Server

Hint

Please first refer to Installation to install sherpa before proceeding.

The server is responsible for accepting audio samples from the client, decoding it, and sending the recognition results back to the client.

Usage

cd /path/to/sherpa
./sherpa/bin/streaming_pruned_transducer_statelessX/streaming_server.py --help

shows the usage message.

You need two files to start the server:

  1. The neural network model, which is a torchscript file.

  2. The BPE model or the tokens file.

The above two files can be obtained after training your model with https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless4.

If you don’t want to train a model by yourself, you can try the pretrained model: https://huggingface.co/pkufool/icefall_librispeech_streaming_pruned_transducer_stateless4_20220625

Hint

You can find pretrained models in RESULTS.md for all the recipes in icefall.

For instance, the pretrained models for the LibriSpeech dataset can be found at https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md.

The following shows you how to start the server with the above pretrained model.

cd /path/to/sherpa

git lfs install
git clone https://huggingface.co/pkufool/icefall_librispeech_streaming_pruned_transducer_stateless4_20220625

./sherpa/bin/streaming_pruned_transducer_statelessX/streaming_server.py \
  --endpoint.rule3.min-utterance-length 1000.0 \
  --port 6006 \
  --max-batch-size 50 \
  --max-wait-ms 5 \
  --nn-pool-size 1 \
  --nn-model-filename ./icefall_librispeech_streaming_pruned_transducer_stateless4_20220625/exp/cpu_jit-epoch-25-avg-3.pt \
  --bpe-model-filename ./icefall_librispeech_streaming_pruned_transducer_stateless4_20220625/data/lang_bpe_500/bpe.model

That’s it!

Now you can start the Client, record your voice in real-time, and check the recognition results from the server.