semvec/pypi-packages huggingface dataset:
llama-voice-llamasearch 0.1.0
On-device voice processing pipeline for fast, private voice interaction
License: MIT
Requires Python: >=3.8

# Llama Voice

[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![Python Version](https://img.shields.io/badge/python-3.8+-blue.svg)]() <!-- Add appropriate Python version support -->

**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

```bash
# 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:

```bash
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.

```python
# 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.