tensorflow-speech-recognition

Introduction to Tensorflow Speech Recognition

Tensorflow Speech Recognition is a project focused on developing a speech recognition system using Google’s powerful Tensorflow deep learning framework, particularly through sequence-to-sequence neural networks. This project serves as a successor to the Caffe Speech Recognition project, providing users with a more advanced approach to understanding and implementing speech recognition systems.

Historical Context and Updates

As of 2024, the original Tensorflow Speech Recognition project is no longer maintained with the latest developments in technology. The framework initially utilized, Tensorflow 1.0, has become outdated, with the underlying theory also experiencing advancements. Therefore, users are recommended to explore more contemporary solutions such as Whisper, a modern speech recognition tool.

In 2020, Mozilla launched DeepSpeech, achieving commendable error rates, signaling an important step forward for open-source speech recognition. As a result, while Tensorflow Speech Recognition remains useful for educational purposes, users looking for practical applications may find Mozilla DeepSpeech more suitable.

Project Goals

The ultimate aim of the Tensorflow Speech Recognition project was to create an effective and independent speech recognition system, primarily for Linux-based systems. The developers recognized the availability of extensive training data, such as open-source repositories containing gigabytes of audio and text, and believed that with simple yet robust models, efficient speech recognition was achievable.

Installation and Setup

The setup involves several steps:

1. Clone the Code

   git clone https://github.com/pannous/tensorflow-speech-recognition
   cd tensorflow-speech-recognition
   git clone https://github.com/pannous/layer.git
   git clone https://github.com/pannous/tensorpeers.git
   

2. Install pyaudio and Dependencies

   First, download and configure the portaudio library:

   git clone  https://git.assembla.com/portaudio.git
   ./configure --prefix=/path/to/your/local
   make
   make install
   

   Then update the library paths:

   export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/path/to/your/local/lib
   export LIDRARY_PATH=$LIBRARY_PATH:/path/to/your/local/lib
   export CPATH=$CPATH:/path/to/your/local/include
   source ~/.bashrc
   

   Finally, install pyaudio:

   pip install pyaudio
   

Getting Started

Begin with simpler examples such as:

• ./number_classifier_tflearn.py
• ./speaker_classifier_tflearn.py

For more complex architectures, experiment with:

• ./densenet_layer.py

To start training or recording, use:

• ./train.sh
• ./record.py

Opportunities for Engagement

For those new to the field, engaging with the project provides several opportunities:

• Watch educational videos to better understand the process.
• Dive into code like lstm-tflearn.py and attempt improvements or corrections.
• Experiment with data augmentation techniques such as frequency modulation, adding background noise, and more.

Potential Extensions and Enhancements

Possible extensions include integrating enhanced processing features like WarpCTC on the GPU, developing modular models with persistence, and fostering incremental collaborative snapshots with P2P learning.

Conclusion

Although the project may not be complete, it serves as a foundational platform for learning and experimentation in speech recognition. Enthusiasts and developers are encouraged to explore further possibilities and contribute to evolving technologies using this as a stepping stone.

For collaboration opportunities or consulting, you can reach out via [email protected].