Ocotillo provides a performant and user-friendly speech recognition solution, targeting developers and researchers who need accurate English transcription. It simplifies the process of integrating state-of-the-art speech-to-text capabilities into applications, offering significant speed improvements through TorchScript optimization.

How It Works

Ocotillo leverages a wav2vec2 model, specifically jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli, fine-tuned for speech recognition and punctuation prediction. The core advantage lies in its TorchScript tracing, which compiles the PyTorch model into C++ for highly efficient inference. This approach minimizes overhead and maximizes processing speed, especially on GPUs.

Quick Start & Requirements

• Install PyTorch: https://pytorch.org/get-started/locally/
• Clone and install:

  git clone https://github.com/neonbjb/ocotillo.git
  cd ocotillo
  python setup.py install
  

• Dependencies: PyTorch, Hugging Face Transformers. GPU (NVIDIA recommended) for optimal performance.
• Demo Colab notebook: asr_demo.ipynb

Highlighted Details

• Achieves 329x faster-than-realtime processing on an NVIDIA A5000 GPU with batch size 16.
• Fine-tuned wav2vec2 model predicts punctuation for improved transcription quality.
• Offers a simple CLI, batch processing script (transcribe.py), and a Python API (Transcriber class).
• Includes an HTTP server compatible with Mycroft AI for remote speech-to-text services.

Maintenance & Community

• Key contributions acknowledged from Patrick von Platen for Hugging Face API and blog posts.
• No explicit community links (Discord/Slack) or roadmap provided in the README.

Licensing & Compatibility

• The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The project is focused on English speech recognition and has not been tested on embedded hardware like the Raspberry Pi. The licensing status requires clarification for commercial adoption.