File size: 4,141 Bytes
b703376 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | """Woosh-DFlow text-to-audio BACKEND Space (plain Gradio 6, no pyharp).
This is the *backend* half of the HARP two-Space workflow for Sony AI's Woosh
sound-effect model. It reuses Woosh's own inference code (the same calls the
upstream ``gradio_Woosh-DFlow.py`` demo makes) and exposes a single clean
``/generate`` API endpoint that a thin pyharp HARP frontend proxies to via
``gradio_client``.
Why a separate backend Space at all: Woosh needs ``python>=3.12``,
``torch==2.8.0`` and ``gradio>=6.9.0``, which cannot coexist in one process with
pyharp v0.3.0 (it hard-pins ``gradio==5.28.0``). Isolating Woosh in its own
Space sidesteps that conflict entirely.
Weights (CC-BY-NC, from the official v1.0.0 GitHub release) are baked into the
Docker image at build time (see the Dockerfile), so this file just loads them.
"""
from __future__ import annotations
import logging
import os
import tempfile
import gradio as gr
import numpy as np
import soundfile as sf
import torch
from woosh.components.base import LoadConfig
from woosh.inference.flowmap_sampler import sample_euler
from woosh.model.flowmap_from_pretrained import FlowMapFromPretrained
logging.basicConfig(level=logging.INFO)
log = logging.getLogger("woosh-backend")
# Matches the upstream demo: 128-channel latents, ~5s at 48 kHz after the AE.
SAMPLE_RATE = 48000
LATENT_CHANNELS = 128
LATENT_FRAMES = 501
NUM_STEPS = 4 # Woosh-DFlow is the *distilled* model: 4 sampling steps.
RENOISE = [0, 0.5, 0.5, 0.3]
CHECKPOINT = os.environ.get("WOOSH_CHECKPOINT", "checkpoints/Woosh-DFlow")
if torch.cuda.is_available():
DEVICE = "cuda"
elif getattr(torch.backends, "mps", None) is not None and torch.backends.mps.is_available():
DEVICE = "mps"
else:
DEVICE = "cpu"
log.info("Loading Woosh-DFlow from %s on %s ...", CHECKPOINT, DEVICE)
LDM = FlowMapFromPretrained(LoadConfig(path=CHECKPOINT)).eval().to(DEVICE)
log.info("Model loaded on %s.", DEVICE)
@torch.inference_mode()
def generate(prompt: str, cfg_scale: float = 4.5, seed: int = -1) -> str:
"""Generate one sound effect from a text prompt; return a WAV file path."""
if not prompt or not prompt.strip():
raise gr.Error("Please enter a text prompt describing the sound effect.")
if seed is None or int(seed) < 0:
seed = int.from_bytes(os.urandom(4), "big") % (2**31)
torch.manual_seed(int(seed))
noise = torch.randn(1, LATENT_CHANNELS, LATENT_FRAMES).to(DEVICE)
cond = LDM.get_cond(
{"audio": None, "description": [prompt]}, no_dropout=True, device=DEVICE
)
x_fake = sample_euler(
model=LDM,
noise=noise,
cond=cond,
num_steps=NUM_STEPS,
renoise=RENOISE,
cfg=float(cfg_scale),
)
audio = LDM.autoencoder.inverse(x_fake).cpu().float()
# Peak-normalize to avoid clipping, then write a float32 WAV.
peak = audio.abs().amax(dim=-1, keepdim=True).clamp(min=1.0)
audio = (audio / peak).clamp(-1.0, 1.0)
wav = np.ascontiguousarray(audio[0].squeeze().numpy())
out_path = os.path.join(tempfile.mkdtemp(), "woosh_dflow.wav")
sf.write(out_path, wav, SAMPLE_RATE)
return out_path
with gr.Blocks(title="Woosh-DFlow backend") as demo:
gr.Markdown(
"# Woosh-DFlow \u2014 text-to-audio backend\n"
"Plain-Gradio **backend** for the HARP two-Space workflow. Point a pyharp "
"remote frontend at the `/generate` API endpoint."
)
prompt = gr.Textbox(
label="Prompt",
placeholder="e.g. sportscar engine revving and driving away quickly",
)
cfg = gr.Slider(0.0, 15.0, value=4.5, step=0.1, label="CFG scale")
seed = gr.Number(value=-1, precision=0, label="Seed (-1 = random)")
out = gr.Audio(label="Generated audio", type="filepath")
gr.Button("Generate", variant="primary").click(
generate, inputs=[prompt, cfg, seed], outputs=out, api_name="generate"
)
demo.queue(max_size=8).launch(
server_name="0.0.0.0",
server_port=int(os.environ.get("PORT", "7860")),
show_error=True,
)
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