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import os |
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import gc |
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import re |
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import sys |
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import torch |
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import torch.nn.functional as F |
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import parselmouth |
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import torchcrepe |
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import pyworld |
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import faiss |
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import librosa |
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import numpy as np |
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from scipy import signal |
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from functools import lru_cache |
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from torch import Tensor |
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|
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now_dir = os.getcwd() |
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sys.path.append(now_dir) |
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from rvc.lib.predictors.RMVPE import RMVPE0Predictor |
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from rvc.lib.predictors.FCPE import FCPEF0Predictor |
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FILTER_ORDER = 5 |
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CUTOFF_FREQUENCY = 48 |
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SAMPLE_RATE = 16000 |
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bh, ah = signal.butter( |
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N=FILTER_ORDER, Wn=CUTOFF_FREQUENCY, btype="high", fs=SAMPLE_RATE |
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) |
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|
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input_audio_path2wav = {} |
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|
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class AudioProcessor: |
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""" |
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A class for processing audio signals, specifically for adjusting RMS levels. |
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""" |
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|
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def change_rms( |
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source_audio: np.ndarray, |
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source_rate: int, |
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target_audio: np.ndarray, |
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target_rate: int, |
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rate: float, |
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) -> np.ndarray: |
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""" |
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Adjust the RMS level of target_audio to match the RMS of source_audio, with a given blending rate. |
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|
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Args: |
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source_audio: The source audio signal as a NumPy array. |
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source_rate: The sampling rate of the source audio. |
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target_audio: The target audio signal to adjust. |
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target_rate: The sampling rate of the target audio. |
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rate: The blending rate between the source and target RMS levels. |
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|
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Returns: |
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The adjusted target audio signal with RMS level modified to match the source audio. |
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""" |
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|
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rms1 = librosa.feature.rms( |
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y=source_audio, |
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frame_length=source_rate // 2 * 2, |
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hop_length=source_rate // 2, |
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) |
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rms2 = librosa.feature.rms( |
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y=target_audio, |
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frame_length=target_rate // 2 * 2, |
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hop_length=target_rate // 2, |
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) |
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|
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rms1 = F.interpolate( |
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torch.from_numpy(rms1).float().unsqueeze(0), |
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size=target_audio.shape[0], |
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mode="linear", |
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).squeeze() |
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rms2 = F.interpolate( |
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torch.from_numpy(rms2).float().unsqueeze(0), |
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size=target_audio.shape[0], |
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mode="linear", |
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).squeeze() |
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rms2 = torch.maximum(rms2, torch.zeros_like(rms2) + 1e-6) |
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adjusted_audio = ( |
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target_audio |
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* (torch.pow(rms1, 1 - rate) * torch.pow(rms2, rate - 1)).numpy() |
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) |
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return adjusted_audio |
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class Autotune: |
