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https://github.com/at16k/at16k
Trained models for automatic speech recognition (ASR). A library to quickly build applications that require speech to text conversion.
https://github.com/at16k/at16k
asr asr-model automatic-speech-recognition pretrained-models speech-analysis speech-api speech-recognition speech-recognizer speech-to-text voice-commands voice-recognition
Last synced: about 2 months ago
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Trained models for automatic speech recognition (ASR). A library to quickly build applications that require speech to text conversion.
- Host: GitHub
- URL: https://github.com/at16k/at16k
- Owner: at16k
- License: mit
- Created: 2019-12-03T11:37:10.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2021-03-31T13:02:33.000Z (over 3 years ago)
- Last Synced: 2024-07-28T09:33:05.139Z (about 2 months ago)
- Topics: asr, asr-model, automatic-speech-recognition, pretrained-models, speech-analysis, speech-api, speech-recognition, speech-recognizer, speech-to-text, voice-commands, voice-recognition
- Language: Python
- Homepage: https://at16k.com
- Size: 268 KB
- Stars: 128
- Watchers: 11
- Forks: 19
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
- Code of conduct: CODE_OF_CONDUCT.md
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# 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
- Tensorflow = 1.14
- Scipy (for reading wav files)
## Install via pip
```
$ pip install at16k
```
## Install from source
Requires: [poetry](https://github.com/sdispater/poetry)
```
$ git clone https://github.com/at16k/at16k.git
$ poetry env use python3.6
$ poetry install
```
# Download models
Currently, three models are available for speech to text conversion.
- en_8k (Trained on English audio recorded at 8 KHz, supports offline ASR)
- en_16k (Trained on English audio recorded at 16 KHz, supports offline ASR)
- en_16k_rnnt (Trained on English audio recorded at 16 KHz, supports real-time ASR)
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
$ python -m at16k.download en_16k_rnnt
```
By default, the models will be downloaded and stored at /.at16k. To override the default, set the environment variable AT16K_RESOURCES_DIR.
For example:
```
$ export AT16K_RESOURCES_DIR=/path/to/my/directory
```
You will need to reuse this environment variable while using the API via command-line, library or REST API.
# Preprocessing audio files
at16k accepts wav files with the following specs:
- 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 -ar 8000 -ac 1 -ab 16
# For 16 KHz
$ ffmpeg -i -ar 16000 -ac 1 -ab 16
```
# Usage
at16k supports two modes for performing ASR - offline and real-time. And, it comes with a handy command line utility to quickly try out different models and use cases.
Here are a few examples -
```
# Offline ASR, 8 KHz sampling rate
$ at16k-convert -i -m en_8k
# Offline ASR, 16 KHz sampling rate
$ at16k-convert -i -m en_16k
# Real-time ASR, 16 KHz sampling rate, from a file, beam decoding
$ at16k-convert -i -m en_16k_rnnt -d beam
# Real-time ASR, 16 KHz sampling rate, from mic input, greedy decoding (requires pyaudio)
$ at16k-convert -m en_16k_rnnt -d greedy
```
If the ***at16k-convert*** binary is not available for some reason, replace it with -
```
python -m at16k.bin.speech_to_text ...
```
## Library API
Check [this file](https://github.com/at16k/at16k/blob/master/at16k/bin/speech_to_text.py) for examples on how to use at16k as a library.
# 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)](https://www.tensorflow.org/tfrc) program for providing access to cloud TPUs.