vh
4a4c09177f
Two new audio-generation stacks alongside the TTS slate: ace-step :8210 — Apache 2.0 music generation foundation model (hybrid diffusion + LLM). Lyric-aware multi-minute songs. ~10-12 GB VRAM during inference, A6000-pinned. Custom Dockerfile patches upstream's torch/cu126 resolution bug (--extra-index-url cu126 was falling back to pypi-default cu13 wheels, mismatching torchvision). stable-audio-open :8211 — Stability AI 1.21B latent-diffusion SFX + ambience. Up to 47s clips at 44.1 kHz. ~6 GB VRAM in fp16, A6000-pinned. Custom FastAPI shim around diffusers' StableAudioPipeline (no upstream HTTP server). Dockerfile pins torchsde explicitly — diffusers doesn't pull it as a hard dep but CosineDPMSolverMultistepScheduler needs it.
81 lines
2.2 KiB
Python
81 lines
2.2 KiB
Python
# FastAPI shim around diffusers' StableAudioPipeline.
|
|
# Single endpoint POST /v1/audio/sfx returns a WAV blob.
|
|
# Model is loaded once on startup and held in process memory.
|
|
import io
|
|
import os
|
|
import time
|
|
from contextlib import asynccontextmanager
|
|
from typing import Optional
|
|
|
|
import soundfile as sf
|
|
import torch
|
|
from diffusers import StableAudioPipeline
|
|
from fastapi import FastAPI, HTTPException, Response
|
|
from pydantic import BaseModel, Field
|
|
|
|
MODEL_ID = os.environ.get("SAO_MODEL", "stabilityai/stable-audio-open-1.0")
|
|
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
|
|
DTYPE = torch.float16 if DEVICE == "cuda" else torch.float32
|
|
|
|
state: dict = {}
|
|
|
|
|
|
@asynccontextmanager
|
|
async def lifespan(app: FastAPI):
|
|
print(f"[sao] loading {MODEL_ID} on {DEVICE} ({DTYPE})", flush=True)
|
|
t0 = time.time()
|
|
pipe = StableAudioPipeline.from_pretrained(MODEL_ID, torch_dtype=DTYPE)
|
|
pipe = pipe.to(DEVICE)
|
|
state["pipe"] = pipe
|
|
print(f"[sao] loaded in {time.time() - t0:.1f}s", flush=True)
|
|
yield
|
|
state.clear()
|
|
|
|
|
|
app = FastAPI(lifespan=lifespan)
|
|
|
|
|
|
class SfxRequest(BaseModel):
|
|
prompt: str = Field(..., min_length=1)
|
|
negative_prompt: Optional[str] = "Low quality."
|
|
duration: float = Field(10.0, gt=0.5, le=47.0)
|
|
steps: int = Field(100, ge=10, le=300)
|
|
seed: Optional[int] = None
|
|
cfg_scale: float = Field(7.0, gt=0.0, le=20.0)
|
|
|
|
|
|
@app.get("/health")
|
|
def health():
|
|
return {
|
|
"status": "ok",
|
|
"model": MODEL_ID,
|
|
"device": DEVICE,
|
|
"loaded": "pipe" in state,
|
|
}
|
|
|
|
|
|
@app.post("/v1/audio/sfx")
|
|
def sfx(req: SfxRequest):
|
|
pipe = state.get("pipe")
|
|
if pipe is None:
|
|
raise HTTPException(503, "model not loaded yet")
|
|
|
|
generator = None
|
|
if req.seed is not None:
|
|
generator = torch.Generator(DEVICE).manual_seed(req.seed)
|
|
|
|
audio = pipe(
|
|
req.prompt,
|
|
negative_prompt=req.negative_prompt,
|
|
num_inference_steps=req.steps,
|
|
audio_end_in_s=req.duration,
|
|
num_waveforms_per_prompt=1,
|
|
generator=generator,
|
|
).audios
|
|
|
|
waveform = audio[0].T.float().cpu().numpy()
|
|
buf = io.BytesIO()
|
|
sf.write(buf, waveform, pipe.vae.sampling_rate, format="WAV")
|
|
buf.seek(0)
|
|
return Response(content=buf.read(), media_type="audio/wav")
|