740 lines
26 KiB
Python
740 lines
26 KiB
Python
import numpy as np
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import torch
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import sys
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import os
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import pdb
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import parselmouth
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from time import time as ttime
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import torch.nn.functional as F
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import torchcrepe
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from torch import Tensor
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import scipy.signal as signal
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import pyworld, os, faiss, librosa, torchcrepe
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from scipy import signal
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from functools import lru_cache
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import random
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import gc
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import re
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import traceback
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from pathlib import Path
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# Get the current working directory
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script_dir = Path(__file__).resolve().parent
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now_dir = script_dir.parents[3]
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sys.path.append(now_dir)
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from ..lib.FCPEF0Predictor import FCPEF0Predictor
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bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
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input_audio_path2wav = {}
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@lru_cache
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def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
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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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def change_rms(data1, sr1, data2, sr2, rate):
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# print(data1.max(),data2.max())
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rms1 = librosa.feature.rms(y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2)
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rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
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rms1 = torch.from_numpy(rms1)
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rms1 = F.interpolate(
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rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
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).squeeze()
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rms2 = torch.from_numpy(rms2)
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rms2 = F.interpolate(
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rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
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).squeeze()
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rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
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data2 *= (
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torch.pow(rms1, torch.tensor(1 - rate))
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* torch.pow(rms2, torch.tensor(rate - 1))
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).numpy()
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return data2
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from sklearn.metrics import (
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silhouette_score,
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calinski_harabasz_score,
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davies_bouldin_score,
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fowlkes_mallows_score,
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adjusted_rand_score,
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adjusted_mutual_info_score
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)
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class VC(object):
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def __init__(self, tgt_sr, config, data=None, d=None, train_data=None, debug_rvc=False):
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branding = "AllTalk "
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self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
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config.x_pad,
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config.x_query,
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config.x_center,
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config.x_max,
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config.is_half,
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)
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self.sr = 16000
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self.window = 160
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self.t_pad = self.sr * 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.sr * self.x_query
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self.t_center = self.sr * self.x_center
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self.t_max = self.sr * self.x_max
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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.note_dict = self.generate_interpolated_frequencies()
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if data is not None and d is not None and train_data is not None:
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if debug_rvc:
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print(f"[{branding}Debug] Initializing FAISS quantizer with dimensionality: {d}")
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self.quantizer = faiss.IndexFlatL2(d)
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if debug_rvc:
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print(f"[{branding}Debug] FAISS quantizer initialized: {self.quantizer}")
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# Dynamic adjustment of new_nlist
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new_nlist = max(1, len(train_data) // 39) # Ensure at least 1
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if debug_rvc:
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print(f"[{branding}Debug] Using nlist: {new_nlist}")
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self.index = faiss.IndexIVFFlat(self.quantizer, d, new_nlist)
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if debug_rvc:
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print(f"[{branding}Debug] FAISS IndexIVFFlat initialized: {self.index}")
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if debug_rvc:
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print(f"[{branding}Debug] Training FAISS index with {len(train_data)} training points and {new_nlist} centroids")
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self.index.train(train_data)
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if debug_rvc:
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print(f"[{branding}Debug] FAISS index trained")
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if debug_rvc:
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print(f"[{branding}Debug] Adding {len(data)} data points to FAISS index")
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self.index.add(data)
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if debug_rvc:
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print(f"[{branding}Debug] Data added to FAISS index")
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else:
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self.quantizer = None
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self.index = None
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if debug_rvc:
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print(f"[{branding}Debug] FAISS index not initialized due to missing data.")
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def generate_interpolated_frequencies(self):
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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(freq_low, freq_high, num=10, endpoint=False)
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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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def autotune_f0(self, f0):
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# Autotunes the given fundamental frequency (f0) to the nearest musical note.
