WeNet Python Binding

This is a python binding of WeNet.

WeNet is a production first and production ready end-to-end speech recognition toolkit.

The best things of the binding are:

  1. Multiple languages supports, including English, Chinese. Other languages are in development.

  2. Non-streaming and streaming API

  3. N-best, contextual biasing, and timestamp supports, which are very important for speech productions.

  4. Alignment support. You can get phone level alignments this tool, on developing.


Python 3.6+ is required.

pip3 install wenetruntime


Non-streaming Usage

import sys
import wenetruntime as wenet

wav_file = sys.argv[1]
decoder = wenet.Decoder(lang='chs')
ans = decoder.decode_wav(wav_file)

You can also specify the following parameter in wenet.Decoder

  • lang (str): The language you used, chs for Chinese, and en for English.

  • model_dir (str): is the Runtime Model directory, it contains the following files. If not provided, official model for specific lang will be downloaded automatically.

    • final.zip: runtime TorchScript ASR model.

    • units.txt: modeling units file

    • TLG.fst: optional, it means decoding with LM when TLG.fst is given.

    • words.txt: optional, word level symbol table for decoding with TLG.fst

    Please refer https://github.com/wenet-e2e/wenet/blob/main/docs/pretrained_models.md for the details of Runtime Model.

  • nbest (int): Output the top-n best result.

  • enable_timestamp (bool): Whether to enable the word level timestamp.

  • context (List[str]): a list of context biasing words.

  • context_score (float): context bonus score.

  • continuous_decoding (bool): Whether to enable continuous(long) decoding.

For example:

decoder = wenet.Decoder(model_dir,
                        context=['不忘初心', '牢记使命'],

Streaming Usage

import sys
import wave
import wenetruntime as wenet

test_wav = sys.argv[1]

with wave.open(test_wav, 'rb') as fin:
    assert fin.getnchannels() == 1
    wav = fin.readframes(fin.getnframes())

decoder = wenet.Decoder(lang='chs')
# We suppose the wav is 16k, 16bits, and decode every 0.5 seconds
interval = int(0.5 * 16000) * 2
for i in range(0, len(wav), interval):
    last = False if i + interval < len(wav) else True
    chunk_wav = wav[i: min(i + interval, len(wav))]
    ans = decoder.decode(chunk_wav, last)

You can use the same parameters as we introduced above to control the behavior of wenet.Decoder

Build on Your Local Machine

git clone git@github.com:wenet-e2e/wenet.git
cd wenet/runtime/binding/python
python setup.py install