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""" |
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A class for applying autotune to a given fundamental frequency (F0) contour. |
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""" |
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def __init__(self, ref_freqs): |
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""" |
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Initializes the Autotune class with a set of reference frequencies. |
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Args: |
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ref_freqs: A list of reference frequencies representing musical notes. |
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""" |
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self.ref_freqs = ref_freqs |
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self.note_dict = self.generate_interpolated_frequencies() |
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|
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def generate_interpolated_frequencies(self): |
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""" |
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Generates a dictionary of interpolated frequencies between reference frequencies. |
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|
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Returns: |
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A list of interpolated frequencies, including the original reference frequencies. |
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""" |
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note_dict = [] |
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for i in range(len(self.ref_freqs) - 1): |
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freq_low = self.ref_freqs[i] |
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freq_high = self.ref_freqs[i + 1] |
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interpolated_freqs = np.linspace( |
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freq_low, freq_high, num=10, endpoint=False |
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) |
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note_dict.extend(interpolated_freqs) |
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note_dict.append(self.ref_freqs[-1]) |
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return note_dict |
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|
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def autotune_f0(self, f0): |
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""" |
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Autotunes a given F0 contour by snapping each frequency to the closest reference frequency. |
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Args: |
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f0: The input F0 contour as a NumPy array. |
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Returns: |
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The autotuned F0 contour. |
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""" |
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autotuned_f0 = np.zeros_like(f0) |
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for i, freq in enumerate(f0): |
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closest_note = min(self.note_dict, key=lambda x: abs(x - freq)) |
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autotuned_f0[i] = closest_note |
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return autotuned_f0 |
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class Pipeline: |
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""" |
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The main pipeline class for performing voice conversion, including preprocessing, F0 estimation, |
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voice conversion using a model, and post-processing. |
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""" |
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|
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def __init__(self, tgt_sr, config): |
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""" |
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Initializes the Pipeline class with target sampling rate and configuration parameters. |
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|
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Args: |
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tgt_sr: The target sampling rate for the output audio. |
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config: A configuration object containing various parameters for the pipeline. |
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""" |
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self.x_pad = config.x_pad |
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self.x_query = config.x_query |
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self.x_center = config.x_center |
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self.x_max = config.x_max |
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self.is_half = config.is_half |
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self.sample_rate = 16000 |
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self.window = 160 |
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self.t_pad = self.sample_rate * self.x_pad |
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self.t_pad_tgt = tgt_sr * self.x_pad |
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self.t_pad2 = self.t_pad * 2 |
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self.t_query = self.sample_rate * self.x_query |
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self.t_center = self.sample_rate * self.x_center |
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self.t_max = self.sample_rate * self.x_max |
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self.time_step = self.window / self.sample_rate * 1000 |
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self.f0_min = 50 |
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self.f0_max = 1100 |
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self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700) |
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self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700) |
