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| Author | SHA1 | Date | |
|---|---|---|---|
 Hongsheng Liu
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# Audio Generate API
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vLLM-Omni provides an API for text-to-audio generation using diffusion-based models such as Stable Audio.
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Unlike the [Speech API](speech_api.md) which targets text-to-speech synthesis, the Audio Generate API is designed for general-purpose audio generation from text descriptions (sound effects, music, ambient soundscapes, etc.).
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Each server instance runs a single model (specified at startup via `vllm-omni serve <model> --omni`).
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## Quick Start
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### Start the Server
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```bash
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vllm-omni serve stabilityai/stable-audio-open-1.0 \
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--host 0.0.0.0 \
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--port 8000 \
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--gpu-memory-utilization 0.9 \
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--trust-remote-code \
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--enforce-eager \
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--omni
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```
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### Generate Audio
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**Using curl:**
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```bash
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curl -X POST http://localhost:8000/v1/audio/generate \
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-H "Content-Type: application/json" \
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-d '{
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"input": "The sound of a cat purring",
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"audio_length": 10.0
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}' --output cat.wav
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```
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**Using Python:**
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```python
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import httpx
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response = httpx.post(
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"http://localhost:8000/v1/audio/generate",
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json={
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"input": "The sound of a cat purring",
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"audio_length": 10.0,
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},
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timeout=300.0,
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)
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with open("cat.wav", "wb") as f:
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f.write(response.content)
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```
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## API Reference
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### Endpoint
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```
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POST /v1/audio/generate
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Content-Type: application/json
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```
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### Request Parameters
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| `input` | string | **required** | Text prompt describing the audio to generate |
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| `model` | string | server's model | Model to use (optional, should match server if specified) |
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| `response_format` | string | "wav" | Audio format: wav, mp3, flac, pcm, aac, opus |
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| `speed` | float | 1.0 | Playback speed (0.25 - 4.0) |
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#### Diffusion Parameters
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| `audio_length` | float | null | Audio duration in seconds (default value is the max ~47s for `stable-audio-open-1.0`) |
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| `audio_start` | float | 0.0 | Audio start time in seconds |
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| `negative_prompt` | string | null | Text describing what to avoid in generation |
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| `guidance_scale` | float | model default | Classifier-free guidance scale (higher = more adherence to prompt) |
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| `num_inference_steps` | int | model default | Number of denoising steps (higher = better quality, slower) |
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| `seed` | int | null | Random seed for reproducible generation |
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### Response Format
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Returns binary audio data with the appropriate `Content-Type` header:
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| `response_format` | Content-Type |
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|--------------------|--------------|
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| `wav` | `audio/wav` |
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| `mp3` | `audio/mpeg` |
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| `flac` | `audio/flac` |
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| `pcm` | `audio/pcm` |
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| `aac` | `audio/aac` |
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| `opus` | `audio/opus` |
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## Examples
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### Basic Generation
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Generate audio with only a text prompt (model defaults for all other parameters):
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```bash
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curl -X POST http://localhost:8000/v1/audio/generate \
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-H "Content-Type: application/json" \
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-d '{
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"input": "The sound of ocean waves crashing on a beach"
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}' --output ocean.wav
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```
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### Custom Duration
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Specify an explicit audio length in seconds:
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```bash
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curl -X POST http://localhost:8000/v1/audio/generate \
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-H "Content-Type: application/json" \
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-d '{
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"input": "A dog barking",
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"audio_length": 5.0
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}' --output dog_5s.wav
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```
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### High Quality with Negative Prompt
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Use a negative prompt to steer generation away from undesired characteristics, and increase inference steps for higher quality:
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```bash
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curl -X POST http://localhost:8000/v1/audio/generate \
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-H "Content-Type: application/json" \
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-d '{
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"input": "A piano playing a gentle melody",
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"audio_length": 10.0,
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"negative_prompt": "Low quality, distorted, noisy",
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"guidance_scale": 8.0,
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"num_inference_steps": 150
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}' --output piano_hq.wav
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```
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### Reproducible Generation
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Set a `seed` to get deterministic results across runs:
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```bash
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curl -X POST http://localhost:8000/v1/audio/generate \
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-H "Content-Type: application/json" \
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-d '{
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"input": "Thunder and rain sounds",
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"audio_length": 15.0,
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"seed": 42
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}' --output thunder.wav
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```
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### Full Control
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Combine all parameters for precise control over generation:
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```bash
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curl -X POST http://localhost:8000/v1/audio/generate \
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-H "Content-Type: application/json" \
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-d '{
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"input": "Thunder and rain sounds",
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"audio_length": 15.0,
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"negative_prompt": "Low quality",
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"guidance_scale": 7.0,
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"num_inference_steps": 100,
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"seed": 42
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}' --output thunder_rain.wav
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```
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### Quick Generation (Fewer Steps)
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For faster generation with slightly lower quality:
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```bash
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curl -X POST http://localhost:8000/v1/audio/generate \
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-H "Content-Type: application/json" \
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-d '{
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"input": "Birds chirping in a forest",
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"audio_length": 8.0,
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"num_inference_steps": 50
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}' --output birds_quick.wav
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```
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### Python Client
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```python
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import httpx
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response = httpx.post(
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"http://localhost:8000/v1/audio/generate",
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json={
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"input": "Thunder and rain",
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"audio_length": 15.0,
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"negative_prompt": "Low quality",
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"guidance_scale": 7.0,
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"num_inference_steps": 100,
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"seed": 42,
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"response_format": "wav",
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},
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timeout=300.0,
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)
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with open("thunder.wav", "wb") as f:
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f.write(response.content)
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```
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## Parameter Tuning Guide
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### `guidance_scale`
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Controls how closely the generated audio follows the text prompt.
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| Range | Behaviour |
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|-------|-----------|
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| 3 - 5 | More creative / varied output |
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| 7 (default) | Balanced adherence |
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| 10+ | Strict adherence to the prompt |
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### `num_inference_steps`
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Controls the number of denoising steps in the diffusion process.
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| Steps | Quality | Speed | Use Case |
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|-------|---------|-------|----------|
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| 50 | Good | Fast | Quick previews |
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| 100 | Very Good | Medium | General purpose |
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| 150+ | Excellent | Slow | Final / critical audio |
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### `audio_length`
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Duration of the generated audio clip. For `stable-audio-open-1.0`, the maximum is approximately 47 seconds. If omitted, the model uses its own default length.
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### `negative_prompt`
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Describes characteristics to avoid. Common negative prompts include:
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- `"Low quality, distorted, noisy"`
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- `"Silence, static"`
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- `"Music"` (when generating sound effects only)
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## Supported Models
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| Model | Description |
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|-------|-------------|
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| `stabilityai/stable-audio-open-1.0` | Open-source audio generation model, up to ~47 seconds, 44.1 kHz stereo |
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## Error Responses
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### 400 Bad Request
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Invalid or missing parameters:
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```json
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{
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"error": {
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"message": "Audio generation model did not produce audio output.",
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"type": "BadRequestError",
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"param": null,
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"code": 400
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}
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}
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```
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### 404 Not Found
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Model mismatch:
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```json
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{
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"error": {
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"message": "The model `xxx` does not exist.",
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"type": "NotFoundError",
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"param": "model",
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"code": 404
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}
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}
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```
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### 422 Unprocessable Entity
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Pydantic validation failure (e.g. invalid `response_format`, `speed` out of range):
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```json
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{
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"detail": [
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{
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"type": "literal_error",
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"msg": "Input should be 'wav', 'pcm', 'flac', 'mp3', 'aac' or 'opus'",
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...
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}
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]
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}
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```
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## Troubleshooting
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### "Audio generation model did not produce audio output"
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The model finished but returned no audio data. Verify the server started successfully and the model loaded without errors.
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### Server Not Responding
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```bash
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# Check if the server is healthy
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curl http://localhost:8000/health
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```
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### Audio Quality Issues
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- Increase `num_inference_steps` (e.g. 150).
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- Add a negative prompt: `"Low quality, distorted, noisy"`.
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- Increase `guidance_scale` for stronger prompt adherence.
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### Generation Timeout
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- Reduce `num_inference_steps`.
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- Reduce `audio_length`.
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- Check GPU memory with `nvidia-smi`.
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### Out of Memory
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- Lower `--gpu-memory-utilization` (e.g. 0.8).
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- Reduce `audio_length`.
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## Development
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Enable debug logging:
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```bash
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vllm-omni serve stabilityai/stable-audio-open-1.0 \
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--host 0.0.0.0 \
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--port 8000 \
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--gpu-memory-utilization 0.9 \
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--trust-remote-code \
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--enforce-eager \
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--omni \
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--uvicorn-log-level debug
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```
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@@ -1,193 +0,0 @@
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# Text-To-Audio
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Source <https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/stable_audio>.
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This example demonstrates how to deploy Stable Audio models for online text-to-audio generation using vLLM-Omni.
