target_sample_rate):
raise NotImplementedError()
def pad_silence(self, duration, sides='both'):
raise NotImplementedError()
def subsegment(self, start_sec=None, end_sec=None):
raise NotImplementedError()
def convolve(self, filter, allow_resample=False):
raise NotImplementedError()
def convolve_and_normalize(self, filter, allow_resample=False):
raise NotImplementedError()
@property
def samples(self):
"""Return audio samples.
:return: Audio samples.
:rtype: ndarray
"""
return self._samples.copy()
@property
def sample_rate(self):
"""Return audio sample rate.
:return: Audio sample rate.
:rtype: int
"""
return self._sample_rate
@property
def num_samples(self):
"""Return number of samples.
:return: Number of samples.
:rtype: int
"""
return self._samples.shape(0)
@property
def duration(self):
"""Return audio duration.
:return: Audio duration in seconds.
:rtype: float
"""
return self._samples.shape[0] / float(self._sample_rate)
@property
def rms_db(self):
"""Return root mean square energy of the audio in decibels.
:return: Root mean square energy in decibels.
:rtype: float
"""
# square root => multiply by 10 instead of 20 for dBs
mean_square = np.mean(self._samples**2)
return 10 * np.log10(mean_square)
def _convert_samples_to_float32(self, samples):
"""Convert sample type to float32.
Audio sample type is usually integer or float-point.
Integers will be scaled to [-1, 1] in float32.
"""
float32_samples = samples.astype('float32')
if samples.dtype in np.sctypes['int']:
bits = np.iinfo(samples.dtype).bits
float32_samples *= (1. / 2**(bits - 1))
elif samples.dtype in np.sctypes['float']:
pass
else:
raise TypeError("Unsupported sample type: %s." % samples.dtype)
return float32_samples
def _convert_samples_from_float32(self, samples, dtype):
"""Convert sample type from float32 to dtype.
Audio sample type is usually integer or float-point. For integer
type, float32 will be rescaled from [-1, 1] to the maximum range
supported by the integer type.
This is for writing a audio file.
"""
dtype = np.dtype(dtype)
output_samples = samples.copy()
if dtype in np.sctypes['int']:
bits = np.iinfo(dtype).bits
output_samples *= (2**(bits - 1) / 1.)
min_val = np.iinfo(dtype).min
max_val = np.iinfo(dtype).max
output_samples[output_samples > max_val] = max_val
output_samples[output_samples < min_val] = min_val
elif samples.dtype in np.sctypes['float']:
min_val = np.finfo(dtype).min
max_val = np.finfo(dtype).max
output_samples[output_samples > max_val] = max_val
output_samples[output_samples < min_val] = min_val
else:
raise TypeError("Unsupported sample type: %s." % samples.dtype)
return output_samples.astype(dtype)
deep_speech_2/data_utils/augmentor/augmentation.py 0 → 100644
浏览文件 @ c8b0ae8f
"""Contains the data augmentation pipeline."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import random
from data_utils.augmentor.volume_perturb import VolumePerturbAugmentor
class AugmentationPipeline(object):
"""Build a pre-processing pipeline with various augmentation models.Such a
data augmentation pipeline is oftern leveraged to augment the training
samples to make the model invariant to certain types of perturbations in the
real world, improving model's generalization ability.
The pipeline is built according the the augmentation configuration in json
string, e.g.
.. code-block::
'[{"type": "volume",
"params": {"min_gain_dBFS": -15,
"max_gain_dBFS": 15},
"prob": 0.5},
{"type": "speed",
"params": {"min_speed_rate": 0.8,
"max_speed_rate": 1.2},
"prob": 0.5}
]'
This augmentation configuration inserts two augmentation models
into the pipeline, with one is VolumePerturbAugmentor and the other
SpeedPerturbAugmentor. "prob" indicates the probability of the current
augmentor to take effect.
:param augmentation_config: Augmentation configuration in json string.
:type augmentation_config: str
:param random_seed: Random seed.
:type random_seed: int
:raises ValueError: If the augmentation json config is in incorrect format".
"""
def __init__(self, augmentation_config, random_seed=0):
self._rng = random.Random(random_seed)
self._augmentors, self._rates = self._parse_pipeline_from(
augmentation_config)
def transform_audio(self, audio_segment):
"""Run the pre-processing pipeline for data augmentation.
Note that this is an in-place transformation.
:param audio_segment: Audio segment to process.
:type audio_segment: AudioSegmenet|SpeechSegment
"""
for augmentor, rate in zip(self._augmentors, self._rates):
if self._rng.uniform(0., 1.) <= rate:
augmentor.transform_audio(audio_segment)
def _parse_pipeline_from(self, config_json):
"""Parse the config json to build a augmentation pipelien."""
try:
configs = json.loads(config_json)
augmentors = [
self._get_augmentor(config["type"], config["params"])
for config in configs
]
rates = [config["prob"] for config in configs]
except Exception as e:
raise ValueError("Failed to parse the augmentation config json: "
"%s" % str(e))
return augmentors, rates
def _get_augmentor(self, augmentor_type, params):
"""Return an augmentation model by the type name, and pass in params."""
if augmentor_type == "volume":
return VolumePerturbAugmentor(self._rng, **params)
else:
raise ValueError("Unknown augmentor type [%s]." % augmentor_type)
deep_speech_2/data_utils/augmentor/base.py 0 → 100644
浏览文件 @ c8b0ae8f
"""Contains the abstract base class for augmentation models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from abc import ABCMeta, abstractmethod
class AugmentorBase(object):
"""Abstract base class for augmentation model (augmentor) class.
All augmentor classes should inherit from this class, and implement the
following abstract methods.
"""
__metaclass__ = ABCMeta
@abstractmethod
def __init__(self):
pass
@abstractmethod
def transform_audio(self, audio_segment):
"""Adds various effects to the input audio segment. Such effects
will augment the training data to make the model invariant to certain
types of perturbations in the real world, improving model's
generalization ability.
Note that this is an in-place transformation.
:param audio_segment: Audio segment to add effects to.
:type audio_segment: AudioSegmenet|SpeechSegment
"""
pass
deep_speech_2/data_utils/augmentor/volume_perturb.py 0 → 100644
浏览文件 @ c8b0ae8f
"""Contains the volume perturb augmentation model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from data_utils.augmentor.base import AugmentorBase
class VolumePerturbAugmentor(AugmentorBase):
"""Augmentation model for adding random volume perturbation.
This is used for multi-loudness training of PCEN. See
https://arxiv.org/pdf/1607.05666v1.pdf
for more details.
:param rng: Random generator object.
:type rng: random.Random
:param min_gain_dBFS: Minimal gain in dBFS.
:type min_gain_dBFS: float
:param max_gain_dBFS: Maximal gain in dBFS.
:type max_gain_dBFS: float
"""
def __init__(self, rng, min_gain_dBFS, max_gain_dBFS):
self._min_gain_dBFS = min_gain_dBFS
self._max_gain_dBFS = max_gain_dBFS
self._rng = rng
def transform_audio(self, audio_segment):
"""Change audio loadness.
Note that this is an in-place transformation.
:param audio_segment: Audio segment to add effects to.
:type audio_segment: AudioSegmenet|SpeechSegment
"""
gain = self._rng.uniform(min_gain_dBFS, max_gain_dBFS)
audio_segment.apply_gain(gain)
deep_speech_2/data_utils/data.py 0 → 100644
浏览文件 @ c8b0ae8f
"""Contains data generator for orgnaizing various audio data preprocessing
pipeline and offering data reader interface of PaddlePaddle requirements.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import random
import numpy as np
import paddle.v2 as paddle
from data_utils import utils
from data_utils.augmentor.augmentation import AugmentationPipeline
from data_utils.featurizer.speech_featurizer import SpeechFeaturizer
from data_utils.speech import SpeechSegment
from data_utils.normalizer import FeatureNormalizer
class DataGenerator(object):
"""
DataGenerator provides basic audio data preprocessing pipeline, and offers
data reader interfaces of PaddlePaddle requirements.
:param vocab_filepath: Vocabulary filepath for indexing tokenized
transcripts.
:type vocab_filepath: basestring
:param mean_std_filepath: File containing the pre-computed mean and stddev.
:type mean_std_filepath: None|basestring
:param augmentation_config: Augmentation configuration in json string.
Details see AugmentationPipeline.__doc__.
:type augmentation_config: str
:param max_duration: Audio with duration (in seconds) greater than
this will be discarded.
:type max_duration: float
:param min_duration: Audio with duration (in seconds) smaller than
this will be discarded.
:type min_duration: float
:param stride_ms: Striding size (in milliseconds) for generating frames.
:type stride_ms: float
:param window_ms: Window size (in milliseconds) for generating frames.
:type window_ms: float
:param max_freq: Used when specgram_type is 'linear', only FFT bins
corresponding to frequencies between [0, max_freq] are
returned.
:types max_freq: None|float
:param specgram_type: Specgram feature type. Options: 'linear'.
:type specgram_type: str
:param random_seed: Random seed.
:type random_seed: int
"""
def __init__(self,
vocab_filepath,
mean_std_filepath,
augmentation_config='{}',
max_duration=float('inf'),
min_duration=0.0,
stride_ms=10.0,
window_ms=20.0,
max_freq=None,
specgram_type='linear',
random_seed=0):
self._max_duration = max_duration
self._min_duration = min_duration
self._normalizer = FeatureNormalizer(mean_std_filepath)
self._augmentation_pipeline = AugmentationPipeline(
augmentation_config=augmentation_config, random_seed=random_seed)
self._speech_featurizer = SpeechFeaturizer(
vocab_filepath=vocab_filepath,
specgram_type=specgram_type,
stride_ms=stride_ms,
window_ms=window_ms,
max_freq=max_freq)
self._rng = random.Random(random_seed)
self._epoch = 0
def batch_reader_creator(self,
manifest_path,
batch_size,
min_batch_size=1,
padding_to=-1,
flatten=False,
sortagrad=False,
shuffle_method="batch_shuffle"):
"""
Batch data reader creator for audio data. Return a callable generator
function to produce batches of data.
Audio features within one batch will be padded with zeros to have the
same shape, or a user-defined shape.
:param manifest_path: Filepath of manifest for audio files.
:type manifest_path: basestring
:param batch_size: Number of instances in a batch.
:type batch_size: int
:param min_batch_size: Any batch with batch size smaller than this will
be discarded. (To be deprecated in the future.)
:type min_batch_size: int
:param padding_to: If set -1, the maximun shape in the batch
will be used as the target shape for padding.
Otherwise, `padding_to` will be the target shape.
:type padding_to: int
:param flatten: If set True, audio features will be flatten to 1darray.
:type flatten: bool
:param sortagrad: If set True, sort the instances by audio duration
in the first epoch for speed up training.
:type sortagrad: bool
:param shuffle_method: Shuffle method. Options:
'' or None: no shuffle.
'instance_shuffle': instance-wise shuffle.
'batch_shuffle': similarly-sized instances are
put into batches, and then
batch-wise shuffle the batches.
For more details, please see
``_batch_shuffle.__doc__``.
'batch_shuffle_clipped': 'batch_shuffle' with
head shift and tail
clipping. For more
details, please see
``_batch_shuffle``.
If sortagrad is True, shuffle is disabled
for the first epoch.
:type shuffle_method: None|str
:return: Batch reader function, producing batches of data when called.
:rtype: callable
"""
def batch_reader():
# read manifest
manifest = utils.read_manifest(
manifest_path=manifest_path,
max_duration=self._max_duration,
min_duration=self._min_duration)
# sort (by duration) or batch-wise shuffle the manifest
if self._epoch == 0 and sortagrad:
manifest.sort(key=lambda x: x["duration"])
else:
if shuffle_method == "batch_shuffle":
manifest = self._batch_shuffle(
manifest, batch_size, clipped=False)
elif shuffle_method == "batch_shuffle_clipped":
manifest = self._batch_shuffle(
manifest, batch_size, clipped=True)
elif shuffle_method == "instance_shuffle":
self._rng.shuffle(manifest)
elif not shuffle_method:
pass
else:
raise ValueError("Unknown shuffle method %s." %
shuffle_method)
# prepare batches
instance_reader = self._instance_reader_creator(manifest)
batch = []
for instance in instance_reader():
batch.append(instance)
if len(batch) == batch_size:
yield self._padding_batch(batch, padding_to, flatten)
batch = []
if len(batch) >= min_batch_size:
yield self._padding_batch(batch, padding_to, flatten)
self._epoch += 1
return batch_reader
@property
def feeding(self):
"""Returns data reader's feeding dict.
:return: Data feeding dict.
:rtype: dict
"""
return {"audio_spectrogram": 0, "transcript_text": 1}
@property
def vocab_size(self):
"""Return the vocabulary size.
:return: Vocabulary size.
:rtype: int
"""
return self._speech_featurizer.vocab_size
@property
def vocab_list(self):
"""Return the vocabulary in list.
:return: Vocabulary in list.
:rtype: list
"""
return self._speech_featurizer.vocab_list
def _process_utterance(self, filename, transcript):
"""Load, augment, featurize and normalize for speech data."""
speech_segment = SpeechSegment.from_file(filename, transcript)
self._augmentation_pipeline.transform_audio(speech_segment)
specgram, text_ids = self._speech_featurizer.featurize(speech_segment)
specgram = self._normalizer.apply(specgram)
return specgram, text_ids
def _instance_reader_creator(self, manifest):
"""
Instance reader creator. Create a callable function to produce
instances of data.
Instance: a tuple of ndarray of audio spectrogram and a list of
token indices for transcript.
"""
def reader():
for instance in manifest:
yield self._process_utterance(instance["audio_filepath"],
instance["text"])
return reader
def _padding_batch(self, batch, padding_to=-1, flatten=False):
"""
Padding audio features with zeros to make them have the same shape (or
a user-defined shape) within one bach.
If ``padding_to`` is -1, the maximun shape in the batch will be used
as the target shape for padding. Otherwise, `padding_to` will be the
target shape (only refers to the second axis).
If `flatten` is True, features will be flatten to 1darray.
"""
new_batch = []
# get target shape
max_length = max([audio.shape[1] for audio, text in batch])
if padding_to != -1:
if padding_to < max_length:
raise ValueError("If padding_to is not -1, it should be larger "
"than any instance's shape in the batch")
max_length = padding_to
# padding
for audio, text in batch:
padded_audio = np.zeros([audio.shape[0], max_length])
padded_audio[:, :audio.shape[1]] = audio
if flatten:
padded_audio = padded_audio.flatten()
new_batch.append((padded_audio, text))
return new_batch
def _batch_shuffle(self, manifest, batch_size, clipped=False):
"""Put similarly-sized instances into minibatches for better efficiency
and make a batch-wise shuffle.
1. Sort the audio clips by duration.
2. Generate a random number `k`, k in [0, batch_size).
3. Randomly shift `k` instances in order to create different batches
for different epochs. Create minibatches.
4. Shuffle the minibatches.
:param manifest: Manifest contents. List of dict.
:type manifest: list
:param batch_size: Batch size. This size is also used for generate
a random number for batch shuffle.
:type batch_size: int
:param clipped: Whether to clip the heading (small shift) and trailing
(incomplete batch) instances.
:type clipped: bool
:return: Batch shuffled mainifest.
:rtype: list
"""
manifest.sort(key=lambda x: x["duration"])
shift_len = self._rng.randint(0, batch_size - 1)
batch_manifest = zip(*[iter(manifest[shift_len:])] * batch_size)
self._rng.shuffle(batch_manifest)
batch_manifest = list(sum(batch_manifest, ()))
if not clipped:
res_len = len(manifest) - shift_len - len(batch_manifest)
batch_manifest.extend(manifest[-res_len:])
batch_manifest.extend(manifest[0:shift_len])
return batch_manifest
deep_speech_2/data_utils/featurizer/audio_featurizer.py 0 → 100644
浏览文件 @ c8b0ae8f
"""Contains the audio featurizer class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from data_utils import utils
from data_utils.audio import AudioSegment
class AudioFeaturizer(object):
"""Audio featurizer, for extracting features from audio contents of
AudioSegment or SpeechSegment.
Currently, it only supports feature type of linear spectrogram.
:param specgram_type: Specgram feature type. Options: 'linear'.
:type specgram_type: str
:param stride_ms: Striding size (in milliseconds) for generating frames.
:type stride_ms: float
:param window_ms: Window size (in milliseconds) for generating frames.
:type window_ms: float
:param max_freq: Used when specgram_type is 'linear', only FFT bins
corresponding to frequencies between [0, max_freq] are
returned.
:types max_freq: None|float
"""
def __init__(self,
specgram_type='linear',
stride_ms=10.0,
window_ms=20.0,
max_freq=None):
self._specgram_type = specgram_type
self._stride_ms = stride_ms
self._window_ms = window_ms
self._max_freq = max_freq
def featurize(self, audio_segment):
"""Extract audio features from AudioSegment or SpeechSegment.
:param audio_segment: Audio/speech segment to extract features from.
:type audio_segment: AudioSegment|SpeechSegment
:return: Spectrogram audio feature in 2darray.
