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from typing import Optional, Union
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import numpy as np
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import torch
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from .f0 import F0Predictor
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class FCPE(F0Predictor):
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def __init__(
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self,
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hop_length=512,
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f0_min=50,
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f0_max=1100,
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sampling_rate=44100,
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device="cpu",
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):
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super().__init__(
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hop_length,
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f0_min,
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f0_max,
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sampling_rate,
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device,
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)
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from torchfcpe import (
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spawn_bundled_infer_model,
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)
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self.model = spawn_bundled_infer_model(self.device)
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def compute_f0(
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self,
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wav: np.ndarray,
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p_len: Optional[int] = None,
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filter_radius: Optional[Union[int, float]] = 0.006,
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):
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if p_len is None:
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p_len = wav.shape[0] // self.hop_length
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if not torch.is_tensor(wav):
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wav = torch.from_numpy(wav)
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f0 = (
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self.model.infer(
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wav.float().to(self.device).unsqueeze(0),
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sr=self.sampling_rate,
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decoder_mode="local_argmax",
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threshold=filter_radius,
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)
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.squeeze()
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.cpu()
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.numpy()
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)
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return self._interpolate_f0(self._resize_f0(f0, p_len))[0]
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