optimize(crepe): move crepe into rvc.f0

rvc/f0/crepe.py

@@ -0,0 +1,52 @@
+from typing import Any, Optional, Union
+
+import numpy as np
+import torch
+import torchcrepe
+
+from .f0 import F0Predictor
+
+
+class CRePE(F0Predictor):
+    def __init__(
+        self,
+        hop_length=512,
+        f0_min=50,
+        f0_max=1100,
+        sampling_rate=44100,
+        device="cpu",
+    ):
+        super().__init__(
+            hop_length,
+            f0_min,
+            f0_max,
+            sampling_rate,
+            device,
+        )
+
+    def compute_f0(
+        self,
+        wav: np.ndarray[Any, np.dtype],
+        p_len: Optional[int] = None,
+        filter_radius: Optional[Union[int, float]] = None,
+    ):
+        if p_len is None:
+            p_len = wav.shape[0] // self.hop_length
+        # Pick a batch size that doesn't cause memory errors on your gpu
+        batch_size = 512
+        # Compute pitch using device 'device'
+        f0, pd = torchcrepe.predict(
+            torch.tensor(np.copy(wav))[None].float().to(self.device),
+            self.sampling_rate,
+            self.hop_length,
+            self.f0_min,
+            self.f0_max,
+            batch_size=batch_size,
+            device=self.device,
+            return_periodicity=True,
+        )
+        pd = torchcrepe.filter.median(pd, 3)
+        f0 = torchcrepe.filter.mean(f0, 3)
+        f0[pd < 0.1] = 0
+        f0 = f0[0].cpu().numpy()
+        return self._interpolate_f0(self._resize_f0(f0, p_len))[0]