voice-changer/server/voice_changer/DiffusionSVC/pitchExtractor/HarvestPitchExtractor.py

import pyworld
import numpy as np
import scipy.signal as signal
from const import PitchExtractorType
import torch
from voice_changer.RVC.pitchExtractor.PitchExtractor import PitchExtractor


class HarvestPitchExtractor(PitchExtractor):

    def __init__(self):
        super().__init__()
        self.pitchExtractorType: PitchExtractorType = "harvest"

    def extract(self, audio: torch.Tensor, pitchf, f0_up_key, sr, window, silence_front=0):
        audio = audio.detach().cpu().numpy()
        f0_min = 50
        f0_max = 1100
        # f0_mel_min = 1127 * np.log(1 + f0_min / 700)
        # f0_mel_max = 1127 * np.log(1 + f0_max / 700)

        f0 = self.extract2(audio, uv_interp=True, hop_size=window, silence_front=silence_front)
        f0 = f0 * 2 ** (float(f0_up_key) / 12)

        # pitchf[-f0.shape[0]:] = f0[:pitchf.shape[0]]
        return f0

    def extract2(self, audio,  uv_interp, hop_size: int, silence_front=0):  # audio: 1d numpy array
        n_frames = int(len(audio) // hop_size) + 1

        start_frame = int(silence_front * 16000 / hop_size)
        real_silence_front = start_frame * hop_size / 16000
        audio = audio[int(np.round(real_silence_front * 16000)):]

        f0, _ = pyworld.harvest(
            audio.astype('double'),
            16000,
            f0_floor=50,
            f0_ceil=1100,
            frame_period=(1000 * hop_size / 16000))
        f0 = np.pad(f0.astype('float'), (start_frame, n_frames - len(f0) - start_frame))

        if uv_interp:
            uv = f0 == 0
            if len(f0[~uv]) > 0:
                f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv])
            f0[f0 < 50] = 50

        return f0