Llama Voice

Llama Voice is a [brief description, e.g., real-time voice processing and transcription component] within the LlamaSearch AI ecosystem. It provides capabilities for [list key capabilities, e.g., voice activity detection (VAD), automatic speech recognition (ASR), speaker diarization].

Features

• Real-time Processing: Designed for low-latency voice stream handling.
• Accurate Transcription: Leverages [mention model or technique, e.g., Whisper-based models] for high-quality ASR.
• Speaker Identification: [Describe capability, e.g., Differentiates between multiple speakers in an audio stream].
• Voice Activity Detection: Efficiently detects speech segments to reduce processing load.
• [Add other relevant features]

Installation

# Ensure you are in the root of the llamasearchai-git repository
pip install -e ./batch2/llama-voice

Or, if installing dependencies listed in its pyproject.toml is preferred:

cd batch2/llama-voice
pip install .
cd ../.. 

Dependencies

• Python 3.8+
• [List key dependencies, e.g., PyTorch, Transformers, LibROSA, PyAudio]
• Refer to pyproject.toml for a complete list.

Usage

Provide a basic example of how to use the core functionality.

# Example: Basic ASR usage
# NOTE: This is a hypothetical example, adjust based on actual implementation

from llama_voice.asr_processor import ASRProcessor # Assuming this structure
# from llama_voice.vad import VoiceActivityDetector # Example

# Initialize components (adjust parameters as needed)
# vad = VoiceActivityDetector() 
processor = ASRProcessor(model_size="base")

async def process_audio_stream(stream):
    async for audio_chunk in stream:
        # Optional VAD
        # if vad.is_speech(audio_chunk):
        
        transcription = await processor.transcribe(audio_chunk)
        if transcription:
            print(f"Transcription: {transcription}")

# Example of setting up and running the stream processing
# setup_and_run(process_audio_stream) 

Configuration

Explain any necessary configuration, e.g., model selection, language settings, device selection (CPU/GPU/MPS). Mention if environment variables are used.

Architecture

Briefly describe the main components and their interaction (e.g., VAD module, ASR model loader, processing pipeline).

Contributing

Please refer to the main CONTRIBUTING.md file in the root of the LlamaSearchAI repository for contribution guidelines. Specific notes for Llama Voice development can be added here if necessary.

License

This project is licensed under the MIT License - see the LICENSE file for details.