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## Supported Models
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| Model | Description |
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|-------|-------------|
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| `stabilityai/stable-audio-open-1.0` | Open-source audio generation, up to ~47 seconds, 44.1 kHz stereo |
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## Start Server
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### Basic Start
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```bash
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vllm-omni serve stabilityai/stable-audio-open-1.0 \
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--host 0.0.0.0 \
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--port 8000 \
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--gpu-memory-utilization 0.9 \
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--trust-remote-code \
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--enforce-eager \
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--omni
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```
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## API Calls
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### Method 1: Using curl
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```bash
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# Run all curl examples
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bash curl_examples.sh
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# Or execute directly
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curl -X POST http://localhost:8000/v1/audio/generate \
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-H "Content-Type: application/json" \
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-d '{
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"input": "The sound of a cat purring",
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"audio_length": 10.0
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}' --output cat.wav
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```
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### Method 2: Using Python Client
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```bash
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cd examples/online_serving/stable_audio
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# Simple generation
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python stable_audio_client.py \
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--text "The sound of a cat purring"
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# With custom duration
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python stable_audio_client.py \
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--text "A dog barking" \
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--audio_length 5.0
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# With all parameters
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python stable_audio_client.py \
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--text "Thunder and rain" \
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--audio_length 15.0 \
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--negative_prompt "Low quality" \
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--guidance_scale 7.0 \
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--num_inference_steps 100 \
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--seed 42 \
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--output thunder.wav
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```
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The Python client supports the following command-line arguments:
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- `--api_url`: API endpoint URL (default: `http://localhost:8000/v1/audio/generate`)
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- `--text`: Text prompt for audio generation (default: `"The sound of a cat purring"`)
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- `--audio_length`: Audio length in seconds (default: `10.0`, max ~47s for `stable-audio-open-1.0`)
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- `--audio_start`: Audio start time in seconds (default: `0.0`)
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- `--negative_prompt`: Negative prompt for classifier-free guidance (default: `"Low quality"`)
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- `--guidance_scale`: Guidance scale for diffusion (default: `7.0`)
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- `--num_inference_steps`: Number of inference steps (default: `100`)
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- `--seed`: Random seed for reproducibility (default: `None`)
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- `--response_format`: Audio output format (default: `wav`). Options: `wav`, `mp3`, `flac`, `pcm`
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- `--output`: Output file path (default: `stable_audio_output.wav`)
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|
||||
### Method 3: Using Python httpx
|
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```python
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import httpx
|
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|
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response = httpx.post(
|
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"http://localhost:8000/v1/audio/generate",
|
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json={
|
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"input": "The sound of ocean waves crashing on a beach",
|
||||
"audio_length": 10.0,
|
||||
"negative_prompt": "Low quality, distorted",
|
||||
"guidance_scale": 7.0,
|
||||
"num_inference_steps": 100,
|
||||
},
|
||||
timeout=300.0,
|
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)
|
||||
|
||||
with open("ocean.wav", "wb") as f:
|
||||
f.write(response.content)
|
||||
```
|
||||
|
||||
## Request Format
|
||||
|
||||
### Simple Generation
|
||||
|
||||
```json
|
||||
{
|
||||
"input": "The sound of ocean waves"
|
||||
}
|
||||
```
|
||||
|
||||
### Generation with Parameters
|
||||
|
||||
```json
|
||||
{
|
||||
"input": "A piano playing a gentle melody",
|
||||
"audio_length": 10.0,
|
||||
"negative_prompt": "Low quality, distorted, noisy",
|
||||
"guidance_scale": 8.0,
|
||||
"num_inference_steps": 150,
|
||||
"seed": 42,
|
||||
"response_format": "wav"
|
||||
}
|
||||
```
|
||||
|
||||
## API Reference
|
||||
|
||||
### Endpoint
|
||||
|
||||
```
|
||||
POST /v1/audio/generate
|
||||
Content-Type: application/json
|
||||
```
|
||||
|
||||
### Generation Parameters
|
||||
|
||||
| Parameter | Type | Default | Description |
|
||||
|-----------|------|---------|-------------|
|
||||
| `input` | string | **required** | Text prompt describing the audio to generate |
|
||||
| `model` | string | server's model | Model to use (optional, should match server if specified) |
|
||||
| `response_format` | string | "wav" | Audio format: wav, mp3, flac, pcm, aac, opus |
|
||||
| `speed` | float | 1.0 | Playback speed (0.25 - 4.0) |
|
||||
| `audio_length` | float | null | Audio duration in seconds (max ~47s for `stable-audio-open-1.0`) |
|
||||
| `audio_start` | float | 0.0 | Audio start time in seconds |
|
||||
| `negative_prompt` | string | null | Text describing what to avoid in generation |
|
||||
| `guidance_scale` | float | model default | Classifier-free guidance scale (higher = more adherence to prompt) |
|
||||
| `num_inference_steps` | int | model default | Number of denoising steps (higher = better quality, slower) |
|
||||
| `seed` | int | null | Random seed for reproducible generation |
|
||||
|
||||
### Response Format
|
||||
|
||||
Returns binary audio data with appropriate `Content-Type` header (e.g., `audio/wav`).
|
||||
|
||||
## Tuning Tips
|
||||
|
||||
1. **Audio Length**: Keep under 47 seconds for `stable-audio-open-1.0`.
|
||||
2. **Quality vs Speed**:
|
||||
- 50 steps: Fast, decent quality (quick previews)
|
||||
- 100 steps: Good balance (general purpose)
|
||||
- 150+ steps: High quality, slower (final / critical audio)
|
||||
3. **Guidance Scale**:
|
||||
- Lower (3 - 5): More creative / varied output
|
||||
- Default (7): Good balance
|
||||
- Higher (10+): Strict adherence to the prompt
|
||||
4. **Negative Prompts**: Use to avoid unwanted characteristics such as `"Low quality"`, `"distorted"`, `"noisy"`.
|
||||
5. **Seeds**: Set a fixed seed to get deterministic, reproducible results.
|
||||
|
||||
## File Description
|
||||
|
||||
| File | Description |
|
||||
|------|-------------|
|
||||
| `curl_examples.sh` | Curl examples covering common use cases |
|
||||
| `stable_audio_client.py` | Python client with full CLI argument support |
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
1. **Audio generation model did not produce audio output**: Verify the server started successfully and the model loaded without errors.
|
||||
2. **Connection refused**: Make sure the server is running on the correct port.
|
||||
3. **Generation timeout**: Reduce `num_inference_steps` or `audio_length`, and check GPU memory with `nvidia-smi`.
|
||||
4. **Out of memory**: Lower `--gpu-memory-utilization` or reduce `audio_length`.
|
||||
5. **Audio quality issues**: Increase `num_inference_steps`, add a negative prompt, or raise `guidance_scale`.
|
||||
|
||||
## Example materials
|
||||
|
||||
??? abstract "stable_audio_client.py"
|
||||
``````py
|
||||
--8<-- "examples/online_serving/stable_audio/stable_audio_client.py"
|
||||
``````
|
||||
??? abstract "curl_examples.sh"
|
||||
``````sh
|
||||
--8<-- "examples/online_serving/stable_audio/curl_examples.sh"
|
||||
``````
|
||||
@@ -1,234 +0,0 @@
|
||||
# Stable Audio Online Serving
|
||||
|
||||
Generate audio from text prompts using Stable Audio models via an OpenAI-compatible API endpoint.
|
||||
|
||||
## Features
|
||||
|
||||
- **OpenAI-compatible API**: Use `/v1/audio/generate` endpoint
|
||||
- **Flexible control**: Adjust audio length, guidance scale, inference steps
|
||||
- **Quality control**: Use negative prompts to avoid unwanted characteristics
|
||||
- **Reproducible**: Set random seed for deterministic generation
|
||||
|
||||
## Quick Start
|
||||
|
||||
### 1. Start the Server
|
||||
|
||||
```bash
|
||||
vllm-omni serve stabilityai/stable-audio-open-1.0 \
|
||||
--host 0.0.0.0 \
|
||||
--port 8000 \
|
||||
--gpu-memory-utilization 0.9 \
|
||||
--trust-remote-code \
|
||||
--enforce-eager \
|
||||
--omni
|
||||
```
|
||||
|
||||
### 2. Generate Audio
|
||||
|
||||
#### Using curl
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:8000/v1/audio/generate \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"input": "The sound of a cat purring",
|
||||
"audio_length": 10.0
|
||||
}' --output cat.wav
|
||||
```
|
||||
|
||||
#### Using Python Client
|
||||
|
||||
```bash
|
||||
python stable_audio_client.py \
|
||||
--text "The sound of a cat purring" \
|
||||
--audio_length 10.0 \
|
||||
--output cat.wav
|
||||
```
|
||||
|
||||
#### Using Bash Script
|
||||
|
||||
```bash
|
||||
bash curl_examples.sh
|
||||
```
|
||||
|
||||
## API Reference
|
||||
|
||||
### Endpoint
|
||||
|
||||
```
|
||||
POST /v1/audio/generate
|
||||
```
|
||||
|
||||
### Request Body
|
||||
|
||||
```json
|
||||
{
|
||||
"input": "Text description of the audio",
|
||||
"audio_length": 10.0,
|
||||
"audio_start": 0.0,
|
||||
"negative_prompt": "Low quality",
|
||||
"guidance_scale": 7.0,
|
||||
"num_inference_steps": 100,
|
||||
"seed": 42,
|
||||
"response_format": "wav"
|
||||
}
|
||||
```
|
||||
|
||||
### Parameters
|
||||
|
||||
| Parameter | Type | Default | Description |
|
||||
|-----------|------|---------|-------------|
|
||||
| `input` | string | **required** | Text prompt describing the audio to generate |
|
||||
| `audio_length` | float | ~47s | Audio duration in seconds (max ~47s for stable-audio-open-1.0) |
|
||||
| `audio_start` | float | 0.0 | Audio start time in seconds |
|
||||
| `negative_prompt` | string | null | Text describing what to avoid in generation |
|
||||
| `guidance_scale` | float | 7.0 | Classifier-free guidance scale (higher = more adherence to prompt) |
|
||||
| `num_inference_steps` | int | 50 | Number of denoising steps (higher = better quality, slower) |
|
||||
| `seed` | int | null | Random seed for reproducibility |
|
||||
| `response_format` | string | "wav" | Output format: wav, mp3, flac, pcm |
|
||||
|
||||
### Response
|
||||
|
||||
Returns audio data in the requested format (default: WAV).
|
||||
|
||||
## Usage Examples
|
||||
|
||||
### Basic Generation
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:8000/v1/audio/generate \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"input": "The sound of ocean waves"
|
||||
}' --output ocean.wav
|
||||
```
|
||||
|
||||
### Custom Duration
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:8000/v1/audio/generate \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"input": "A dog barking",
|
||||
"audio_length": 5.0
|
||||
}' --output dog_5s.wav
|
||||
```
|
||||
|
||||
### High Quality with Negative Prompt
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:8000/v1/audio/generate \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"input": "A piano playing a gentle melody",
|
||||
"audio_length": 10.0,
|
||||
"negative_prompt": "Low quality, distorted, noisy",
|
||||
"guidance_scale": 8.0,
|
||||
"num_inference_steps": 150
|
||||
}' --output piano_hq.wav
|
||||
```
|
||||
|
||||
### Reproducible Generation
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:8000/v1/audio/generate \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"input": "Thunder and rain sounds",
|
||||
"audio_length": 15.0,
|
||||
"seed": 42
|
||||
}' --output thunder.wav
|
||||
```
|
||||
|
||||
### Quick Generation (Fewer Steps)
|
||||
|
||||
For faster generation with slightly lower quality:
|
||||
|
||||
```bash
|
||||
curl -X POST http://localhost:8000/v1/audio/generate \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"input": "Birds chirping in a forest",
|
||||
"audio_length": 8.0,
|
||||
"num_inference_steps": 50
|
||||
}' --output birds_quick.wav
|
||||
```
|
||||
|
||||
## Python Client Examples
|
||||
|
||||
### Simple Generation
|
||||
|
||||
```bash
|
||||
python stable_audio_client.py \
|
||||
--text "The sound of a cat purring"
|
||||
```
|
||||
|
||||
### Custom Parameters
|
||||
|
||||
```bash
|
||||
python stable_audio_client.py \
|
||||
--text "Thunder and rain" \
|
||||
--audio_length 15.0 \
|
||||
--negative_prompt "Low quality" \
|
||||
--guidance_scale 7.0 \
|
||||
--num_inference_steps 100 \
|
||||
--seed 42 \
|
||||
--output thunder.wav
|
||||
```
|
||||
|
||||
### Different Output Format
|
||||
|
||||
```bash
|
||||
python stable_audio_client.py \
|
||||
--text "Guitar playing" \
|
||||
--response_format mp3 \
|
||||
--output guitar.mp3
|
||||
```
|
||||
|
||||
## Tips
|
||||
|
||||
1. **Audio Length**: Keep under 47 seconds for `stable-audio-open-1.0`
|
||||
2. **Quality vs Speed**:
|
||||
- 50 steps: Fast, decent quality
|
||||
- 100 steps: Good balance (default)
|
||||
- 150+ steps: High quality, slower
|
||||
3. **Guidance Scale**:
|
||||
- Lower (3-5): More creative/varied
|
||||
- Default (7): Good balance
|
||||
- Higher (10+): More literal to prompt
|
||||
4. **Negative Prompts**: Use to avoid "Low quality", "distorted", "noisy", etc.
