PaddleSpeech/examples/tiny

Tiny Example

1. source path.sh
2. set CUDA_VISIBLE_DEVICES as you need.
3. 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