Signed-off-by: leo.yang <leo.yang.engineer@gmail.com>
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@@ -105,3 +105,45 @@ voice-cloning e2e test ([`Voice cloning (capability gated upstream)`](../../exam
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) or adding a `ref_audio` request parameter will fail
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because the public checkpoint does not provide the encoder weights needed to
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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.`
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### 1x NVIDIA RTX A6000 48GB
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#### Environment
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- OS: Ubuntu 22.04
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- Python: 3.12
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- Driver / runtime: NVIDIA driver 580.126.09, CUDA 13.0
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- GPU: NVIDIA RTX A6000, 48 GB
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- vLLM version: 0.22.0
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- vLLM-Omni version or commit: 0.22.0rc2.dev8+g41a795b5d
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- Required package: `pip install mistral-common`
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#### Command
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```bash
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vllm serve mistralai/Voxtral-4B-TTS-2603 --omni --port 8091
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```
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#### Verification
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```bash
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curl -X POST http://localhost:8091/v1/audio/speech \
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-H "Content-Type: application/json" \
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-d '{
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"model": "mistralai/Voxtral-4B-TTS-2603",
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"input": "Artificial intelligence is transforming the way we interact with technology.",
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"voice": "neutral_female",
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"response_format": "wav"
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}' -o voxtral_test.wav
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```
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Supported voice presets: `ar_male`, `casual_female`, `casual_male`, `cheerful_female`,
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`de_female`, `de_male`, `es_female`, `es_male`, `fr_female`, `fr_male`, `hi_female`,
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`hi_male`, `it_female`, `it_male`, `neutral_female`, `neutral_male`, `nl_female`,
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`nl_male`, `pt_female`, `pt_male`.
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#### Notes
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- Memory usage: Peak VRAM ~40.5 GiB during inference.
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- Generation time: ~8 seconds for ~200 words of English text.
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- Audio quality is high; mono 24 kHz output.
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