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autotuned_f0 = np.zeros_like(f0)
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for i, freq in enumerate(f0):
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# Find the closest note
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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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def get_optimal_torch_device(self, index: int = 0) -> torch.device:
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#print("GETTING OPTIMAL DEVICE")
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if torch.cuda.is_available():
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#print("GETTING OPTIMAL DEVICE CUDA")
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return torch.device(f"cuda:{index % torch.cuda.device_count()}")
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elif torch.backends.mps.is_available():
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#print("GETTING OPTIMAL DEVICE MPS")
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return torch.device("mps")
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#print("GETTING OPTIMAL DEVICE CPU")
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return torch.device("cpu")
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def get_f0_crepe_computation(
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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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x = x.astype(np.float32)
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x /= np.quantile(np.abs(x), 0.999)
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torch_device = self.get_optimal_torch_device()
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audio = torch.from_numpy(x).to(torch_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.sr,
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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=torch_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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#print("LEAVING get_f0_crepe_computation")
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return f0
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def get_f0_official_crepe_computation(
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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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model="full",
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):
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batch_size = 512
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audio = torch.tensor(np.copy(x))[None].float()
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f0, pd = torchcrepe.predict(
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audio,
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self.sr,
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self.window,
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f0_min,
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f0_max,
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model,
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batch_size=batch_size,
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device=self.device,
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return_periodicity=True,
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)
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pd = torchcrepe.filter.median(pd, 3)
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f0 = torchcrepe.filter.mean(f0, 3)
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f0[pd < 0.1] = 0
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f0 = f0[0].cpu().numpy()
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#print("LEAVING get_f0_official_crepe_computation")
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return f0
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def get_f0_hybrid_computation(
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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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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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if hasattr(self, "model_rmvpe") == False:
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from ..lib.rmvpe import RMVPE
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model_path = os.path.join(now_dir, "models", "rvc_base", "rmvpe.pt")
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self.model_rmvpe = RMVPE(
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model_path, is_half=self.is_half, 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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model_path = os.path.join(now_dir, "models", "rvc_base", "fcpe.pt")
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self.model_fcpe = FCPEF0Predictor(
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model_path,
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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.sr,
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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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#print(f"Calculating hybrid median f0 from the stack of {str(methods)}")
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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:
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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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#print("LEAVING get_f0_hybrid_computation")
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return f0_median_hybrid
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def get_f0(
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self,
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input_audio_path,
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x,
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p_len,
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f0_up_key,
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f0_method,
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filter_radius,
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hop_length,
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f0autotune,
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inp_f0=None,
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):
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global input_audio_path2wav
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time_step = self.window / self.sr * 1000
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f0_min = 50
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f0_max = 1100
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f0_mel_min = 1127 * np.log(1 + f0_min / 700)
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f0_mel_max = 1127 * np.log(1 + f0_max / 700)
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if f0_method == "pm":
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f0 = (
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parselmouth.Sound(x, self.sr)
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.to_pitch_ac(
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time_step=time_step / 1000,
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voicing_threshold=0.6,
