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Delete voice_conversion/train directory

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Justin John
2023-06-19 19:43:12 -07:00
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commit 6c0086d5b5
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voice_conversion/train/cmd.txt
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python train_nsf_sim_cache_sid.py -c configs/mi_mix40k_nsf_co256_cs1sid_ms2048.json -m ft-mi
voice_conversion/train/data_utils.py
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import os, traceback
import numpy as np
import torch
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
2) normalizes text and converts them to sequences of integers
3) computes spectrograms from audio files.
"""
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._filter()
def _filter(self):
"""
Filter text & store spec lengths
"""
# Store spectrogram lengths for Bucketing
# wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
# spec_length = wav_length // hop_length
audiopaths_and_text_new = []
lengths = []
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])
lengths.append(os.path.getsize(audiopath) // (3 * 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
def get_audio_text_pair(self, audiopath_and_text):
# separate filename and text
file = audiopath_and_text[0]
phone = audiopath_and_text[1]
pitch = audiopath_and_text[2]
pitchf = audiopath_and_text[3]
dv = audiopath_and_text[4]
phone, pitch, pitchf = self.get_labels(phone, pitch, pitchf)
spec, wav = self.get_audio(file)
dv = self.get_sid(dv)
len_phone = phone.size()[0]
len_spec = spec.size()[-1]
# print(123,phone.shape,pitch.shape,spec.shape)
if len_phone != len_spec:
len_min = min(len_phone, len_spec)
# amor
len_wav = len_min * self.hop_length
spec = spec[:, :len_min]
wav = wav[:, :len_wav]
phone = phone[:len_min, :]
pitch = pitch[:len_min]
pitchf = pitchf[:len_min]
return (spec, wav, phone, pitch, pitchf, dv)
def get_labels(self, phone, pitch, pitchf):
phone = np.load(phone)
phone = np.repeat(phone, 2, axis=0)
pitch = np.load(pitch)
pitchf = np.load(pitchf)
n_num = min(phone.shape[0], 900) # DistributedBucketSampler
# print(234,phone.shape,pitch.shape)
phone = phone[:n_num, :]
pitch = pitch[:n_num]
pitchf = pitchf[:n_num]
phone = torch.FloatTensor(phone)
pitch = torch.LongTensor(pitch)
pitchf = torch.FloatTensor(pitchf)
return phone, pitch, pitchf
def get_audio(self, filename):
audio, sampling_rate = load_wav_to_torch(filename)
if sampling_rate != self.sampling_rate:
raise ValueError(
"{} SR doesn't match target {} SR".format(
sampling_rate, self.sampling_rate
)
)
audio_norm = audio
# audio_norm = audio / self.max_wav_value
# audio_norm = audio / np.abs(audio).max()
audio_norm = audio_norm.unsqueeze(0)
spec_filename = filename.replace(".wav", ".spec.pt")
if os.path.exists(spec_filename):
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,
)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
else:
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)
return spec, audio_norm
def __getitem__(self, index):
return self.get_audio_text_pair(self.audiopaths_and_text[index])
def __len__(self):
return len(self.audiopaths_and_text)
class TextAudioCollateMultiNSFsid:
"""Zero-pads model inputs and targets"""
def __init__(self, return_ids=False):
self.return_ids = return_ids
def __call__(self, batch):
"""Collate's training batch from normalized text and aduio
PARAMS
------
batch: [text_normalized, spec_normalized, wav_normalized]
"""
# Right zero-pad all one-hot text sequences to max input length
_, ids_sorted_decreasing = torch.sort(
torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True
)
max_spec_len = max([x[0].size(1) for x in batch])
max_wave_len = max([x[1].size(1) for x in batch])
spec_lengths = torch.LongTensor(len(batch))
wave_lengths = torch.LongTensor(len(batch))
spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
spec_padded.zero_()
wave_padded.zero_()
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)
pitch_padded = torch.LongTensor(len(batch), max_phone_len)
pitchf_padded = torch.FloatTensor(len(batch), max_phone_len)
phone_padded.zero_()
pitch_padded.zero_()
pitchf_padded.zero_()
# dv = torch.FloatTensor(len(batch), 256)#gin=256
sid = torch.LongTensor(len(batch))
for i in range(len(ids_sorted_decreasing)):
row = batch[ids_sorted_decreasing[i]]
spec = row[0]
spec_padded[i, :, : spec.size(1)] = spec
spec_lengths[i] = spec.size(1)
wave = row[1]
wave_padded[i, :, : wave.size(1)] = wave
wave_lengths[i] = wave.size(1)
phone = row[2]
phone_padded[i, : phone.size(0), :] = phone
phone_lengths[i] = phone.size(0)
pitch = row[3]
pitch_padded[i, : pitch.size(0)] = pitch
pitchf = row[4]
pitchf_padded[i, : pitchf.size(0)] = pitchf
# dv[i] = row[5]
sid[i] = row[5]
return (
phone_padded,
phone_lengths,
pitch_padded,
pitchf_padded,
spec_padded,
spec_lengths,
wave_padded,
wave_lengths,
# dv
sid,
)
class TextAudioLoader(torch.utils.data.Dataset):
"""
1) loads audio, text pairs
2) normalizes text and converts them to sequences of integers
3) computes spectrograms from audio files.
