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README.md
Tiny Example
source path.sh
- set
CUDA_VISIBLE_DEVICES
as you need. - demo scrpt is
bash run.sh
. You can run commond separately as needed.
Steps
-
Prepare the data
bash local/data.sh
data.sh
will download dataset, generate manifests, collect normalizer's statistics and build vocabulary. Once the data preparation is done, you will find the data (only part of LibriSpeech) downloaded in${MAIN_ROOT}/dataset/librispeech
and the corresponding manifest files generated in${PWD}/data
as well as a mean stddev file and a vocabulary file. It has to be run for the very first time you run this dataset and is reusable for all further experiments. -
Train your own ASR model
bash local/train.sh
train.sh
will start a training job, with training logs printed to stdout and model checkpoint of every pass/epoch saved to${PWD}/checkpoints
. These checkpoints could be used for training resuming, inference, evaluation and deployment. -
Case inference with an existing model
bash local/infer.sh
infer.sh
will show us some speech-to-text decoding results for several (default: 10) samples with the trained model. The performance might not be good now as the current model is only trained with a toy subset of LibriSpeech. To see the results with a better model, you can download a well-trained (trained for several days, with the complete LibriSpeech) model and do the inference. -
Evaluate an existing model
bash local/test.sh
test.sh
will evaluate the model with Word Error Rate (or Character Error Rate) measurement. Similarly, you can also download a well-trained model and test its performance: -
Export jit model
bash local/export.sh ckpt_path saved_jit_model_path
-
Tune hyper paerameter
bash local/tune.sh