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pitch_floor=f0_min,
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pitch_ceiling=f0_max,
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)
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.selected_array["frequency"]
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)
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pad_size = (p_len - len(f0) + 1) // 2
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if pad_size > 0 or p_len - len(f0) - pad_size > 0:
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f0 = np.pad(
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f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
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)
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elif f0_method == "harvest":
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input_audio_path2wav[input_audio_path] = x.astype(np.double)
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f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
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if int(filter_radius) > 2:
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f0 = signal.medfilt(f0, 3)
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elif f0_method == "dio":
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f0, t = pyworld.dio(
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x.astype(np.double),
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fs=self.sr,
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f0_ceil=f0_max,
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f0_floor=f0_min,
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frame_period=10,
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)
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f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
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f0 = signal.medfilt(f0, 3)
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elif f0_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 f0_method == "crepe-tiny":
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f0 = self.get_f0_crepe_computation(
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x, f0_min, f0_max, p_len, int(hop_length), "tiny"
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)
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elif f0_method == "rmvpe":
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if hasattr(self, "model_rmvpe") == False:
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from ..lib.rmvpe import RMVPE
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model_path = os.path.join(now_dir, "models", "rvc_base", "rmvpe.pt")
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self.model_rmvpe = RMVPE(
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model_path, is_half=self.is_half, 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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elif f0_method == "fcpe":
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model_path = os.path.join(now_dir, "models", "rvc_base", "fcpe.pt")
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self.model_fcpe = FCPEF0Predictor(
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model_path,
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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.sr,
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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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elif "hybrid" in f0_method:
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input_audio_path2wav[input_audio_path] = x.astype(np.double)
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f0 = self.get_f0_hybrid_computation(
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f0_method,
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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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if f0autotune == "True":
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f0 = self.autotune_f0(f0)
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f0 *= pow(2, f0_up_key / 12)
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tf0 = self.sr // self.window
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if inp_f0 is not None:
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delta_t = np.round(
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(inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
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).astype("int16")
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replace_f0 = np.interp(
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list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
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)
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shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
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f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
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:shape
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]
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f0bak = f0.copy()
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f0_mel = 1127 * np.log(1 + f0 / 700)
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f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
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f0_mel_max - f0_mel_min
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) + 1
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f0_mel[f0_mel <= 1] = 1
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f0_mel[f0_mel > 255] = 255
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f0_coarse = np.rint(f0_mel).astype(int)
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#print("LEAVING get_f0")
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return f0_coarse, f0bak
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def vc(
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self,
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model,
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net_g,
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sid,
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audio0,
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pitch,
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pitchf,
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index,
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big_npy,
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index_rate,
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version,
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protect,
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):
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#print("ENTERING pipeline vc")
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#print(f"audio0 shape: {audio0.shape}")
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feats = torch.from_numpy(audio0)
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#print(f"feats initial dtype: {feats.dtype}")
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if self.is_half:
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feats = feats.half()
