SpeeQ

"SpeeQ", pronounced as "speekiu", is a Python-based speech recognition framework that allows developers and researchers to experiment and train various speech recognition models. It offers pre-implemented model architectures that can be trained with just a few lines of code, making it a suitable option for quick prototyping and testing of speech recognition models.

To get started, refer to the documentation. If you need assistance or want to stay connected, please join our Discord Server.

Installation

To install this package, you can follow the steps below:

1. Create and activate a Python environment using the following commands:

python3 -m venv env
source env/bin/activate

2. Install the packge from source

   git clone https://github.com/msalhab96/SpeeQ.git
   cd SpeeQ
   pip install -r requirements.txt
   pip install -e .

Implemented Models/Papers

Model name	Paper	Type
Deep Speech 1	Deep Speech: Scaling up end-to-end speech recognition	CTC
Deep Speech 2	Deep Speech 2: End-to-End Speech Recognition in English and Mandarin	CTC
Conformer	Conformer: Convolution-augmented Transformer for Speech Recognition	CTC
Jasper	Jasper: An End-to-End Convolutional Neural Acoustic Model	CTC
Wav2Letter	Wav2Letter: an End-to-End ConvNet-based Speech Recognition System	CTC
QuartzNet	QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions	CTC
Squeezeformer	Squeezeformer: An Efficient Transformer for Automatic Speech Recognition	CTC
RNNTransducer	Sequence Transduction with Recurrent Neural Networks	Transducer
ConformerTransducer	Conformer: Convolution-augmented Transformer for Speech Recognition	Transducer
ContextNet	ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context	Transducer
VGGTransformer-Transducer	Transformer-Transducer: End-to-End Speech Recognition with Self-Attention	Transducer
Transformer-Transducer	Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Loss	Transducer
BasicAttSeq2SeqRNN	N/A	Seq2Seq (encoder/decoder)
LAS	Listen, Attend and Spell	Seq2Seq (encoder/decoder)
RNNWithLocationAwareAtt	Attention-Based Models for Speech Recognition	Seq2Seq (encoder/decoder)
SpeechTransformer	Speech-Transformer: A No-Recurrence Sequence-to-Sequence Model for Speech Recognition	Seq2Seq (encoder/decoder)

Contributiuon

Your contributions are highly valued and appreciated! Our aim is to create an open and transparent environment that facilitates easy and straightforward contributions to this project. This can include reporting any issues or bugs you encounter, engaging in discussions regarding the current codebase, submitting fixes, proposing new features, or even becoming a maintainer yourself. We believe that your input is crucial to the continued growth and success of this framework. To start contributing to the framework, please consult the guidelines for contributions.

License & Citation

The framework is licensed under MIT. Therefore, if you use the framework, please consider citing it using the following bitex.

@software{Salhab_SpeeQ_A_framework_2023,
author = {Salhab, Mahmoud},
doi = {10.5281/zenodo.7708780},
license = {MIT},
month = {3},
title = {{SpeeQ: A framework for automatic speech recognition}},
url = {https://github.com/msalhab96/SpeeQ},
version = {0.0.1},
year = {2023}
}