1.9 KiB
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
Evaluating
CUDA_VISIBLE_DEVICES=0 python evaluate.py
More help for arguments:
python evaluate.py --help
Parameters tuning
Parameters tuning for the CTC beam search decoder
CUDA_VISIBLE_DEVICES=0 python tune.py
More help for arguments:
python tune.py --help