:rtype: ndarray
"""
return self._compute_specgram(audio_segment.samples,
audio_segment.sample_rate)
def _compute_specgram(self, samples, sample_rate):
"""Extract various audio features."""
if self._specgram_type == 'linear':
return self._compute_linear_specgram(
samples, sample_rate, self._stride_ms, self._window_ms,
self._max_freq)
else:
raise ValueError("Unknown specgram_type %s. "
"Supported values: linear." % self._specgram_type)
def _compute_linear_specgram(self,
samples,
sample_rate,
stride_ms=10.0,
window_ms=20.0,
max_freq=None,
eps=1e-14):
"""Compute the linear spectrogram from FFT energy."""
if max_freq is None:
max_freq = sample_rate / 2
if max_freq > sample_rate / 2:
raise ValueError("max_freq must be greater than half of "
"sample rate.")
if stride_ms > window_ms:
raise ValueError("Stride size must not be greater than "
"window size.")
stride_size = int(0.001 * sample_rate * stride_ms)
window_size = int(0.001 * sample_rate * window_ms)
specgram, freqs = self._specgram_real(
samples,
window_size=window_size,
stride_size=stride_size,
sample_rate=sample_rate)
ind = np.where(freqs <= max_freq)[0][-1] + 1
return np.log(specgram[:ind, :] + eps)
def _specgram_real(self, samples, window_size, stride_size, sample_rate):
"""Compute the spectrogram for samples from a real signal."""
# extract strided windows
truncate_size = (len(samples) - window_size) % stride_size
samples = samples[:len(samples) - truncate_size]
nshape = (window_size, (len(samples) - window_size) // stride_size + 1)
nstrides = (samples.strides[0], samples.strides[0] * stride_size)
windows = np.lib.stride_tricks.as_strided(
samples, shape=nshape, strides=nstrides)
assert np.all(
windows[:, 1] == samples[stride_size:(stride_size + window_size)])
# window weighting, squared Fast Fourier Transform (fft), scaling
weighting = np.hanning(window_size)[:, None]
fft = np.fft.rfft(windows * weighting, axis=0)
fft = np.absolute(fft)**2
scale = np.sum(weighting**2) * sample_rate
fft[1:-1, :] *= (2.0 / scale)
fft[(0, -1), :] /= scale
# prepare fft frequency list
freqs = float(sample_rate) / window_size * np.arange(fft.shape[0])
return fft, freqs
deep_speech_2/data_utils/featurizer/speech_featurizer.py 0 → 100644
浏览文件 @ c8b0ae8f
"""Contains the speech featurizer class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from data_utils.featurizer.audio_featurizer import AudioFeaturizer
from data_utils.featurizer.text_featurizer import TextFeaturizer
class SpeechFeaturizer(object):
"""Speech featurizer, for extracting features from both audio and transcript
contents of SpeechSegment.
Currently, for audio parts, it only supports feature type of linear
spectrogram; for transcript parts, it only supports char-level tokenizing
and conversion into a list of token indices. Note that the token indexing
order follows the given vocabulary file.
:param vocab_filepath: Filepath to load vocabulary for token indices
conversion.
:type specgram_type: basestring
:param specgram_type: Specgram feature type. Options: 'linear'.
:type specgram_type: str
:param stride_ms: Striding size (in milliseconds) for generating frames.
:type stride_ms: float
:param window_ms: Window size (in milliseconds) for generating frames.
:type window_ms: float
:param max_freq: Used when specgram_type is 'linear', only FFT bins
corresponding to frequencies between [0, max_freq] are
returned.
:types max_freq: None|float
"""
def __init__(self,
vocab_filepath,
specgram_type='linear',
stride_ms=10.0,
window_ms=20.0,
max_freq=None):
self._audio_featurizer = AudioFeaturizer(specgram_type, stride_ms,
window_ms, max_freq)
self._text_featurizer = TextFeaturizer(vocab_filepath)
def featurize(self, speech_segment):
"""Extract features for speech segment.
1. For audio parts, extract the audio features.
2. For transcript parts, convert text string to a list of token indices
in char-level.
:param audio_segment: Speech segment to extract features from.
:type audio_segment: SpeechSegment
:return: A tuple of 1) spectrogram audio feature in 2darray, 2) list of
char-level token indices.
:rtype: tuple
"""
audio_feature = self._audio_featurizer.featurize(speech_segment)
text_ids = self._text_featurizer.featurize(speech_segment.transcript)
return audio_feature, text_ids
@property
def vocab_size(self):
"""Return the vocabulary size.
:return: Vocabulary size.
:rtype: int
"""
return self._text_featurizer.vocab_size
@property
def vocab_list(self):
"""Return the vocabulary in list.
:return: Vocabulary in list.
:rtype: list
"""
return self._text_featurizer.vocab_list
deep_speech_2/data_utils/featurizer/text_featurizer.py 0 → 100644
浏览文件 @ c8b0ae8f
"""Contains the text featurizer class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
class TextFeaturizer(object):
"""Text featurizer, for processing or extracting features from text.
Currently, it only supports char-level tokenizing and conversion into
a list of token indices. Note that the token indexing order follows the
given vocabulary file.
:param vocab_filepath: Filepath to load vocabulary for token indices
conversion.
:type specgram_type: basestring
"""
def __init__(self, vocab_filepath):
self._vocab_dict, self._vocab_list = self._load_vocabulary_from_file(
vocab_filepath)
def featurize(self, text):
"""Convert text string to a list of token indices in char-level.Note
that the token indexing order follows the given vocabulary file.
:param text: Text to process.
:type text: basestring
:return: List of char-level token indices.
:rtype: list
"""
tokens = self._char_tokenize(text)
return [self._vocab_dict[token] for token in tokens]
@property
def vocab_size(self):
"""Return the vocabulary size.
:return: Vocabulary size.
:rtype: int
"""
return len(self._vocab_list)
@property
def vocab_list(self):
"""Return the vocabulary in list.
:return: Vocabulary in list.
:rtype: list
"""
return self._vocab_list
def _char_tokenize(self, text):
"""Character tokenizer."""
return list(text.strip())
def _load_vocabulary_from_file(self, vocab_filepath):
"""Load vocabulary from file."""
vocab_lines = []
with open(vocab_filepath, 'r') as file:
vocab_lines.extend(file.readlines())
vocab_list = [line[:-1] for line in vocab_lines]
vocab_dict = dict(
[(token, id) for (id, token) in enumerate(vocab_list)])
return vocab_dict, vocab_list
deep_speech_2/data_utils/normalizer.py 0 → 100644
浏览文件 @ c8b0ae8f
"""Contains feature normalizers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import random
import data_utils.utils as utils
from data_utils.audio import AudioSegment
class FeatureNormalizer(object):
"""Feature normalizer. Normalize features to be of zero mean and unit
stddev.
if mean_std_filepath is provided (not None), the normalizer will directly
initilize from the file. Otherwise, both manifest_path and featurize_func
should be given for on-the-fly mean and stddev computing.
:param mean_std_filepath: File containing the pre-computed mean and stddev.
:type mean_std_filepath: None|basestring
:param manifest_path: Manifest of instances for computing mean and stddev.
:type meanifest_path: None|basestring
:param featurize_func: Function to extract features. It should be callable
with ``featurize_func(audio_segment)``.
:type featurize_func: None|callable
:param num_samples: Number of random samples for computing mean and stddev.
:type num_samples: int
:param random_seed: Random seed for sampling instances.
:type random_seed: int
:raises ValueError: If both mean_std_filepath and manifest_path
(or both mean_std_filepath and featurize_func) are None.
"""
def __init__(self,
mean_std_filepath,
manifest_path=None,
featurize_func=None,
num_samples=500,
random_seed=0):
if not mean_std_filepath:
if not (manifest_path and featurize_func):
raise ValueError("If mean_std_filepath is None, meanifest_path "
"and featurize_func should not be None.")
self._rng = random.Random(random_seed)
self._compute_mean_std(manifest_path, featurize_func, num_samples)
else:
self._read_mean_std_from_file(mean_std_filepath)
def apply(self, features, eps=1e-14):
"""Normalize features to be of zero mean and unit stddev.
:param features: Input features to be normalized.
:type features: ndarray
:param eps: added to stddev to provide numerical stablibity.
:type eps: float
:return: Normalized features.
:rtype: ndarray
"""
return (features - self._mean) / (self._std + eps)
def write_to_file(self, filepath):
"""Write the mean and stddev to the file.
:param filepath: File to write mean and stddev.
:type filepath: basestring
"""
np.savez(filepath, mean=self._mean, std=self._std)
def _read_mean_std_from_file(self, filepath):
"""Load mean and std from file."""
npzfile = np.load(filepath)
self._mean = npzfile["mean"]
self._std = npzfile["std"]
def _compute_mean_std(self, manifest_path, featurize_func, num_samples):
"""Compute mean and std from randomly sampled instances."""
manifest = utils.read_manifest(manifest_path)
sampled_manifest = self._rng.sample(manifest, num_samples)
features = []
for instance in sampled_manifest:
features.append(
featurize_func(
AudioSegment.from_file(instance["audio_filepath"])))
features = np.hstack(features)
self._mean = np.mean(features, axis=1).reshape([-1, 1])
self._std = np.std(features, axis=1).reshape([-1, 1])
deep_speech_2/data_utils/speech.py 0 → 100644
浏览文件 @ c8b0ae8f
"""Contains the speech segment class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from data_utils.audio import AudioSegment
class SpeechSegment(AudioSegment):
"""Speech segment abstraction, a subclass of AudioSegment,
with an additional transcript.
:param samples: Audio samples [num_samples x num_channels].
:type samples: ndarray.float32
:param sample_rate: Audio sample rate.
:type sample_rate: int
:param transcript: Transcript text for the speech.
:type transript: basestring
:raises TypeError: If the sample data type is not float or int.
"""
def __init__(self, samples, sample_rate, transcript):
AudioSegment.__init__(self, samples, sample_rate)
self._transcript = transcript
def __eq__(self, other):
"""Return whether two objects are equal.
"""
if not AudioSegment.__eq__(self, other):
return False
if self._transcript != other._transcript:
return False
return True
def __ne__(self, other):
"""Return whether two objects are unequal."""
return not self.__eq__(other)
@classmethod
def from_file(cls, filepath, transcript):
"""Create speech segment from audio file and corresponding transcript.
:param filepath: Filepath or file object to audio file.
:type filepath: basestring|file
:param transcript: Transcript text for the speech.
:type transript: basestring
:return: Audio segment instance.
:rtype: AudioSegment
"""
audio = AudioSegment.from_file(filepath)
return cls(audio.samples, audio.sample_rate, transcript)
@classmethod
def from_bytes(cls, bytes, transcript):
"""Create speech segment from a byte string and corresponding
transcript.
:param bytes: Byte string containing audio samples.
:type bytes: str
:param transcript: Transcript text for the speech.
:type transript: basestring
:return: Audio segment instance.
:rtype: AudioSegment
"""
audio = AudioSegment.from_bytes(bytes)
return cls(audio.samples, audio.sample_rate, transcript)
@property
def transcript(self):
"""Return the transcript text.
:return: Transcript text for the speech.
:rtype: basestring
"""
return self._transcript
deep_speech_2/data_utils/utils.py 0 → 100644
浏览文件 @ c8b0ae8f
"""Contains data helper functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
def read_manifest(manifest_path, max_duration=float('inf'), min_duration=0.0):
"""Load and parse manifest file.
Instances with durations outside [min_duration, max_duration] will be
filtered out.
:param manifest_path: Manifest file to load and parse.
:type manifest_path: basestring
:param max_duration: Maximal duration in seconds for instance filter.
:type max_duration: float
:param min_duration: Minimal duration in seconds for instance filter.
:type min_duration: float
:return: Manifest parsing results. List of dict.
:rtype: list
:raises IOError: If failed to parse the manifest.
"""
manifest = []
for json_line in open(manifest_path):
try:
json_data = json.loads(json_line)
except Exception as e:
raise IOError("Error reading manifest: %s" % str(e))
if (json_data["duration"] <= max_duration and
json_data["duration"] >= min_duration):
manifest.append(json_data)
return manifest
deep_speech_2/data/librispeech.py → deep_speech_2/datasets/librispeech/librispeech.py
浏览文件 @ c8b0ae8f
"""
Download, unpack and create manifest json files for the Librespeech dataset.
"""Prepare Librispeech ASR datasets.
A manifest is a json file summarizing filelist in a data set, with each line
containing the meta data (i.e. audio filepath, transcription text, audio
duration) of each audio file in the data set.
Download, unpack and create manifest files.
Manifest file is a json-format file with each line containing the
meta data (i.e. audio filepath, transcript and audio duration)
of each audio file in the data set.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle.v2 as paddle
from paddle.v2.dataset.common import md5file
import distutils.util
import os
import wget
......@@ -15,6 +16,7 @@ import tarfile
import argparse
import soundfile
import json
from paddle.v2.dataset.common import md5file
DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset/speech')
......@@ -35,8 +37,7 @@ MD5_TRAIN_CLEAN_100 = "2a93770f6d5c6c964bc36631d331a522"
MD5_TRAIN_CLEAN_360 = "c0e676e450a7ff2f54aeade5171606fa"
MD5_TRAIN_OTHER_500 = "d1a0fd59409feb2c614ce4d30c387708"
parser = argparse.ArgumentParser(
description='Downloads and prepare LibriSpeech dataset.')
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--target_dir",
default=DATA_HOME + "/Libri",
......@@ -44,7 +45,7 @@ parser.add_argument(
help="Directory to save the dataset. (default: %(default)s)")
parser.add_argument(
"--manifest_prefix",
default="manifest.libri",
default="manifest",
type=str,
help="Filepath prefix for output manifests. (default: %(default)s)")
parser.add_argument(
......
deep_speech_2/datasets/run_all.sh 0 → 100644
浏览文件 @ c8b0ae8f
cd librispeech
python librispeech.py
if [ $? -ne 0 ]; then
echo "Prepare LibriSpeech failed. Terminated."
exit 1
fi
cd -
cat librispeech/manifest.train* | shuf > manifest.train
cat librispeech/manifest.dev-clean > manifest.dev
cat librispeech/manifest.test-clean > manifest.test
echo "All done."
deep_speech_2/decoder.py 100755 → 100644
浏览文件 @ c8b0ae8f
"""
CTC-like decoder utilitis.
"""
"""Contains various CTC decoder."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from itertools import groupby
......@@ -10,8 +11,7 @@ import multiprocessing
def ctc_best_path_decode(probs_seq, vocabulary):
"""
Best path decoding, also called argmax decoding or greedy decoding.
"""Best path decoding, also called argmax decoding or greedy decoding.
Path consisting of the most probable tokens are further post-processed to
remove consecutive repetitions and all blanks.
......@@ -40,8 +40,7 @@ def ctc_best_path_decode(probs_seq, vocabulary):
class Scorer(object):
"""
External defined scorer to evaluate a sentence in beam search
"""External defined scorer to evaluate a sentence in beam search
decoding, consisting of language model and word count.
:param alpha: Parameter associated with language model.
......@@ -73,8 +72,7 @@ class Scorer(object):
# execute evaluation
def __call__(self, sentence, log=False):
"""
Evaluation function
"""Evaluation function, gathering all the scores.
:param sentence: The input sentence for evalutation
:type sentence: basestring
......@@ -101,8 +99,7 @@ def ctc_beam_search_decoder(probs_seq,
cutoff_prob=1.0,
ext_scoring_func=None,
nproc=False):
'''
Beam search decoder for CTC-trained network, using beam search with width
'''Beam search decoder for CTC-trained network, using beam search with width
beam_size to find many paths to one label, return beam_size labels in
the descending order of probabilities. The implementation is based on Prefix
Beam Search(https://arxiv.org/abs/1408.2873), and the unclear part is
......@@ -129,7 +126,6 @@ def ctc_beam_search_decoder(probs_seq,
:type nproc: bool
:return: Decoding log probabilities and result sentences in descending order.
:rtype: list
'''
# dimension check
for prob_list in probs_seq:
......@@ -242,8 +238,7 @@ def ctc_beam_search_decoder_nproc(probs_split,
cutoff_prob=1.0,
ext_scoring_func=None,
num_processes=None):
'''
Beam search decoder using multiple processes.
'''Beam search decoder using multiple processes.
:param probs_seq: 3-D list with length batch_size, each element
is a 2-D list of probabilities can be used by
......
deep_speech_2/error_rate.py 0 → 100644
浏览文件 @ c8b0ae8f
# -*- coding: utf-8 -*-
"""This module provides functions to calculate error rate in different level.
e.g. wer for word-level, cer for char-level.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
def _levenshtein_distance(ref, hyp):
"""Levenshtein distance is a string metric for measuring the difference between
two sequences. Informally, the levenshtein disctance is defined as the minimum
number of single-character edits (substitutions, insertions or deletions)
required to change one word into the other. We can naturally extend the edits to
word level when calculate levenshtein disctance for two sentences.
