[Recipe] add mistralai voxtral tts recipe (#3498)

Signed-off-by: Dmaner <2663515256@qq.com>

recipes/README.md

@@ -34,6 +34,7 @@ recipes/
 | [`inclusionAI/Ming-flash-omni-2.0.md`](./inclusionAI/Ming-flash-omni-2.0.md) | Online serving for multimodal chat + standalone TTS | 4x H100 / 1x H100 80GB |
 | [`LTX/LTX-2.md`](./LTX/LTX-2.md) | Text-to-video and image-to-video serving | 1x H200 141GB |
 | [`LTX/LTX-2.3.md`](./LTX/LTX-2.3.md) | Text-to-video with audio generation (22B) | 1x GPU (96GB VRAM) |
+| [`mistralai/Voxtral-TTS.md`](./mistralai/Voxtral-TTS.md) | Online serving for TTS | 1x RTX 4090 24GB |
 | [`Qwen/Qwen-Image.md`](./Qwen/Qwen-Image.md) | Text-to-image serving with step-wise continuous batching replay and ModelOpt mixed FP8/NVFP4 | 1x A100 80GB / 2x B200 |
 | [`Qwen/Qwen-Image.md`](./Qwen/Qwen-Image.md) | Text-to-image serving with step-wise continuous batching replay | 1x A100 80GB |
 | [`Qwen/Qwen-Image-Edit.md`](./Qwen/Qwen-Image-Edit.md) | Text-guided single-image editing | 1x or 2x H200 141GB |

recipes/mistralai/Voxtral-TTS.md

@@ -0,0 +1,107 @@
+# Voxtral TTS for text-to-speech
+
+## Summary
+
+- Vendor: Mistral AI
+- Model: `mistralai/Voxtral-4B-TTS-2603`
+- Task: Text-to-speech with model-provided voice presets or voice cloning
+- Mode: Online serving with the OpenAI-compatible `/v1/audio/speech` API
+- Maintainer: Community
+
+## When to use this recipe
+
+Use this recipe when you want a known-good starting point for serving Voxtral TTS
+models with vLLM-Omni and validate the deployment with the existing TTS client
+examples in this repository.
+
+## References
+
+- Upstream model card: <https://huggingface.co/mistralai/Voxtral-4B-TTS-2603>
+- Related examples under `examples/`:
+  [`examples/offline_inference/text_to_speech/voxtral_tts/end2end.py`](../../examples/offline_inference/text_to_speech/voxtral_tts/end2end.py)
+  [`examples/online_serving/text_to_speech/voxtral_tts/gradio_demo.py`](../../examples/online_serving/text_to_speech/voxtral_tts/gradio_demo.py)
+- Related issue or discussion:
+  [RFC: add recipes folder](https://github.com/vllm-project/vllm-omni/issues/2645)
+
+## Hardware Support
+
+## GPU
+
+### 1x RTX 4090 24GB
+
+#### Environment
+
+- OS: Ubuntu 22.04.5 LTS
+- Python: 3.12.11
+- PyTorch: 2.11.0+cu130
+- Driver / runtime: CUDA 13.0
+- GPU: NVIDIA GeForce RTX 4090, 24 GB
+- vLLM version: 0.20.0
+- vLLM-Omni version or commit: 0.20.0rc2.dev99+g857356d5b
+- Required package: mistral_common >= 1.10.0
+
+#### Command
+
+Online:
+
+```bash
+vllm serve mistralai/Voxtral-4B-TTS-2603 \
+    --omni \
+    --port 8091
+```
+
+The deploy config is auto-loaded from `vllm_omni/deploy/voxtral_tts.yaml`
+
+#### Verification
+
+**Quick smoke test with curl:**
+
+```bash
+curl -X POST http://127.0.0.1:8091/v1/audio/speech \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "mistralai/Voxtral-4B-TTS-2603",
+    "input": "Hello, this is Voxtral TTS running with vLLM-Omni.",
+    "voice": "casual_female",
+    "language": "English",
+    "response_format": "wav"
+  }' \
+  --output voxtral.wav
+```
+
+**Streaming audio:**
+
+```bash
+curl -X POST http://127.0.0.1:8091/v1/audio/speech \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "mistralai/Voxtral-4B-TTS-2603",
+    "input": "Hello, this is Voxtral TTS streaming PCM.",
+    "voice": "casual_female",
+    "language": "English",
+    "stream": true,
+    "response_format": "pcm"
+  }' \
+  --output voxtral_stream.pcm
+```
+Convert the raw PCM output to WAV
+```bash
+ffmpeg -f s16le -ar 24000 -ac 1 -i voxtral_stream.pcm voxtral_stream.wav -y
+ffprobe -hide_banner voxtral_stream.wav
+```
+
+#### Notes
+
+- Memory usage: Stage 0 (`audio_generation`) ~18.95 GiB, Stage 1
+  (`audio_tokenizer`) ~1.55 GiB. The observed server-startup peak was
+  `21006 MiB / 24564 MiB` (`20.51 GiB / 23.99 GiB`, about 85.5%).
+  Both stages share GPU 0 via the deploy config (`gpu_memory_utilization: 0.8`
+  for Stage 0, `0.1` for Stage 1).
+- Key flags: `--omni` is required. `--port 8091` is optional and only selects
+  the HTTP port.
+- Known limitation: Voxtral TTS outputs mono audio at 24 kHz. And with the public
+mistralai/Voxtral-4B-TTS-2603 checkpoint. running the
+voice-cloning e2e test ([`Voice cloning (capability gated upstream)`](../../examples/offline_inference/text_to_speech/README.md#voice-cloning-capability-gated-upstream)
+) or adding a `ref_audio` request parameter will fail
+because the public checkpoint does not provide the encoder weights needed to
+convert reference audio into conditioning features. The expected failure is: `RuntimeError: encode_waveforms requires encoder weights which are not available in the open-source checkpoint.`