docs: update README with voxtral backend, benchmarks, testing sections

- Add Voxtral Backend section explaining voxtral-mlx and voxtral (HF).
- Add Testing & Benchmarks section with commands to run tests/benchmarks.
- Update --backend parameter docs to include voxtral-mlx and voxtral.
- Update optional dependencies table with Voxtral entry.
- Link to BENCHMARK.md for detailed performance comparisons.

README.md

@@ -75,15 +75,35 @@ Go to `chrome-extension` for instructions.
 |-----------|-------------|
 | **Windows/Linux optimizations** | `faster-whisper` |
 | **Apple Silicon optimizations** | `mlx-whisper` |
+| **Voxtral (multilingual, auto-detect)** | `transformers torch` (or use built-in `voxtral-mlx` on Apple Silicon) |
 | **Translation** | `nllw` |
 | **Speaker diarization** | `git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[asr]` |
 | OpenAI API | `openai` |
 | *[Not recommanded]*  Speaker diarization with Diart | `diart` |
 
-See  **Parameters & Configuration** below on how to use them.
+See **Parameters & Configuration** below on how to use them.
+See **[BENCHMARK.md](BENCHMARK.md)** for detailed performance comparisons across all backends.
 
 
 
+### Voxtral Backend
+
+WhisperLiveKit supports [Voxtral Mini](https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602),
+a 4B-parameter speech model from Mistral AI that natively handles 100+ languages with automatic
+language detection. Unlike whisper-based backends, Voxtral does not require specifying `--language`.
+
+```bash
+# Apple Silicon (native MLX, recommended)
+wlk --backend voxtral-mlx
+
+# Linux/GPU (HuggingFace transformers)
+pip install transformers torch
+wlk --backend voxtral
+```
+
+Voxtral uses its own streaming policy and does not use LocalAgreement or SimulStreaming.
+See [BENCHMARK.md](BENCHMARK.md) for performance numbers.
+
 ### Usage Examples
 
 **Command-line Interface**: Start the transcription server with various options:
@@ -92,8 +112,11 @@ See  **Parameters & Configuration** below on how to use them.
 # Large model and translate from french to danish
 wlk --model large-v3 --language fr --target-language da
 
-# Diarization and server listening on */80 
+# Diarization and server listening on */80
 wlk --host 0.0.0.0 --port 80 --model medium --diarization --language fr
+
+# Voxtral multilingual (auto-detects language)
+wlk --backend voxtral-mlx
 ```
 
 
@@ -151,7 +174,7 @@ async def websocket_endpoint(websocket: WebSocket):
 | `--target-language` | If sets, translates using [NLLW](https://github.com/QuentinFuxa/NoLanguageLeftWaiting). [200 languages available](docs/supported_languages.md). If you want to translate to english, you can also use `--direct-english-translation`. The STT model will try to directly output the translation. | `None` |
 | `--diarization` | Enable speaker identification | `False` |
 | `--backend-policy` | Streaming strategy: `1`/`simulstreaming` uses AlignAtt SimulStreaming, `2`/`localagreement` uses the LocalAgreement policy | `simulstreaming` |
-| `--backend` | Whisper implementation selector. `auto` picks MLX on macOS (if installed), otherwise Faster-Whisper, otherwise vanilla Whisper. You can also force `mlx-whisper`, `faster-whisper`, `whisper`, or `openai-api` (LocalAgreement only) | `auto` |
+| `--backend` | ASR backend selector. `auto` picks MLX on macOS (if installed), otherwise Faster-Whisper, otherwise vanilla Whisper. Options: `mlx-whisper`, `faster-whisper`, `whisper`, `openai-api` (LocalAgreement only), `voxtral-mlx` (Apple Silicon), `voxtral` (HuggingFace) | `auto` |
 | `--no-vac` | Disable Voice Activity Controller. NOT ADVISED | `False` |
 | `--no-vad` | Disable Voice Activity Detection. NOT ADVISED | `False` |
 | `--warmup-file` | Audio file path for model warmup | `jfk.wav` |
@@ -271,5 +294,29 @@ docker run --gpus all -p 8000:8000 --name wlk wlk --model large-v3 --language fr
   - `HF_PRECACHE_DIR="./.cache/"` - Pre-load a model cache for faster first-time start
   - `HF_TKN_FILE="./token"` - Add your Hugging Face Hub access token to download gated models
 
-## 🔮 Use Cases
+## Testing & Benchmarks
+
+WhisperLiveKit includes a unit test suite and an offline benchmark harness.
+
+```bash
+# Install test dependencies
+pip install -e ".[test]"
+
+# Run unit tests (no model download required)
+pytest tests/ -v
+
+# Benchmark a single backend
+python test_backend_offline.py --backend faster-whisper --no-realtime
+
+# Benchmark all installed backends
+python test_backend_offline.py --benchmark --no-realtime
+
+# Export benchmark results as JSON
+python test_backend_offline.py --benchmark --no-realtime --json results.json
+```
+
+See [BENCHMARK.md](BENCHMARK.md) for a full comparison of backends, policies, WER, speed, and
+timestamp accuracy on Apple Silicon.
+
+## Use Cases
 Capture discussions in real-time for meeting transcription, help hearing-impaired users follow conversations through accessibility tools, transcribe podcasts or videos automatically for content creation, transcribe support calls with speaker identification for customer service...

whisperlivekit/core.py

@@ -92,7 +92,12 @@ class TranscriptionEngine:
         }
 
         if config.transcription:
-            if config.backend == "voxtral":
+            if config.backend == "voxtral-mlx":
+                from whisperlivekit.voxtral_mlx_asr import VoxtralMLXASR
+                self.tokenizer = None
+                self.asr = VoxtralMLXASR(**transcription_common_params)
+                logger.info("Using Voxtral MLX native backend")
+            elif config.backend == "voxtral":
                 from whisperlivekit.voxtral_hf_streaming import VoxtralHFStreamingASR
                 self.tokenizer = None
                 self.asr = VoxtralHFStreamingASR(**transcription_common_params)
@@ -169,6 +174,9 @@ class TranscriptionEngine:
 
 
 def online_factory(args, asr):
+    if getattr(args, 'backend', None) == "voxtral-mlx":
+        from whisperlivekit.voxtral_mlx_asr import VoxtralMLXOnlineProcessor
+        return VoxtralMLXOnlineProcessor(asr)
     if getattr(args, 'backend', None) == "voxtral":
         from whisperlivekit.voxtral_hf_streaming import VoxtralHFStreamingOnlineProcessor
         return VoxtralHFStreamingOnlineProcessor(asr)

whisperlivekit/parse_args.py

@@ -147,8 +147,8 @@ def parse_args():
         "--backend",
         type=str,
         default="auto",
-        choices=["auto", "mlx-whisper", "faster-whisper", "whisper", "openai-api", "voxtral"],
+        choices=["auto", "mlx-whisper", "faster-whisper", "whisper", "openai-api", "voxtral", "voxtral-mlx"],
-        help="Select the ASR backend implementation (auto: prefer MLX on macOS, otherwise Faster-Whisper, else Whisper). Use 'voxtral' for Voxtral streaming via HuggingFace Transformers (CUDA/CPU/MPS).",
+        help="Select the ASR backend implementation (auto: prefer MLX on macOS, otherwise Faster-Whisper, else Whisper). Use 'voxtral' for HF Transformers Voxtral (CUDA/CPU/MPS). Use 'voxtral-mlx' for native MLX Voxtral on Apple Silicon.",
     )
     parser.add_argument(
         "--no-vac",