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.

Features

Live Transcription: Real-time speech-to-text with automatic language detection.
On-the-fly Translation: Bridge translations to English and then to target languages (Spanish, Arabic, French, etc.).
Voice Activity Detection (VAD): Intelligent audio buffering using Silero VAD to process only actual speech.
Apple Silicon Optimized: Uses MLX for high performance on Mac (MPS).
Background Ingestion: Optional background thread to send JSON payloads to a remote server.
Configurable: Command-line parameters to select languages, devices, and ingestion.

Installation

Clone the repository:

git clone <repository-url>
cd pythonwhisper

Install dependencies: Ensure you have Python 3.9+ and the required libraries:
```
pip install mlx-whisper numpy sounddevice torch requests transformers silero-vad
```
Note: On Apple Silicon, ensure mlx and torch with MPS support are correctly installed.

Usage

Run the script using python3 transcribe.py with optional flags.

Common Commands

List available audio devices:
```
python3 transcribe.py -l
```
Transcribe and translate to Spanish (screen only):
```
python3 transcribe.py -es
```
Enable Spanish, Arabic, and French with English bridging, and send to server:
```
python3 transcribe.py -es -ar -fr -en -i
```
Use a specific input device (e.g., index 3) and translate to Spanish:
```
python3 transcribe.py -d 3 -es
```

Arguments

-es: Enable Spanish translation.
-en: Enable English (shows original if detected, or bridged if not).
-ar: Enable Arabic translation.
-fr: Enable French translation.
-i, --ingest: Enable data transmission to the remote server.
-l, --list-devices: Show available audio devices and exit.
-d, --device [ID]: Input device index (bypasses selection prompt).

Technical Note: Universal Translation Models

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.

Universal Model Alternative (e.g., Meta's NLLB-200)

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.

We have the option to switch the translation engine to a single, universal model such as NLLB-200 (No Language Left Behind):

Model ID: facebook/nllb-200-distilled-600M
Benefits:
- Supports over 200 languages in a single model.
- Simplified code: no need to load/manage multiple model objects.
- More efficient for complex multilingual environments.
Trade-off: Slightly higher memory footprint for the single model compared to a single specialized model, but more efficient than 3+ specialized models.

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.