|
||||
5. **Seeds**: Use same seed for reproducible results
|
||||
|
||||
## Performance
|
||||
|
||||
| Inference Steps | Quality | Speed | Use Case |
|
||||
|----------------|---------|-------|----------|
|
||||
| 50 | Good | Fast | Quick previews |
|
||||
| 100 (default) | Very Good | Medium | Production |
|
||||
| 150+ | Excellent | Slow | Final/critical audio |
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Server not responding
|
||||
- Check if server is running: `curl http://localhost:8000/health`
|
||||
- Check server logs for errors
|
||||
|
||||
### Audio quality issues
|
||||
- Increase `num_inference_steps` (e.g., 150)
|
||||
- Add negative prompts: `"Low quality, distorted, noisy"`
|
||||
- Increase `guidance_scale` for more prompt adherence
|
||||
|
||||
### Generation timeout
|
||||
- Reduce `num_inference_steps`
|
||||
- Reduce `audio_length`
|
||||
- Check GPU memory with `nvidia-smi`
|
||||
|
||||
### Wrong audio length
|
||||
- Ensure `audio_length` is within model limits (~47s max)
|
||||
- Adjust `audio_start` if trimming is needed
|
||||
|
||||
## See Also
|
||||
|
||||
- [Offline Inference Example](../../offline_inference/text_to_audio/README.md)
|
||||
- [Stable Audio Model Card](https://huggingface.co/stabilityai/stable-audio-open-1.0)
|
||||
- [vLLM-Omni Documentation](https://github.com/vllm-project/vllm-omni)
|
||||
@@ -1,54 +0,0 @@
|
||||
#!/bin/bash
|
||||
# Examples for using Stable Audio with curl via /v1/audio/generate endpoint
|
||||
|
||||
# Example 1: Simple request with default parameters
|
||||
echo "Example 1: Simple request with default parameters"
|
||||
curl -X POST http://localhost:8000/v1/audio/generate \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"input": "The sound audience clapping and cheering in a stadium"
|
||||
}' --output stadium.wav
|
||||
|
||||
# Example 2: Request with custom audio_length
|
||||
echo "Example 2: Custom audio length (5 seconds)"
|
||||
curl -X POST http://localhost:8000/v1/audio/generate \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"input": "The sound of a dog barking",
|
||||
"audio_length": 5.0
|
||||
}' --output dog_5s.wav
|
||||
|
||||
# Example 3: Request with negative prompt for quality control
|
||||
echo "Example 3: With negative prompt"
|
||||
curl -X POST http://localhost:8000/v1/audio/generate \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"input": "A piano playing a gentle melody",
|
||||
"audio_length": 10.0,
|
||||
"negative_prompt": "Low quality, distorted, noisy"
|
||||
}' --output piano.wav
|
||||
|
||||
# Example 4: Full control with all parameters
|
||||
echo "Example 4: Full control (custom length, guidance, steps, seed)"
|
||||
curl -X POST http://localhost:8000/v1/audio/generate \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"input": "Thunder and rain sounds",
|
||||
"audio_length": 15.0,
|
||||
"negative_prompt": "Low quality",
|
||||
"guidance_scale": 7.0,
|
||||
"num_inference_steps": 100,
|
||||
"seed": 42
|
||||
}' --output thunder_rain.wav
|
||||
|
||||
# Example 5: Quick generation with fewer steps (faster but lower quality)
|
||||
echo "Example 5: Quick generation (fewer steps)"
|
||||
curl -X POST http://localhost:8000/v1/audio/generate \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"input": "Ocean waves crashing on a beach",
|
||||
"audio_length": 8.0,
|
||||
"num_inference_steps": 50
|
||||
}' --output ocean.wav
|
||||
|
||||
echo "All examples completed!"
|
||||
@@ -1,170 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
OpenAI-compatible client for Stable Audio via /v1/audio/generate endpoint.
|
||||
|
||||
This script demonstrates how to use the OpenAI-compatible speech API
|
||||
to generate audio from text using Stable Audio models.
|
||||
|
||||
Examples:
|
||||
# Simple generation
|
||||
python stable_audio_client.py --text "The sound of a cat purring"
|
||||
|
||||
# With custom duration
|
||||
python stable_audio_client.py --text "A dog barking" --audio_length 5.0
|
||||
|
||||
# With all parameters
|
||||
python stable_audio_client.py --text "Thunder and rain" \
|
||||
--audio_length 15.0 \
|
||||
--negative_prompt "Low quality" \
|
||||
--guidance_scale 7.0 \
|
||||
--num_inference_steps 100 \
|
||||
--seed 42 \
|
||||
--output thunder.wav
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import sys
|
||||
|
||||
import requests
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser(description="Generate audio with Stable Audio via OpenAI-compatible API")
|
||||
parser.add_argument(
|
||||
"--api_url",
|
||||
default="http://localhost:8000/v1/audio/generate",
|
||||
help="API endpoint URL",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--text",
|
||||
default="The sound of a cat purring",
|
||||
help="Text prompt for audio generation",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--audio_length",
|
||||
type=float,
|
||||
default=10.0,
|
||||
help="Audio length in seconds (max ~47s for stable-audio-open-1.0)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--audio_start",
|
||||
type=float,
|
||||
default=0.0,
|
||||
help="Audio start time in seconds",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--negative_prompt",
|
||||
default="Low quality",
|
||||
help="Negative prompt for classifier-free guidance",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--guidance_scale",
|
||||
type=float,
|
||||
default=7.0,
|
||||
help="Guidance scale for diffusion (higher = more adherence to prompt)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--num_inference_steps",
|
||||
type=int,
|
||||
default=100,
|
||||
help="Number of inference steps (higher = better quality, slower)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--seed",
|
||||
type=int,
|
||||
default=None,
|
||||
help="Random seed for reproducibility",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output",
|
||||
default="stable_audio_output.wav",
|
||||
help="Output file path",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--response_format",
|
||||
default="wav",
|
||||
choices=["wav", "mp3", "flac", "pcm"],
|
||||
help="Audio output format",
|
||||
)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def generate_audio(args):
|
||||
"""Generate audio using the API."""
|
||||
|
||||
# Build request payload
|
||||
payload = {
|
||||
"input": args.text,
|
||||
"audio_length": args.audio_length,
|
||||
"audio_start": args.audio_start,
|
||||
"response_format": args.response_format,
|
||||
}
|
||||
|
||||
# Add optional parameters
|
||||
if args.negative_prompt:
|
||||
payload["negative_prompt"] = args.negative_prompt
|
||||
if args.guidance_scale:
|
||||
payload["guidance_scale"] = args.guidance_scale
|
||||
if args.num_inference_steps:
|
||||
payload["num_inference_steps"] = args.num_inference_steps
|
||||
if args.seed is not None:
|
||||
payload["seed"] = args.seed
|
||||
|
||||
print(f"\n{'=' * 60}")
|
||||
print("Stable Audio - Text-to-Audio Generation")
|
||||
print(f"{'=' * 60}")
|
||||
print(f"API URL: {args.api_url}")
|
||||
print(f"Prompt: {args.text}")
|
||||
print(f"Audio length: {args.audio_length}s")
|
||||
print(f"Negative prompt: {args.negative_prompt}")
|
||||
print(f"Guidance scale: {args.guidance_scale}")
|
||||
print(f"Inference steps: {args.num_inference_steps}")
|
||||
if args.seed is not None:
|
||||
print(f"Seed: {args.seed}")
|
||||
print(f"Output: {args.output}")
|
||||
print(f"{'=' * 60}\n")
|
||||
|
||||
try:
|
||||
# Make the API request
|
||||
print("Generating audio...")
|
||||
response = requests.post(
|
||||
args.api_url,
|
||||
json=payload,
|
||||
headers={"Content-Type": "application/json"},
|
||||
timeout=300, # 5 minute timeout for long generations
|
||||
)
|
||||
|
||||
# Check for errors
|
||||
if response.status_code != 200:
|
||||
print(f"Error: API returned status code {response.status_code}")
|
||||
print(f"Response: {response.text}")
|
||||
return False
|
||||
|
||||
# Save the audio
|
||||
with open(args.output, "wb") as f:
|
||||
f.write(response.content)
|
||||
|
||||
print(f"✓ Audio saved to {args.output}")
|
||||
print(f" File size: {len(response.content) / 1024:.1f} KB")
|
||||
return True
|
||||
|
||||
except requests.exceptions.Timeout:
|
||||
print("Error: Request timed out. Try reducing inference steps or audio length.")
|
||||
return False
|
||||
except requests.exceptions.ConnectionError:
|
||||
print(f"Error: Could not connect to {args.api_url}")
|
||||
print("Make sure the server is running.")
|
||||
return False
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
return False
|
||||
|
||||
|
||||
def main():
|
||||
args = parse_args()
|
||||
success = generate_audio(args)
|
||||
sys.exit(0 if success else 1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,509 +0,0 @@
|
||||
# tests/entrypoints/openai_api/test_serving_audio_generate.py
|
||||
import logging
|
||||
from inspect import Signature, signature
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
from fastapi import FastAPI
|
||||
from fastapi.testclient import TestClient
|
||||
|
||||
from vllm_omni.entrypoints.openai.protocol.audio import (
|
||||
CreateAudio,
|
||||
OpenAICreateAudioGenerateRequest,
|
||||
)
|
||||
from vllm_omni.entrypoints.openai.serving_audio_generate import (
|
||||
OmniOpenAIServingAudioGenerate,
|
||||
)
|
||||
from vllm_omni.inputs.data import OmniDiffusionSamplingParams
|
||||
from vllm_omni.outputs import OmniRequestOutput
|
||||
|
||||
pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# Helper: create a mock audio output for endpoint tests
|
||||
def create_mock_audio_output(
|
||||
request_id: str = "audiogen-mock-123",
|
||||
sample_rate: int = 44100,
|
||||
num_samples: int = 44100,
|
||||
audio_key: str = "audio",
|
||||
) -> OmniRequestOutput:
|
||||
"""Return an OmniRequestOutput mimicking diffusion audio model output."""
|
||||
|
||||
audio_tensor = torch.sin(torch.linspace(0, 440 * 2 * torch.pi, num_samples))
|
||||
|
||||
return OmniRequestOutput.from_diffusion(
|
||||
request_id=request_id,
|
||||
images=[],
|
||||
prompt=None,
|
||||
metrics={},
|
||||
multimodal_output={audio_key: audio_tensor, "sr": sample_rate},
|
||||
)
|
||||
|
||||
|
||||
def _make_engine_client(*, audio_key: str = "audio", sample_rate: int = 44100):
|
||||
"""Build a mock engine client producing audio output."""
