Xinghai Sun
92eacf548b
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8 years ago | |
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data_utils | 8 years ago | |
datasets | 8 years ago | |
lm | 8 years ago | |
tests | 8 years ago | |
README.md | 8 years ago | |
compute_mean_std.py | 8 years ago | |
decoder.py | 8 years ago | |
error_rate.py | 8 years ago | |
evaluate.py | 8 years ago | |
infer.py | 8 years ago | |
model.py | 8 years ago | |
requirements.txt | 8 years ago | |
setup.sh | 8 years ago | |
train.py | 8 years ago | |
tune.py | 8 years ago | |
utils.py | 8 years ago |
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
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
It will compute 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. The default feature of audio data is power spectrum, and the mfcc feature is also supported. To train and infer based on mfcc feature, please generate this file by
python compute_mean_std.py --specgram_type mfcc
and specify --specgram_type mfcc
when running train.py, infer.py, evaluator.py or tune.py.
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
Preparing language model
The following steps, inference, parameters tuning and evaluating, will require a language model during decoding. A compressed language model is provided and can be accessed by
cd ./lm
sh run.sh
cd ..
Inference
For GPU inference
CUDA_VISIBLE_DEVICES=0 python infer.py
For CPU inference
python infer.py --use_gpu=False
More help for arguments:
python infer.py --help
Evaluating
CUDA_VISIBLE_DEVICES=0 python evaluate.py
More help for arguments:
python evaluate.py --help
Parameters tuning
Usually, the parameters \alpha
and \beta
for the CTC prefix beam search decoder need to be tuned after retraining the acoustic model.
For GPU tuning
CUDA_VISIBLE_DEVICES=0 python tune.py
For CPU tuning
python tune.py --use_gpu=False
More help for arguments:
python tune.py --help
Then reset parameters with the tuning result before inference or evaluating.