optimize(infer): move attentions into rvc

rvc/onnx/f0predictors/dio.py

@@ -0,0 +1,44 @@
+import numpy as np
+import pyworld
+import typing
+
+from .f0 import F0Predictor
+
+
+class DioF0Predictor(F0Predictor):
+    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
+        super().__init__(hop_length, f0_min, f0_max, sampling_rate)
+
+    def compute_f0(
+        self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None
+    ):
+        if p_len is None:
+            p_len = wav.shape[0] // self.hop_length
+        f0, t = pyworld.dio(
+            wav.astype(np.double),
+            fs=self.sampling_rate,
+            f0_floor=self.f0_min,
+            f0_ceil=self.f0_max,
+            frame_period=1000 * self.hop_length / self.sampling_rate,
+        )
+        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
+        for index, pitch in enumerate(f0):
+            f0[index] = round(pitch, 1)
+        return self.__interpolate_f0(self.__resize_f0(f0, p_len))[0]
+
+    def compute_f0_uv(
+        self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None
+    ):
+        if p_len is None:
+            p_len = wav.shape[0] // self.hop_length
+        f0, t = pyworld.dio(
+            wav.astype(np.double),
+            fs=self.sampling_rate,
+            f0_floor=self.f0_min,
+            f0_ceil=self.f0_max,
+            frame_period=1000 * self.hop_length / self.sampling_rate,
+        )
+        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
+        for index, pitch in enumerate(f0):
+            f0[index] = round(pitch, 1)
+        return self.__interpolate_f0(self.__resize_f0(f0, p_len))