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import dataclasses |
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import pathlib |
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import libf0 |
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import librosa |
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import numpy as np |
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import resampy |
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import torch |
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import torchcrepe |
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import torchfcpe |
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import os |
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from rvc.lib.predictors.RMVPE import RMVPE0Predictor |
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from rvc.configs.config import Config |
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config = Config() |
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@dataclasses.dataclass |
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class F0Extractor: |
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wav_path: pathlib.Path |
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sample_rate: int = 44100 |
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hop_length: int = 512 |
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f0_min: int = 50 |
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f0_max: int = 1600 |
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method: str = "rmvpe" |
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x: np.ndarray = dataclasses.field(init=False) |
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def __post_init__(self): |
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self.x, self.sample_rate = librosa.load(self.wav_path, sr=self.sample_rate) |
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@property |
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def hop_size(self) -> float: |
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return self.hop_length / self.sample_rate |
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@property |
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def wav16k(self) -> np.ndarray: |
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return resampy.resample(self.x, self.sample_rate, 16000) |
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def extract_f0(self) -> np.ndarray: |
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f0 = None |
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method = self.method |
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if method == "crepe": |
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wav16k_torch = torch.FloatTensor(self.wav16k).unsqueeze(0).to(config.device) |
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f0 = torchcrepe.predict( |
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wav16k_torch, |
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sample_rate=16000, |
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hop_length=160, |
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batch_size=512, |
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fmin=self.f0_min, |
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fmax=self.f0_max, |
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device=config.device, |
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) |
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f0 = f0[0].cpu().numpy() |
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elif method == "fcpe": |
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audio = librosa.to_mono(self.x) |
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audio_length = len(audio) |
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f0_target_length = (audio_length // self.hop_length) + 1 |
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audio = ( |
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torch.from_numpy(audio) |
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.float() |
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.unsqueeze(0) |
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.unsqueeze(-1) |
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.to(config.device) |
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) |
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model = torchfcpe.spawn_bundled_infer_model(device=config.device) |
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f0 = model.infer( |
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audio, |
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sr=self.sample_rate, |
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decoder_mode="local_argmax", |
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threshold=0.006, |
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f0_min=self.f0_min, |
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f0_max=self.f0_max, |
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interp_uv=False, |
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output_interp_target_length=f0_target_length, |
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) |
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f0 = f0.squeeze().cpu().numpy() |
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elif method == "rmvpe": |
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model_rmvpe = RMVPE0Predictor( |
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os.path.join("rvc", "models", "predictors", "rmvpe.pt"), |
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is_half=config.is_half, |
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device=config.device, |
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) |
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f0 = model_rmvpe.infer_from_audio(self.wav16k, thred=0.03) |
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else: |
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raise ValueError(f"Unknown method: {self.method}") |
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return libf0.hz_to_cents(f0, librosa.midi_to_hz(0)) |
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def plot_f0(self, f0): |
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from matplotlib import pyplot as plt |
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plt.figure(figsize=(10, 4)) |
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plt.plot(f0) |
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plt.title(self.method) |
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plt.xlabel("Time (frames)") |
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plt.ylabel("F0 (cents)") |
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plt.show() |
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