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Easy-to-use Speech Toolkit including SOTA/Streaming ASR with punctuation, influential TTS with text frontend, Speaker Verification System and End-to-End Speech Simultaneous Translation.
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Yibing Liu 8ba98cb518
fix decoders' unittest 		7 years ago
data_utils Patch for adding missing shift_perturb.py in last commmit (pull request #114). 7 years ago
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lm refine ctc_beam_search_decoder 7 years ago
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README.md Improve audio featurizer and add shift augmentor. 7 years ago
compute_mean_std.py Enable min_batch_num in train.py and update train info print. 7 years ago
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model.py Add function, class and module docs for data parts in DS2. 7 years ago
requirements.txt refine ctc_beam_search_decoder 7 years ago
setup.sh Improve audio featurizer and add shift augmentor. 7 years ago
train.py Improve audio featurizer and add shift augmentor. 7 years ago
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README.md

Deep Speech 2 on PaddlePaddle

Installation

Please replace $PADDLE_INSTALL_DIR with your own paddle installation directory.

sh setup.sh
export LD_LIBRARY_PATH=$PADDLE_INSTALL_DIR/Paddle/third_party/install/warpctc/lib:$LD_LIBRARY_PATH

For some machines, we also need to install libsndfile1. Details to be added.

Usage

Preparing Data

cd datasets
sh run_all.sh
cd ..

sh run_all.sh prepares all ASR datasets (currently, only LibriSpeech available). After running, we have several summarization manifest files in json-format.

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 compute_mean_std.py --help

Training

For GPU Training:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python train.py

For CPU Training:

python train.py --use_gpu False

More help for arguments:

python train.py --help

Inferencing

CUDA_VISIBLE_DEVICES=0 python infer.py

More help for arguments:

python infer.py --help