| import dataclasses |
| import pathlib |
| import librosa |
| import numpy as np |
| import resampy |
| import torch |
| import torchcrepe |
| import torchfcpe |
| import os |
|
|
| |
| from rvc.lib.predictors.RMVPE import RMVPE0Predictor |
| from rvc.configs.config import Config |
|
|
| |
| from djcm import DJCMExtractor |
|
|
| config = Config() |
|
|
|
|
| @dataclasses.dataclass |
| 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) |
|
|
| @property |
| def hop_size(self): |
| return self.hop_length / self.sample_rate |
|
|
| @property |
| def wav16k(self): |
| return resampy.resample(self.x, self.sample_rate, 16000) |
|
|
| def extract_f0(self): |
| 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"), |
| device=config.device, |
| |
| ) |
| f0 = model_rmvpe.infer_from_audio(self.wav16k, thred=0.03) |
|
|
| elif method == "djcm": |
| if not hasattr(self, "_djcm"): |
| self._djcm = DJCMExtractor("rvc/models/predictors/djcm.pt", device=config.device) |
| f0 = self._djcm(self.wav16k, sr=16000) |
|
|
| else: |
| raise ValueError(f"Unknown method: {self.method}") |
|
|
| return self.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() |
|
|
| @staticmethod |
| def hz_to_cents(F, F_ref=55.0): |
| F_temp = np.array(F).astype(float) |
| F_temp[F_temp == 0] = np.nan |
| F_cents = 1200 * np.log2(F_temp / F_ref) |
| return F_cents |
|
|