"""
ref_len = len(ref)
hyp_len = len(hyp)
# special case
if ref == hyp:
return 0
if ref_len == 0:
return hyp_len
if hyp_len == 0:
return ref_len
distance = np.zeros((ref_len + 1, hyp_len + 1), dtype=np.int32)
# initialize distance matrix
for j in xrange(hyp_len + 1):
distance[0][j] = j
for i in xrange(ref_len + 1):
distance[i][0] = i
# calculate levenshtein distance
for i in xrange(1, ref_len + 1):
for j in xrange(1, hyp_len + 1):
if ref[i - 1] == hyp[j - 1]:
distance[i][j] = distance[i - 1][j - 1]
else:
s_num = distance[i - 1][j - 1] + 1
i_num = distance[i][j - 1] + 1
d_num = distance[i - 1][j] + 1
distance[i][j] = min(s_num, i_num, d_num)
return distance[ref_len][hyp_len]
def wer(reference, hypothesis, ignore_case=False, delimiter=' '):
"""Calculate word error rate (WER). WER compares reference text and
hypothesis text in word-level. WER is defined as:
.. math::
WER = (Sw + Dw + Iw) / Nw
where
.. code-block:: text
Sw is the number of words subsituted,
Dw is the number of words deleted,
Iw is the number of words inserted,
Nw is the number of words in the reference
We can use levenshtein distance to calculate WER. Please draw an attention that
empty items will be removed when splitting sentences by delimiter.
:param reference: The reference sentence.
:type reference: basestring
:param hypothesis: The hypothesis sentence.
:type hypothesis: basestring
:param ignore_case: Whether case-sensitive or not.
:type ignore_case: bool
:param delimiter: Delimiter of input sentences.
:type delimiter: char
:return: Word error rate.
:rtype: float
:raises ValueError: If the reference length is zero.
"""
if ignore_case == True:
reference = reference.lower()
hypothesis = hypothesis.lower()
ref_words = filter(None, reference.split(delimiter))
hyp_words = filter(None, hypothesis.split(delimiter))
if len(ref_words) == 0:
raise ValueError("Reference's word number should be greater than 0.")
edit_distance = _levenshtein_distance(ref_words, hyp_words)
wer = float(edit_distance) / len(ref_words)
return wer
def cer(reference, hypothesis, ignore_case=False):
"""Calculate charactor error rate (CER). CER compares reference text and
hypothesis text in char-level. CER is defined as:
.. math::
CER = (Sc + Dc + Ic) / Nc
where
.. code-block:: text
Sc is the number of characters substituted,
Dc is the number of characters deleted,
Ic is the number of characters inserted
Nc is the number of characters in the reference
We can use levenshtein distance to calculate CER. Chinese input should be
encoded to unicode. Please draw an attention that the leading and tailing
white space characters will be truncated and multiple consecutive white
space characters in a sentence will be replaced by one white space character.
:param reference: The reference sentence.
:type reference: basestring
:param hypothesis: The hypothesis sentence.
:type hypothesis: basestring
:param ignore_case: Whether case-sensitive or not.
:type ignore_case: bool
:return: Character error rate.
:rtype: float
:raises ValueError: If the reference length is zero.
"""
if ignore_case == True:
reference = reference.lower()
hypothesis = hypothesis.lower()
reference = ' '.join(filter(None, reference.split(' ')))
hypothesis = ' '.join(filter(None, hypothesis.split(' ')))
if len(reference) == 0:
raise ValueError("Length of reference should be greater than 0.")
edit_distance = _levenshtein_distance(reference, hypothesis)
cer = float(edit_distance) / len(reference)
return cer
deep_speech_2/evaluate.py
浏览文件 @ c8b0ae8f
"""
Evaluation for a simplifed version of Baidu DeepSpeech2 model.
"""
"""Evaluation for DeepSpeech2 model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle.v2 as paddle
import distutils.util
import argparse
import gzip
from audio_data_utils import DataGenerator
from data_utils.data import DataGenerator
from model import deep_speech2
from decoder import *
from error_rate import wer
parser = argparse.ArgumentParser(
description='Simplified version of DeepSpeech2 evaluation.')
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--num_samples",
default=100,
......@@ -38,6 +38,11 @@ parser.add_argument(
default=True,
type=distutils.util.strtobool,
help="Use gpu or not. (default: %(default)s)")
parser.add_argument(
"--mean_std_filepath",
default='mean_std.npz',
type=str,
help="Manifest path for normalizer. (default: %(default)s)")
parser.add_argument(
"--decode_method",
default='beam_search_nproc',
......@@ -46,7 +51,7 @@ parser.add_argument(
"beam_search or beam_search_nproc. (default: %(default)s)")
parser.add_argument(
"--language_model_path",
default="./data/1Billion.klm",
default="data/1Billion.klm",
type=str,
help="Path for language model. (default: %(default)s)")
parser.add_argument(
......@@ -87,41 +92,34 @@ parser.add_argument(
help="Model filepath. (default: %(default)s)")
parser.add_argument(
"--vocab_filepath",
default='data/eng_vocab.txt',
default='datasets/vocab/eng_vocab.txt',
type=str,
help="Vocabulary filepath. (default: %(default)s)")
args = parser.parse_args()
def evaluate():
"""
Evaluate on whole test data for DeepSpeech2.
"""
"""Evaluate on whole test data for DeepSpeech2."""
# initialize data generator
data_generator = DataGenerator(
vocab_filepath=args.vocab_filepath,
normalizer_manifest_path=args.normalizer_manifest_path,
normalizer_num_samples=200,
max_duration=20.0,
min_duration=0.0,
stride_ms=10,
window_ms=20)
mean_std_filepath=args.mean_std_filepath,
augmentation_config='{}')
# create network config
dict_size = data_generator.vocabulary_size()
vocab_list = data_generator.vocabulary_list()
# paddle.data_type.dense_array is used for variable batch input.
# The size 161 * 161 is only an placeholder value and the real shape
# of input batch data will be induced during training.
audio_data = paddle.layer.data(
name="audio_spectrogram",
height=161,
width=2000,
type=paddle.data_type.dense_vector(322000))
name="audio_spectrogram", type=paddle.data_type.dense_array(161 * 161))
text_data = paddle.layer.data(
name="transcript_text",
type=paddle.data_type.integer_value_sequence(dict_size))
type=paddle.data_type.integer_value_sequence(data_generator.vocab_size))
output_probs = deep_speech2(
audio_data=audio_data,
text_data=text_data,
dict_size=dict_size,
dict_size=data_generator.vocab_size,
num_conv_layers=args.num_conv_layers,
num_rnn_layers=args.num_rnn_layers,
rnn_size=args.rnn_layer_size,
......@@ -132,14 +130,11 @@ def evaluate():
gzip.open(args.model_filepath))
# prepare infer data
feeding = data_generator.data_name_feeding()
test_batch_reader = data_generator.batch_reader_creator(
batch_reader = data_generator.batch_reader_creator(
manifest_path=args.decode_manifest_path,
batch_size=args.num_samples,
padding_to=2000,
flatten=True,
sort_by_duration=False,
shuffle=False)
sortagrad=False,
shuffle_method=None)
# define inferer
inferer = paddle.inference.Inference(
......@@ -151,10 +146,10 @@ def evaluate():
ext_scorer = Scorer(args.alpha, args.beta, args.language_model_path)
wer_counter, wer_sum = 0, 0.0
for infer_data in test_batch_reader():
for infer_data in batch_reader():
# run inference
infer_results = inferer.infer(input=infer_data)
num_steps = len(infer_results) / len(infer_data)
num_steps = len(infer_results) // len(infer_data)
probs_split = [
infer_results[i * num_steps:(i + 1) * num_steps]
for i in xrange(0, len(infer_data))
......@@ -164,22 +159,26 @@ def evaluate():
# best path decode
if args.decode_method == "best_path":
for i, probs in enumerate(probs_split):
output_transcription = ctc_decode(
probs_seq=probs, vocabulary=vocab_list, method="best_path")
target_transcription = ''.join(
[vocab_list[index] for index in infer_data[i][1]])
output_transcription = ctc_best_path_decode(
probs_seq=probs, vocabulary=data_generator.vocab_list)
target_transcription = ''.join([
data_generator.vocab_list[index]
for index in infer_data[i][1]
])
wer_sum += wer(target_transcription, output_transcription)
wer_counter += 1
# beam search decode in single process
elif args.decode_method == "beam_search":
for i, probs in enumerate(probs_split):
target_transcription = ''.join(
[vocab_list[index] for index in infer_data[i][1]])
target_transcription = ''.join([
data_generator.vocab_list[index]
for index in infer_data[i][1]
])
beam_search_result = ctc_beam_search_decoder(
probs_seq=probs,
vocabulary=vocab_list,
vocabulary=data_generator.vocab_list,
beam_size=args.beam_size,
blank_id=len(vocab_list),
blank_id=len(data_generator.vocab_list),
ext_scoring_func=ext_scorer,
cutoff_prob=args.cutoff_prob, )
wer_sum += wer(target_transcription, beam_search_result[0][1])
......@@ -188,18 +187,21 @@ def evaluate():
elif args.decode_method == "beam_search_nproc":
beam_search_nproc_results = ctc_beam_search_decoder_nproc(
probs_split=probs_split,
vocabulary=vocab_list,
vocabulary=data_generator.vocab_list,
beam_size=args.beam_size,
blank_id=len(vocab_list),
blank_id=len(data_generator.vocab_list),
ext_scoring_func=ext_scorer,
cutoff_prob=args.cutoff_prob, )
for i, beam_search_result in enumerate(beam_search_nproc_results):
target_transcription = ''.join(
[vocab_list[index] for index in infer_data[i][1]])
target_transcription = ''.join([
data_generator.vocab_list[index]
for index in infer_data[i][1]
])
wer_sum += wer(target_transcription, beam_search_result[0][1])
wer_counter += 1
else:
raise ValueError("Decoding method [%s] is not supported." % method)
raise ValueError("Decoding method [%s] is not supported." %
decode_method)
print("Cur WER = %f" % (wer_sum / wer_counter))
print("Final WER = %f" % (wer_sum / wer_counter))
......
deep_speech_2/infer.py
浏览文件 @ c8b0ae8f
"""
Inference for a simplifed version of Baidu DeepSpeech2 model.
"""
"""Inferer for DeepSpeech2 model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle.v2 as paddle
import distutils.util
import argparse
import gzip
from audio_data_utils import DataGenerator
import distutils.util
import paddle.v2 as paddle
from data_utils.data import DataGenerator
from model import deep_speech2
from decoder import *
from error_rate import wer
import time
import utils
parser = argparse.ArgumentParser(
description='Simplified version of DeepSpeech2 inference.')
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--num_samples",
default=100,
......@@ -40,8 +40,8 @@ parser.add_argument(
type=distutils.util.strtobool,
help="Use gpu or not. (default: %(default)s)")
parser.add_argument(
"--normalizer_manifest_path",
default='data/manifest.libri.train-clean-100',
"--mean_std_filepath",
default='mean_std.npz',
type=str,
help="Manifest path for normalizer. (default: %(default)s)")
parser.add_argument(
......@@ -56,12 +56,12 @@ parser.add_argument(
help="Model filepath. (default: %(default)s)")
parser.add_argument(
"--vocab_filepath",
default='data/eng_vocab.txt',
default='datasets/vocab/eng_vocab.txt',
type=str,
help="Vocabulary filepath. (default: %(default)s)")
parser.add_argument(
"--decode_method",
default='beam_search_nproc',
default='best_path',
type=str,
help="Method for ctc decoding:"
" best_path,"
......@@ -79,7 +79,7 @@ parser.add_argument(
help="Number of output per sample in beam search. (default: %(default)d)")
parser.add_argument(
"--language_model_path",
default="./data/1Billion.klm",
default="data/1Billion.klm",
type=str,
help="Path for language model. (default: %(default)s)")
parser.add_argument(
......@@ -102,34 +102,26 @@ args = parser.parse_args()
def infer():
"""
Inference for DeepSpeech2.
"""
"""Inference for DeepSpeech2."""
# initialize data generator
data_generator = DataGenerator(
vocab_filepath=args.vocab_filepath,
normalizer_manifest_path=args.normalizer_manifest_path,
normalizer_num_samples=200,
max_duration=20.0,
min_duration=0.0,
stride_ms=10,
window_ms=20)
mean_std_filepath=args.mean_std_filepath,
augmentation_config='{}')
# create network config
dict_size = data_generator.vocabulary_size()
vocab_list = data_generator.vocabulary_list()
# paddle.data_type.dense_array is used for variable batch input.
# The size 161 * 161 is only an placeholder value and the real shape
# of input batch data will be induced during training.
audio_data = paddle.layer.data(
name="audio_spectrogram",
height=161,
width=2000,
type=paddle.data_type.dense_vector(322000))
name="audio_spectrogram", type=paddle.data_type.dense_array(161 * 161))
text_data = paddle.layer.data(
name="transcript_text",
type=paddle.data_type.integer_value_sequence(dict_size))
type=paddle.data_type.integer_value_sequence(data_generator.vocab_size))
output_probs = deep_speech2(
audio_data=audio_data,
text_data=text_data,
dict_size=dict_size,
dict_size=data_generator.vocab_size,
num_conv_layers=args.num_conv_layers,
num_rnn_layers=args.num_rnn_layers,
rnn_size=args.rnn_layer_size,
......@@ -140,23 +132,20 @@ def infer():
gzip.open(args.model_filepath))
# prepare infer data
feeding = data_generator.data_name_feeding()
test_batch_reader = data_generator.batch_reader_creator(
batch_reader = data_generator.batch_reader_creator(
manifest_path=args.decode_manifest_path,
batch_size=args.num_samples,
padding_to=2000,
flatten=True,
sort_by_duration=False,
shuffle=False)
infer_data = test_batch_reader().next()
sortagrad=False,
shuffle_method=None)
infer_data = batch_reader().next()
# run inference
infer_results = paddle.infer(
output_layer=output_probs, parameters=parameters, input=infer_data)
num_steps = len(infer_results) / len(infer_data)
num_steps = len(infer_results) // len(infer_data)
probs_split = [
infer_results[i * num_steps:(i + 1) * num_steps]
for i in xrange(0, len(infer_data))
for i in xrange(len(infer_data))
]
## decode and print
......@@ -165,10 +154,11 @@ def infer():
total_time = 0.0
if args.decode_method == "best_path":
for i, probs in enumerate(probs_split):
target_transcription = ''.join(
[vocab_list[index] for index in infer_data[i][1]])
target_transcription = ''.join([
data_generator.vocab_list[index] for index in infer_data[i][1]
])
best_path_transcription = ctc_best_path_decode(
probs_seq=probs, vocabulary=vocab_list)
probs_seq=probs, vocabulary=data_generator.vocab_list)
print("\nTarget Transcription: %s\nOutput Transcription: %s" %
(target_transcription, best_path_transcription))
wer_cur = wer(target_transcription, best_path_transcription)
......@@ -180,13 +170,14 @@ def infer():
elif args.decode_method == "beam_search":
ext_scorer = Scorer(args.alpha, args.beta, args.language_model_path)
for i, probs in enumerate(probs_split):
target_transcription = ''.join(
[vocab_list[index] for index in infer_data[i][1]])
target_transcription = ''.join([
data_generator.vocab_list[index] for index in infer_data[i][1]
])
beam_search_result = ctc_beam_search_decoder(
probs_seq=probs,
vocabulary=vocab_list,
vocabulary=data_generator.vocab_list,
beam_size=args.beam_size,
blank_id=len(vocab_list),
blank_id=len(data_generator.vocab_list),
cutoff_prob=args.cutoff_prob,
ext_scoring_func=ext_scorer, )
print("\nTarget Transcription:\t%s" % target_transcription)
......@@ -204,14 +195,15 @@ def infer():
ext_scorer = Scorer(args.alpha, args.beta, args.language_model_path)
beam_search_nproc_results = ctc_beam_search_decoder_nproc(
probs_split=probs_split,
vocabulary=vocab_list,
vocabulary=data_generator.vocab_list,
beam_size=args.beam_size,
blank_id=len(vocab_list),
blank_id=len(data_generator.vocab_list),
cutoff_prob=args.cutoff_prob,
ext_scoring_func=ext_scorer, )
for i, beam_search_result in enumerate(beam_search_nproc_results):
target_transcription = ''.join(
[vocab_list[index] for index in infer_data[i][1]])
target_transcription = ''.join([
data_generator.vocab_list[index] for index in infer_data[i][1]
])
print("\nTarget Transcription:\t%s" % target_transcription)
for index in xrange(args.num_results_per_sample):
......@@ -224,10 +216,12 @@ def infer():
print("cur wer = %f , average wer = %f" %
(wer_cur, wer_sum / wer_counter))
else:
raise ValueError("Decoding method [%s] is not supported." % method)
raise ValueError("Decoding method [%s] is not supported." %
decode_method)
def main():
utils.print_arguments(args)
paddle.init(use_gpu=args.use_gpu, trainer_count=1)
infer()
......
deep_speech_2/model.py
浏览文件 @ c8b0ae8f
"""
A simplifed version of Baidu DeepSpeech2 model.
