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 ff the duration exceeds the limits, however, your transcript may contain errors and missing text.