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self.device = config.device |
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self.ref_freqs = [ |
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65.41, |
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82.41, |
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110.00, |
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146.83, |
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196.00, |
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246.94, |
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329.63, |
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440.00, |
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587.33, |
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783.99, |
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1046.50, |
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] |
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self.autotune = Autotune(self.ref_freqs) |
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self.note_dict = self.autotune.note_dict |
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|
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@staticmethod |
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@lru_cache |
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def get_f0_harvest(input_audio_path, fs, f0max, f0min, frame_period): |
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""" |
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Estimates the fundamental frequency (F0) of a given audio file using the Harvest algorithm. |
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|
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Args: |
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input_audio_path: Path to the input audio file. |
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fs: Sampling rate of the audio file. |
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f0max: Maximum F0 value to consider. |
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f0min: Minimum F0 value to consider. |
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frame_period: Frame period in milliseconds for F0 analysis. |
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|
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Returns: |
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The estimated F0 contour as a NumPy array. |
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""" |
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audio = input_audio_path2wav[input_audio_path] |
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f0, t = pyworld.harvest( |
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audio, |
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fs=fs, |
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f0_ceil=f0max, |
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f0_floor=f0min, |
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frame_period=frame_period, |
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) |
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f0 = pyworld.stonemask(audio, f0, t, fs) |
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return f0 |
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|
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def get_f0_crepe( |
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self, |
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x, |
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f0_min, |
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f0_max, |
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p_len, |
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hop_length, |
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model="full", |
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): |
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""" |
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Estimates the fundamental frequency (F0) of a given audio signal using the Crepe model. |
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|
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Args: |
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x: The input audio signal as a NumPy array. |
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f0_min: Minimum F0 value to consider. |
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f0_max: Maximum F0 value to consider. |
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p_len: Desired length of the F0 output. |
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hop_length: Hop length for the Crepe model. |
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model: Crepe model size to use ("full" or "tiny"). |
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|
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Returns: |
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The estimated F0 contour as a NumPy array. |
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""" |
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x = x.astype(np.float32) |
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x /= np.quantile(np.abs(x), 0.999) |
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audio = torch.from_numpy(x).to(self.device, copy=True) |
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audio = torch.unsqueeze(audio, dim=0) |
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if audio.ndim == 2 and audio.shape[0] > 1: |
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audio = torch.mean(audio, dim=0, keepdim=True).detach() |
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audio = audio.detach() |
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pitch: Tensor = torchcrepe.predict( |
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audio, |
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self.sample_rate, |
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hop_length, |
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f0_min, |
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f0_max, |
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model, |
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batch_size=hop_length * 2, |
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device=self.device, |
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pad=True, |
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) |
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p_len = p_len or x.shape[0] // hop_length |
