Delete F0Extractor.py

F0Extractor.py

@@ -1,115 +0,0 @@
-import dataclasses
-import os
-import pathlib
-
-import resampy
-import torchfcpe
-
-import numpy as np
-
-import torch
-import torchcrepe
-
-import librosa
-
-from ultimate_rvc.common import RVC_MODELS_DIR
-from ultimate_rvc.rvc.configs.config import Config
-
-# from tools.anyf0.rmvpe import RMVPE
-from ultimate_rvc.rvc.lib.predictors.RMVPE import RMVPE0Predictor
-
-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(str(RVC_MODELS_DIR), "predictors", "rmvpe.pt"),
-                device=config.device,
-            )
-            f0 = model_rmvpe.infer_from_audio(self.wav16k, thred=0.03)
-        elif method == "djcm":
-            from ultimate_rvc.rvc.lib.predictors.djcm_module import DJCM
-            model_djcm = DJCM(
-                model_path=os.path.join(str(RVC_MODELS_DIR), "predictors", "djcm.pt"),
-                device=config.device
-            )
-            f0 = model_djcm.infer_from_audio(self.wav16k)
-        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()
-
-    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