"""
"""Contains DeepSpeech2 model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle.v2 as paddle
#TODO: add bidirectional rnn.
def conv_bn_layer(input, filter_size, num_channels_in, num_channels_out, stride,
padding, act):
......
deep_speech_2/tests/test_error_rate.py 0 → 100644
浏览文件 @ c8b0ae8f
# -*- coding: utf-8 -*-
"""Test error rate."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import error_rate
class TestParse(unittest.TestCase):
def test_wer_1(self):
ref = 'i UM the PHONE IS i LEFT THE portable PHONE UPSTAIRS last night'
hyp = 'i GOT IT TO the FULLEST i LOVE TO portable FROM OF STORES last night'
word_error_rate = error_rate.wer(ref, hyp)
self.assertTrue(abs(word_error_rate - 0.769230769231) < 1e-6)
def test_wer_2(self):
ref = 'i UM the PHONE IS i LEFT THE portable PHONE UPSTAIRS last night'
word_error_rate = error_rate.wer(ref, ref)
self.assertEqual(word_error_rate, 0.0)
def test_wer_3(self):
ref = ' '
hyp = 'Hypothesis sentence'
with self.assertRaises(ValueError):
word_error_rate = error_rate.wer(ref, hyp)
def test_cer_1(self):
ref = 'werewolf'
hyp = 'weae wolf'
char_error_rate = error_rate.cer(ref, hyp)
self.assertTrue(abs(char_error_rate - 0.25) < 1e-6)
def test_cer_2(self):
ref = 'werewolf'
char_error_rate = error_rate.cer(ref, ref)
self.assertEqual(char_error_rate, 0.0)
def test_cer_3(self):
ref = u'我是中国人'
hyp = u'我是 美洲人'
char_error_rate = error_rate.cer(ref, hyp)
self.assertTrue(abs(char_error_rate - 0.6) < 1e-6)
def test_cer_4(self):
ref = u'我是中国人'
char_error_rate = error_rate.cer(ref, ref)
self.assertFalse(char_error_rate, 0.0)
def test_cer_5(self):
ref = ''
hyp = 'Hypothesis'
with self.assertRaises(ValueError):
char_error_rate = error_rate.cer(ref, hyp)
if __name__ == '__main__':
unittest.main()
deep_speech_2/train.py
浏览文件 @ c8b0ae8f
"""
Trainer for a simplifed version of Baidu DeepSpeech2 model.
"""
"""Trainer for DeepSpeech2 model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle.v2 as paddle
import distutils.util
import sys
import os
import argparse
import gzip
import time
import sys
import distutils.util
import paddle.v2 as paddle
from model import deep_speech2
from audio_data_utils import DataGenerator
import numpy as np
import os
#TODO: add WER metric
from data_utils.data import DataGenerator
import utils
parser = argparse.ArgumentParser(
description='Simplified version of DeepSpeech2 trainer.')
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--batch_size", default=32, type=int, help="Minibatch size.")
parser.add_argument(
......@@ -51,32 +49,38 @@ parser.add_argument(
help="Use gpu or not. (default: %(default)s)")
parser.add_argument(
"--use_sortagrad",
default=False,
default=True,
type=distutils.util.strtobool,
help="Use sortagrad or not. (default: %(default)s)")
parser.add_argument(
"--shuffle_method",
default='instance_shuffle',
type=str,
help="Shuffle method: 'instance_shuffle', 'batch_shuffle', "
"'batch_shuffle_batch'. (default: %(default)s)")
parser.add_argument(
"--trainer_count",
default=4,
type=int,
help="Trainer number. (default: %(default)s)")
parser.add_argument(
"--normalizer_manifest_path",
default='data/manifest.libri.train-clean-100',
"--mean_std_filepath",
default='mean_std.npz',
type=str,
help="Manifest path for normalizer. (default: %(default)s)")
parser.add_argument(
"--train_manifest_path",
default='data/manifest.libri.train-clean-100',
default='datasets/manifest.train',
type=str,
help="Manifest path for training. (default: %(default)s)")
parser.add_argument(
"--dev_manifest_path",
default='data/manifest.libri.dev-clean',
default='datasets/manifest.dev',
type=str,
help="Manifest path for validation. (default: %(default)s)")
parser.add_argument(
"--vocab_filepath",
default='data/eng_vocab.txt',
default='datasets/vocab/eng_vocab.txt',
type=str,
help="Vocabulary filepath. (default: %(default)s)")
parser.add_argument(
......@@ -86,37 +90,42 @@ parser.add_argument(
help="If set None, the training will start from scratch. "
"Otherwise, the training will resume from "
"the existing model of this path. (default: %(default)s)")
parser.add_argument(
"--augmentation_config",
default='{}',
type=str,
help="Augmentation configuration in json-format. "
"(default: %(default)s)")
args = parser.parse_args()
def train():
"""
DeepSpeech2 training.
"""
"""DeepSpeech2 training."""
# initialize data generator
data_generator = DataGenerator(
vocab_filepath=args.vocab_filepath,
normalizer_manifest_path=args.normalizer_manifest_path,
normalizer_num_samples=200,
max_duration=20.0,
min_duration=0.0,
stride_ms=10,
window_ms=20)
def data_generator():
return DataGenerator(
vocab_filepath=args.vocab_filepath,
mean_std_filepath=args.mean_std_filepath,
augmentation_config=args.augmentation_config)
train_generator = data_generator()
test_generator = data_generator()
# create network config
dict_size = data_generator.vocabulary_size()
# paddle.data_type.dense_array is used for variable batch input.
# The size 161 * 161 is only an placeholder value and the real shape
# of input batch data will be induced during training.
audio_data = paddle.layer.data(
name="audio_spectrogram",
height=161,
width=2000,
type=paddle.data_type.dense_vector(322000))
name="audio_spectrogram", type=paddle.data_type.dense_array(161 * 161))
text_data = paddle.layer.data(
name="transcript_text",
type=paddle.data_type.integer_value_sequence(dict_size))
type=paddle.data_type.integer_value_sequence(
train_generator.vocab_size))
cost = deep_speech2(
audio_data=audio_data,
text_data=text_data,
dict_size=dict_size,
dict_size=train_generator.vocab_size,
num_conv_layers=args.num_conv_layers,
num_rnn_layers=args.num_rnn_layers,
rnn_size=args.rnn_layer_size,
......@@ -136,28 +145,18 @@ def train():
cost=cost, parameters=parameters, update_equation=optimizer)
# prepare data reader
train_batch_reader_sortagrad = data_generator.batch_reader_creator(
manifest_path=args.train_manifest_path,
batch_size=args.batch_size,
padding_to=2000,
flatten=True,
sort_by_duration=True,
shuffle=False)
train_batch_reader_nosortagrad = data_generator.batch_reader_creator(
train_batch_reader = train_generator.batch_reader_creator(
manifest_path=args.train_manifest_path,
batch_size=args.batch_size,
padding_to=2000,
flatten=True,
sort_by_duration=False,
shuffle=True)
test_batch_reader = data_generator.batch_reader_creator(
min_batch_size=args.trainer_count,
sortagrad=args.use_sortagrad if args.init_model_path is None else False,
shuffle_method=args.shuffle_method)
test_batch_reader = test_generator.batch_reader_creator(
manifest_path=args.dev_manifest_path,
batch_size=args.batch_size,
padding_to=2000,
flatten=True,
sort_by_duration=False,
shuffle=False)
feeding = data_generator.data_name_feeding()
min_batch_size=1, # must be 1, but will have errors.
sortagrad=False,
shuffle_method=None)
# create event handler
def event_handler(event):
......@@ -165,9 +164,9 @@ def train():
if isinstance(event, paddle.event.EndIteration):
cost_sum += event.cost
cost_counter += 1
if event.batch_id % 50 == 0:
print "\nPass: %d, Batch: %d, TrainCost: %f" % (
event.pass_id, event.batch_id, cost_sum / cost_counter)
if (event.batch_id + 1) % 100 == 0:
print("\nPass: %d, Batch: %d, TrainCost: %f" % (
event.pass_id, event.batch_id + 1, cost_sum / cost_counter))
cost_sum, cost_counter = 0.0, 0
with gzip.open("params.tar.gz", 'w') as f:
parameters.to_tar(f)
......@@ -178,28 +177,21 @@ def train():
start_time = time.time()
cost_sum, cost_counter = 0.0, 0
if isinstance(event, paddle.event.EndPass):
result = trainer.test(reader=test_batch_reader, feeding=feeding)
print "\n------- Time: %d sec, Pass: %d, ValidationCost: %s" % (
time.time() - start_time, event.pass_id, result.cost)
result = trainer.test(
reader=test_batch_reader, feeding=test_generator.feeding)
print("\n------- Time: %d sec, Pass: %d, ValidationCost: %s" %
(time.time() - start_time, event.pass_id, result.cost))
# run train
# first pass with sortagrad
if args.use_sortagrad:
trainer.train(
reader=train_batch_reader_sortagrad,
event_handler=event_handler,
num_passes=1,
feeding=feeding)
args.num_passes -= 1
# other passes without sortagrad
trainer.train(
reader=train_batch_reader_nosortagrad,
reader=train_batch_reader,
event_handler=event_handler,
num_passes=args.num_passes,
feeding=feeding)
feeding=train_generator.feeding)
def main():
utils.print_arguments(args)
paddle.init(use_gpu=args.use_gpu, trainer_count=args.trainer_count)
train()
......
deep_speech_2/tune.py
浏览文件 @ c8b0ae8f
"""
Parameters tuning for beam search decoder in Deep Speech 2.
"""
"""Parameters tuning for DeepSpeech2 model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle.v2 as paddle
import distutils.util
import argparse
import gzip
from audio_data_utils import DataGenerator
from data_utils.data import DataGenerator
from model import deep_speech2
from decoder import *
from error_rate import wer
parser = argparse.ArgumentParser(
description='Parameters tuning for ctc beam search decoder in Deep Speech 2.'
)
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--num_samples",
default=100,
......@@ -39,6 +38,11 @@ parser.add_argument(
default=True,
type=distutils.util.strtobool,
help="Use gpu or not. (default: %(default)s)")
parser.add_argument(
"--mean_std_filepath",
default='mean_std.npz',
type=str,
help="Manifest path for normalizer. (default: %(default)s)")
parser.add_argument(
"--normalizer_manifest_path",
default='data/manifest.libri.train-clean-100',
......@@ -56,7 +60,7 @@ parser.add_argument(
help="Model filepath. (default: %(default)s)")
parser.add_argument(
"--vocab_filepath",
default='data/eng_vocab.txt',
default='datasets/vocab/eng_vocab.txt',
type=str,
help="Vocabulary filepath. (default: %(default)s)")
parser.add_argument(
......@@ -77,7 +81,7 @@ parser.add_argument(
help="Number of outputs per sample in beam search. (default: %(default)d)")
parser.add_argument(
"--language_model_path",
default="./data/1Billion.klm",
default="data/1Billion.klm",
type=str,
help="Path for language model. (default: %(default)s)")
parser.add_argument(
......@@ -120,9 +124,7 @@ args = parser.parse_args()
def tune():
"""
Tune parameters alpha and beta on one minibatch.
"""
"""Tune parameters alpha and beta on one minibatch."""
if not args.num_alphas >= 0:
raise ValueError("num_alphas must be non-negative!")
......@@ -133,28 +135,22 @@ def tune():
# initialize data generator
data_generator = DataGenerator(
vocab_filepath=args.vocab_filepath,
normalizer_manifest_path=args.normalizer_manifest_path,
normalizer_num_samples=200,
max_duration=20.0,
min_duration=0.0,
stride_ms=10,
window_ms=20)
mean_std_filepath=args.mean_std_filepath,
augmentation_config='{}')
# create network config
dict_size = data_generator.vocabulary_size()
vocab_list = data_generator.vocabulary_list()
# paddle.data_type.dense_array is used for variable batch input.
# The size 161 * 161 is only an placeholder value and the real shape
# of input batch data will be induced during training.
audio_data = paddle.layer.data(
name="audio_spectrogram",
height=161,
width=2000,
type=paddle.data_type.dense_vector(322000))
name="audio_spectrogram", type=paddle.data_type.dense_array(161 * 161))
text_data = paddle.layer.data(
name="transcript_text",
type=paddle.data_type.integer_value_sequence(dict_size))
type=paddle.data_type.integer_value_sequence(data_generator.vocab_size))
output_probs = deep_speech2(
audio_data=audio_data,
text_data=text_data,
dict_size=dict_size,
dict_size=data_generator.vocab_size,
num_conv_layers=args.num_conv_layers,
num_rnn_layers=args.num_rnn_layers,
rnn_size=args.rnn_layer_size,
......@@ -165,21 +161,18 @@ def tune():
gzip.open(args.model_filepath))
# prepare infer data
feeding = data_generator.data_name_feeding()
test_batch_reader = data_generator.batch_reader_creator(
batch_reader = data_generator.batch_reader_creator(
manifest_path=args.decode_manifest_path,
batch_size=args.num_samples,
padding_to=2000,
flatten=True,
sort_by_duration=False,
shuffle=False)
sortagrad=False,
shuffle_method=None)
# get one batch data for tuning
infer_data = test_batch_reader().next()
infer_data = batch_reader().next()
# run inference
infer_results = paddle.infer(
output_layer=output_probs, parameters=parameters, input=infer_data)
num_steps = len(infer_results) / len(infer_data)
num_steps = len(infer_results) // len(infer_data)
probs_split = [
infer_results[i * num_steps:(i + 1) * num_steps]
for i in xrange(0, len(infer_data))
......@@ -198,13 +191,15 @@ def tune():
# beam search decode
if args.decode_method == "beam_search":
for i, probs in enumerate(probs_split):
target_transcription = ''.join(
[vocab_list[index] for index in infer_data[i][1]])
target_transcription = ''.join([
data_generator.vocab_list[index]
for index in infer_data[i][1]
])
beam_search_result = ctc_beam_search_decoder(
probs_seq=probs,
vocabulary=vocab_list,
vocabulary=data_generator.vocab_list,
beam_size=args.beam_size,
blank_id=len(vocab_list),
blank_id=len(data_generator.vocab_list),
cutoff_prob=args.cutoff_prob,
ext_scoring_func=ext_scorer, )
wer_sum += wer(target_transcription, beam_search_result[0][1])
......@@ -213,18 +208,21 @@ def tune():
elif args.decode_method == "beam_search_nproc":
beam_search_nproc_results = ctc_beam_search_decoder_nproc(
probs_split=probs_split,
vocabulary=vocab_list,
vocabulary=data_generator.vocab_list,
beam_size=args.beam_size,
cutoff_prob=args.cutoff_prob,
blank_id=len(vocab_list),
blank_id=len(data_generator.vocab_list),
ext_scoring_func=ext_scorer, )
for i, beam_search_result in enumerate(beam_search_nproc_results):
target_transcription = ''.join(
[vocab_list[index] for index in infer_data[i][1]])
target_transcription = ''.join([
data_generator.vocab_list[index]
for index in infer_data[i][1]
])
wer_sum += wer(target_transcription, beam_search_result[0][1])
wer_counter += 1
else:
raise ValueError("Decoding method [%s] is not supported." % method)
raise ValueError("Decoding method [%s] is not supported." %
decode_method)
print("alpha = %f\tbeta = %f\tWER = %f" %
(alpha, beta, wer_sum / wer_counter))
......
deep_speech_2/utils.py 0 → 100644
浏览文件 @ c8b0ae8f
"""Contains common utility functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
def print_arguments(args):
"""Print argparse's arguments.
Usage:
.. code-block:: python
parser = argparse.ArgumentParser()
parser.add_argument("name", default="Jonh", type=str, help="User name.")
args = parser.parse_args()
print_arguments(args)
:param args: Input argparse.Namespace for printing.
