| from typing import Dict, List, Any |
| from tangoflux import TangoFluxInference |
| import torchaudio |
|
|
| from huggingface_inference_toolkit.logging import logger |
| import io |
| import base64 |
|
|
| class EndpointHandler(): |
| def __init__(self, path=""): |
| |
| |
| |
| self.model = TangoFluxInference(name='declare-lab/TangoFlux',device='cuda') |
|
|
|
|
| def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
| """ |
| data args: |
| inputs (:obj: `str` | `PIL.Image` | `np.array`) |
| kwargs |
| Return: |
| A :obj:`list` | `dict`: will be serialized and returned |
| """ |
|
|
| logger.info(f"Received incoming request with {data=}") |
|
|
| if "inputs" in data and isinstance(data["inputs"], str): |
| prompt = data.pop("inputs") |
| elif "prompt" in data and isinstance(data["prompt"], str): |
| prompt = data.pop("prompt") |
| else: |
| raise ValueError( |
| "Provided input body must contain either the key `inputs` or `prompt` with the" |
| " prompt to use for the audio generation, and it needs to be a non-empty string." |
| ) |
|
|
| parameters = data.pop("parameters", {}) |
|
|
| num_inference_steps = parameters.get("num_inference_steps", 50) |
| duration = parameters.get("duration", 10) |
| guidance_scale = parameters.get("guidance_scale", 3.5) |
|
|
| audio= self.model.generate(prompt,steps=num_inference_steps, |
| duration=duration, |
| guidance_scale=guidance_scale) |
| |
| buffer = io.BytesIO() |
| torchaudio.save(buffer, audio, 44100, format="wav") |
| buffer.seek(0) |
| audio_base64 = base64.b64encode(buffer.read()).decode('utf-8') |
| return audio_base64 |
