RVC/Retrieval-based-Voice-Conversion-WebUI
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Reformat and rewrite _get_name_params (#57)

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* Reformat

* rewrite _get_name_params

* Add workflow for automatic formatting

* Revert "Add workflow for automatic formatting"

This reverts commit 9111c5dbc1.

* revert Retrieval_based_Voice_Conversion_WebUI.ipynb

---------

Co-authored-by: 源文雨 <41315874+fumiama@users.noreply.github.com>
This commit is contained in:
Ftps
2023-04-15 20:44:24 +09:00
committed by GitHub
parent aaa893c4b1
commit c8261b2ccc
45 changed files with 4878 additions and 2456 deletions
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105
train/data_utils.py
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@@ -1,4 +1,4 @@
import os,traceback
import os, traceback
import numpy as np
import torch
import torch.utils.data
@@ -6,6 +6,7 @@ import torch.utils.data
from mel_processing import spectrogram_torch
from utils import load_wav_to_torch, load_filepaths_and_text
class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
"""
1) loads audio, text pairs
@@ -15,14 +16,14 @@ class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
def __init__(self, audiopaths_and_text, hparams):
self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
self.max_wav_value = hparams.max_wav_value
self.sampling_rate = hparams.sampling_rate
self.filter_length = hparams.filter_length
self.hop_length = hparams.hop_length
self.win_length = hparams.win_length
self.sampling_rate = hparams.sampling_rate
self.min_text_len = getattr(hparams, "min_text_len", 1)
self.max_text_len = getattr(hparams, "max_text_len", 5000)
self.max_wav_value = hparams.max_wav_value
self.sampling_rate = hparams.sampling_rate
self.filter_length = hparams.filter_length
self.hop_length = hparams.hop_length
self.win_length = hparams.win_length
self.sampling_rate = hparams.sampling_rate
self.min_text_len = getattr(hparams, "min_text_len", 1)
self.max_text_len = getattr(hparams, "max_text_len", 5000)
self._filter()
def _filter(self):
@@ -34,12 +35,13 @@ class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
# spec_length = wav_length // hop_length
audiopaths_and_text_new = []
lengths = []
for audiopath, text, pitch,pitchf,dv in self.audiopaths_and_text:
for audiopath, text, pitch, pitchf, dv in self.audiopaths_and_text:
if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
audiopaths_and_text_new.append([audiopath, text, pitch,pitchf,dv])
audiopaths_and_text_new.append([audiopath, text, pitch, pitchf, dv])
lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
self.audiopaths_and_text = audiopaths_and_text_new
self.lengths = lengths
def get_sid(self, sid):
sid = torch.LongTensor([int(sid)])
return sid
@@ -54,7 +56,7 @@ class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
phone, pitch, pitchf = self.get_labels(phone, pitch, pitchf)
spec, wav = self.get_audio(file)
dv=self.get_sid(dv)
dv = self.get_sid(dv)
len_phone = phone.size()[0]
len_spec = spec.size()[-1]
@@ -71,9 +73,9 @@ class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
pitch = pitch[:len_min]
pitchf = pitchf[:len_min]
return (spec, wav, phone, pitch,pitchf,dv)
return (spec, wav, phone, pitch, pitchf, dv)
def get_labels(self, phone, pitch,pitchf):
def get_labels(self, phone, pitch, pitchf):
phone = np.load(phone)
phone = np.repeat(phone, 2, axis=0)
pitch = np.load(pitch)
@@ -86,7 +88,7 @@ class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
phone = torch.FloatTensor(phone)
pitch = torch.LongTensor(pitch)
pitchf = torch.FloatTensor(pitchf)
return phone, pitch,pitchf
return phone, pitch, pitchf
def get_audio(self, filename):
audio, sampling_rate = load_wav_to_torch(filename)
@@ -103,10 +105,15 @@ class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
try:
spec = torch.load(spec_filename)
except:
print (spec_filename,traceback.format_exc())
spec = spectrogram_torch(audio_norm, self.filter_length,
self.sampling_rate, self.hop_length, self.win_length,
center=False)
print(spec_filename, traceback.format_exc())
spec = spectrogram_torch(
audio_norm,
self.filter_length,
self.sampling_rate,
self.hop_length,
self.win_length,
center=False,
)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
else:
@@ -127,6 +134,8 @@ class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset):
def __len__(self):
return len(self.audiopaths_and_text)
class TextAudioCollateMultiNSFsid:
"""Zero-pads model inputs and targets"""
