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add preprocess and training

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babysor00
2022-02-13 11:28:41 +08:00
parent 379fd2b9fd
commit 19eaa68202
9 changed files with 156 additions and 51 deletions
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3
utils/audio_utils.py
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@@ -49,8 +49,7 @@ def mel_spectrogram(
y = y.squeeze(1)
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)],
center=center, pad_mode='reflect', normalized=False, onesided=True)
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
mel_spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec)
mel_spec = _spectral_normalize_torch(mel_spec)

40
utils/data_load.py
View File

@@ -3,7 +3,9 @@ import numpy as np
import torch
from utils.f0_utils import get_cont_lf0
import resampy
from .audio_utils import MAX_WAV_VALUE, load_wav, mel_spectrogram, normalize
from .audio_utils import MAX_WAV_VALUE, load_wav, mel_spectrogram
from librosa.util import normalize
import os
def read_fids(fid_list_f):
@@ -11,7 +13,6 @@ def read_fids(fid_list_f):
fids = [l.strip().split()[0] for l in f if l.strip()]
return fids
class OneshotVcDataset(torch.utils.data.Dataset):
def __init__(
self,
@@ -61,17 +62,17 @@ class OneshotVcDataset(torch.utils.data.Dataset):
return len(self.fid_list)
def get_spk_dvec(self, fid):
spk_name = fid.split("_")[0]
spk_name = fid
if spk_name.startswith("p"):
spk_dvec_path = f"{self.vctk_spk_dvec_dir}/{spk_name}.npy"
spk_dvec_path = f"{self.vctk_spk_dvec_dir}{os.sep}{spk_name}.npy"
else:
spk_dvec_path = f"{self.libri_spk_dvec_dir}/{spk_name}.npy"
spk_dvec_path = f"{self.libri_spk_dvec_dir}{os.sep}{spk_name}.npy"
return torch.from_numpy(np.load(spk_dvec_path))
def compute_mel(self, wav_path):
audio, sr = load_wav(wav_path)
if sr != 16000:
audio = resampy.resample(audio, sr, 16000)
if sr != 24000:
audio = resampy.resample(audio, sr, 24000)
audio = audio / MAX_WAV_VALUE
audio = normalize(audio) * 0.95
audio = torch.FloatTensor(audio).unsqueeze(0)
@@ -79,11 +80,11 @@ class OneshotVcDataset(torch.utils.data.Dataset):
audio,
n_fft=1024,
num_mels=80,
sampling_rate=16000,
hop_size=200,
win_size=800,
sampling_rate=24000,
hop_size=240,
win_size=1024,
fmin=0,
fmax=7600,
fmax=8000,
)
return melspec.squeeze(0).numpy().T
@@ -98,14 +99,16 @@ class OneshotVcDataset(torch.utils.data.Dataset):
# 1. Load features
if fid.startswith("p"):
# vctk
ppg = np.load(f"{self.vctk_ppg_dir}/{fid}.{self.ppg_file_ext}")
f0 = np.load(f"{self.vctk_f0_dir}/{fid}.{self.f0_file_ext}")
mel = self.compute_mel(f"{self.vctk_wav_dir}/{fid}.{self.wav_file_ext}")
sub = fid.split("_")[0]
ppg = np.load(f"{self.vctk_ppg_dir}{os.sep}{fid}.{self.ppg_file_ext}")
f0 = np.load(f"{self.vctk_f0_dir}{os.sep}{fid}.{self.f0_file_ext}")
mel = self.compute_mel(f"{self.vctk_wav_dir}{os.sep}{sub}{os.sep}{fid}.{self.wav_file_ext}")
else:
# libritts
ppg = np.load(f"{self.libri_ppg_dir}/{fid}.{self.ppg_file_ext}")
f0 = np.load(f"{self.libri_f0_dir}/{fid}.{self.f0_file_ext}")
mel = self.compute_mel(f"{self.libri_wav_dir}/{fid}.{self.wav_file_ext}")
# aidatatang
sub = fid[5:10]
ppg = np.load(f"{self.libri_ppg_dir}{os.sep}{fid}.{self.ppg_file_ext}")
f0 = np.load(f"{self.libri_f0_dir}{os.sep}{fid}.{self.f0_file_ext}")
mel = self.compute_mel(f"{self.libri_wav_dir}{os.sep}{sub}{os.sep}{fid}.{self.wav_file_ext}")
if self.min_max_norm_mel:
mel = self.bin_level_min_max_norm(mel)
@@ -148,7 +151,6 @@ class OneshotVcDataset(torch.utils.data.Dataset):
mel = mel[:min_len]
return f0, ppg, mel
class MultiSpkVcCollate():
"""Zero-pads model inputs and targets based on number of frames per step
"""