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#print("Converting feats to half precision")
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else:
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feats = feats.float()
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#print("Converting feats to float precision")
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#print(f"feats dtype after conversion: {feats.dtype}")
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if feats.dim() == 2:
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#print("feats has 2 dimensions, taking mean along last dimension")
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feats = feats.mean(-1)
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assert feats.dim() == 1, feats.dim()
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#print(f"feats shape after mean: {feats.shape}")
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feats = feats.view(1, -1)
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#print(f"feats shape after view: {feats.shape}")
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padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
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#print(f"padding_mask shape: {padding_mask.shape}")
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#print(f"padding_mask device: {padding_mask.device}")
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inputs = {
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"source": feats.to(self.device),
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"padding_mask": padding_mask,
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"output_layer": 9 if version == "v1" else 12,
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}
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#print(f"inputs source shape: {inputs['source'].shape}")
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#print(f"inputs source device: {inputs['source'].device}")
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|
#print(f"output_layer: {inputs['output_layer']}")
|
|
t0 = ttime()
|
|
#print("Pipeline vc STEP 1:")
|
|
with torch.no_grad():
|
|
logits = model.extract_features(**inputs)
|
|
#print(f"logits type: {type(logits)}")
|
|
#print(f"logits length: {len(logits)}")
|
|
#print(f"logits[0] shape: {logits[0].shape}")
|
|
feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
|
|
#print(f"feats shape after final_proj or logits[0]: {feats.shape}")
|
|
#print("Pipeline vc STEP 1a:")
|
|
if protect < 0.5 and pitch is not None and pitchf is not None:
|
|
#print("Cloning feats to feats0")
|
|
feats0 = feats.clone()
|
|
#print(f"feats0 shape: {feats0.shape}")
|
|
#print("Pipeline vc STEP 1b:")
|
|
if (
|
|
isinstance(index, type(None)) == False
|
|
and isinstance(big_npy, type(None)) == False
|
|
and index_rate != 0
|
|
):
|
|
#print("Pipeline vc STEP 1c:")
|
|
#print(f"feats shape: {feats.shape}")
|
|
npy = feats[0, :feats.shape[1], :].cpu().numpy()
|
|
#print(f"npy shape: {npy.shape}")
|
|
#print("Pipeline vc STEP 1c1:")
|
|
if self.is_half:
|
|
#print("Pipeline vc STEP 1c2:")
|
|
npy = npy.astype("float32")
|
|
#print(f"Converted npy to float32. npy dtype: {npy.dtype}")
|
|
#print("Pipeline vc STEP 1c3:")
|
|
#print(f"npy size: {npy.size}")
|
|
|
|
score, ix = self.index.search(npy, k=4)
|
|
|
|
#print(f"score shape: {score.shape}")
|
|
#print(f"ix shape: {ix.shape}")
|
|
#print("Pipeline vc STEP 1c4:")
|
|
weight = np.square(1 / score)
|
|
#print(f"weight shape: {weight.shape}")
|
|
#print("Pipeline vc STEP 1c5:")
|
|
weight /= weight.sum(axis=1, keepdims=True)
|
|
#print(f"weight shape after normalization: {weight.shape}")
|
|
#print("Pipeline vc STEP 1d:")
|
|
npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
|
|
#print(f"npy shape after weighted sum: {npy.shape}")
|
|
#print("Pipeline vc STEP 1e:")
|
|
if self.is_half:
|
|
npy = npy.astype("float16")
|
|
#print(f"Converted npy to float16. npy dtype: {npy.dtype}")
|
|
#print("Pipeline vc STEP 1f:")
|
|
feats = (
|
|
torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
|
|
+ (1 - index_rate) * feats
|
|
)
|
|
#print(f"feats shape after updating with npy: {feats.shape}")
|
|
#print("Pipeline vc STEP 2:")
|
|
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
|
|
#print(f"feats shape after interpolation: {feats.shape}")
|
|
if protect < 0.5 and pitch is not None and pitchf is not None:
|
|
feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
|
|
#print(f"feats0 shape after interpolation: {feats0.shape}")
|
|
t1 = ttime()
|
|
p_len = audio0.shape[0] // self.window
|
|
#print(f"Initial p_len: {p_len}")
|
|
if feats.shape[1] < p_len:
|
|
p_len = feats.shape[1]
|
|
#print(f"Updated p_len to match feats shape: {p_len}")
|
|
if pitch is not None and pitchf is not None:
|
|
pitch = pitch[:, :p_len]
|
|
pitchf = pitchf[:, :p_len]
|
|
#print(f"Updated pitch shape: {pitch.shape}")
|
|
#print(f"Updated pitchf shape: {pitchf.shape}")
|
|
#print("Pipeline vc STEP 3:")
|
|
if protect < 0.5 and pitch is not None and pitchf is not None:
|
|
pitchff = pitchf.clone()
|
|
#print(f"pitchff shape: {pitchff.shape}")
|
|
pitchff[pitchf > 0] = 1
|
|
pitchff[pitchf < 1] = protect
|
|
pitchff = pitchff.unsqueeze(-1)
|
|
#print(f"pitchff shape after unsqueeze: {pitchff.shape}")
|
|
feats = feats * pitchff + feats0 * (1 - pitchff)
|
|
#print(f"feats shape after updating with pitchff: {feats.shape}")
|
|
feats = feats.to(feats0.dtype)
|
|
#print(f"feats dtype after conversion: {feats.dtype}")
|
|
p_len = torch.tensor([p_len], device=self.device).long()
|
|
#print(f"p_len tensor: {p_len}")
|
|
#print(f"p_len device: {p_len.device}")
|
|
#print("Pipeline vc p_len is:", p_len)
|
|
with torch.no_grad():
|
|
if pitch is not None and pitchf is not 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()
|
|
)
|
|
#print(f"audio1 shape: {audio1.shape}")
|
|
del feats, p_len, padding_mask
|
|
if torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
t2 = ttime()
|
|
#print(f"Pipeline vc time breakdown:")
|
|
#print(f" Step 1: {t1 - t0:.3f}s")
|
|
#print(f" Step 2: {t2 - t1:.3f}s")
|
|
#print(f" Total: {t2 - t0:.3f}s")
|
|
#print("Leaving pipeline vc")
|
|
return audio1
|
|
|
|
def pipeline(
|
|
self,
|
|
model,
|
|
net_g,
|
|
sid,
|
|
audio,
|
|
input_audio_path,
|
|
f0_up_key,
|
|
f0_method,
|
|
file_index,
|
|
index_rate,
|
|
if_f0,
|
|
filter_radius,
|
|
tgt_sr,
|
|
resample_sr,
|
|
rms_mix_rate,
|
|
version,
|
|
protect,
|
|
hop_length,
|
|
f0autotune,
|
|
f0_file=None,
|
|
):
|
|
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
|
|
t1 = ttime()
|
|
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 if_f0 == 1:
|
|
pitch, pitchf = self.get_f0(
|
|
input_audio_path,
|
|
audio_pad,
|
|
p_len,
|
|
f0_up_key,
|
|
f0_method,
|
|
filter_radius,
|
|
hop_length,
|
|
f0autotune,
|
|
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()
|
|
t2 = ttime()
|
|
for t in opt_ts:
|
|
t = t // self.window * self.window
|
|
if if_f0 == 1:
|
|
audio_opt.append(
|
|
self.vc(
|
|
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.vc(
|
|
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 if_f0 == 1:
|
|
audio_opt.append(
|
|
self.vc(
|
|
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.vc(
|
|
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 = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
|
|
if resample_sr >= 16000 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 |