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source = np.array(pitch.squeeze(0).cpu().float().numpy()) |
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source[source < 0.001] = np.nan |
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target = np.interp( |
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np.arange(0, len(source) * p_len, len(source)) / p_len, |
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np.arange(0, len(source)), |
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source, |
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) |
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f0 = np.nan_to_num(target) |
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return f0 |
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|
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def get_f0_hybrid( |
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self, |
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methods_str, |
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x, |
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f0_min, |
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f0_max, |
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p_len, |
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hop_length, |
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): |
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""" |
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Estimates the fundamental frequency (F0) using a hybrid approach combining multiple methods. |
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|
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Args: |
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methods_str: A string specifying the methods to combine (e.g., "hybrid[crepe+rmvpe]"). |
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x: The input audio signal as a NumPy array. |
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f0_min: Minimum F0 value to consider. |
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f0_max: Maximum F0 value to consider. |
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p_len: Desired length of the F0 output. |
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hop_length: Hop length for F0 estimation methods. |
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|
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Returns: |
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The estimated F0 contour as a NumPy array, obtained by combining the specified methods. |
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""" |
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methods_str = re.search("hybrid\[(.+)\]", methods_str) |
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if methods_str: |
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methods = [method.strip() for method in methods_str.group(1).split("+")] |
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f0_computation_stack = [] |
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print(f"Calculating f0 pitch estimations for methods {str(methods)}") |
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x = x.astype(np.float32) |
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x /= np.quantile(np.abs(x), 0.999) |
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for method in methods: |
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f0 = None |
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if method == "crepe": |
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f0 = self.get_f0_crepe_computation( |
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x, f0_min, f0_max, p_len, int(hop_length) |
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) |
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elif method == "rmvpe": |
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self.model_rmvpe = RMVPE0Predictor( |
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os.path.join("rvc", "models", "predictors", "rmvpe.pt"), |
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is_half=self.is_half, |
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device=self.device, |
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) |
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f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03) |
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f0 = f0[1:] |
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elif method == "fcpe": |
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self.model_fcpe = FCPEF0Predictor( |
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os.path.join("rvc", "models", "predictors", "fcpe.pt"), |
|
f0_min=int(f0_min), |
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f0_max=int(f0_max), |
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dtype=torch.float32, |
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device=self.device, |
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sampling_rate=self.sample_rate, |
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threshold=0.03, |
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) |
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f0 = self.model_fcpe.compute_f0(x, p_len=p_len) |
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del self.model_fcpe |
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gc.collect() |
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f0_computation_stack.append(f0) |
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|
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f0_computation_stack = [fc for fc in f0_computation_stack if fc is not None] |
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f0_median_hybrid = None |
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if len(f0_computation_stack) == 1: |
|
f0_median_hybrid = f0_computation_stack[0] |
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else: |
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f0_median_hybrid = np.nanmedian(f0_computation_stack, axis=0) |
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return f0_median_hybrid |
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|
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def get_f0( |
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self, |
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input_audio_path, |
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x, |
|
p_len, |
