Open source library and trained models for speech recognition

at16k

Pronounced as at sixteen k

What is at16k?

at16k is a Python library to perform automatic speech recognition or speech to text conversion. The goal of this project is to provide the community with a production quality speech-to-text library.

Installation

It is recommended that you install at16k in a virtual environment.

Prerequisites

  • Python = 3.6 (not tested on other versions)
  • Tensorflow = 1.14
  • Scipy (for reading wav files)

Install via pip

$ pip install at16k

Install from source

Requires: poetry

$ git clone https://github.com/at16k/at16k.git
$ poetry env use python3.6
$ poetry install

Download models

Currently, two models are available for speech to text conversion.

  • en_8k (Trained on english audio recorded at 8 KHz)
  • en_16k (Trained on english audio recorded at 16 KHz)

To download all the models:

$ python -m at16k.download all

Alternatively, you can download only the model you need. For example:

$ python -m at16k.download en_8k
$ python -m at16k.download en_16k

Preprocessing audio files

at16k accepts wav files with the following spces:

  • Channels: 1
  • Bits per sample: 16
  • Sample rate: 8000 (en_8k) or 16000 (en_16k)

Use ffmpeg to convert your audio/video files to an acceptable format. For example,

# For 8 KHz
$ ffmpeg -i <input_file> -ar 8000 -ac 1 -ab 16 <output_file>

# For 16 KHz
$ ffmpeg -i <input_file> -ar 16000 -ac 1 -ab 16 <output_file>

Usage

Command line

There are two ways to invoke at16k speech-to-text via the command line.

at16k-convert -i <input_wav_file> -m <model_name>

Alternatively,

python -m at16k.bin.speech_to_text -i <input_wav_file> -m <model_name>

Library API

from at16k.api import SpeechToText

# One-time initialization
STT = SpeechToText('en_16k') # or en_8k

# Run STT on an audio file, returns a dict
print(STT('./samples/test_16k.wav'))

Check example.py for details on how to use the API.

Limitations

The max duration of your audio file should be less than 30 seconds when using en_8k , and less than 15 seconds when using en_16k . An error will not be thrown if the duration exceeds the limits, however, your transcript may contain errors and missing text.

License

This software is distributed under the MIT license.

Acknowledgements

We would like to thank Google TensorFlow Research Cloud (TFRC) program for providing access to cloud TPUs.

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