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52 lines
1.9 KiB
52 lines
1.9 KiB
# Tiny Example
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1. `source path.sh`
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3. set `CUDA_VISIBLE_DEVICES` as you need.
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2. demo scrpt is `bash run.sh`. You can run commond separately as needed.
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## Steps
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- Prepare the data
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```bash
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bash local/data.sh
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```
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`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.
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- Train your own ASR model
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```bash
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bash local/train.sh
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```
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`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.
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- Case inference with an existing model
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```bash
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bash local/infer.sh
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```
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`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.
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- Evaluate an existing model
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```bash
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bash local/test.sh
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```
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`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:
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- Export jit model
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```bash
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bash local/export.sh ckpt_path saved_jit_model_path
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```
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- Tune hyper paerameter
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```bash
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bash local/tune.sh
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```
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