|
||||
mock_engine_client = MagicMock()
|
||||
mock_engine_client.errored = False
|
||||
mock_engine_client.model_type = "StableAudioPipeline"
|
||||
mock_engine_client.default_sampling_params_list = [{}]
|
||||
|
||||
async def mock_generate_fn(*args, **kwargs):
|
||||
yield create_mock_audio_output(
|
||||
request_id=kwargs.get("request_id", "audiogen-mock"),
|
||||
sample_rate=sample_rate,
|
||||
audio_key=audio_key,
|
||||
)
|
||||
|
||||
mock_engine_client.generate = MagicMock(side_effect=mock_generate_fn)
|
||||
return mock_engine_client
|
||||
|
||||
|
||||
def _make_server(engine_client=None):
|
||||
"""Build an OmniOpenAIServingAudioGenerate with mocks."""
|
||||
if engine_client is None:
|
||||
engine_client = _make_engine_client()
|
||||
|
||||
mock_models = MagicMock()
|
||||
mock_models.is_base_model.return_value = True
|
||||
|
||||
return OmniOpenAIServingAudioGenerate(
|
||||
engine_client=engine_client,
|
||||
models=mock_models,
|
||||
request_logger=MagicMock(),
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def test_app():
|
||||
server = _make_server()
|
||||
|
||||
original_fn = server.create_audio_generate
|
||||
sig = signature(original_fn)
|
||||
new_params = [p for name, p in sig.parameters.items() if name != "raw_request"]
|
||||
new_sig = Signature(parameters=new_params, return_annotation=sig.return_annotation)
|
||||
|
||||
async def patched_create_audio_generate(*args, **kwargs):
|
||||
return await original_fn(*args, **kwargs)
|
||||
|
||||
patched_create_audio_generate.__signature__ = new_sig
|
||||
server.create_audio_generate = patched_create_audio_generate
|
||||
|
||||
app = FastAPI()
|
||||
app.add_api_route(
|
||||
"/v1/audio/generate",
|
||||
server.create_audio_generate,
|
||||
methods=["POST"],
|
||||
response_model=None,
|
||||
)
|
||||
return app
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def client(test_app):
|
||||
return TestClient(test_app)
|
||||
|
||||
|
||||
# Request Validation (Pydantic model)
|
||||
class TestRequestValidation:
|
||||
"""Validate OpenAICreateAudioGenerateRequest pydantic constraints."""
|
||||
|
||||
def test_valid_minimal_request(self):
|
||||
req = OpenAICreateAudioGenerateRequest(input="A calm piano melody")
|
||||
assert req.input == "A calm piano melody"
|
||||
assert req.response_format == "wav"
|
||||
assert req.speed == 1.0
|
||||
|
||||
def test_fields_are_wired_correctly(self):
|
||||
req = OpenAICreateAudioGenerateRequest(
|
||||
input="rain sounds",
|
||||
model="stable-audio",
|
||||
response_format="flac",
|
||||
speed=1.5,
|
||||
audio_length=10.0,
|
||||
audio_start=2.0,
|
||||
negative_prompt="noise",
|
||||
guidance_scale=7.5,
|
||||
num_inference_steps=100,
|
||||
seed=42,
|
||||
)
|
||||
assert req.input == "rain sounds"
|
||||
assert req.model == "stable-audio"
|
||||
assert req.response_format == "flac"
|
||||
assert req.speed == 1.5
|
||||
assert req.audio_length == 10.0
|
||||
assert req.audio_start == 2.0
|
||||
assert req.negative_prompt == "noise"
|
||||
assert req.guidance_scale == 7.5
|
||||
assert req.num_inference_steps == 100
|
||||
assert req.seed == 42
|
||||
|
||||
def test_invalid_response_format(self):
|
||||
with pytest.raises(Exception):
|
||||
OpenAICreateAudioGenerateRequest(input="test", response_format="invalid_format")
|
||||
|
||||
def test_speed_lower_bound(self):
|
||||
with pytest.raises(Exception):
|
||||
OpenAICreateAudioGenerateRequest(input="test", speed=0.1)
|
||||
|
||||
def test_speed_upper_bound(self):
|
||||
with pytest.raises(Exception):
|
||||
OpenAICreateAudioGenerateRequest(input="test", speed=5.0)
|
||||
|
||||
def test_speed_at_boundaries(self):
|
||||
req_low = OpenAICreateAudioGenerateRequest(input="test", speed=0.25)
|
||||
assert req_low.speed == 0.25
|
||||
req_high = OpenAICreateAudioGenerateRequest(input="test", speed=4.0)
|
||||
assert req_high.speed == 4.0
|
||||
|
||||
def test_stream_format_sse_rejected(self):
|
||||
with pytest.raises(Exception):
|
||||
OpenAICreateAudioGenerateRequest(input="test", stream_format="sse")
|
||||
|
||||
def test_stream_format_audio_accepted(self):
|
||||
req = OpenAICreateAudioGenerateRequest(input="test", stream_format="audio")
|
||||
assert req.stream_format == "audio"
|
||||
|
||||
|
||||
# Constructor & Class Methods
|
||||
class TestConstructor:
|
||||
def test_default_init(self):
|
||||
server = _make_server()
|
||||
assert server.diffusion_mode is False
|
||||
|
||||
def test_for_diffusion_factory(self):
|
||||
engine_client = _make_engine_client()
|
||||
mock_models = MagicMock()
|
||||
mock_models.is_base_model.return_value = True
|
||||
|
||||
server = OmniOpenAIServingAudioGenerate.for_diffusion(
|
||||
engine_client=engine_client,
|
||||
models=mock_models,
|
||||
request_logger=MagicMock(),
|
||||
)
|
||||
assert server.diffusion_mode is True
|
||||
|
||||
def test_is_stable_audio_model_true(self):
|
||||
server = _make_server()
|
||||
assert server._is_stable_audio_model() is True
|
||||
|
||||
def test_is_stable_audio_model_false(self):
|
||||
engine = _make_engine_client()
|
||||
engine.model_type = "SomeOtherModel"
|
||||
server = _make_server(engine_client=engine)
|
||||
assert server._is_stable_audio_model() is False
|
||||
|
||||
|
||||
# Parameter Wiring — verify request params reach the engine
|
||||
class TestParameterWiring:
|
||||
"""Ensure request parameters are correctly forwarded to the engine."""
|
||||
|
||||
@pytest.fixture
|
||||
def server_and_engine(self):
|
||||
engine = _make_engine_client()
|
||||
server = _make_server(engine_client=engine)
|
||||
return server, engine
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_prompt_wiring(self, server_and_engine):
|
||||
server, engine = server_and_engine
|
||||
req = OpenAICreateAudioGenerateRequest(input="birds chirping")
|
||||
await server.create_audio_generate(req)
|
||||
|
||||
engine.generate.assert_called_once()
|
||||
call_kwargs = engine.generate.call_args[1]
|
||||
assert call_kwargs["prompt"]["prompt"] == "birds chirping"
|
||||
assert call_kwargs["output_modalities"] == ["audio"]
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_negative_prompt_wiring(self, server_and_engine):
|
||||
server, engine = server_and_engine
|
||||
req = OpenAICreateAudioGenerateRequest(input="a calm ocean", negative_prompt="noise distortion")
|
||||
await server.create_audio_generate(req)
|
||||
|
||||
call_kwargs = engine.generate.call_args[1]
|
||||
assert call_kwargs["prompt"]["negative_prompt"] == "noise distortion"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_negative_prompt_absent(self, server_and_engine):
|
||||
server, engine = server_and_engine
|
||||
req = OpenAICreateAudioGenerateRequest(input="a calm ocean")
|
||||
await server.create_audio_generate(req)
|
||||
|
||||
call_kwargs = engine.generate.call_args[1]
|
||||
assert "negative_prompt" not in call_kwargs["prompt"]
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_guidance_scale_wiring(self, server_and_engine):
|
||||
server, engine = server_and_engine
|
||||
req = OpenAICreateAudioGenerateRequest(input="test", guidance_scale=12.0)
|
||||
await server.create_audio_generate(req)
|
||||
|
||||
call_kwargs = engine.generate.call_args[1]
|
||||
sp = call_kwargs["sampling_params_list"][0]
|
||||
assert isinstance(sp, OmniDiffusionSamplingParams)
|
||||
assert sp.guidance_scale == 12.0
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_num_inference_steps_wiring(self, server_and_engine):
|
||||
server, engine = server_and_engine
|
||||
req = OpenAICreateAudioGenerateRequest(input="test", num_inference_steps=200)
|
||||
await server.create_audio_generate(req)
|
||||
|
||||
sp = engine.generate.call_args[1]["sampling_params_list"][0]
|
||||
assert sp.num_inference_steps == 200
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_seed_creates_generator(self, server_and_engine):
|
||||
server, engine = server_and_engine
|
||||
req = OpenAICreateAudioGenerateRequest(input="test", seed=42)
|
||||
|
||||
with patch("vllm_omni.entrypoints.openai.serving_audio_generate.torch") as mock_torch:
|
||||
mock_gen = MagicMock()
|
||||
mock_gen.manual_seed.return_value = mock_gen
|
||||
mock_torch.Generator.return_value = mock_gen
|
||||
|
||||
await server.create_audio_generate(req)
|
||||
|
||||
mock_torch.Generator.assert_called_once()
|
||||
mock_gen.manual_seed.assert_called_once_with(42)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_seed_none_skips_generator(self, server_and_engine):
|
||||
server, engine = server_and_engine
|
||||
req = OpenAICreateAudioGenerateRequest(input="test")
|
||||
|
||||
await server.create_audio_generate(req)
|
||||
|
||||
sp = engine.generate.call_args[1]["sampling_params_list"][0]
|
||||
assert sp.generator is None
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_audio_length_wiring(self, server_and_engine):
|
||||
server, engine = server_and_engine
|
||||
req = OpenAICreateAudioGenerateRequest(input="test", audio_length=10.0, audio_start=2.0)
|
||||
await server.create_audio_generate(req)
|
||||
|
||||
sp = engine.generate.call_args[1]["sampling_params_list"][0]
|
||||
assert sp.extra_args["audio_start_in_s"] == 2.0
|
||||
assert sp.extra_args["audio_end_in_s"] == 12.0 # start + length
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_audio_length_default_start(self, server_and_engine):
|
||||
server, engine = server_and_engine
|
||||
req = OpenAICreateAudioGenerateRequest(input="test", audio_length=5.0)
|
||||
await server.create_audio_generate(req)
|
||||
|
||||
sp = engine.generate.call_args[1]["sampling_params_list"][0]
|
||||
assert sp.extra_args["audio_start_in_s"] == 0.0
|
||||
assert sp.extra_args["audio_end_in_s"] == 5.0
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_no_audio_length_skips_extra_args(self, server_and_engine):
|
||||
server, engine = server_and_engine
|
||||
req = OpenAICreateAudioGenerateRequest(input="test")
|
||||
await server.create_audio_generate(req)
|
||||
|
||||
sp = engine.generate.call_args[1]["sampling_params_list"][0]
|
||||
assert sp.extra_args == {}
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_defaults_not_set_when_omitted(self, server_and_engine):
|
||||
"""Guidance scale and num_inference_steps keep dataclass defaults when not in request."""