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f0_up_key, |
|
f0_method, |
|
filter_radius, |
|
hop_length, |
|
f0_autotune, |
|
inp_f0=None, |
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): |
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""" |
|
Estimates the fundamental frequency (F0) of a given audio signal using various methods. |
|
|
|
Args: |
|
input_audio_path: Path to the input audio file. |
|
x: The input audio signal as a NumPy array. |
|
p_len: Desired length of the F0 output. |
|
f0_up_key: Key to adjust the pitch of the F0 contour. |
|
f0_method: Method to use for F0 estimation (e.g., "pm", "harvest", "crepe"). |
|
filter_radius: Radius for median filtering the F0 contour. |
|
hop_length: Hop length for F0 estimation methods. |
|
f0_autotune: Whether to apply autotune to the F0 contour. |
|
inp_f0: Optional input F0 contour to use instead of estimating. |
|
|
|
Returns: |
|
A tuple containing the quantized F0 contour and the original F0 contour. |
|
""" |
|
global input_audio_path2wav |
|
if f0_method == "pm": |
|
f0 = ( |
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parselmouth.Sound(x, self.sample_rate) |
|
.to_pitch_ac( |
|
time_step=self.time_step / 1000, |
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voicing_threshold=0.6, |
|
pitch_floor=self.f0_min, |
|
pitch_ceiling=self.f0_max, |
|
) |
|
.selected_array["frequency"] |
|
) |
|
pad_size = (p_len - len(f0) + 1) // 2 |
|
if pad_size > 0 or p_len - len(f0) - pad_size > 0: |
|
f0 = np.pad( |
|
f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant" |
|
) |
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elif f0_method == "harvest": |
|
input_audio_path2wav[input_audio_path] = x.astype(np.double) |
|
f0 = self.get_f0_harvest( |
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input_audio_path, self.sample_rate, self.f0_max, self.f0_min, 10 |
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) |
|
if int(filter_radius) > 2: |
|
f0 = signal.medfilt(f0, 3) |
|
elif f0_method == "dio": |
|
f0, t = pyworld.dio( |
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x.astype(np.double), |
|
fs=self.sample_rate, |
|
f0_ceil=self.f0_max, |
|
f0_floor=self.f0_min, |
|
frame_period=10, |
|
) |
|
f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sample_rate) |
|
f0 = signal.medfilt(f0, 3) |
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elif f0_method == "crepe": |
|
f0 = self.get_f0_crepe(x, self.f0_min, self.f0_max, p_len, int(hop_length)) |
|
elif f0_method == "crepe-tiny": |
|
f0 = self.get_f0_crepe( |
|
x, self.f0_min, self.f0_max, p_len, int(hop_length), "tiny" |
|
) |
|
elif f0_method == "rmvpe": |
|
self.model_rmvpe = RMVPE0Predictor( |
|
os.path.join("rvc", "models", "predictors", "rmvpe.pt"), |
|
is_half=self.is_half, |
|
device=self.device, |
|
) |
|
f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03) |
|
elif f0_method == "fcpe": |
|
self.model_fcpe = FCPEF0Predictor( |
|
os.path.join("rvc", "models", "predictors", "fcpe.pt"), |
|
f0_min=int(self.f0_min), |
|
f0_max=int(self.f0_max), |
|
dtype=torch.float32, |
|
device=self.device, |
|
sampling_rate=self.sample_rate, |
|
threshold=0.03, |
|
) |
|
f0 = self.model_fcpe.compute_f0(x, p_len=p_len) |
|
del self.model_fcpe |
|
gc.collect() |
|
elif "hybrid" in f0_method: |
|
input_audio_path2wav[input_audio_path] = x.astype(np.double) |
|
f0 = self.get_f0_hybrid( |
|
f0_method, |
|
x, |
|
self.f0_min, |
|
self.f0_max, |
|
p_len, |
|
hop_length, |
|
) |
|
|
|
if f0_autotune == "True": |
|
f0 = Autotune.autotune_f0(self, f0) |
|
|
|
f0 *= pow(2, f0_up_key / 12) |
|
tf0 = self.sample_rate // self.window |
|
if inp_f0 is not None: |
|
delta_t = np.round( |
|
(inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1 |
|
).astype("int16") |
|
replace_f0 = np.interp( |
|
list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1] |
|
) |
|
shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0] |
|
f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[ |
|
:shape |
|
] |
|
f0bak = f0.copy() |
|
f0_mel = 1127 * np.log(1 + f0 / 700) |
|
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * 254 / ( |
|
self.f0_mel_max - self.f0_mel_min |
|
) + 1 |
|
f0_mel[f0_mel <= 1] = 1 |
|
f0_mel[f0_mel > 255] = 255 |
|
f0_coarse = np.rint(f0_mel).astype(np.int) |
|
|
|
return f0_coarse, f0bak |
|
|
|
def voice_conversion( |
|
self, |
|
model, |
|
net_g, |
|
sid, |
|
audio0, |
|
pitch, |
|
pitchf, |
|
index, |
|
big_npy, |
|
index_rate, |
|
version, |
|
protect, |
|
): |
|
""" |
|
Performs voice conversion on a given audio segment. |
|
|
|
Args: |
|
model: The feature extractor model. |
|
net_g: The generative model for synthesizing speech. |
|
sid: Speaker ID for the target voice. |
|
audio0: The input audio segment. |
|
pitch: Quantized F0 contour for pitch guidance. |
|
pitchf: Original F0 contour for pitch guidance. |
|
index: FAISS index for speaker embedding retrieval. |
|
big_npy: Speaker embeddings stored in a NumPy array. |
|
index_rate: Blending rate for speaker embedding retrieval. |
|
version: Model version ("v1" or "v2"). |
|
protect: Protection level for preserving the original pitch. |
|
|
|
Returns: |
|
The voice-converted audio segment. |
|
""" |
|
feats = torch.from_numpy(audio0) |
|
if self.is_half: |
|
feats = feats.half() |
|
else: |
|
feats = feats.float() |
|
if feats.dim() == 2: |
|
feats = feats.mean(-1) |
|
assert feats.dim() == 1, feats.dim() |
|
feats = feats.view(1, -1) |
|
padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False) |
|
|
|
inputs = { |
|
"source": feats.to(self.device), |
|
"padding_mask": padding_mask, |
|
"output_layer": 9 if version == "v1" else 12, |
|
} |
|
with torch.no_grad(): |
|
logits = model.extract_features(**inputs) |
|
feats = model.final_proj(logits[0]) if version == "v1" else logits[0] |
|
if protect < 0.5 and pitch != None and pitchf != None: |
|
feats0 = feats.clone() |
|
if ( |
|
isinstance(index, type(None)) == False |
|
and isinstance(big_npy, type(None)) == False |
|
and index_rate != 0 |
|
): |
|
npy = feats[0].cpu().numpy() |
|
if self.is_half: |
|
npy = npy.astype("float32") |
|
|
|
score, ix = index.search(npy, k=8) |
|
weight = np.square(1 / score) |
|
weight /= weight.sum(axis=1, keepdims=True) |
|
npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1) |
|
|
|
if self.is_half: |
|
npy = npy.astype("float16") |
|
feats = ( |
|
torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate |
|
+ (1 - index_rate) * feats |
|
) |
|
|
|
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1) |
