Spaces:
Running
Running
| import dataclasses | |
| import pathlib | |
| import libf0 | |
| import librosa | |
| import numpy as np | |
| import resampy | |
| import torch | |
| import torchcrepe | |
| import torchfcpe | |
| import os | |
| # from tools.anyf0.rmvpe import RMVPE | |
| from rvc.lib.predictors.RMVPE import RMVPE0Predictor | |
| from rvc.configs.config import Config | |
| config = Config() | |
| class F0Extractor: | |
| wav_path: pathlib.Path | |
| sample_rate: int = 44100 | |
| hop_length: int = 512 | |
| f0_min: int = 50 | |
| f0_max: int = 1600 | |
| method: str = "rmvpe" | |
| x: np.ndarray = dataclasses.field(init=False) | |
| def __post_init__(self): | |
| self.x, self.sample_rate = librosa.load(self.wav_path, sr=self.sample_rate) | |
| def hop_size(self) -> float: | |
| return self.hop_length / self.sample_rate | |
| def wav16k(self) -> np.ndarray: | |
| return resampy.resample(self.x, self.sample_rate, 16000) | |
| def extract_f0(self) -> np.ndarray: | |
| f0 = None | |
| method = self.method | |
| if method == "crepe": | |
| wav16k_torch = torch.FloatTensor(self.wav16k).unsqueeze(0).to(config.device) | |
| f0 = torchcrepe.predict( | |
| wav16k_torch, | |
| sample_rate=16000, | |
| hop_length=160, | |
| batch_size=512, | |
| fmin=self.f0_min, | |
| fmax=self.f0_max, | |
| device=config.device, | |
| ) | |
| f0 = f0[0].cpu().numpy() | |
| elif method == "fcpe": | |
| audio = librosa.to_mono(self.x) | |
| audio_length = len(audio) | |
| f0_target_length = (audio_length // self.hop_length) + 1 | |
| audio = ( | |
| torch.from_numpy(audio) | |
| .float() | |
| .unsqueeze(0) | |
| .unsqueeze(-1) | |
| .to(config.device) | |
| ) | |
| model = torchfcpe.spawn_bundled_infer_model(device=config.device) | |
| f0 = model.infer( | |
| audio, | |
| sr=self.sample_rate, | |
| decoder_mode="local_argmax", | |
| threshold=0.006, | |
| f0_min=self.f0_min, | |
| f0_max=self.f0_max, | |
| interp_uv=False, | |
| output_interp_target_length=f0_target_length, | |
| ) | |
| f0 = f0.squeeze().cpu().numpy() | |
| elif method == "rmvpe": | |
| model_rmvpe = RMVPE0Predictor( | |
| os.path.join("rvc", "models", "predictors", "rmvpe.pt"), | |
| is_half=config.is_half, | |
| device=config.device, | |
| # hop_length=80 | |
| ) | |
| f0 = model_rmvpe.infer_from_audio(self.wav16k, thred=0.03) | |
| else: | |
| raise ValueError(f"Unknown method: {self.method}") | |
| return libf0.hz_to_cents(f0, librosa.midi_to_hz(0)) | |
| def plot_f0(self, f0): | |
| from matplotlib import pyplot as plt | |
| plt.figure(figsize=(10, 4)) | |
| plt.plot(f0) | |
| plt.title(self.method) | |
| plt.xlabel("Time (frames)") | |
| plt.ylabel("F0 (cents)") | |
| plt.show() | |