:type args: argparse.Namespace
"""
print("----- Configuration Arguments -----")
for arg, value in vars(args).iteritems():
print("%s: %s" % (arg, value))
print("------------------------------------")
image_classification/README.md
浏览文件 @ c8b0ae8f
TBD
图像分类
=======================
这里将介绍如何在PaddlePaddle下使用AlexNet、VGG、GoogLeNet和ResNet模型进行图像分类。图像分类问题的描述和这四种模型的介绍可以参考[PaddlePaddle book](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification)。
## 训练模型
### 初始化
在初始化阶段需要导入所用的包，并对PaddlePaddle进行初始化。
```python
import gzip
import paddle.v2.dataset.flowers as flowers
import paddle.v2 as paddle
import reader
import vgg
import resnet
import alexnet
import googlenet
# PaddlePaddle init
paddle.init(use_gpu=False, trainer_count=1)
```
### 定义参数和输入
设置算法参数（如数据维度、类别数目和batch size等参数），定义数据输入层`image`和类别标签`lbl`。
```python
DATA_DIM = 3 * 224 * 224
CLASS_DIM = 102
BATCH_SIZE = 128
image = paddle.layer.data(
name="image", type=paddle.data_type.dense_vector(DATA_DIM))
lbl = paddle.layer.data(
name="label", type=paddle.data_type.integer_value(CLASS_DIM))
```
### 获得所用模型
这里可以选择使用AlexNet、VGG、GoogLeNet和ResNet模型中的一个模型进行图像分类。通过调用相应的方法可以获得网络最后的Softmax层。
1. 使用AlexNet模型
指定输入层`image`和类别数目`CLASS_DIM`后，可以通过下面的代码得到AlexNet的Softmax层。
```python
out = alexnet.alexnet(image, class_dim=CLASS_DIM)
```
2. 使用VGG模型
根据层数的不同，VGG分为VGG13、VGG16和VGG19。使用VGG16模型的代码如下：
```python
out = vgg.vgg16(image, class_dim=CLASS_DIM)
```
类似地，VGG13和VGG19可以分别通过`vgg.vgg13`和`vgg.vgg19`方法获得。
3. 使用GoogLeNet模型
GoogLeNet在训练阶段使用两个辅助的分类器强化梯度信息并进行额外的正则化。因此`googlenet.googlenet`共返回三个Softmax层，如下面的代码所示：
```python
out, out1, out2 = googlenet.googlenet(image, class_dim=CLASS_DIM)
loss1 = paddle.layer.cross_entropy_cost(
input=out1, label=lbl, coeff=0.3)
paddle.evaluator.classification_error(input=out1, label=lbl)
loss2 = paddle.layer.cross_entropy_cost(
input=out2, label=lbl, coeff=0.3)
paddle.evaluator.classification_error(input=out2, label=lbl)
extra_layers = [loss1, loss2]
```
对于两个辅助的输出，这里分别对其计算损失函数并评价错误率，然后将损失作为后文SGD的extra_layers。
4. 使用ResNet模型
ResNet模型可以通过下面的代码获取：
```python
out = resnet.resnet_imagenet(image, class_dim=CLASS_DIM)
```
### 定义损失函数
```python
cost = paddle.layer.classification_cost(input=out, label=lbl)
```
### 创建参数和优化方法
```python
# Create parameters
parameters = paddle.parameters.create(cost)
# Create optimizer
optimizer = paddle.optimizer.Momentum(
momentum=0.9,
regularization=paddle.optimizer.L2Regularization(rate=0.0005 *
BATCH_SIZE),
learning_rate=0.001 / BATCH_SIZE,
learning_rate_decay_a=0.1,
learning_rate_decay_b=128000 * 35,
learning_rate_schedule="discexp", )
```
通过 `learning_rate_decay_a` (简写$a$） 、`learning_rate_decay_b` (简写$b$) 和 `learning_rate_schedule` 指定学习率调整策略，这里采用离散指数的方式调节学习率，计算公式如下， $n$ 代表已经处理过的累计总样本数，$lr_{0}$ 即为参数里设置的 `learning_rate`。
$$ lr = lr_{0} * a^ {\lfloor \frac{n}{ b}\rfloor} $$
### 定义数据读取
首先以[花卉数据](http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html)为例说明如何定义输入。下面的代码定义了花卉数据训练集和验证集的输入：
```python
train_reader = paddle.batch(
paddle.reader.shuffle(
flowers.train(),
buf_size=1000),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
flowers.valid(),
batch_size=BATCH_SIZE)
```
若需要使用其他数据，则需要先建立图像列表文件。`reader.py`定义了这种文件的读取方式，它从图像列表文件中解析出图像路径和类别标签。
图像列表文件是一个文本文件，其中每一行由一个图像路径和类别标签构成，二者以跳格符（Tab）隔开。类别标签用整数表示，其最小值为0。下面给出一个图像列表文件的片段示例：
```
dataset_100/train_images/n03982430_23191.jpeg 1
dataset_100/train_images/n04461696_23653.jpeg 7
dataset_100/train_images/n02441942_3170.jpeg 8
dataset_100/train_images/n03733281_31716.jpeg 2
dataset_100/train_images/n03424325_240.jpeg 0
dataset_100/train_images/n02643566_75.jpeg 8
```
训练时需要分别指定训练集和验证集的图像列表文件。这里假设这两个文件分别为`train.list`和`val.list`，数据读取方式如下：
```python
train_reader = paddle.batch(
paddle.reader.shuffle(
reader.train_reader('train.list'),
buf_size=1000),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
reader.test_reader('val.list'),
batch_size=BATCH_SIZE)
```
### 定义事件处理程序
```python
# End batch and end pass event handler
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 1 == 0:
print "\nPass %d, Batch %d, Cost %f, %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics)
if isinstance(event, paddle.event.EndPass):
with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
parameters.to_tar(f)
result = trainer.test(reader=test_reader)
print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
```
### 定义训练方法
对于AlexNet、VGG和ResNet，可以按下面的代码定义训练方法：
```python
# Create trainer
trainer = paddle.trainer.SGD(
cost=cost,
parameters=parameters,
update_equation=optimizer)
```
GoogLeNet有两个额外的输出层，因此需要指定`extra_layers`，如下所示：
```python
# Create trainer
trainer = paddle.trainer.SGD(
cost=cost,
parameters=parameters,
update_equation=optimizer,
extra_layers=extra_layers)
```
### 开始训练
```python
trainer.train(
reader=train_reader, num_passes=200, event_handler=event_handler)
```
## 应用模型
模型训练好后，可以使用下面的代码预测给定图片的类别。
```python
# load parameters
with gzip.open('params_pass_10.tar.gz', 'r') as f:
parameters = paddle.parameters.Parameters.from_tar(f)
file_list = [line.strip() for line in open(image_list_file)]
test_data = [(paddle.image.load_and_transform(image_file, 256, 224, False)
.flatten().astype('float32'), )
for image_file in file_list]
probs = paddle.infer(
output_layer=out, parameters=parameters, input=test_data)
lab = np.argsort(-probs)
for file_name, result in zip(file_list, lab):
print "Label of %s is: %d" % (file_name, result[0])
```
首先从文件中加载训练好的模型（代码里以第10轮迭代的结果为例），然后读取`image_list_file`中的图像。`image_list_file`是一个文本文件，每一行为一个图像路径。代码使用`paddle.infer`判断`image_list_file`中每个图像的类别，并进行输出。
image_classification/alexnet.py 0 → 100644
浏览文件 @ c8b0ae8f
import paddle.v2 as paddle
__all__ = ['alexnet']
def alexnet(input, class_dim):
conv1 = paddle.layer.img_conv(
input=input,
filter_size=11,
num_channels=3,
num_filters=96,
stride=4,
padding=1)
cmrnorm1 = paddle.layer.img_cmrnorm(
input=conv1, size=5, scale=0.0001, power=0.75)
pool1 = paddle.layer.img_pool(input=cmrnorm1, pool_size=3, stride=2)
conv2 = paddle.layer.img_conv(
input=pool1,
filter_size=5,
num_filters=256,
stride=1,
padding=2,
groups=1)
cmrnorm2 = paddle.layer.img_cmrnorm(
input=conv2, size=5, scale=0.0001, power=0.75)
pool2 = paddle.layer.img_pool(input=cmrnorm2, pool_size=3, stride=2)
pool3 = paddle.networks.img_conv_group(
input=pool2,
pool_size=3,
pool_stride=2,
conv_num_filter=[384, 384, 256],
conv_filter_size=3,
pool_type=paddle.pooling.Max())
fc1 = paddle.layer.fc(
input=pool3,
size=4096,
act=paddle.activation.Relu(),
layer_attr=paddle.attr.Extra(drop_rate=0.5))
fc2 = paddle.layer.fc(
input=fc1,
size=4096,
act=paddle.activation.Relu(),
layer_attr=paddle.attr.Extra(drop_rate=0.5))
out = paddle.layer.fc(
input=fc2, size=class_dim, act=paddle.activation.Softmax())
return out
image_classification/googlenet.py 0 → 100644
浏览文件 @ c8b0ae8f
import paddle.v2 as paddle
__all__ = ['googlenet']
def inception(name, input, channels, filter1, filter3R, filter3, filter5R,
filter5, proj):
cov1 = paddle.layer.img_conv(
name=name + '_1',
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter1,
stride=1,
padding=0)
cov3r = paddle.layer.img_conv(
name=name + '_3r',
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter3R,
stride=1,
padding=0)
cov3 = paddle.layer.img_conv(
name=name + '_3',
input=cov3r,
filter_size=3,
num_filters=filter3,
stride=1,
padding=1)
cov5r = paddle.layer.img_conv(
name=name + '_5r',
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter5R,
stride=1,
padding=0)
cov5 = paddle.layer.img_conv(
name=name + '_5',
input=cov5r,
filter_size=5,
num_filters=filter5,
stride=1,
padding=2)
pool1 = paddle.layer.img_pool(
name=name + '_max',
input=input,
pool_size=3,
num_channels=channels,
stride=1,
padding=1)
covprj = paddle.layer.img_conv(
name=name + '_proj',
input=pool1,
filter_size=1,
num_filters=proj,
stride=1,
padding=0)
cat = paddle.layer.concat(name=name, input=[cov1, cov3, cov5, covprj])
return cat
def googlenet(input, class_dim):
# stage 1
conv1 = paddle.layer.img_conv(
name="conv1",
input=input,
filter_size=7,
num_channels=3,
num_filters=64,
stride=2,
padding=3)
pool1 = paddle.layer.img_pool(
name="pool1", input=conv1, pool_size=3, num_channels=64, stride=2)
# stage 2
conv2_1 = paddle.layer.img_conv(
name="conv2_1",
input=pool1,
filter_size=1,
num_filters=64,
stride=1,
padding=0)
conv2_2 = paddle.layer.img_conv(
name="conv2_2",
input=conv2_1,
filter_size=3,
num_filters=192,
stride=1,
padding=1)
pool2 = paddle.layer.img_pool(
name="pool2", input=conv2_2, pool_size=3, num_channels=192, stride=2)
# stage 3
ince3a = inception("ince3a", pool2, 192, 64, 96, 128, 16, 32, 32)
ince3b = inception("ince3b", ince3a, 256, 128, 128, 192, 32, 96, 64)
pool3 = paddle.layer.img_pool(
name="pool3", input=ince3b, num_channels=480, pool_size=3, stride=2)
# stage 4
ince4a = inception("ince4a", pool3, 480, 192, 96, 208, 16, 48, 64)
ince4b = inception("ince4b", ince4a, 512, 160, 112, 224, 24, 64, 64)
ince4c = inception("ince4c", ince4b, 512, 128, 128, 256, 24, 64, 64)
ince4d = inception("ince4d", ince4c, 512, 112, 144, 288, 32, 64, 64)
ince4e = inception("ince4e", ince4d, 528, 256, 160, 320, 32, 128, 128)
pool4 = paddle.layer.img_pool(
name="pool4", input=ince4e, num_channels=832, pool_size=3, stride=2)
# stage 5
ince5a = inception("ince5a", pool4, 832, 256, 160, 320, 32, 128, 128)
ince5b = inception("ince5b", ince5a, 832, 384, 192, 384, 48, 128, 128)
pool5 = paddle.layer.img_pool(
name="pool5",
input=ince5b,
num_channels=1024,
pool_size=7,
stride=7,
pool_type=paddle.pooling.Avg())
dropout = paddle.layer.addto(
input=pool5,
layer_attr=paddle.attr.Extra(drop_rate=0.4),
act=paddle.activation.Linear())
out = paddle.layer.fc(
input=dropout, size=class_dim, act=paddle.activation.Softmax())
# fc for output 1
pool_o1 = paddle.layer.img_pool(
name="pool_o1",
input=ince4a,
num_channels=512,
pool_size=5,
stride=3,
pool_type=paddle.pooling.Avg())
conv_o1 = paddle.layer.img_conv(
name="conv_o1",
input=pool_o1,
filter_size=1,
num_filters=128,
stride=1,
padding=0)
fc_o1 = paddle.layer.fc(
name="fc_o1",
input=conv_o1,
size=1024,
layer_attr=paddle.attr.Extra(drop_rate=0.7),
act=paddle.activation.Relu())
out1 = paddle.layer.fc(
input=fc_o1, size=class_dim, act=paddle.activation.Softmax())
# fc for output 2
pool_o2 = paddle.layer.img_pool(
name="pool_o2",
input=ince4d,
num_channels=528,
pool_size=5,
stride=3,
pool_type=paddle.pooling.Avg())
conv_o2 = paddle.layer.img_conv(
name="conv_o2",
input=pool_o2,
filter_size=1,
num_filters=128,
stride=1,
padding=0)
fc_o2 = paddle.layer.fc(
name="fc_o2",
input=conv_o2,
size=1024,
layer_attr=paddle.attr.Extra(drop_rate=0.7),
act=paddle.activation.Relu())
out2 = paddle.layer.fc(
input=fc_o2, size=class_dim, act=paddle.activation.Softmax())
return out, out1, out2
image_classification/infer.py 0 → 100644
浏览文件 @ c8b0ae8f
import gzip
import paddle.v2 as paddle
import reader
import vgg
import resnet
import alexnet
import googlenet
import argparse
import os
from PIL import Image
import numpy as np
WIDTH = 224
HEIGHT = 224
DATA_DIM = 3 * WIDTH * HEIGHT
CLASS_DIM = 102
def main():
# parse the argument
parser = argparse.ArgumentParser()
parser.add_argument(
'data_list',
help='The path of data list file, which consists of one image path per line'
)
parser.add_argument(
'model',
help='The model for image classification',
choices=['alexnet', 'vgg13', 'vgg16', 'vgg19', 'resnet', 'googlenet'])
parser.add_argument(
'params_path', help='The file which stores the parameters')
args = parser.parse_args()
# PaddlePaddle init
paddle.init(use_gpu=True, trainer_count=1)
image = paddle.layer.data(
name="image", type=paddle.data_type.dense_vector(DATA_DIM))
if args.model == 'alexnet':
out = alexnet.alexnet(image, class_dim=CLASS_DIM)
elif args.model == 'vgg13':
out = vgg.vgg13(image, class_dim=CLASS_DIM)
elif args.model == 'vgg16':
out = vgg.vgg16(image, class_dim=CLASS_DIM)
elif args.model == 'vgg19':
out = vgg.vgg19(image, class_dim=CLASS_DIM)
elif args.model == 'resnet':
out = resnet.resnet_imagenet(image, class_dim=CLASS_DIM)
elif args.model == 'googlenet':
out, _, _ = googlenet.googlenet(image, class_dim=CLASS_DIM)
# load parameters
with gzip.open(args.params_path, 'r') as f:
parameters = paddle.parameters.Parameters.from_tar(f)
file_list = [line.strip() for line in open(args.data_list)]
test_data = [(paddle.image.load_and_transform(image_file, 256, 224, False)
.flatten().astype('float32'), ) for image_file in file_list]
probs = paddle.infer(
output_layer=out, parameters=parameters, input=test_data)
lab = np.argsort(-probs)
for file_name, result in zip(file_list, lab):
print "Label of %s is: %d" % (file_name, result[0])
if __name__ == '__main__':
main()
image_classification/reader.py
浏览文件 @ c8b0ae8f
# Copyright (c) 2016 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 random
from paddle.v2.image import load_and_transform
import paddle.v2 as paddle
from multiprocessing import cpu_count
def train_mapper(sample):
'''
map image path to type needed by model input layer for the training set
'''
img, label = sample
img = paddle.image.load_image(img)
img = paddle.image.simple_transform(img, 256, 224, True)
return img.flatten().astype('float32'), label
def test_mapper(sample):
'''
map image path to type needed by model input layer for the test set
'''
img, label = sample
img = paddle.image.load_image(img)
img = paddle.image.simple_transform(img, 256, 224, True)
return img.flatten().astype('float32'), label
def train_reader(train_list):
def train_reader(train_list, buffered_size=1024):
def reader():
with open(train_list, 'r') as f:
lines = [line.strip() for line in f]
random.shuffle(lines)
for line in lines:
img_path, lab = line.strip().split('\t')
im = load_and_transform(img_path, 256, 224, True)
yield im.flatten().astype('float32'), int(lab)
yield img_path, int(lab)
return reader
return paddle.reader.xmap_readers(train_mapper, reader,
cpu_count(), buffered_size)
def test_reader(test_list):
def test_reader(test_list, buffered_size=1024):
def reader():
with open(test_list, 'r') as f:
lines = [line.strip() for line in f]
for line in lines:
img_path, lab = line.strip().split('\t')
im = load_and_transform(img_path, 256, 224, False)
yield im.flatten().astype('float32'), int(lab)
yield img_path, int(lab)
return reader
return paddle.reader.xmap_readers(test_mapper, reader,
cpu_count(), buffered_size)
if __name__ == '__main__':
......