|
if protect < 0.5 and pitch != None and pitchf != None: |
|
feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute( |
|
0, 2, 1 |
|
) |
|
p_len = audio0.shape[0] // self.window |
|
if feats.shape[1] < p_len: |
|
p_len = feats.shape[1] |
|
if pitch != None and pitchf != None: |
|
pitch = pitch[:, :p_len] |
|
pitchf = pitchf[:, :p_len] |
|
|
|
if protect < 0.5 and pitch != None and pitchf != None: |
|
pitchff = pitchf.clone() |
|
pitchff[pitchf > 0] = 1 |
|
pitchff[pitchf < 1] = protect |
|
pitchff = pitchff.unsqueeze(-1) |
|
feats = feats * pitchff + feats0 * (1 - pitchff) |
|
feats = feats.to(feats0.dtype) |
|
p_len = torch.tensor([p_len], device=self.device).long() |
|
with torch.no_grad(): |
|
if pitch != None and pitchf != None: |
|
audio1 = ( |
|
(net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]) |
|
.data.cpu() |
|
.float() |
|
.numpy() |
|
) |
|
else: |
|
audio1 = ( |
|
(net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy() |
|
) |
|
del feats, p_len, padding_mask |
|
if torch.cuda.is_available(): |
|
torch.cuda.empty_cache() |
|
return audio1 |
|
|
|
def pipeline( |
|
self, |
|
model, |
|
net_g, |
|
sid, |
|
audio, |
|
input_audio_path, |
|
f0_up_key, |
|
f0_method, |
|
file_index, |
|
index_rate, |
|
pitch_guidance, |
|
filter_radius, |
|
tgt_sr, |
|
resample_sr, |
|
rms_mix_rate, |
|
version, |
|
protect, |
|
hop_length, |
|
f0_autotune, |
|
f0_file, |
|
): |
|
""" |
|
The main pipeline function for performing voice conversion. |
|
|
|
Args: |
|
model: The feature extractor model. |
|
net_g: The generative model for synthesizing speech. |
|
sid: Speaker ID for the target voice. |
|
audio: The input audio signal. |
|
input_audio_path: Path to the input audio file. |
|
f0_up_key: Key to adjust the pitch of the F0 contour. |
|
f0_method: Method to use for F0 estimation. |
|
file_index: Path to the FAISS index file for speaker embedding retrieval. |
|
index_rate: Blending rate for speaker embedding retrieval. |
|
pitch_guidance: Whether to use pitch guidance during voice conversion. |
|
filter_radius: Radius for median filtering the F0 contour. |
|
tgt_sr: Target sampling rate for the output audio. |
|
resample_sr: Resampling rate for the output audio. |
|
rms_mix_rate: Blending rate for adjusting the RMS level of the output audio. |
|
version: Model version. |
|
protect: Protection level for preserving the original pitch. |
|
hop_length: Hop length for F0 estimation methods. |
|
f0_autotune: Whether to apply autotune to the F0 contour. |
|
f0_file: Path to a file containing an F0 contour to use. |
|
|
|
Returns: |
|
The voice-converted audio signal. |
|
""" |
|
if file_index != "" and os.path.exists(file_index) == True and index_rate != 0: |
|
try: |
|
index = faiss.read_index(file_index) |
|
big_npy = index.reconstruct_n(0, index.ntotal) |
|
except Exception as error: |
|
print(error) |
|
index = big_npy = None |
|
else: |
|
index = big_npy = None |
|
audio = signal.filtfilt(bh, ah, audio) |
|
audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect") |
|
opt_ts = [] |
|
if audio_pad.shape[0] > self.t_max: |
|
audio_sum = np.zeros_like(audio) |
|
for i in range(self.window): |
|
audio_sum += audio_pad[i : i - self.window] |
|
for t in range(self.t_center, audio.shape[0], self.t_center): |
|
opt_ts.append( |
|
t |
|
- self.t_query |
|
+ np.where( |
|
np.abs(audio_sum[t - self.t_query : t + self.t_query]) |
|
== np.abs(audio_sum[t - self.t_query : t + self.t_query]).min() |
|
)[0][0] |
|
) |
|
s = 0 |
|
audio_opt = [] |
|
t = None |
|
audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect") |
|
p_len = audio_pad.shape[0] // self.window |
|
inp_f0 = None |
|
if hasattr(f0_file, "name") == True: |
|
try: |
|
with open(f0_file.name, "r") as f: |
|
lines = f.read().strip("\n").split("\n") |
|
inp_f0 = [] |
|
for line in lines: |
|
inp_f0.append([float(i) for i in line.split(",")]) |
|
inp_f0 = np.array(inp_f0, dtype="float32") |
|
except Exception as error: |
|
print(error) |
|
sid = torch.tensor(sid, device=self.device).unsqueeze(0).long() |
|
pitch, pitchf = None, None |
|
if pitch_guidance == 1: |
|
pitch, pitchf = self.get_f0( |
|
input_audio_path, |
|
audio_pad, |
|
p_len, |
|
f0_up_key, |
|
f0_method, |
|
filter_radius, |
|
hop_length, |
|
f0_autotune, |
|
inp_f0, |
|
) |
|
pitch = pitch[:p_len] |
|
pitchf = pitchf[:p_len] |
|
if self.device == "mps": |
|
pitchf = pitchf.astype(np.float32) |
|
pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long() |
|
pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float() |
|
for t in opt_ts: |
|
t = t // self.window * self.window |
|
if pitch_guidance == 1: |
|
audio_opt.append( |
|
self.voice_conversion( |
|
model, |
|
net_g, |
|
sid, |
|
audio_pad[s : t + self.t_pad2 + self.window], |
|
pitch[:, s // self.window : (t + self.t_pad2) // self.window], |
|
pitchf[:, s // self.window : (t + self.t_pad2) // self.window], |
|
index, |
|
big_npy, |
|
index_rate, |
|
version, |
|
protect, |
|
)[self.t_pad_tgt : -self.t_pad_tgt] |
|
) |
|
else: |
|
audio_opt.append( |
|
self.voice_conversion( |
|
model, |
|
net_g, |
|
sid, |
|
audio_pad[s : t + self.t_pad2 + self.window], |
|
None, |
|
None, |
|
index, |
|
big_npy, |
|
index_rate, |
|
version, |
|
protect, |
|
)[self.t_pad_tgt : -self.t_pad_tgt] |
|
) |
|
s = t |
|
if pitch_guidance == 1: |
|
audio_opt.append( |
|
self.voice_conversion( |
|
model, |
|
net_g, |
|
sid, |
|
audio_pad[t:], |
|
pitch[:, t // self.window :] if t is not None else pitch, |
|
pitchf[:, t // self.window :] if t is not None else pitchf, |
|
index, |
|
big_npy, |
|
index_rate, |
|
version, |
|
protect, |
|
)[self.t_pad_tgt : -self.t_pad_tgt] |
|
) |
|
else: |
|
audio_opt.append( |
|
self.voice_conversion( |
|
model, |
|
net_g, |
|
sid, |
|
audio_pad[t:], |
|
None, |
|
None, |
|
index, |
|
big_npy, |
|
index_rate, |
|
version, |
|
protect, |
|
)[self.t_pad_tgt : -self.t_pad_tgt] |
|
) |
|
audio_opt = np.concatenate(audio_opt) |
|
if rms_mix_rate != 1: |
|
audio_opt = AudioProcessor.change_rms( |
|
audio, self.sample_rate, audio_opt, tgt_sr, rms_mix_rate |
|
) |
|
if resample_sr >= self.sample_rate and tgt_sr != resample_sr: |
|
audio_opt = librosa.resample( |
|
audio_opt, orig_sr=tgt_sr, target_sr=resample_sr |
|
) |
|
audio_max = np.abs(audio_opt).max() / 0.99 |
|
max_int16 = 32768 |
|
if audio_max > 1: |
|
max_int16 /= audio_max |
|
audio_opt = (audio_opt * max_int16).astype(np.int16) |
|
del pitch, pitchf, sid |
|
if torch.cuda.is_available(): |
|
torch.cuda.empty_cache() |
|
return audio_opt |