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predict.py
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predict.py
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import os
import random
import numpy as np
import soundfile as sf
import torch
from cog import BasePredictor, Input, Path
from audiosr import build_model, super_resolution
os.environ["TOKENIZERS_PARALLELISM"] = "true"
torch.set_float32_matmul_precision("high")
class Predictor(BasePredictor):
def setup(self, model_name="basic", device="auto"):
self.model_name = model_name
self.device = device
self.sr = 48000
self.audiosr = build_model(model_name=self.model_name, device=self.device)
def predict(self,
input_file: Path = Input(description="Audio to upsample"),
ddim_steps: int = Input(description="Number of inference steps", default=50, ge=10, le=500),
guidance_scale: float = Input(description="Scale for classifier free guidance", default=3.5, ge=1.0, le=20.0),
seed: int = Input(description="Random seed. Leave blank to randomize the seed", default=None)
) -> Path:
"""Run a single prediction on the model"""
if seed is None:
seed = random.randint(0, 2**32 - 1)
print(f"Setting seed to: {seed}")
waveform = super_resolution(
self.audiosr,
input_file,
seed=seed,
guidance_scale=guidance_scale,
ddim_steps=ddim_steps,
latent_t_per_second=12.8
)
out_wav = (waveform[0] * 32767).astype(np.int16).T
sf.write("out.wav", data=out_wav, samplerate=48000)
return Path("out.wav")
if __name__ == "__main__":
p = Predictor()
p.setup()
out = p.predict(
"example/music.wav",
ddim_steps=50,
guidance_scale=3.5,
seed=42
)