|
||||
server, engine = server_and_engine
|
||||
req = OpenAICreateAudioGenerateRequest(input="test")
|
||||
await server.create_audio_generate(req)
|
||||
|
||||
sp = engine.generate.call_args[1]["sampling_params_list"][0]
|
||||
defaults = OmniDiffusionSamplingParams()
|
||||
assert sp.guidance_scale == defaults.guidance_scale
|
||||
assert sp.num_inference_steps == defaults.num_inference_steps
|
||||
|
||||
|
||||
# Audio Response Format
|
||||
class TestAudioResponseFormat:
|
||||
def test_wav_response(self, client):
|
||||
payload = {"input": "a gentle rain", "response_format": "wav"}
|
||||
response = client.post("/v1/audio/generate", json=payload)
|
||||
assert response.status_code == 200
|
||||
assert response.headers["content-type"] == "audio/wav"
|
||||
assert len(response.content) > 0
|
||||
|
||||
def test_mp3_response(self, client):
|
||||
payload = {"input": "a gentle rain", "response_format": "mp3"}
|
||||
response = client.post("/v1/audio/generate", json=payload)
|
||||
assert response.status_code == 200
|
||||
assert response.headers["content-type"] == "audio/mpeg"
|
||||
assert len(response.content) > 0
|
||||
|
||||
def test_flac_response(self, client):
|
||||
payload = {"input": "a gentle rain", "response_format": "flac"}
|
||||
response = client.post("/v1/audio/generate", json=payload)
|
||||
assert response.status_code == 200
|
||||
assert response.headers["content-type"] == "audio/flac"
|
||||
assert len(response.content) > 0
|
||||
|
||||
def test_invalid_format_rejected(self, client):
|
||||
payload = {"input": "test", "response_format": "banana"}
|
||||
response = client.post("/v1/audio/generate", json=payload)
|
||||
assert response.status_code == 422
|
||||
|
||||
@patch("vllm_omni.entrypoints.openai.serving_audio_generate.OmniOpenAIServingAudioGenerate.create_audio")
|
||||
def test_speed_parameter_forwarded(self, mock_create_audio, test_app):
|
||||
mock_audio_response = MagicMock()
|
||||
mock_audio_response.audio_data = b"dummy_audio"
|
||||
mock_audio_response.media_type = "audio/wav"
|
||||
mock_create_audio.return_value = mock_audio_response
|
||||
|
||||
c = TestClient(test_app)
|
||||
payload = {"input": "test", "response_format": "wav", "speed": 2.5}
|
||||
c.post("/v1/audio/generate", json=payload)
|
||||
|
||||
mock_create_audio.assert_called_once()
|
||||
audio_obj = mock_create_audio.call_args[0][0]
|
||||
assert isinstance(audio_obj, CreateAudio)
|
||||
assert audio_obj.speed == 2.5
|
||||
|
||||
@patch("vllm_omni.entrypoints.openai.serving_audio_generate.OmniOpenAIServingAudioGenerate.create_audio")
|
||||
def test_sample_rate_from_output(self, mock_create_audio, test_app):
|
||||
mock_audio_response = MagicMock()
|
||||
mock_audio_response.audio_data = b"dummy"
|
||||
mock_audio_response.media_type = "audio/wav"
|
||||
mock_create_audio.return_value = mock_audio_response
|
||||
|
||||
c = TestClient(test_app)
|
||||
payload = {"input": "test"}
|
||||
c.post("/v1/audio/generate", json=payload)
|
||||
|
||||
audio_obj = mock_create_audio.call_args[0][0]
|
||||
assert audio_obj.sample_rate == 44100 # Stable Audio default
|
||||
|
||||
|
||||
# Error Handling
|
||||
class TestErrorHandling:
|
||||
@pytest.mark.asyncio
|
||||
async def test_no_output_returns_error(self):
|
||||
engine = _make_engine_client()
|
||||
|
||||
async def empty_gen(*args, **kwargs):
|
||||
return
|
||||
yield # noqa: unreachable – makes this an async generator
|
||||
|
||||
engine.generate = MagicMock(side_effect=empty_gen)
|
||||
server = _make_server(engine_client=engine)
|
||||
req = OpenAICreateAudioGenerateRequest(input="test")
|
||||
resp = await server.create_audio_generate(req)
|
||||
|
||||
# create_error_response returns an ErrorResponse with .error.message
|
||||
assert "No output generated" in resp.error.message
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_no_audio_in_output_returns_error(self):
|
||||
engine = _make_engine_client()
|
||||
|
||||
async def gen_without_audio(*args, **kwargs):
|
||||
yield OmniRequestOutput.from_diffusion(
|
||||
request_id="test",
|
||||
images=[],
|
||||
prompt=None,
|
||||
metrics={},
|
||||
multimodal_output={}, # no audio key
|
||||
)
|
||||
|
||||
engine.generate = MagicMock(side_effect=gen_without_audio)
|
||||
server = _make_server(engine_client=engine)
|
||||
req = OpenAICreateAudioGenerateRequest(input="test")
|
||||
resp = await server.create_audio_generate(req)
|
||||
|
||||
assert "did not produce audio" in resp.error.message
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_engine_errored_raises(self):
|
||||
engine = _make_engine_client()
|
||||
engine.errored = True
|
||||
engine.dead_error = RuntimeError("engine is dead")
|
||||
server = _make_server(engine_client=engine)
|
||||
|
||||
req = OpenAICreateAudioGenerateRequest(input="test")
|
||||
with pytest.raises(RuntimeError, match="engine is dead"):
|
||||
await server.create_audio_generate(req)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_model_outputs_key_fallback(self):
|
||||
"""Audio data under 'model_outputs' key should be accepted."""
|
||||
engine = _make_engine_client(audio_key="model_outputs")
|
||||
server = _make_server(engine_client=engine)
|
||||
req = OpenAICreateAudioGenerateRequest(input="test")
|
||||
resp = await server.create_audio_generate(req)
|
||||
|
||||
# Should succeed and return a Response with audio bytes
|
||||
assert hasattr(resp, "body")
|
||||
assert len(resp.body) > 0
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_value_error_returns_error_response(self):
|
||||
engine = _make_engine_client()
|
||||
|
||||
async def gen_value_error(*args, **kwargs):
|
||||
raise ValueError("bad value")
|
||||
yield # noqa: unreachable
|
||||
|
||||
engine.generate = MagicMock(side_effect=gen_value_error)
|
||||
server = _make_server(engine_client=engine)
|
||||
req = OpenAICreateAudioGenerateRequest(input="test")
|
||||
resp = await server.create_audio_generate(req)
|
||||
|
||||
assert "bad value" in resp.error.message
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_generic_exception_returns_error_response(self):
|
||||
engine = _make_engine_client()
|
||||
|
||||
async def gen_runtime_error(*args, **kwargs):
|
||||
raise RuntimeError("something went wrong")
|
||||
yield # noqa: unreachable
|
||||
|
||||
engine.generate = MagicMock(side_effect=gen_runtime_error)
|
||||
server = _make_server(engine_client=engine)
|
||||
req = OpenAICreateAudioGenerateRequest(input="test")
|
||||
resp = await server.create_audio_generate(req)
|
||||
|
||||
assert "Audio generation failed" in resp.error.message
|
||||
|
||||
|
||||
# End-to-End via TestClient
|
||||
class TestAudioGenerateAPI:
|
||||
def test_basic_success(self, client):
|
||||
payload = {"input": "ambient forest sounds"}
|
||||
response = client.post("/v1/audio/generate", json=payload)
|
||||
assert response.status_code == 200
|
||||
assert len(response.content) > 0
|
||||
|
||||
def test_with_all_params(self, client):
|
||||
payload = {
|
||||
"input": "gentle piano",
|
||||
"response_format": "wav",
|
||||
"speed": 1.0,
|
||||
"audio_length": 5.0,
|
||||
"audio_start": 0.0,
|
||||
"negative_prompt": "noise",
|
||||
"guidance_scale": 7.0,
|
||||
"num_inference_steps": 50,
|
||||
"seed": 123,
|
||||
}
|
||||
response = client.post("/v1/audio/generate", json=payload)
|
||||
assert response.status_code == 200
|
||||
assert response.headers["content-type"] == "audio/wav"
|
||||
|
||||
def test_missing_input_rejected(self, client):
|
||||
payload = {}
|
||||
response = client.post("/v1/audio/generate", json=payload)
|
||||
assert response.status_code == 422
|
||||
|
||||
def test_extra_unknown_fields_ignored(self, client):
|
||||
payload = {"input": "test", "unknown_field": "value"}
|
||||
response = client.post("/v1/audio/generate", json=payload)
|
||||
# Pydantic v2 ignores extra fields by default
|
||||
assert response.status_code == 200
|
||||
@@ -612,7 +612,7 @@ def test_initialize_stage_configs_called_when_none(
|
||||
"""Test that stage configs are auto-loaded when stage_configs_path is None."""
|
||||
|
||||
def _fake_loader(
|
||||
config_path: str,
|
||||
model: str,
|
||||
stage_configs_path: str | None = None,
|
||||
base_engine_args: dict | None = None,
|
||||
default_stage_cfg_factory=None,
|
||||
@@ -687,7 +687,7 @@ def test_generate_raises_on_length_mismatch(monkeypatch: pytest.MonkeyPatch, moc
|
||||
"""Test that generate raises ValueError when sampling_params_list length doesn't match."""
|
||||
|
||||
def _fake_loader(
|
||||
config_path: str,
|
||||
model: str,
|
||||
stage_configs_path: str | None = None,
|
||||
base_engine_args: dict | None = None,
|
||||
default_stage_cfg_factory=None,
|
||||
@@ -742,7 +742,7 @@ def test_generate_pipeline_and_final_outputs(monkeypatch: pytest.MonkeyPatch, mo
|
||||
stage_cfg1["processed_input"] = ["processed-for-stage-1"]
|
||||
|
||||
def _fake_loader(
|
||||
config_path: str,
|
||||
model: str,
|
||||
stage_configs_path: str | None = None,
|
||||
base_engine_args: dict | None = None,
|
||||
default_stage_cfg_factory=None,
|
||||
@@ -848,7 +848,7 @@ def test_generate_pipeline_with_batch_input(monkeypatch: pytest.MonkeyPatch, moc
|
||||
stage_cfg1["stage_id"] = 1
|
||||
|
||||
def _fake_loader(
|
||||
config_path: str,
|
||||
model: str,
|
||||
stage_configs_path: str | None = None,
|
||||
base_engine_args: dict | None = None,
|
||||
default_stage_cfg_factory=None,
|
||||
@@ -969,7 +969,7 @@ def test_generate_no_final_output_returns_empty(
|
||||
stage_cfg1["final_output"] = False
|
||||
|
||||
def _fake_loader(
|
||||
config_path: str,
|
||||
model: str,
|
||||
stage_configs_path: str | None = None,
|
||||
base_engine_args: dict | None = None,
|
||||
default_stage_cfg_factory=None,
|
||||
@@ -1062,7 +1062,7 @@ def test_generate_sampling_params_none_use_default(
|
||||
stage_cfg1["final_output"] = False
|
||||
|
||||
def _fake_loader(
|
||||
config_path: str,
|
||||
model: str,
|
||||
stage_configs_path: str | None = None,
|
||||
base_engine_args: dict | None = None,
|
||||
default_stage_cfg_factory=None,
|
||||
@@ -1140,7 +1140,7 @@ def test_wait_for_stages_ready_timeout(monkeypatch: pytest.MonkeyPatch, mocker:
|
||||
"""Test that _wait_for_stages_ready handles timeout correctly."""
