Beta Software

Simple Python library, distributed via binary wheels with few direct dependencies, for easily using WeNet models for speech recognition.

Requirements:

• Python 3.7+ x64
• Platform: Windows/Linux/MacOS
• Python package requirements: cffi, numpy
• Wenet Model (must be "runtime" format)
  • Several are available ready-to-go on this project's releases page and below.

Features:

• Synchronous decoding of single utterance
• Streaming decoding, using separate thread

Models:

from wenet_stt import WenetSTTModel
model = WenetSTTModel(WenetSTTModel.build_config('model_dir'))

import wave
with wave.open('tests/test.wav', 'rb') as wav_file:
    wav_samples = wav_file.readframes(wav_file.getnframes())

assert model.decode(wav_samples).lower() == 'it depends on the context'

Also contains a simple CLI interface for recognizing wav files:

$ python -m wenet_stt decode model test.wav
IT DEPENDS ON THE CONTEXT
$ python -m wenet_stt decode model test.wav test.wav
IT DEPENDS ON THE CONTEXT
IT DEPENDS ON THE CONTEXT
$ python -m wenet_stt -h
usage: python -m wenet_stt [-h] {decode} ...

positional arguments:
  {decode}    sub-command
    decode    decode one or more WAV files

optional arguments:
  -h, --help  show this help message and exit

Recommended installation via binary wheel from pip (requires a recent version of pip):

python -m pip install wenet_stt

For details on building from source, see the Github Actions build workflow.

• David Zurow (@daanzu)

This project is licensed under the GNU Affero General Public License v3 (AGPL-3.0-or-later). See the LICENSE file for details. If this license is problematic for you, please contact me.

• Contains and uses code from WeNet, licensed under the Apache-2.0 License, and other transitive dependencies (see source).