@@ -155,7 +164,9 @@ class TextAudioCollateMultiNSFsid:
max_phone_len = max([x[2].size(0) for x in batch])
phone_lengths = torch.LongTensor(len(batch))
phone_padded = torch.FloatTensor(len(batch), max_phone_len, batch[0][2].shape[1])#(spec, wav, phone, pitch)
phone_padded = torch.FloatTensor(
len(batch), max_phone_len, batch[0][2].shape[1]
) # (spec, wav, phone, pitch)
pitch_padded = torch.LongTensor(len(batch), max_phone_len)
pitchf_padded = torch.FloatTensor(len(batch), max_phone_len)
phone_padded.zero_()
@@ -187,7 +198,6 @@ class TextAudioCollateMultiNSFsid:
# dv[i] = row[5]
sid[i] = row[5]
return (
phone_padded,
phone_lengths,
@@ -198,9 +208,10 @@ class TextAudioCollateMultiNSFsid:
wave_padded,
wave_lengths,
# dv
sid
sid,
)
class TextAudioLoader(torch.utils.data.Dataset):
"""
1) loads audio, text pairs
@@ -210,14 +221,14 @@ class TextAudioLoader(torch.utils.data.Dataset):
def __init__(self, audiopaths_and_text, hparams):
self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text)
self.max_wav_value = hparams.max_wav_value
self.sampling_rate = hparams.sampling_rate
self.filter_length = hparams.filter_length
self.hop_length = hparams.hop_length
self.win_length = hparams.win_length
self.sampling_rate = hparams.sampling_rate
self.min_text_len = getattr(hparams, "min_text_len", 1)
self.max_text_len = getattr(hparams, "max_text_len", 5000)
self.max_wav_value = hparams.max_wav_value
self.sampling_rate = hparams.sampling_rate
self.filter_length = hparams.filter_length
self.hop_length = hparams.hop_length
self.win_length = hparams.win_length
self.sampling_rate = hparams.sampling_rate
self.min_text_len = getattr(hparams, "min_text_len", 1)
self.max_text_len = getattr(hparams, "max_text_len", 5000)
self._filter()
def _filter(self):
@@ -229,12 +240,13 @@ class TextAudioLoader(torch.utils.data.Dataset):
# spec_length = wav_length // hop_length
audiopaths_and_text_new = []
lengths = []
for audiopath, text,dv in self.audiopaths_and_text:
for audiopath, text, dv in self.audiopaths_and_text:
if self.min_text_len <= len(text) and len(text) <= self.max_text_len:
audiopaths_and_text_new.append([audiopath, text,dv])
audiopaths_and_text_new.append([audiopath, text, dv])
lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
self.audiopaths_and_text = audiopaths_and_text_new
self.lengths = lengths
def get_sid(self, sid):
sid = torch.LongTensor([int(sid)])
return sid
@@ -247,7 +259,7 @@ class TextAudioLoader(torch.utils.data.Dataset):
phone = self.get_labels(phone)
spec, wav = self.get_audio(file)
dv=self.get_sid(dv)
dv = self.get_sid(dv)
len_phone = phone.size()[0]
len_spec = spec.size()[-1]
@@ -257,7 +269,7 @@ class TextAudioLoader(torch.utils.data.Dataset):
spec = spec[:, :len_min]
wav = wav[:, :len_wav]
phone = phone[:len_min, :]
return (spec, wav, phone,dv)
return (spec, wav, phone, dv)
def get_labels(self, phone):
phone = np.load(phone)
@@ -282,10 +294,15 @@ class TextAudioLoader(torch.utils.data.Dataset):
try:
spec = torch.load(spec_filename)
except:
print (spec_filename,traceback.format_exc())
spec = spectrogram_torch(audio_norm, self.filter_length,
self.sampling_rate, self.hop_length, self.win_length,
center=False)
print(spec_filename, traceback.format_exc())
spec = spectrogram_torch(
audio_norm,
self.filter_length,
self.sampling_rate,
self.hop_length,
self.win_length,
center=False,
)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
else:
@@ -306,6 +323,8 @@ class TextAudioLoader(torch.utils.data.Dataset):
def __len__(self):
return len(self.audiopaths_and_text)
class TextAudioCollate:
"""Zero-pads model inputs and targets"""
@@ -334,7 +353,9 @@ class TextAudioCollate:
max_phone_len = max([x[2].size(0) for x in batch])
phone_lengths = torch.LongTensor(len(batch))
phone_padded = torch.FloatTensor(len(batch), max_phone_len, batch[0][2].shape[1])
phone_padded = torch.FloatTensor(
len(batch), max_phone_len, batch[0][2].shape[1]
)
phone_padded.zero_()
sid = torch.LongTensor(len(batch))
@@ -355,7 +376,6 @@ class TextAudioCollate:
sid[i] = row[3]
return (
phone_padded,
phone_lengths,
@@ -363,9 +383,10 @@ class TextAudioCollate:
spec_lengths,
wave_padded,
wave_lengths,
sid
sid,
)
class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
"""
Maintain similar input lengths in a batch.
@@ -402,7 +423,7 @@ class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
if idx_bucket != -1:
buckets[idx_bucket].append(i)
for i in range(len(buckets) - 1, -1, -1):#
for i in range(len(buckets) - 1, -1, -1): #
if len(buckets[i]) == 0:
buckets.pop(i)
self.boundaries.pop(i + 1)