|
||||
|
||||
def _fake_loader(
|
||||
config_path: str,
|
||||
model: str,
|
||||
stage_configs_path: str | None = None,
|
||||
base_engine_args: dict | None = None,
|
||||
default_stage_cfg_factory=None,
|
||||
@@ -1202,7 +1202,7 @@ def test_generate_handles_error_messages(monkeypatch: pytest.MonkeyPatch, mocker
|
||||
"""Test that generate handles error messages from stages correctly."""
|
||||
|
||||
def _fake_loader(
|
||||
config_path: str,
|
||||
model: str,
|
||||
stage_configs_path: str | None = None,
|
||||
base_engine_args: dict | None = None,
|
||||
default_stage_cfg_factory=None,
|
||||
@@ -1283,7 +1283,7 @@ def test_close_sends_shutdown_signal(monkeypatch: pytest.MonkeyPatch, mocker: Mo
|
||||
"""Test that close() sends shutdown signal to all input queues."""
|
||||
|
||||
def _fake_loader(
|
||||
config_path: str,
|
||||
model: str,
|
||||
stage_configs_path: str | None = None,
|
||||
base_engine_args: dict | None = None,
|
||||
default_stage_cfg_factory=None,
|
||||
|
||||
@@ -577,7 +577,7 @@ def test_initialize_stage_configs_called_when_none(
|
||||
"""Test that stage configs are auto-loaded when stage_configs_path is None."""
|
||||
|
||||
def _fake_loader(
|
||||
config_path: str,
|
||||
model: str,
|
||||
stage_configs_path: str | None = None,
|
||||
base_engine_args: dict | None = None,
|
||||
default_stage_cfg_factory=None,
|
||||
@@ -654,7 +654,7 @@ def test_generate_raises_on_length_mismatch(monkeypatch: pytest.MonkeyPatch, moc
|
||||
"""Test that generate raises ValueError when sampling_params_list length doesn't match."""
|
||||
|
||||
def _fake_loader(
|
||||
config_path: str,
|
||||
model: str,
|
||||
stage_configs_path: str | None = None,
|
||||
base_engine_args: dict | None = None,
|
||||
default_stage_cfg_factory=None,
|
||||
@@ -711,7 +711,7 @@ def test_generate_pipeline_and_final_outputs(monkeypatch: pytest.MonkeyPatch, mo
|
||||
stage_cfg1["processed_input"] = ["processed-for-stage-1"]
|
||||
|
||||
def _fake_loader(
|
||||
config_path: str,
|
||||
model: str,
|
||||
stage_configs_path: str | None = None,
|
||||
base_engine_args: dict | None = None,
|
||||
default_stage_cfg_factory=None,
|
||||
@@ -819,7 +819,7 @@ def test_generate_no_final_output_returns_empty(
|
||||
stage_cfg1["final_output"] = False
|
||||
|
||||
def _fake_loader(
|
||||
config_path: str,
|
||||
model: str,
|
||||
stage_configs_path: str | None = None,
|
||||
base_engine_args: dict | None = None,
|
||||
default_stage_cfg_factory=None,
|
||||
@@ -911,7 +911,7 @@ def test_generate_sampling_params_none_use_default(
|
||||
stage_cfg1["final_output"] = False
|
||||
|
||||
def _fake_loader(
|
||||
config_path: str,
|
||||
model: str,
|
||||
stage_configs_path: str | None = None,
|
||||
base_engine_args: dict | None = None,
|
||||
default_stage_cfg_factory=None,
|
||||
@@ -988,7 +988,7 @@ def test_wait_for_stages_ready_timeout(monkeypatch: pytest.MonkeyPatch, mocker:
|
||||
"""Test that _wait_for_stages_ready handles timeout correctly."""
|
||||
|
||||
def _fake_loader(
|
||||
config_path: str,
|
||||
model: str,
|
||||
stage_configs_path: str | None = None,
|
||||
base_engine_args: dict | None = None,
|
||||
default_stage_cfg_factory=None,
|
||||
@@ -1052,7 +1052,7 @@ def test_generate_handles_error_messages(monkeypatch: pytest.MonkeyPatch, mocker
|
||||
"""Test that generate handles error messages from stages correctly."""
|
||||
|
||||
def _fake_loader(
|
||||
config_path: str,
|
||||
model: str,
|
||||
stage_configs_path: str | None = None,
|
||||
base_engine_args: dict | None = None,
|
||||
default_stage_cfg_factory=None,
|
||||
@@ -1135,7 +1135,7 @@ def test_close_sends_shutdown_signal(monkeypatch: pytest.MonkeyPatch, mocker: Mo
|
||||
"""Test that close() sends shutdown signal to all input queues."""
|
||||
|
||||
def _fake_loader(
|
||||
config_path: str,
|
||||
model: str,
|
||||
stage_configs_path: str | None = None,
|
||||
base_engine_args: dict | None = None,
|
||||
default_stage_cfg_factory=None,
|
||||
|
||||
@@ -356,6 +356,7 @@ class StableAudioPipeline(nn.Module, SupportAudioOutput):
|
||||
negative_prompt: str | list[str] | None = None,
|
||||
audio_end_in_s: float | None = None,
|
||||
audio_start_in_s: float = 0.0,
|
||||
num_inference_steps: int = 100,
|
||||
guidance_scale: float = 7.0,
|
||||
num_waveforms_per_prompt: int = 1,
|
||||
generator: torch.Generator | list[torch.Generator] | None = None,
|
||||
@@ -373,6 +374,7 @@ class StableAudioPipeline(nn.Module, SupportAudioOutput):
|
||||
negative_prompt: Negative prompt for CFG
|
||||
audio_end_in_s: Audio end time in seconds (max ~47s for stable-audio-open-1.0)
|
||||
audio_start_in_s: Audio start time in seconds
|
||||
num_inference_steps: Number of denoising steps
|
||||
guidance_scale: CFG scale
|
||||
num_waveforms_per_prompt: Number of audio outputs per prompt
|
||||
generator: Random generator for reproducibility
|
||||
@@ -393,7 +395,7 @@ class StableAudioPipeline(nn.Module, SupportAudioOutput):
|
||||
elif req.prompts:
|
||||
negative_prompt = ["" if isinstance(p, str) else (p.get("negative_prompt") or "") for p in req.prompts]
|
||||
|
||||
num_inference_steps = req.sampling_params.num_inference_steps
|
||||
num_inference_steps = req.sampling_params.num_inference_steps or num_inference_steps
|
||||
if req.sampling_params.guidance_scale_provided:
|
||||
guidance_scale = req.sampling_params.guidance_scale
|
||||
|
||||
|
||||
@@ -28,7 +28,6 @@ from vllm_omni.entrypoints.utils import (
|
||||
load_stage_configs_from_model,
|
||||
load_stage_configs_from_yaml,
|
||||
resolve_model_config_path,
|
||||
resolve_model_type,
|
||||
)
|
||||
|
||||
logger = init_logger(__name__)
|
||||
@@ -85,13 +84,12 @@ class OmniLLM(LLM):
|
||||
self.worker_backend = kwargs.get("worker_backend", "multi_process")
|
||||
self.ray_address = kwargs.get("ray_address", None)
|
||||
self.batch_timeout = batch_timeout
|
||||
self.model_type = resolve_model_type(model)
|
||||
self.log_stats: bool = bool(log_stats)
|
||||
|
||||
# Load stage configurations
|
||||
if stage_configs_path is None:
|
||||
self.config_path = resolve_model_config_path(self.model_type)
|
||||
self.stage_configs = load_stage_configs_from_model(config_path=self.config_path)
|
||||
self.config_path = resolve_model_config_path(model)
|
||||
self.stage_configs = load_stage_configs_from_model(model)
|
||||
else:
|
||||
self.config_path = stage_configs_path
|
||||
self.stage_configs = load_stage_configs_from_yaml(stage_configs_path)
|
||||
|
||||
@@ -87,10 +87,7 @@ from vllm_omni.entrypoints.openai.image_api_utils import (
|
||||
encode_image_base64,
|
||||
parse_size,
|
||||
)
|
||||
from vllm_omni.entrypoints.openai.protocol.audio import (
|
||||
OpenAICreateAudioGenerateRequest,
|
||||
OpenAICreateSpeechRequest,
|
||||
)
|
||||
from vllm_omni.entrypoints.openai.protocol.audio import OpenAICreateSpeechRequest
|
||||
from vllm_omni.entrypoints.openai.protocol.images import (
|
||||
ImageData,
|
||||
ImageGenerationRequest,
|
||||
@@ -100,7 +97,6 @@ from vllm_omni.entrypoints.openai.protocol.videos import (
|
||||
VideoGenerationRequest,
|
||||
VideoGenerationResponse,
|
||||
)
|
||||
from vllm_omni.entrypoints.openai.serving_audio_generate import OmniOpenAIServingAudioGenerate
|
||||
from vllm_omni.entrypoints.openai.serving_chat import OmniOpenAIServingChat
|
||||
from vllm_omni.entrypoints.openai.serving_speech import OmniOpenAIServingSpeech
|
||||
from vllm_omni.entrypoints.openai.serving_video import OmniOpenAIServingVideo
|
||||
@@ -179,29 +175,8 @@ class _DiffusionServingModels:
|
||||
provide a lightweight fallback.
|
||||
"""
|
||||
|
||||
class _NullModelConfig:
|
||||
def __getattr__(self, name):
|
||||
return None
|
||||
|
||||
class _Unsupported:
|
||||
def __init__(self, name: str):
|
||||
self.name = name
|
||||
|
||||
def __call__(self, *args, **kwargs):
|
||||
raise NotImplementedError(f"{self.name} is not supported in diffusion mode")
|
||||
|
||||
def __getattr__(self, attr):
|
||||
raise NotImplementedError(f"{self.name}.{attr} is not supported in diffusion mode")
|
||||
|
||||
def __init__(self, base_model_paths: list[BaseModelPath]) -> None:
|
||||
self._base_model_paths = base_model_paths
|
||||
self.model_config = self._NullModelConfig()
|
||||
|
||||
def __getattr__(self, name):
|
||||
"""Return a sentinel that raises NotImplementedError if called or
|
||||
accessed, so any use of unsupported OpenAIServingModels features in
|
||||
diffusion mode fails loudly with a descriptive message."""
