| | import dataclasses |
| | import pathlib |
| | import libf0 |
| | 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 |
| |
|
| | 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) |
| |
|
| | 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() |
| |
|