image_classification/resnet.py 0 → 100644
浏览文件 @ c8b0ae8f
import paddle.v2 as paddle
__all__ = ['resnet_imagenet', 'resnet_cifar10']
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
active_type=paddle.activation.Relu(),
ch_in=None):
tmp = paddle.layer.img_conv(
input=input,
filter_size=filter_size,
num_channels=ch_in,
num_filters=ch_out,
stride=stride,
padding=padding,
act=paddle.activation.Linear(),
bias_attr=False)
return paddle.layer.batch_norm(input=tmp, act=active_type)
def shortcut(input, ch_in, ch_out, stride):
if ch_in != ch_out:
return conv_bn_layer(input, ch_out, 1, stride, 0,
paddle.activation.Linear())
else:
return input
def basicblock(input, ch_in, ch_out, stride):
short = shortcut(input, ch_in, ch_out, stride)
conv1 = conv_bn_layer(input, ch_out, 3, stride, 1)
conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1, paddle.activation.Linear())
return paddle.layer.addto(
input=[short, conv2], act=paddle.activation.Relu())
def bottleneck(input, ch_in, ch_out, stride):
short = shortcut(input, ch_in, ch_out * 4, stride)
conv1 = conv_bn_layer(input, ch_out, 1, stride, 0)
conv2 = conv_bn_layer(conv1, ch_out, 3, 1, 1)
conv3 = conv_bn_layer(conv2, ch_out * 4, 1, 1, 0,
paddle.activation.Linear())
return paddle.layer.addto(
input=[short, conv3], act=paddle.activation.Relu())
def layer_warp(block_func, input, ch_in, ch_out, count, stride):
conv = block_func(input, ch_in, ch_out, stride)
for i in range(1, count):
conv = block_func(conv, ch_out, ch_out, 1)
return conv
def resnet_imagenet(input, class_dim, depth=50):
cfg = {
18: ([2, 2, 2, 1], basicblock),
34: ([3, 4, 6, 3], basicblock),
50: ([3, 4, 6, 3], bottleneck),
101: ([3, 4, 23, 3], bottleneck),
152: ([3, 8, 36, 3], bottleneck)
}
stages, block_func = cfg[depth]
conv1 = conv_bn_layer(
input, ch_in=3, ch_out=64, filter_size=7, stride=2, padding=3)
pool1 = paddle.layer.img_pool(input=conv1, pool_size=3, stride=2)
res1 = layer_warp(block_func, pool1, 64, 64, stages[0], 1)
res2 = layer_warp(block_func, res1, 64, 128, stages[1], 2)
res3 = layer_warp(block_func, res2, 128, 256, stages[2], 2)
res4 = layer_warp(block_func, res3, 256, 512, stages[3], 2)
pool2 = paddle.layer.img_pool(
input=res4, pool_size=7, stride=1, pool_type=paddle.pooling.Avg())
out = paddle.layer.fc(
input=pool2, size=class_dim, act=paddle.activation.Softmax())
return out
def resnet_cifar10(input, class_dim, depth=32):
# depth should be one of 20, 32, 44, 56, 110, 1202
assert (depth - 2) % 6 == 0
n = (depth - 2) / 6
nStages = {16, 64, 128}
conv1 = conv_bn_layer(
input, ch_in=3, ch_out=16, filter_size=3, stride=1, padding=1)
res1 = layer_warp(basicblock, conv1, 16, 16, n, 1)
res2 = layer_warp(basicblock, res1, 16, 32, n, 2)
res3 = layer_warp(basicblock, res2, 32, 64, n, 2)
pool = paddle.layer.img_pool(
input=res3, pool_size=8, stride=1, pool_type=paddle.pooling.Avg())
out = paddle.layer.fc(
input=pool, size=class_dim, act=paddle.activation.Softmax())
return out
image_classification/train.py 100644 → 100755
浏览文件 @ c8b0ae8f
# Copyright (c) 2016 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 gzip
import paddle.v2.dataset.flowers as flowers
import paddle.v2 as paddle
import reader
import vgg
import resnet
import alexnet
import googlenet
import argparse
DATA_DIM = 3 * 224 * 224
CLASS_DIM = 1000
CLASS_DIM = 102
BATCH_SIZE = 128
def main():
# parse the argument
parser = argparse.ArgumentParser()
parser.add_argument(
'model',
help='The model for image classification',
choices=['alexnet', 'vgg13', 'vgg16', 'vgg19', 'resnet', 'googlenet'])
args = parser.parse_args()
# PaddlePaddle init
paddle.init(use_gpu=True, trainer_count=4)
paddle.init(use_gpu=True, trainer_count=1)
image = paddle.layer.data(
name="image", type=paddle.data_type.dense_vector(DATA_DIM))
lbl = paddle.layer.data(
name="label", type=paddle.data_type.integer_value(CLASS_DIM))
net = vgg.vgg13(image)
out = paddle.layer.fc(
input=net, size=CLASS_DIM, act=paddle.activation.Softmax())
extra_layers = None
learning_rate = 0.01
if args.model == 'alexnet':
out = alexnet.alexnet(image, class_dim=CLASS_DIM)
elif args.model == 'vgg13':
out = vgg.vgg13(image, class_dim=CLASS_DIM)
elif args.model == 'vgg16':
out = vgg.vgg16(image, class_dim=CLASS_DIM)
elif args.model == 'vgg19':
out = vgg.vgg19(image, class_dim=CLASS_DIM)
elif args.model == 'resnet':
out = resnet.resnet_imagenet(image, class_dim=CLASS_DIM)
learning_rate = 0.1
elif args.model == 'googlenet':
out, out1, out2 = googlenet.googlenet(image, class_dim=CLASS_DIM)
loss1 = paddle.layer.cross_entropy_cost(
input=out1, label=lbl, coeff=0.3)
paddle.evaluator.classification_error(input=out1, label=lbl)
loss2 = paddle.layer.cross_entropy_cost(
input=out2, label=lbl, coeff=0.3)
paddle.evaluator.classification_error(input=out2, label=lbl)
extra_layers = [loss1, loss2]
cost = paddle.layer.classification_cost(input=out, label=lbl)
# Create parameters
......@@ -45,16 +63,23 @@ def main():
momentum=0.9,
regularization=paddle.optimizer.L2Regularization(rate=0.0005 *
BATCH_SIZE),
learning_rate=0.01 / BATCH_SIZE,
learning_rate=learning_rate / BATCH_SIZE,
learning_rate_decay_a=0.1,
learning_rate_decay_b=128000 * 35,
learning_rate_schedule="discexp", )
train_reader = paddle.batch(
paddle.reader.shuffle(reader.test_reader("train.list"), buf_size=1000),
paddle.reader.shuffle(
flowers.train(),
# To use other data, replace the above line with:
# reader.train_reader('train.list'),
buf_size=1000),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
reader.train_reader("test.list"), batch_size=BATCH_SIZE)
flowers.valid(),
# To use other data, replace the above line with:
# reader.test_reader('val.list'),
batch_size=BATCH_SIZE)
# End batch and end pass event handler
def event_handler(event):
......@@ -71,11 +96,14 @@ def main():
# Create trainer
trainer = paddle.trainer.SGD(
cost=cost, parameters=parameters, update_equation=optimizer)
cost=cost,
parameters=parameters,
update_equation=optimizer,
extra_layers=extra_layers)
trainer.train(
reader=train_reader, num_passes=200, event_handler=event_handler)
if __name__ == '__main__':
main()
main()
\ No newline at end of file
image_classification/vgg.py
浏览文件 @ c8b0ae8f
# Copyright (c) 2016 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.v2 as paddle
__all__ = ['vgg13', 'vgg16', 'vgg19']
def vgg(input, nums):
def vgg(input, nums, class_dim):
def conv_block(input, num_filter, groups, num_channels=None):
return paddle.networks.img_conv_group(
input=input,
......@@ -48,19 +34,21 @@ def vgg(input, nums):
size=fc_dim,
act=paddle.activation.Relu(),
layer_attr=paddle.attr.Extra(drop_rate=0.5))
return fc2
out = paddle.layer.fc(
input=fc2, size=class_dim, act=paddle.activation.Softmax())
return out
def vgg13(input):
def vgg13(input, class_dim):
nums = [2, 2, 2, 2, 2]
return vgg(input, nums)
return vgg(input, nums, class_dim)
def vgg16(input):
def vgg16(input, class_dim):
nums = [2, 2, 3, 3, 3]
return vgg(input, nums)
return vgg(input, nums, class_dim)
def vgg19(input):
def vgg19(input, class_dim):
nums = [2, 2, 4, 4, 4]
return vgg(input, nums)
return vgg(input, nums, class_dim)
language_model/README.md
浏览文件 @ c8b0ae8f
TBD
# 语言模型
## 简介
语言模型即 Language Model，简称LM。它是一个概率分布模型，简单来说，就是用来计算一个句子的概率的模型。利用它可以确定哪个词序列的可能性更大，或者给定若干个词，可以预测下一个最可能出现的词。语言模型是自然语言处理领域里一个重要的基础模型。
## 应用场景
**语言模型被应用在很多领域**，如：
* **自动写作**：语言模型可以根据上文生成下一个词，递归下去可以生成整个句子、段落、篇章。
* **QA**：语言模型可以根据Question生成Answer。
* **机器翻译**：当前主流的机器翻译模型大多基于Encoder-Decoder模式，其中Decoder就是一个语言模型，用来生成目标语言。
* **拼写检查**：语言模型可以计算出词序列的概率，一般在拼写错误处序列的概率会骤减，可以用来识别拼写错误并提供改正候选集。
* **词性标注、句法分析、语音识别......**
## 关于本例
Language Model 常见的实现方式有 N-Gram、RNN、seq2seq。本例中实现了基于N-Gram、RNN的语言模型。**本例的文件结构如下**（`images` 文件夹与使用无关可不关心）：
```text
.
├── data # toy、demo数据，用户可据此格式化自己的数据
│ ├── chinese.test.txt # test用的数据demo
| ├── chinese.train.txt # train用的数据demo
│ └── input.txt # infer用的输入数据demo
├── config.py # 配置文件，包括data、train、infer相关配置
├── infer.py # 预测任务脚本，即生成文本
├── network_conf.py # 本例中涉及的各种网络结构均定义在此文件中，希望进一步修改模型结构，请修改此文件
├── reader.py # 读取数据接口
├── README.md # 文档
├── train.py # 训练任务脚本
└── utils.py # 定义通用的函数，例如：构建字典、加载字典等
```
**注：一般情况下基于N-Gram的语言模型不如基于RNN的语言模型效果好，所以实际使用时建议使用基于RNN的语言模型，本例中也将着重介绍基于RNN的模型，简略介绍基于N-Gram的模型。**
## RNN 语言模型
### 简介
RNN是一个序列模型，基本思路是：在时刻t，将前一时刻t-1的隐藏层输出和t时刻的词向量一起输入到隐藏层从而得到时刻t的特征表示，然后用这个特征表示得到t时刻的预测输出，如此在时间维上递归下去。可以看出RNN善于使用上文信息、历史知识，具有“记忆”功能。理论上RNN能实现“长依赖”（即利用很久之前的知识），但在实际应用中发现效果并不理想，于是出现了很多RNN的变种，如常用的LSTM和GRU，它们对传统RNN的cell进行了改进，弥补了传统RNN的不足，本例中即使用了LSTM、GRU。下图是RNN（广义上包含了LSTM、GRU等）语言模型“循环”思想的示意图：
<p align=center><img src='images/rnn.png' width='500px'/></p>
### 模型实现
本例中RNN语言模型的实现简介如下：
* **定义模型参数**：`config.py`中的`Config_rnn`**类**中定义了模型的参数变量。
* **定义模型结构**：`network_conf.py`中的`rnn_lm`**函数**中定义了模型的**结构**，如下：
* 输入层：将输入的词（或字）序列映射成向量，即embedding。
* 中间层：根据配置实现RNN层，将上一步得到的embedding向量序列作为输入。
* 输出层：使用softmax归一化计算单词的概率，将output结果返回
* loss：定义模型的cost为多类交叉熵损失函数。
* **训练模型**：`train.py`中的`main`方法实现了模型的训练，实现流程如下：
* 准备输入数据：建立并保存词典、构建train和test数据的reader。
* 初始化模型：包括模型的结构、参数。
* 构建训练器：demo中使用的是Adam优化算法。
* 定义回调函数：构建`event_handler`来跟踪训练过程中loss的变化，并在每轮训练结束时保存模型的参数。
* 训练：使用trainer训练模型。
* **生成文本**：`infer.py`中的`main`方法实现了文本的生成，实现流程如下：
* 根据配置选择生成方法：RNN模型 or N-Gram模型。
* 加载train好的模型和词典文件。
* 读取`input_file`文件（每行为一个sentence的前缀），用启发式图搜索算法`beam_search`根据各sentence的前缀生成文本。
* 将生成的文本及其前缀保存到文件`output_file`。
## N-Gram 语言模型
### 简介
N-Gram模型也称为N-1阶马尔科夫模型，它有一个有限历史假设：当前词的出现概率仅仅与前面N-1个词相关。一般采用最大似然估计（Maximum Likelihood Estimation，MLE）方法对模型的参数进行估计。当N取1、2、3时，N-Gram模型分别称为unigram、bigram和trigram语言模型。一般情况下，N越大、训练语料的规模越大，参数估计的结果越可靠，但由于模型较简单、表达能力不强以及数据稀疏等问题。一般情况下用N-Gram实现的语言模型不如RNN、seq2seq效果好。下图是基于神经网络的N-Gram语言模型结构示意图：
<p align=center><img src='images/ngram.png' width='500px'/></p>
### 模型实现
本例中N-Gram语言模型的实现简介如下：
* **定义模型参数**：`config.py`中的`Config_ngram`**类**中定义了模型的参数变量。
* **定义模型结构**：`network_conf.py`中的`ngram_lm`**函数**中定义了模型的**结构**，如下：
* 输入层：本例中N取5，将前四个词分别做embedding，然后连接起来作为输入。
* 中间层：根据配置实现DNN层，将上一步得到的embedding向量序列作为输入。
* 输出层：使用softmax归一化计算单词的概率，将output结果返回
* loss：定义模型的cost为多类交叉熵损失函数。
* **训练模型**：`train.py`中的`main`方法实现了模型的训练，实现流程与上文中RNN语言模型基本一致。
* **生成文本**：`infer.py`中的`main`方法实现了文本的生成，实现流程与上文中RNN语言模型基本一致，区别在于构建input时本例会取每个前缀的最后4（N-1）个词作为输入。
## 使用说明
运行本例的方法如下：
* 1，运行`python train.py`命令，开始train模型（默认使用RNN），待训练结束。
* 2，运行`python infer.py`命令做prediction。（输入的文本默认为`data/input.txt`，生成的文本默认保存到`data/output.txt`中。）
**如果用户需要使用自己的语料、定制模型，需要修改的地方主要是`语料`和`config.py`中的配置，需要注意的细节和适配工作详情如下：**
### 语料适配
* 清洗语料：去除原文中空格、tab、乱码，按需去除数字、标点符号、特殊符号等。
* 编码格式：utf-8，本例中已经对中文做了适配。
* 内容格式：每个句子占一行；每行中的各词之间使用一个空格符分开。
* 按需要配置`config.py`中对于data的配置：
```python
# -- config : data --
train_file = 'data/chinese.train.txt'
test_file = 'data/chinese.test.txt'
vocab_file = 'data/vocab_cn.txt' # the file to save vocab
build_vocab_method = 'fixed_size' # 'frequency' or 'fixed_size'
vocab_max_size = 3000 # when build_vocab_method = 'fixed_size'
unk_threshold = 1 # # when build_vocab_method = 'frequency'
min_sentence_length = 3
max_sentence_length = 60
```
其中，`build_vocab_method `指定了构建词典的方法：**1，按词频**，即将出现次数小于`unk_threshold `的词视为`<UNK>`；**2，按词典长度**，`vocab_max_size`定义了词典的最大长度，如果语料中出现的不同词的个数大于这个值，则根据各词的词频倒序排，取`top(vocab_max_size)`个词纳入词典。
其中`min_sentence_length`和`max_sentence_length `分别指定了句子的最小和最大长度，小于最小长度的和大于最大长度的句子将被过滤掉、不参与训练。
*注：需要注意的是词典越大生成的内容越丰富但训练耗时越久，一般中文分词之后，语料中不同的词能有几万乃至几十万，如果vocab\_max\_size取值过小则导致\<UNK\>占比过高，如果vocab\_max\_size取值较大则严重影响训练速度（对精度也有影响），所以也有“按字”训练模型的方式，即：把每个汉字当做一个词，常用汉字也就几千个，使得字典的大小不会太大、不会丢失太多信息，但汉语中同一个字在不同词中语义相差很大，有时导致模型效果不理想。建议用户多试试、根据实际情况选择是“按词训练”还是“按字训练”。*
### 模型适配、训练
* 按需调整`config.py`中对于模型的配置，详解如下：
```python
# -- config : train --
use_which_model = 'rnn' # must be: 'rnn' or 'ngram'
use_gpu = False # whether to use gpu
trainer_count = 1 # number of trainer
class Config_rnn(object):
"""
config for RNN language model
"""
rnn_type = 'gru' # or 'lstm'
emb_dim = 200
hidden_size = 200
num_layer = 2
num_passs = 2
batch_size = 32
model_file_name_prefix = 'lm_' + rnn_type + '_params_pass_'
class Config_ngram(object):
"""
config for N-Gram language model
"""
emb_dim = 200
hidden_size = 200
num_layer = 2
N = 5
num_passs = 2
batch_size = 32
model_file_name_prefix = 'lm_ngram_pass_'
```
其中，`use_which_model`指定了要train的模型，如果使用RNN语言模型则设置为'rnn'，如果使用N-Gram语言模型则设置为'ngram'；`use_gpu`指定了train的时候是否使用gpu；`trainer_count`指定了并行度、用几个trainer去train模型；`rnn_type` 用于配置rnn cell类型，可以取‘lstm’或‘gru’；`hidden_size`配置unit个数；`num_layer`配置RNN的层数；`num_passs`配置训练的轮数；`emb_dim`配置embedding的dimension；`batch_size `配置了train model时每个batch的大小；`model_file_name_prefix `配置了要保存的模型的名字前缀。
* 运行`python train.py`命令训练模型，模型将被保存到当前目录。
### 按需生成文本
* 按需调整`config.py`中对于infer的配置，详解如下：
```python
# -- config : infer --
input_file = 'data/input.txt' # input file contains sentence prefix each line
output_file = 'data/output.txt' # the file to save results
num_words = 10 # the max number of words need to generate
beam_size = 5 # beam_width, the number of the prediction sentence for each prefix
```
其中，`input_file`中保存的是待生成的文本前缀，utf-8编码，每个前缀占一行，形如：
```text
我
我 是
```
用户将需要生成的文本前缀按此格式存入文件即可；
`num_words`指定了要生成多少个单词（实际生成过程中遇到结束符会停止生成，所以实际生成的词个数可能会比此值小）；`beam_size`指定了beam search方法的width，即每个前缀生成多少个候选词序列；`output_file`指定了生成结果的存放位置。
* 运行`python infer.py`命令生成文本，生成的结果格式如下：
```text
我
我 <EOS> 0.107702672482
我 爱 。我 中国 中国 <EOS> 0.000177299271939
我 爱 中国 。我 是 中国 <EOS> 4.51695544709e-05
我 爱 中国 中国 <EOS> 0.000910127729821
我 爱 中国 。我 是 <EOS> 0.00015957862922
```
其中，‘我’是前缀，其下方的五个句子时补全的结果，每个句子末尾的浮点数表示此句子的生成概率。
language_model/config.py 0 → 100644
浏览文件 @ c8b0ae8f
# coding=utf-8
# -- config : data --
train_file = 'data/chinese.train.txt'
test_file = 'data/chinese.test.txt'
vocab_file = 'data/vocab_cn.txt' # the file to save vocab
build_vocab_method = 'fixed_size' # 'frequency' or 'fixed_size'
vocab_max_size = 3000 # when build_vocab_method = 'fixed_size'
unk_threshold = 1 # # when build_vocab_method = 'frequency'
min_sentence_length = 3
max_sentence_length = 60
# -- config : train --
use_which_model = 'ngram' # must be: 'rnn' or 'ngram'
use_gpu = False # whether to use gpu
trainer_count = 1 # number of trainer
class Config_rnn(object):
"""
config for RNN language model
"""
rnn_type = 'gru' # or 'lstm'
emb_dim = 200
hidden_size = 200
num_layer = 2
num_passs = 2
batch_size = 32
model_file_name_prefix = 'lm_' + rnn_type + '_params_pass_'
class Config_ngram(object):
"""
config for N-Gram language model
"""
emb_dim = 200
hidden_size = 200
num_layer = 2
N = 5
num_passs = 2
batch_size = 32
model_file_name_prefix = 'lm_ngram_pass_'
# -- config : infer --
input_file = 'data/input.txt' # input file contains sentence prefix each line
output_file = 'data/output.txt' # the file to save results
num_words = 10 # the max number of words need to generate
beam_size = 5 # beam_width, the number of the prediction sentence for each prefix
language_model/data/chinese.test.txt 0 → 100755
浏览文件 @ c8b0ae8f
我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。
\ No newline at end of file
language_model/data/chinese.train.txt 0 → 100755
浏览文件 @ c8b0ae8f
我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。我 是 中国 人 。
我 爱 中国 。
\ No newline at end of file
language_model/data/input.txt 0 → 100644
浏览文件 @ c8b0ae8f
我
我 是
我 是 中国
我 爱
我 是 中国 人。
我 爱 中国
我 爱 中国 。我
我 爱 中国 。我 爱
我 爱 中国 。我 是
我 爱 中国 。我 是 中国
\ No newline at end of file
language_model/infer.py 0 → 100644
浏览文件 @ c8b0ae8f
# coding=utf-8
import paddle.v2 as paddle
import gzip
import numpy as np
from utils import *
import network_conf
from config import *
def generate_using_rnn(word_id_dict, num_words, beam_size):
"""
Demo: use RNN model to do prediction.
