Adolfo Reyna
82 lines
4.0 KiB
Markdown
82 lines
4.0 KiB
Markdown
# Python Whisper Live Transcription & Translation
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A real-time, low-latency audio transcription and translation tool utilizing OpenAI's Whisper (via `mlx-whisper` for Apple Silicon optimization), Silero VAD for speech detection, and Helsinki-NLP's Opus-MT models for translation.
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## Features
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- **Live Transcription:** Real-time speech-to-text with automatic language detection.
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- **On-the-fly Translation:** Bridge translations to English and then to target languages (Spanish, Arabic, French, etc.).
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- **Voice Activity Detection (VAD):** Intelligent audio buffering using Silero VAD to process only actual speech.
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- **Apple Silicon Optimized:** Uses MLX for high performance on Mac (MPS).
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- **Background Ingestion:** Optional background thread to send JSON payloads to a remote server.
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- **Configurable:** Command-line parameters to select languages, devices, and ingestion.
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## Installation
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1. **Clone the repository:**
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```bash
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git clone <repository-url>
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cd pythonwhisper
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```
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2. **Install dependencies:**
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Ensure you have Python 3.9+ and the required libraries:
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```bash
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pip install mlx-whisper numpy sounddevice torch requests transformers silero-vad
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```
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*Note: On Apple Silicon, ensure `mlx` and `torch` with MPS support are correctly installed.*
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## Usage
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Run the script using `python3 transcribe.py` with optional flags.
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### Common Commands
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- **List available audio devices:**
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```bash
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python3 transcribe.py -l
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```
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- **Transcribe and translate to Spanish (screen only):**
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```bash
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python3 transcribe.py -es
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```
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- **Enable Spanish, Arabic, and French with English bridging, and send to server:**
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```bash
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python3 transcribe.py -es -ar -fr -en -i
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```
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- **Use a specific input device (e.g., index 3) and translate to Spanish:**
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```bash
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python3 transcribe.py -d 3 -es
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```
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### Arguments
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- `-es`: Enable Spanish translation.
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- `-en`: Enable English (shows original if detected, or bridged if not).
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- `-ar`: Enable Arabic translation.
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- `-fr`: Enable French translation.
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- `-i`, `--ingest`: Enable data transmission to the remote server.
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- `-l`, `--list-devices`: Show available audio devices and exit.
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- `-d`, `--device [ID]`: Input device index (bypasses selection prompt).
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- `-q`, `--quantize`: Use 4-bit quantized Whisper model for faster transcription (Strategy 3).
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- `-s`, `--stream`: Enable real-time streaming transcription/draft mode (Strategy 1).
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- `-c`, `--context`: Enable prompt caching/rolling context to help the model maintain sentence continuity across chunks.
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- `--lang [CODE]`: Hardcode the source language (e.g., `en`, `es`) to bypass automatic language detection for faster processing.
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- `--silence [MS]`: Set the minimum silence duration in milliseconds to end a chunk. Defaults to 1000ms. Increase to force longer sentences before translation.
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---
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## Technical Note: Universal Translation Models
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While the current implementation uses specialized, per-language models from the **Helsinki-NLP Opus-MT** project (e.g., `opus-mt-en-es`, `opus-mt-en-ar`), there is an alternative approach: **Universal Models**.
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### Universal Model Alternative (e.g., Meta's NLLB-200)
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The current per-language model approach is highly accurate and memory-efficient if you only need 1 or 2 target languages. However, if you require support for many languages simultaneously, loading multiple specialized models can consume significant RAM/VRAM.
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We have the option to switch the translation engine to a single, universal model such as **NLLB-200 (No Language Left Behind)**:
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- **Model ID:** `facebook/nllb-200-distilled-600M`
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- **Benefits:**
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- Supports over **200 languages** in a single model.
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- Simplified code: no need to load/manage multiple model objects.
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- More efficient for complex multilingual environments.
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- **Trade-off:** Slightly higher memory footprint for the single model compared to a single specialized model, but more efficient than 3+ specialized models.
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If you wish to switch to a universal model, the `transcribe.py` logic can be updated to use a single `M2M100` or `NLLB` pipeline instead of the current `MarianMT` loop.
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