|
||||
return self._Unsupported(name)
|
||||
|
||||
async def show_available_models(self) -> ModelList:
|
||||
return ModelList(
|
||||
@@ -459,10 +434,10 @@ async def omni_init_app_state(
|
||||
|
||||
# For omni models
|
||||
state.stage_configs = engine_client.stage_configs if hasattr(engine_client, "stage_configs") else None
|
||||
model_name = served_model_names[0] if served_model_names else args.model
|
||||
|
||||
# Pure Diffusion mode: use simplified initialization logic
|
||||
if is_pure_diffusion:
|
||||
model_name = served_model_names[0] if served_model_names else args.model
|
||||
state.vllm_config = None
|
||||
state.diffusion_engine = engine_client
|
||||
state.openai_serving_models = _DiffusionServingModels(base_model_paths)
|
||||
@@ -475,16 +450,6 @@ async def omni_init_app_state(
|
||||
model_name=model_name,
|
||||
)
|
||||
|
||||
# audio related
|
||||
state.openai_serving_speech = None
|
||||
state.openai_serving_audio_generate = OmniOpenAIServingAudioGenerate.for_diffusion(
|
||||
engine_client,
|
||||
state.openai_serving_models,
|
||||
request_logger=request_logger,
|
||||
model_name=model_name,
|
||||
)
|
||||
|
||||
# video related
|
||||
diffusion_stage_configs = engine_client.stage_configs if hasattr(engine_client, "stage_configs") else None
|
||||
state.openai_serving_video = OmniOpenAIServingVideo.for_diffusion(
|
||||
diffusion_engine=engine_client, # type: ignore
|
||||
@@ -492,6 +457,7 @@ async def omni_init_app_state(
|
||||
stage_configs=diffusion_stage_configs,
|
||||
)
|
||||
|
||||
state.enable_server_load_tracking = getattr(args, "enable_server_load_tracking", False)
|
||||
state.server_load_metrics = 0
|
||||
logger.info("Pure diffusion API server initialized for model: %s", model_name)
|
||||
return
|
||||
@@ -766,11 +732,7 @@ async def omni_init_app_state(
|
||||
)
|
||||
|
||||
state.openai_serving_speech = OmniOpenAIServingSpeech(
|
||||
engine_client, state.openai_serving_models, request_logger=request_logger, model_name=model_name
|
||||
)
|
||||
|
||||
state.openai_serving_audio_generate = OmniOpenAIServingAudioGenerate(
|
||||
engine_client, state.openai_serving_models, request_logger=request_logger, model_name=model_name
|
||||
engine_client, state.openai_serving_models, request_logger=request_logger
|
||||
)
|
||||
|
||||
state.openai_serving_video = OmniOpenAIServingVideo(
|
||||
@@ -795,10 +757,6 @@ def Omnispeech(request: Request) -> OmniOpenAIServingSpeech | None:
|
||||
return request.app.state.openai_serving_speech
|
||||
|
||||
|
||||
def OmniAudioGenerate(request: Request) -> OmniOpenAIServingAudioGenerate | None:
|
||||
return getattr(request.app.state, "openai_serving_audio_generate", None)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/v1/chat/completions",
|
||||
dependencies=[Depends(validate_json_request)],
|
||||
@@ -909,34 +867,6 @@ async def create_speech(request: OpenAICreateSpeechRequest, raw_request: Request
|
||||
raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)) from e
|
||||
|
||||
|
||||
@router.post(
|
||||
"/v1/audio/generate",
|
||||
dependencies=[Depends(validate_json_request)],
|
||||
responses={
|
||||
HTTPStatus.OK.value: {"content": {"audio/*": {}}},
|
||||
HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
|
||||
HTTPStatus.NOT_FOUND.value: {"model": ErrorResponse},
|
||||
HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
|
||||
},
|
||||
)
|
||||
@with_cancellation
|
||||
@load_aware_call
|
||||
async def create_audio_generate(request: OpenAICreateAudioGenerateRequest, raw_request: Request):
|
||||
handler = OmniAudioGenerate(raw_request)
|
||||
if handler is None:
|
||||
base_server = getattr(raw_request.app.state, "openai_serving_tokenization", None)
|
||||
if base_server is None:
|
||||
raise HTTPException(
|
||||
status_code=HTTPStatus.NOT_FOUND.value,
|
||||
detail="The model does not support Audio Generate API",
|
||||
)
|
||||
return base_server.create_error_response(message="The model does not support Audio Generate API")
|
||||
try:
|
||||
return await handler.create_audio_generate(request, raw_request)
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)) from e
|
||||
|
||||
|
||||
@router.get(
|
||||
"/v1/audio/voices",
|
||||
responses={
|
||||
|
||||
@@ -45,10 +45,6 @@ class AudioMixin:
|
||||
"Only mono (1D) and stereo (2D) are supported."
|
||||
)
|
||||
|
||||
if audio_tensor.ndim == 2 and audio_tensor.shape[0] == 2:
|
||||
# Convert from [channels, samples] to [samples, channels]
|
||||
audio_tensor = audio_tensor.T
|
||||
|
||||
audio_tensor, sample_rate = self._apply_speed_adjustment(audio_tensor, speed, sample_rate)
|
||||
|
||||
supported_formats = {
|
||||
|
||||
@@ -85,53 +85,6 @@ class OpenAICreateSpeechRequest(BaseModel):
|
||||
return self
|
||||
|
||||
|
||||
class OpenAICreateAudioGenerateRequest(BaseModel):
|
||||
"""Request model for audio generation via diffusion models (e.g. Stable Audio)."""
|
||||
|
||||
input: str = Field(
|
||||
description="Text prompt describing the audio to generate",
|
||||
)
|
||||
model: str | None = None
|
||||
response_format: Literal["wav", "pcm", "flac", "mp3", "aac", "opus"] = "wav"
|
||||
speed: float | None = Field(
|
||||
default=1.0,
|
||||
ge=0.25,
|
||||
le=4.0,
|
||||
)
|
||||
stream_format: Literal["sse", "audio"] | None = "audio"
|
||||
audio_length: float | None = Field(
|
||||
default=None,
|
||||
description="Audio length in seconds",
|
||||
)
|
||||
audio_start: float | None = Field(
|
||||
default=0.0,
|
||||
description="Audio start time in seconds",
|
||||
)
|
||||
negative_prompt: str | None = Field(
|
||||
default=None,
|
||||
description="Negative prompt for classifier-free guidance",
|
||||
)
|
||||
guidance_scale: float | None = Field(
|
||||
default=None,
|
||||
description="Guidance scale for diffusion models",
|
||||
)
|
||||
num_inference_steps: int | None = Field(
|
||||
default=None,
|
||||
description="Number of inference steps",
|
||||
)
|
||||
seed: int | None = Field(
|
||||
default=None,
|
||||
description="Random seed for reproducibility",
|
||||
)
|
||||
|
||||
@field_validator("stream_format")
|
||||
@classmethod
|
||||
def validate_stream_format(cls, v: str) -> str:
|
||||
if v == "sse":
|
||||
raise ValueError("'sse' is not a supported stream_format yet. Please use 'audio'.")
|
||||
return v
|
||||
|
||||
|
||||
class CreateAudio(BaseModel):
|
||||
audio_tensor: np.ndarray
|
||||
sample_rate: int = 24000
|
||||
|
||||
@@ -1,168 +0,0 @@
|
||||
import asyncio
|
||||
|
||||
import torch
|
||||
from fastapi import Request
|
||||
from fastapi.responses import Response
|
||||
from vllm.entrypoints.openai.engine.serving import OpenAIServing
|
||||
from vllm.logger import init_logger
|
||||
from vllm.utils import random_uuid
|
||||
|
||||
from vllm_omni.entrypoints.openai.audio_utils_mixin import AudioMixin
|
||||
from vllm_omni.entrypoints.openai.protocol.audio import (
|
||||
AudioResponse,
|
||||
CreateAudio,
|
||||
OpenAICreateAudioGenerateRequest,
|
||||
)
|
||||
from vllm_omni.inputs.data import OmniDiffusionSamplingParams
|
||||
from vllm_omni.outputs import OmniRequestOutput
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
|
||||
class OmniOpenAIServingAudioGenerate(OpenAIServing, AudioMixin):
|
||||
"""Serving class for audio generation via diffusion models (e.g. Stable Audio)."""
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
self.model_name = kwargs.pop("model_name", None)
|
||||
super().__init__(*args, **kwargs)
|
||||
self.diffusion_mode = False
|
||||
|
||||
@classmethod
|
||||
def for_diffusion(cls, *args, **kwargs) -> "OmniOpenAIServingAudioGenerate":
|
||||
"""Create an instance configured to run in diffusion mode."""
|
||||
instance = cls(*args, **kwargs)
|
||||
instance.diffusion_mode = True
|
||||
return instance
|
||||
|
||||
def _is_stable_audio_model(self) -> bool:
|
||||
return self.engine_client.model_type == "StableAudioPipeline"
|
||||
|
||||
async def create_audio_generate(
|
||||
self,
|
||||
request: OpenAICreateAudioGenerateRequest,
|
||||
raw_request: Request | None = None,
|
||||
):
|
||||
"""
|
||||
Generate audio using diffusion-based models (e.g. Stable Audio).
|
||||
|
||||
This endpoint is designed for audio generation models as
|
||||
opposed to TTS models that specifically generate speech.
|
||||
"""
|
||||
|
||||
error_check_ret = await self._check_model(request)
|
||||
if error_check_ret is not None:
|
||||
logger.error("Error with model %s", error_check_ret)
|
||||
return error_check_ret
|
||||
|
||||
if self.engine_client.errored:
|
||||
raise self.engine_client.dead_error
|
||||
|
||||
request_id = f"audiogen-{random_uuid()}"
|
||||
|
||||
try:
|
||||
default_sr = 44100 # Default sample rate for Stable Audio
|
||||
|
||||
# Build prompt for diffusion audio generation
|
||||
prompt = {
|
||||
"prompt": request.input,
|
||||
}
|
||||
if request.negative_prompt:
|
||||
prompt["negative_prompt"] = request.negative_prompt
|
||||
|
||||
# Build sampling params for diffusion
|
||||
sampling_params_list = [OmniDiffusionSamplingParams(num_outputs_per_prompt=1)]
|
||||
|
||||
# Create generator if seed provided
|
||||
if request.seed is not None:
|
||||
from vllm_omni.platforms import current_omni_platform
|
||||
|
||||
generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(request.seed)
|
||||
sampling_params_list[0].generator = generator
|
||||
|
||||
if request.guidance_scale is not None:
|
||||
sampling_params_list[0].guidance_scale = request.guidance_scale
|
||||
|
||||
if request.num_inference_steps is not None:
|
||||
sampling_params_list[0].num_inference_steps = request.num_inference_steps
|
||||
|
||||
# Set up audio duration parameters
|
||||
if request.audio_length is not None:
|
||||
audio_length = request.audio_length
|
||||
audio_start = request.audio_start if request.audio_start is not None else 0.0
|
||||
audio_end_in_s = audio_start + audio_length
|
||||
sampling_params_list[0].extra_args = {
|
||||
"audio_start_in_s": audio_start,
|
||||
"audio_end_in_s": audio_end_in_s,
|
||||
}
|
||||
|
||||
logger.info(
|
||||
"Audio generation request %s: prompt=%r",
|
||||
request_id,
|
||||
request.input[:50] + "..." if len(request.input) > 50 else request.input,
|
||||
)
|
||||
|
||||
generator = self.engine_client.generate(
|
||||
prompt=prompt,
|
||||
request_id=request_id,
|
||||
sampling_params_list=sampling_params_list,
|
||||
output_modalities=["audio"],
|
||||
)
|
||||
|
||||
final_output: OmniRequestOutput | None = None
|
||||
async for res in generator:
|
||||
final_output = res
|
||||
|
||||
if final_output is None:
|
||||
return self.create_error_response("No output generated from the model.")