:param word_id_dict: vocab.
:type word_id_dict: dictionary with content of '{word, id}', 'word' is string type , 'id' is int type.
:param num_words: the number of the words to generate.
:type num_words: int
:param beam_size: beam width.
:type beam_size: int
:return: save prediction results to output_file
"""
# prepare and cache model
config = Config_rnn()
_, output_layer = network_conf.rnn_lm(
vocab_size=len(word_id_dict),
emb_dim=config.emb_dim,
rnn_type=config.rnn_type,
hidden_size=config.hidden_size,
num_layer=config.num_layer) # network config
model_file_name = config.model_file_name_prefix + str(config.num_passs -
1) + '.tar.gz'
parameters = paddle.parameters.Parameters.from_tar(
gzip.open(model_file_name)) # load parameters
inferer = paddle.inference.Inference(
output_layer=output_layer, parameters=parameters)
# tools, different from generate_using_ngram's tools
id_word_dict = dict(
[(v, k) for k, v in word_id_dict.items()]) # {id : word}
def str2ids(str):
return [[[
word_id_dict.get(w, word_id_dict['<UNK>']) for w in str.split()
]]]
def ids2str(ids):
return [[[id_word_dict.get(id, ' ') for id in ids]]]
# generate text
with open(input_file) as file:
output_f = open(output_file, 'w')
for line in file:
line = line.decode('utf-8').strip()
# generate
texts = {} # type: {text : probability}
texts[line] = 1
for _ in range(num_words):
texts_new = {}
for (text, prob) in texts.items():
if '<EOS>' in text: # stop prediction when <EOS> appear
texts_new[text] = prob
continue
# next word's probability distribution
predictions = inferer.infer(input=str2ids(text))
predictions[-1][word_id_dict['<UNK>']] = -1 # filter <UNK>
# find next beam_size words
for _ in range(beam_size):
cur_maxProb_index = np.argmax(
predictions[-1]) # next word's id
text_new = text + ' ' + id_word_dict[
cur_maxProb_index] # text append next word
texts_new[text_new] = texts[text] * predictions[-1][
cur_maxProb_index]
predictions[-1][cur_maxProb_index] = -1
texts.clear()
if len(texts_new) <= beam_size:
texts = texts_new
else: # cutting
texts = dict(
sorted(
texts_new.items(), key=lambda d: d[1], reverse=True)
[:beam_size])
# save results to output file
output_f.write(line.encode('utf-8') + '\n')
for (sentence, prob) in texts.items():
output_f.write('\t' + sentence.encode('utf-8', 'replace') + '\t'
+ str(prob) + '\n')
output_f.write('\n')
output_f.close()
print('already saved results to ' + output_file)
def generate_using_ngram(word_id_dict, num_words, beam_size):
"""
Demo: use N-Gram model to do prediction.
:param word_id_dict: vocab.
:type word_id_dict: dictionary with content of '{word, id}', 'word' is string type , 'id' is int type.
:param num_words: the number of the words to generate.
:type num_words: int
:param beam_size: beam width.
:type beam_size: int
:return: save prediction results to output_file
"""
# prepare and cache model
config = Config_ngram()
_, output_layer = network_conf.ngram_lm(
vocab_size=len(word_id_dict),
emb_dim=config.emb_dim,
hidden_size=config.hidden_size,
num_layer=config.num_layer) # network config
model_file_name = config.model_file_name_prefix + str(config.num_passs -
1) + '.tar.gz'
parameters = paddle.parameters.Parameters.from_tar(
gzip.open(model_file_name)) # load parameters
inferer = paddle.inference.Inference(
output_layer=output_layer, parameters=parameters)
# tools, different from generate_using_rnn's tools
id_word_dict = dict(
[(v, k) for k, v in word_id_dict.items()]) # {id : word}
def str2ids(str):
return [[
word_id_dict.get(w, word_id_dict['<UNK>']) for w in str.split()
]]
def ids2str(ids):
return [[id_word_dict.get(id, ' ') for id in ids]]
# generate text
with open(input_file) as file:
output_f = open(output_file, 'w')
for line in file:
line = line.decode('utf-8').strip()
words = line.split()
if len(words) < config.N:
output_f.write(line.encode('utf-8') + "\n\tnone\n")
continue
# generate
texts = {} # type: {text : probability}
texts[line] = 1
for _ in range(num_words):
texts_new = {}
for (text, prob) in texts.items():
if '<EOS>' in text: # stop prediction when <EOS> appear
texts_new[text] = prob
continue
# next word's probability distribution
predictions = inferer.infer(
input=str2ids(' '.join(text.split()[-config.N:])))
predictions[-1][word_id_dict['<UNK>']] = -1 # filter <UNK>
# find next beam_size words
for _ in range(beam_size):
cur_maxProb_index = np.argmax(
predictions[-1]) # next word's id
text_new = text + ' ' + id_word_dict[
cur_maxProb_index] # text append nextWord
texts_new[text_new] = texts[text] * predictions[-1][
cur_maxProb_index]
predictions[-1][cur_maxProb_index] = -1
texts.clear()
if len(texts_new) <= beam_size:
texts = texts_new
else: # cutting
texts = dict(
sorted(
texts_new.items(), key=lambda d: d[1], reverse=True)
[:beam_size])
# save results to output file
output_f.write(line.encode('utf-8') + '\n')
for (sentence, prob) in texts.items():
output_f.write('\t' + sentence.encode('utf-8', 'replace') + '\t'
+ str(prob) + '\n')
output_f.write('\n')
output_f.close()
print('already saved results to ' + output_file)
def main():
# init paddle
paddle.init(use_gpu=use_gpu, trainer_count=trainer_count)
# prepare and cache vocab
if os.path.isfile(vocab_file):
word_id_dict = load_vocab(vocab_file) # load word dictionary
else:
if build_vocab_method == 'fixed_size':
word_id_dict = build_vocab_with_fixed_size(
train_file, vocab_max_size) # build vocab
else:
word_id_dict = build_vocab_using_threshhold(
train_file, unk_threshold) # build vocab
save_vocab(word_id_dict, vocab_file) # save vocab
# generate
if use_which_model == 'rnn':
generate_using_rnn(
word_id_dict=word_id_dict, num_words=num_words, beam_size=beam_size)
elif use_which_model == 'ngram':
generate_using_ngram(
word_id_dict=word_id_dict, num_words=num_words, beam_size=beam_size)
else:
raise Exception('use_which_model must be rnn or ngram!')
if __name__ == "__main__":
main()
language_model/network_conf.py 0 → 100644
浏览文件 @ c8b0ae8f
# coding=utf-8
import paddle.v2 as paddle
def rnn_lm(vocab_size, emb_dim, rnn_type, hidden_size, num_layer):
"""
RNN language model definition.
:param vocab_size: size of vocab.
:param emb_dim: embedding vector's dimension.
:param rnn_type: the type of RNN cell.
:param hidden_size: number of unit.
:param num_layer: layer number.
:return: cost and output layer of model.
"""
assert emb_dim > 0 and hidden_size > 0 and vocab_size > 0 and num_layer > 0
# input layers
input = paddle.layer.data(
name="input", type=paddle.data_type.integer_value_sequence(vocab_size))
target = paddle.layer.data(
name="target", type=paddle.data_type.integer_value_sequence(vocab_size))
# embedding layer
input_emb = paddle.layer.embedding(input=input, size=emb_dim)
# rnn layer
if rnn_type == 'lstm':
rnn_cell = paddle.networks.simple_lstm(
input=input_emb, size=hidden_size)
for _ in range(num_layer - 1):
rnn_cell = paddle.networks.simple_lstm(
input=rnn_cell, size=hidden_size)
elif rnn_type == 'gru':
rnn_cell = paddle.networks.simple_gru(input=input_emb, size=hidden_size)
for _ in range(num_layer - 1):
rnn_cell = paddle.networks.simple_gru(
input=rnn_cell, size=hidden_size)
else:
raise Exception('rnn_type error!')
# fc(full connected) and output layer
output = paddle.layer.fc(
input=[rnn_cell], size=vocab_size, act=paddle.activation.Softmax())
# loss
cost = paddle.layer.classification_cost(input=output, label=target)
return cost, output
def ngram_lm(vocab_size, emb_dim, hidden_size, num_layer):
"""
N-Gram language model definition.
:param vocab_size: size of vocab.
:param emb_dim: embedding vector's dimension.
:param hidden_size: size of unit.
:param num_layer: layer number.
:return: cost and output layer of model.
"""
assert emb_dim > 0 and hidden_size > 0 and vocab_size > 0 and num_layer > 0
def wordemb(inlayer):
wordemb = paddle.layer.table_projection(
input=inlayer,
size=emb_dim,
param_attr=paddle.attr.Param(
name="_proj", initial_std=0.001, learning_rate=1, l2_rate=0))
return wordemb
# input layers
first_word = paddle.layer.data(
name="first_word", type=paddle.data_type.integer_value(vocab_size))
second_word = paddle.layer.data(
name="second_word", type=paddle.data_type.integer_value(vocab_size))
third_word = paddle.layer.data(
name="third_word", type=paddle.data_type.integer_value(vocab_size))
fourth_word = paddle.layer.data(
name="fourth_word", type=paddle.data_type.integer_value(vocab_size))
next_word = paddle.layer.data(
name="next_word", type=paddle.data_type.integer_value(vocab_size))
# embedding layer
first_emb = wordemb(first_word)
second_emb = wordemb(second_word)
third_emb = wordemb(third_word)
fourth_emb = wordemb(fourth_word)
context_emb = paddle.layer.concat(
input=[first_emb, second_emb, third_emb, fourth_emb])
# hidden layer
hidden = paddle.layer.fc(
input=context_emb, size=hidden_size, act=paddle.activation.Relu())
for _ in range(num_layer - 1):
hidden = paddle.layer.fc(
input=hidden, size=hidden_size, act=paddle.activation.Relu())
# fc(full connected) and output layer
predict_word = paddle.layer.fc(
input=[hidden], size=vocab_size, act=paddle.activation.Softmax())
# loss
cost = paddle.layer.classification_cost(input=predict_word, label=next_word)
return cost, predict_word
language_model/reader.py 0 → 100644
浏览文件 @ c8b0ae8f
# coding=utf-8
import collections
import os
def rnn_reader(file_name, min_sentence_length, max_sentence_length,
word_id_dict):
"""
create reader for RNN, each line is a sample.
:param file_name: file name.
:param min_sentence_length: sentence's min length.
:param max_sentence_length: sentence's max length.
:param word_id_dict: vocab with content of '{word, id}', 'word' is string type , 'id' is int type.
:return: data reader.
"""
def reader():
UNK = word_id_dict['<UNK>']
with open(file_name) as file:
for line in file:
words = line.decode('utf-8', 'ignore').strip().split()
if len(words) < min_sentence_length or len(
words) > max_sentence_length:
continue
ids = [word_id_dict.get(w, UNK) for w in words]
ids.append(word_id_dict['<EOS>'])
target = ids[1:]
target.append(word_id_dict['<EOS>'])
yield ids[:], target[:]
return reader
def ngram_reader(file_name, N, word_id_dict):
"""
create reader for N-Gram.
:param file_name: file name.
:param N: N-Gram's N.
:param word_id_dict: vocab with content of '{word, id}', 'word' is string type , 'id' is int type.
:return: data reader.
"""
assert N >= 2
def reader():
ids = []
UNK_ID = word_id_dict['<UNK>']
cache_size = 10000000
with open(file_name) as file:
for line in file:
words = line.decode('utf-8', 'ignore').strip().split()
ids += [word_id_dict.get(w, UNK_ID) for w in words]
ids_len = len(ids)
if ids_len > cache_size: # output
for i in range(ids_len - N - 1):
yield tuple(ids[i:i + N])
ids = []
ids_len = len(ids)
for i in range(ids_len - N - 1):
yield tuple(ids[i:i + N])
return reader
language_model/train.py 0 → 100644
浏览文件 @ c8b0ae8f
# coding=utf-8
import sys
import paddle.v2 as paddle
import reader
from utils import *
import network_conf
import gzip
from config import *
def train(model_cost, train_reader, test_reader, model_file_name_prefix,
num_passes):
"""
train model.
:param model_cost: cost layer of the model to train.
:param train_reader: train data reader.
:param test_reader: test data reader.
:param model_file_name_prefix: model's prefix name.
:param num_passes: epoch.
:return:
"""
# init paddle
paddle.init(use_gpu=use_gpu, trainer_count=trainer_count)
# create parameters
parameters = paddle.parameters.create(model_cost)
# create optimizer
adam_optimizer = paddle.optimizer.Adam(
learning_rate=1e-3,
regularization=paddle.optimizer.L2Regularization(rate=1e-3),
model_average=paddle.optimizer.ModelAverage(
average_window=0.5, max_average_window=10000))
# create trainer
trainer = paddle.trainer.SGD(
cost=model_cost, parameters=parameters, update_equation=adam_optimizer)
# define event_handler callback
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print("\nPass %d, Batch %d, Cost %f, %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics))
else:
sys.stdout.write('.')
sys.stdout.flush()
# save model each pass
if isinstance(event, paddle.event.EndPass):
result = trainer.test(reader=test_reader)
print("\nTest with Pass %d, %s" % (event.pass_id, result.metrics))
with gzip.open(
model_file_name_prefix + str(event.pass_id) + '.tar.gz',
'w') as f:
parameters.to_tar(f)
# start to train
print('start training...')
trainer.train(
reader=train_reader, event_handler=event_handler, num_passes=num_passes)
print("Training finished.")
def main():
# prepare vocab
print('prepare vocab...')
if build_vocab_method == 'fixed_size':
word_id_dict = build_vocab_with_fixed_size(
train_file, vocab_max_size) # build vocab
else:
word_id_dict = build_vocab_using_threshhold(
train_file, unk_threshold) # build vocab
save_vocab(word_id_dict, vocab_file) # save vocab
# init model and data reader
if use_which_model == 'rnn':
# init RNN model
print('prepare rnn model...')
config = Config_rnn()
cost, _ = network_conf.rnn_lm(
len(word_id_dict), config.emb_dim, config.rnn_type,
config.hidden_size, config.num_layer)
# init RNN data reader
train_reader = paddle.batch(
paddle.reader.shuffle(
reader.rnn_reader(train_file, min_sentence_length,
max_sentence_length, word_id_dict),
buf_size=65536),
batch_size=config.batch_size)
test_reader = paddle.batch(
paddle.reader.shuffle(
reader.rnn_reader(test_file, min_sentence_length,
max_sentence_length, word_id_dict),
buf_size=65536),
batch_size=config.batch_size)
elif use_which_model == 'ngram':
# init N-Gram model
print('prepare ngram model...')
config = Config_ngram()
assert config.N == 5
cost, _ = network_conf.ngram_lm(
vocab_size=len(word_id_dict),
emb_dim=config.emb_dim,
hidden_size=config.hidden_size,
num_layer=config.num_layer)
# init N-Gram data reader
train_reader = paddle.batch(
paddle.reader.shuffle(
reader.ngram_reader(train_file, config.N, word_id_dict),
buf_size=65536),
batch_size=config.batch_size)
test_reader = paddle.batch(
paddle.reader.shuffle(
reader.ngram_reader(test_file, config.N, word_id_dict),
buf_size=65536),
batch_size=config.batch_size)
else:
raise Exception('use_which_model must be rnn or ngram!')
# train model
train(
model_cost=cost,
train_reader=train_reader,
test_reader=test_reader,
model_file_name_prefix=config.model_file_name_prefix,
num_passes=config.num_passs)
if __name__ == "__main__":
main()
language_model/utils.py 0 → 100644
浏览文件 @ c8b0ae8f
# coding=utf-8
import os
import collections
def save_vocab(word_id_dict, vocab_file_name):
"""
save vocab.
:param word_id_dict: dictionary with content of '{word, id}', 'word' is string type , 'id' is int type.
:param vocab_file_name: vocab file name.
"""
f = open(vocab_file_name, 'w')
for (k, v) in word_id_dict.items():
f.write(k.encode('utf-8') + '\t' + str(v) + '\n')
print('save vocab to ' + vocab_file_name)
f.close()
def load_vocab(vocab_file_name):
"""
load vocab from file.
:param vocab_file_name: vocab file name.
:return: dictionary with content of '{word, id}', 'word' is string type , 'id' is int type.
"""
assert os.path.isfile(vocab_file_name)
dict = {}
with open(vocab_file_name) as file:
for line in file:
if len(line) < 2:
continue
kv = line.decode('utf-8').strip().split('\t')
dict[kv[0]] = int(kv[1])
return dict
def build_vocab_using_threshhold(file_name, unk_threshold):
"""
build vacab using_<UNK> threshhold.
:param file_name:
:param unk_threshold: <UNK> threshhold.
:type unk_threshold: int.
:return: dictionary with content of '{word, id}', 'word' is string type , 'id' is int type.
"""
counter = {}
with open(file_name) as file:
for line in file:
words = line.decode('utf-8', 'ignore').strip().split()
for word in words:
if word in counter:
counter[word] += 1
else:
counter[word] = 1
counter_new = {}
for (word, frequency) in counter.items():
if frequency >= unk_threshold:
counter_new[word] = frequency
counter.clear()
counter_new = sorted(counter_new.items(), key=lambda d: -d[1])
words = [word_frequency[0] for word_frequency in counter_new]
word_id_dict = dict(zip(words, range(2, len(words) + 2)))
word_id_dict['<UNK>'] = 0
word_id_dict['<EOS>'] = 1
return word_id_dict
def build_vocab_with_fixed_size(file_name, vocab_max_size):
"""
build vacab with assigned max size.
:param vocab_max_size: vocab's max size.
:return: dictionary with content of '{word, id}', 'word' is string type , 'id' is int type.
"""
words = []
for line in open(file_name):
words += line.decode('utf-8', 'ignore').strip().split()
counter = collections.Counter(words)
counter = sorted(counter.items(), key=lambda x: -x[1])
if len(counter) > vocab_max_size:
counter = counter[:vocab_max_size]
words, counts = zip(*counter)
word_id_dict = dict(zip(words, range(2, len(words) + 2)))
word_id_dict['<UNK>'] = 0
word_id_dict['<EOS>'] = 1
return word_id_dict
scheduled_sampling/README.md
浏览文件 @ c8b0ae8f
TBD
# Scheduled Sampling
## 概述
序列生成任务的生成目标是在给定源输入的条件下，最大化目标序列的概率。训练时该模型将目标序列中的真实元素作为解码器每一步的输入，然后最大化下一个元素的概率。生成时上一步解码得到的元素被用作当前的输入，然后生成下一个元素。可见这种情况下训练阶段和生成阶段的解码器输入数据的概率分布并不一致。
Scheduled Sampling\[[1](#参考文献)\]是一种解决训练和生成时输入数据分布不一致的方法。在训练早期该方法主要使用目标序列中的真实元素作为解码器输入，可以将模型从随机初始化的状态快速引导至一个合理的状态。随着训练的进行，该方法会逐渐更多地使用生成的元素作为解码器输入，以解决数据分布不一致的问题。
标准的序列到序列模型中，如果序列前面生成了错误的元素，后面的输入状态将会收到影响，而该误差会随着生成过程不断向后累积。Scheduled Sampling以一定概率将生成的元素作为解码器输入，这样即使前面生成错误，其训练目标仍然是最大化真实目标序列的概率，模型会朝着正确的方向进行训练。因此这种方式增加了模型的容错能力。
## 算法简介
Scheduled Sampling主要应用在序列到序列模型的训练阶段，而生成阶段则不需要使用。
训练阶段解码器在最大化第$t$个元素概率时，标准序列到序列模型使用上一时刻的真实元素$y_{t-1}$作为输入。设上一时刻生成的元素为$g_{t-1}$，Scheduled Sampling算法会以一定概率使用$g_{t-1}$作为解码器输入。
设当前已经训练到了第$i$个mini-batch，Scheduled Sampling定义了一个概率$\epsilon_i$控制解码器的输入。$\epsilon_i$是一个随着$i$增大而衰减的变量，常见的定义方式有：
- 线性衰减：$\epsilon_i=max(\epsilon,k-c*i)$，其中$\epsilon$限制$\epsilon_i$的最小值，$k$和$c$控制线性衰减的幅度。
- 指数衰减：$\epsilon_i=k^i$，其中$0<k<1$，$k$控制着指数衰减的幅度。
- 反向Sigmoid衰减：$\epsilon_i=k/(k+exp(i/k))$，其中$k>1$，$k$同样控制衰减的幅度。
图1给出了这三种方式的衰减曲线，
<p align="center">
<img src="img/decay.jpg" width="50%" align="center"><br>
图1. 线性衰减、指数衰减和反向Sigmoid衰减的衰减曲线
</p>
如图2所示，在解码器的$t$时刻Scheduled Sampling以概率$\epsilon_i$使用上一时刻的真实元素$y_{t-1}$作为解码器输入，以概率$1-\epsilon_i$使用上一时刻生成的元素$g_{t-1}$作为解码器输入。从图1可知随着$i$的增大$\epsilon_i$会不断减小，解码器将不断倾向于使用生成的元素作为输入，训练阶段和生成阶段的数据分布将变得越来越一致。
<p align="center">
<img src="img/Scheduled_Sampling.jpg" width="50%" align="center"><br>
图2. Scheduled Sampling选择不同元素作为解码器输入示意图
</p>
## 模型实现
由于Scheduled Sampling是对序列到序列模型的改进，其整体实现框架与序列到序列模型较为相似。为突出本文重点，这里仅介绍与Scheduled Sampling相关的部分，完整的代码见`scheduled_sampling.py`。
首先导入需要的包，并定义控制衰减概率的类`RandomScheduleGenerator`，如下：
```python
import numpy as np
import math
class RandomScheduleGenerator:
"""
The random sampling rate for scheduled sampling algoithm, which uses devcayed
sampling rate.
"""
...
```
下面将分别定义类`RandomScheduleGenerator`的`__init__`、`getScheduleRate`和`processBatch`三个方法。
`__init__`方法对类进行初始化，其`schedule_type`参数指定了使用哪种衰减方式，可选的方式有`constant`、`linear`、`exponential`和`inverse_sigmoid`。`constant`指对所有的mini-batch使用固定的$\epsilon_i$，`linear`指线性衰减方式，`exponential`表示指数衰减方式，`inverse_sigmoid`表示反向Sigmoid衰减。`__init__`方法的参数`a`和`b`表示衰减方法的参数，需要在验证集上调优。`self.schedule_computers`将衰减方式映射为计算$\epsilon_i$的函数。最后一行根据`schedule_type`将选择的衰减函数赋给`self.schedule_computer`变量。
```python
def __init__(self, schedule_type, a, b):
"""
schduled_type: is the type of the decay. It supports constant, linear,
exponential, and inverse_sigmoid right now.
a: parameter of the decay (MUST BE DOUBLE)
b: parameter of the decay (MUST BE DOUBLE)
"""
self.schedule_type = schedule_type
self.a = a
self.b = b
self.data_processed_ = 0
self.schedule_computers = {
"constant": lambda a, b, d: a,
"linear": lambda a, b, d: max(a, 1 - d / b),
"exponential": lambda a, b, d: pow(a, d / b),
"inverse_sigmoid": lambda a, b, d: b / (b + math.exp(d * a / b)),
}
assert (self.schedule_type in self.schedule_computers)
self.schedule_computer = self.schedule_computers[self.schedule_type]
```
`getScheduleRate`根据衰减函数和已经处理的数据量计算$\epsilon_i$。
```python
def getScheduleRate(self):
"""
Get the schedule sampling rate. Usually not needed to be called by the users
"""
return self.schedule_computer(self.a, self.b, self.data_processed_)
```
`processBatch`方法根据概率值$\epsilon_i$进行采样，得到`indexes`，`indexes`中每个元素取值为`0`的概率为$\epsilon_i$，取值为`1`的概率为$1-\epsilon_i$。`indexes`决定了解码器的输入是真实元素还是生成的元素，取值为`0`表示使用真实元素，取值为`1`表示使用生成的元素。
```python
def processBatch(self, batch_size):
"""
Get a batch_size of sampled indexes. These indexes can be passed to a
MultiplexLayer to select from the grouth truth and generated samples
from the last time step.
"""
rate = self.getScheduleRate()
numbers = np.random.rand(batch_size)
indexes = (numbers >= rate).astype('int32').tolist()
self.data_processed_ += batch_size
return indexes
```
Scheduled Sampling需要在序列到序列模型的基础上增加一个输入`true_token_flag`，以控制解码器输入。
```python
true_token_flags = paddle.layer.data(
name='true_token_flag',
type=paddle.data_type.integer_value_sequence(2))
```
这里还需要对原始reader进行封装，增加`true_token_flag`的数据生成器。下面以线性衰减为例说明如何调用上面定义的`RandomScheduleGenerator`产生`true_token_flag`的输入数据。
```python
schedule_generator = RandomScheduleGenerator("linear", 0.75, 1000000)
def gen_schedule_data(reader):
"""
Creates a data reader for scheduled sampling.
Output from the iterator that created by original reader will be
appended with "true_token_flag" to indicate whether to use true token.
:param reader: the original reader.
:type reader: callable
:return: the new reader with the field "true_token_flag".
:rtype: callable
"""
def data_reader():
for src_ids, trg_ids, trg_ids_next in reader():
yield src_ids, trg_ids, trg_ids_next, \
[0] + schedule_generator.processBatch(len(trg_ids) - 1)
return data_reader
```
这段代码在原始输入数据（即源序列元素`src_ids`、目标序列元素`trg_ids`和目标序列下一个元素`trg_ids_next`）后追加了控制解码器输入的数据。由于解码器第一个元素是序列开始符，因此将追加的数据第一个元素设置为`0`，表示解码器第一步始终使用真实目标序列的第一个元素（即序列开始符）。
训练时`recurrent_group`每一步调用的解码器函数如下：
```python
def gru_decoder_with_attention_train(enc_vec, enc_proj, true_word,
true_token_flag):
"""
The decoder step for training.
:param enc_vec: the encoder vector for attention
:type enc_vec: LayerOutput
:param enc_proj: the encoder projection for attention
:type enc_proj: LayerOutput
:param true_word: the ground-truth target word
:type true_word: LayerOutput
:param true_token_flag: the flag of using the ground-truth target word
:type true_token_flag: LayerOutput
:return: the softmax output layer
:rtype: LayerOutput
"""
decoder_mem = paddle.layer.memory(
name='gru_decoder', size=decoder_size, boot_layer=decoder_boot)
context = paddle.networks.simple_attention(
encoded_sequence=enc_vec,
encoded_proj=enc_proj,
decoder_state=decoder_mem)
gru_out_memory = paddle.layer.memory(
name='gru_out', size=target_dict_dim)
generated_word = paddle.layer.max_id(input=gru_out_memory)
generated_word_emb = paddle.layer.embedding(
input=generated_word,
size=word_vector_dim,
param_attr=paddle.attr.ParamAttr(name='_target_language_embedding'))
current_word = paddle.layer.multiplex(
input=[true_token_flag, true_word, generated_word_emb])
with paddle.layer.mixed(size=decoder_size * 3) as decoder_inputs:
decoder_inputs += paddle.layer.full_matrix_projection(input=context)
decoder_inputs += paddle.layer.full_matrix_projection(
input=current_word)
gru_step = paddle.layer.gru_step(
name='gru_decoder',
input=decoder_inputs,
output_mem=decoder_mem,
size=decoder_size)
with paddle.layer.mixed(
name='gru_out',
size=target_dict_dim,
bias_attr=True,
act=paddle.activation.Softmax()) as out:
out += paddle.layer.full_matrix_projection(input=gru_step)
return out
```
该函数使用`memory`层`gru_out_memory`记忆上一时刻生成的元素，根据`gru_out_memory`选择概率最大的词语`generated_word`作为生成的词语。`multiplex`层会在真实元素`true_word`和生成的元素`generated_word`之间做出选择，并将选择的结果作为解码器输入。`multiplex`层使用了三个输入，分别为`true_token_flag`、`true_word`和`generated_word_emb`。对于这三个输入中每个元素，若`true_token_flag`中的值为`0`，则`multiplex`层输出`true_word`中的相应元素；若`true_token_flag`中的值为`1`，则`multiplex`层输出`generated_word_emb`中的相应元素。
## 参考文献
[1] Bengio S, Vinyals O, Jaitly N, et al. [Scheduled sampling for sequence prediction with recurrent neural networks](http://papers.nips.cc/paper/5956-scheduled-sampling-for-sequence-prediction-with-recurrent-neural-networks)//Advances in Neural Information Processing Systems. 2015: 1171-1179.
scheduled_sampling/random_schedule_generator.py 0 → 100644
浏览文件 @ c8b0ae8f
import numpy as np
import math
class RandomScheduleGenerator:
"""
The random sampling rate for scheduled sampling algoithm, which uses devcayed
sampling rate.
"""
def __init__(self, schedule_type, a, b):
"""
schduled_type: is the type of the decay. It supports constant, linear,
exponential, and inverse_sigmoid right now.
a: parameter of the decay (MUST BE DOUBLE)
b: parameter of the decay (MUST BE DOUBLE)
"""
self.schedule_type = schedule_type
self.a = a
self.b = b
self.data_processed_ = 0
self.schedule_computers = {
"constant": lambda a, b, d: a,
"linear": lambda a, b, d: max(a, 1 - d / b),
"exponential": lambda a, b, d: pow(a, d / b),
"inverse_sigmoid": lambda a, b, d: b / (b + math.exp(d * a / b)),
}
assert (self.schedule_type in self.schedule_computers)
self.schedule_computer = self.schedule_computers[self.schedule_type]
def getScheduleRate(self):
"""
Get the schedule sampling rate. Usually not needed to be called by the users
"""
return self.schedule_computer(self.a, self.b, self.data_processed_)
def processBatch(self, batch_size):
"""
Get a batch_size of sampled indexes. These indexes can be passed to a
MultiplexLayer to select from the grouth truth and generated samples
from the last time step.
"""
rate = self.getScheduleRate()
numbers = np.random.rand(batch_size)
indexes = (numbers >= rate).astype('int32').tolist()
self.data_processed_ += batch_size
return indexes
scheduled_sampling/scheduled_sampling.py 0 → 100644
浏览文件 @ c8b0ae8f
此差异已折叠。
text_classification/.gitignore 0 → 100644
浏览文件 @ c8b0ae8f
data
*.tar.gz
*.log
*.pyc
text_classification/README.md
浏览文件 @ c8b0ae8f
此差异已折叠。
text_classification/index.html
浏览文件 @ c8b0ae8f
此差异已折叠。
text_classification/infer.py 0 → 100644
浏览文件 @ c8b0ae8f
此差异已折叠。
text_classification/network_conf.py 0 → 100644
浏览文件 @ c8b0ae8f
此差异已折叠。
text_classification/reader.py 0 → 100644
浏览文件 @ c8b0ae8f
此差异已折叠。
text_classification/run.sh 0 → 100644
浏览文件 @ c8b0ae8f
#!/bin/sh
python train.py \
--nn_type="dnn" \
--batch_size=64 \
--num_passes=10 \
2>&1 | tee train.log
text_classification/train.py 0 → 100644
浏览文件 @ c8b0ae8f
此差异已折叠。
text_classification/utils.py 0 → 100644
浏览文件 @ c8b0ae8f
此差异已折叠。
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或