|
||||
|
||||
# Extract audio from output
|
||||
audio_output = None
|
||||
if hasattr(final_output, "multimodal_output") and final_output.multimodal_output:
|
||||
audio_output = final_output.multimodal_output
|
||||
if not audio_output and hasattr(final_output, "request_output"):
|
||||
if final_output.request_output and hasattr(final_output.request_output, "multimodal_output"):
|
||||
audio_output = final_output.request_output.multimodal_output
|
||||
|
||||
# Check for audio data using either "audio" or "model_outputs" key
|
||||
audio_key = None
|
||||
if audio_output:
|
||||
if "audio" in audio_output:
|
||||
audio_key = "audio"
|
||||
elif "model_outputs" in audio_output:
|
||||
audio_key = "model_outputs"
|
||||
|
||||
if not audio_output or audio_key is None:
|
||||
return self.create_error_response("Audio generation model did not produce audio output.")
|
||||
|
||||
audio_tensor = audio_output[audio_key]
|
||||
sample_rate = audio_output.get("sr", default_sr)
|
||||
if hasattr(sample_rate, "item"):
|
||||
sample_rate = sample_rate.item()
|
||||
|
||||
# Convert tensor to numpy
|
||||
if hasattr(audio_tensor, "float"):
|
||||
audio_tensor = audio_tensor.float().detach().cpu().numpy()
|
||||
|
||||
# Squeeze batch dimension if present, but preserve channel dimension for stereo
|
||||
if audio_tensor.ndim > 1:
|
||||
audio_tensor = audio_tensor.squeeze()
|
||||
|
||||
audio_obj = CreateAudio(
|
||||
audio_tensor=audio_tensor,
|
||||
sample_rate=int(sample_rate),
|
||||
response_format=request.response_format or "wav",
|
||||
speed=request.speed or 1.0,
|
||||
stream_format=request.stream_format,
|
||||
base64_encode=False,
|
||||
)
|
||||
|
||||
audio_response: AudioResponse = self.create_audio(audio_obj)
|
||||
return Response(content=audio_response.audio_data, media_type=audio_response.media_type)
|
||||
|
||||
except asyncio.CancelledError:
|
||||
return self.create_error_response("Client disconnected")
|
||||
except ValueError as e:
|
||||
return self.create_error_response(e)
|
||||
except Exception as e:
|
||||
logger.exception("Audio generation failed: %s", e)
|
||||
return self.create_error_response(f"Audio generation failed: {e}")
|
||||
@@ -84,7 +84,6 @@ def _validate_path_within_directory(file_path: Path, directory: Path) -> bool:
|
||||
|
||||
class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
|
||||
def __init__(self, *args, **kwargs):
|
||||
self.model_name = kwargs.pop("model_name", None)
|
||||
super().__init__(*args, **kwargs)
|
||||
# Initialize uploaded speakers storage
|
||||
speech_voice_samples_dir = os.environ.get("SPEECH_VOICE_SAMPLES", "/tmp/voice_samples")
|
||||
@@ -757,8 +756,6 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
|
||||
request_id = f"speech-{random_uuid()}"
|
||||
|
||||
try:
|
||||
sampling_params_list = self.engine_client.default_sampling_params_list
|
||||
default_sr = 24000 # Default sample rate for TTS models
|
||||
if self._is_tts:
|
||||
# Validate TTS parameters
|
||||
validation_error = self._validate_tts_request(request)
|
||||
@@ -784,6 +781,8 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
|
||||
tts_params.get("task_type", ["unknown"])[0],
|
||||
)
|
||||
|
||||
sampling_params_list = self.engine_client.default_sampling_params_list
|
||||
|
||||
generator = self.engine_client.generate(
|
||||
prompt=prompt,
|
||||
request_id=request_id,
|
||||
@@ -810,7 +809,7 @@ class OmniOpenAIServingSpeech(OpenAIServing, AudioMixin):
|
||||
return self.create_error_response("TTS model did not produce audio output.")
|
||||
|
||||
audio_tensor = audio_output[audio_key]
|
||||
sr_raw = audio_output.get("sr", default_sr)
|
||||
sr_raw = audio_output.get("sr", 24000)
|
||||
sr_val = sr_raw[-1] if isinstance(sr_raw, list) and sr_raw else sr_raw
|
||||
sample_rate = sr_val.item() if hasattr(sr_val, "item") else int(sr_val)
|
||||
|
||||
|
||||
@@ -169,46 +169,22 @@ def _convert_dataclasses_to_dict(obj: Any) -> Any:
|
||||
return obj
|
||||
|
||||
|
||||
def resolve_model_config_path(model_type: str) -> str | None:
|
||||
"""Resolve the stage config file path from the model type.
|
||||
def resolve_model_config_path(model: str) -> str:
|
||||
"""Resolve the stage config file path from the model name.
|
||||
|
||||
Resolves stage configuration path based on the model type and device type.
|
||||
First tries to find a device-specific YAML file from stage_configs/{device_type}/
|
||||
directory. If not found, falls back to the default config file.
|
||||
|
||||
Args:
|
||||
model_type: Model type string
|
||||
model: Model name or path (used to determine model_type)
|
||||
|
||||
Returns:
|
||||
String path to the stage configuration file if found, None otherwise
|
||||
"""
|
||||
default_config_path = current_omni_platform.get_default_stage_config_path()
|
||||
config_file_name = f"{model_type}.yaml"
|
||||
complete_config_path = PROJECT_ROOT / default_config_path / config_file_name
|
||||
if os.path.exists(complete_config_path):
|
||||
return str(complete_config_path)
|
||||
|
||||
# Fall back to default config
|
||||
stage_config_file = f"vllm_omni/model_executor/stage_configs/{model_type}.yaml"
|
||||
stage_config_path = PROJECT_ROOT / stage_config_file
|
||||
if not os.path.exists(stage_config_path):
|
||||
return None
|
||||
return str(stage_config_path)
|
||||
|
||||
|
||||
def resolve_model_type(model: str) -> str:
|
||||
"""Resolve the model type from the model name.
|
||||
|
||||
Args:
|
||||
model: Model name or path
|
||||
|
||||
Returns:
|
||||
Model type string (e.g. ``"Qwen3TTSForConditionalGeneration"``,
|
||||
``"StableAudioPipeline"``).
|
||||
String path to the stage configuration file
|
||||
|
||||
Raises:
|
||||
ValueError: If the model type cannot be determined from any
|
||||
available configuration file.
|
||||
ValueError: If model_type cannot be determined
|
||||
FileNotFoundError: If no stage config file exists for the model type
|
||||
"""
|
||||
# Try to get config from standard transformers format first
|
||||
try:
|
||||
@@ -241,26 +217,42 @@ def resolve_model_type(model: str) -> str:
|
||||
f"Please ensure the model has proper configuration files with 'model_type' field"
|
||||
)
|
||||
|
||||
return model_type
|
||||
default_config_path = current_omni_platform.get_default_stage_config_path()
|
||||
model_type_str = f"{model_type}.yaml"
|
||||
complete_config_path = PROJECT_ROOT / default_config_path / model_type_str
|
||||
if os.path.exists(complete_config_path):
|
||||
return str(complete_config_path)
|
||||
|
||||
# Fall back to default config
|
||||
stage_config_file = f"vllm_omni/model_executor/stage_configs/{model_type}.yaml"
|
||||
stage_config_path = PROJECT_ROOT / stage_config_file
|
||||
if not os.path.exists(stage_config_path):
|
||||
return None
|
||||
return str(stage_config_path)
|
||||
|
||||
|
||||
def load_stage_configs_from_model(config_path: str | None, base_engine_args: dict | None = None) -> list:
|
||||
"""Load stage configurations from a resolved config file path.
|
||||
def load_stage_configs_from_model(model: str, base_engine_args: dict | None = None) -> list:
|
||||
"""Load stage configurations from model's default config file.
|
||||
|
||||
Loads stage configurations based on the model type and device type.
|
||||
First tries to load a device-specific YAML file from stage_configs/{device_type}/
|
||||
directory. If not found, falls back to the default config file.
|
||||
|
||||
Args:
|
||||
config_path: Path to the YAML configuration file, or None.
|
||||
When None, returns an empty list.
|
||||
base_engine_args: Optional engine arguments to merge with stage configs.
|
||||
model: Model name or path (used to determine model_type)
|
||||
|
||||
Returns:
|
||||
List of stage configuration dictionaries, or empty list if
|
||||
config_path is None.
|
||||
List of stage configuration dictionaries
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If no stage config file exists for the model type
|
||||
"""
|
||||
if base_engine_args is None:
|
||||
base_engine_args = {}
|
||||
if config_path is None:
|
||||
stage_config_path = resolve_model_config_path(model)
|
||||
if stage_config_path is None:
|
||||
return []
|
||||
stage_configs = load_stage_configs_from_yaml(config_path=config_path, base_engine_args=base_engine_args)
|
||||
stage_configs = load_stage_configs_from_yaml(config_path=stage_config_path, base_engine_args=base_engine_args)
|
||||
return stage_configs
|
||||
|
||||
|
||||
@@ -314,10 +306,9 @@ def load_and_resolve_stage_configs(
|
||||
Returns:
|
||||
Tuple of (config_path, stage_configs)
|
||||
"""
|
||||
model_type = resolve_model_type(model)
|
||||
if stage_configs_path is None:
|
||||
config_path = resolve_model_config_path(model_type)
|
||||
stage_configs = load_stage_configs_from_model(config_path, base_engine_args=kwargs)
|
||||
config_path = resolve_model_config_path(model)
|
||||
stage_configs = load_stage_configs_from_model(model, base_engine_args=kwargs)
|
||||
if not stage_configs:
|
||||
if default_stage_cfg_factory is not None:
|
||||
default_stage_cfg = default_stage_cfg_factory()
|
||||
|
||||
Reference in new issue
Block a user