"""
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._filter()
def _filter(self):
"""
Filter text & store spec lengths
"""
# Store spectrogram lengths for Bucketing
# wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
# spec_length = wav_length // hop_length
audiopaths_and_text_new = []
lengths = []
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])
lengths.append(os.path.getsize(audiopath) // (3 * 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
def get_audio_text_pair(self, audiopath_and_text):
# separate filename and text
file = audiopath_and_text[0]
phone = audiopath_and_text[1]
dv = audiopath_and_text[2]
phone = self.get_labels(phone)
spec, wav = self.get_audio(file)
dv = self.get_sid(dv)
len_phone = phone.size()[0]
len_spec = spec.size()[-1]
if len_phone != len_spec:
len_min = min(len_phone, len_spec)
len_wav = len_min * self.hop_length
spec = spec[:, :len_min]
wav = wav[:, :len_wav]
phone = phone[:len_min, :]
return (spec, wav, phone, dv)
def get_labels(self, phone):
phone = np.load(phone)
phone = np.repeat(phone, 2, axis=0)
n_num = min(phone.shape[0], 900) # DistributedBucketSampler
phone = phone[:n_num, :]
phone = torch.FloatTensor(phone)
return phone
def get_audio(self, filename):
audio, sampling_rate = load_wav_to_torch(filename)
if sampling_rate != self.sampling_rate:
raise ValueError(
"{} SR doesn't match target {} SR".format(
sampling_rate, self.sampling_rate
)
)
audio_norm = audio
# audio_norm = audio / self.max_wav_value
# audio_norm = audio / np.abs(audio).max()
audio_norm = audio_norm.unsqueeze(0)
spec_filename = filename.replace(".wav", ".spec.pt")
if os.path.exists(spec_filename):
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,
)
spec = torch.squeeze(spec, 0)
torch.save(spec, spec_filename, _use_new_zipfile_serialization=False)
else:
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)
return spec, audio_norm
def __getitem__(self, index):
return self.get_audio_text_pair(self.audiopaths_and_text[index])
def __len__(self):
return len(self.audiopaths_and_text)
class TextAudioCollate:
"""Zero-pads model inputs and targets"""
def __init__(self, return_ids=False):
self.return_ids = return_ids
def __call__(self, batch):
"""Collate's training batch from normalized text and aduio
PARAMS
------
batch: [text_normalized, spec_normalized, wav_normalized]
"""
# Right zero-pad all one-hot text sequences to max input length
_, ids_sorted_decreasing = torch.sort(
torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True
)
max_spec_len = max([x[0].size(1) for x in batch])
max_wave_len = max([x[1].size(1) for x in batch])
spec_lengths = torch.LongTensor(len(batch))
wave_lengths = torch.LongTensor(len(batch))
spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len)
wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len)
spec_padded.zero_()
wave_padded.zero_()
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.zero_()
sid = torch.LongTensor(len(batch))
for i in range(len(ids_sorted_decreasing)):
row = batch[ids_sorted_decreasing[i]]
spec = row[0]
spec_padded[i, :, : spec.size(1)] = spec
spec_lengths[i] = spec.size(1)
wave = row[1]
wave_padded[i, :, : wave.size(1)] = wave
wave_lengths[i] = wave.size(1)
phone = row[2]
phone_padded[i, : phone.size(0), :] = phone
phone_lengths[i] = phone.size(0)
sid[i] = row[3]
return (
phone_padded,
phone_lengths,
spec_padded,
spec_lengths,
wave_padded,
wave_lengths,
sid,
)
class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
"""
Maintain similar input lengths in a batch.
Length groups are specified by boundaries.
Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
It removes samples which are not included in the boundaries.
Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
"""
def __init__(
self,
dataset,
batch_size,
boundaries,
num_replicas=None,
rank=None,
shuffle=True,
):
super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
self.lengths = dataset.lengths
self.batch_size = batch_size
self.boundaries = boundaries
self.buckets, self.num_samples_per_bucket = self._create_buckets()
self.total_size = sum(self.num_samples_per_bucket)
self.num_samples = self.total_size // self.num_replicas
def _create_buckets(self):
buckets = [[] for _ in range(len(self.boundaries) - 1)]
for i in range(len(self.lengths)):
length = self.lengths[i]
idx_bucket = self._bisect(length)
if idx_bucket != -1:
buckets[idx_bucket].append(i)
for i in range(len(buckets) - 1, -1, -1): #
if len(buckets[i]) == 0:
buckets.pop(i)
self.boundaries.pop(i + 1)
num_samples_per_bucket = []
for i in range(len(buckets)):
len_bucket = len(buckets[i])
total_batch_size = self.num_replicas * self.batch_size
rem = (
total_batch_size - (len_bucket % total_batch_size)
) % total_batch_size
num_samples_per_bucket.append(len_bucket + rem)
return buckets, num_samples_per_bucket
def __iter__(self):
# deterministically shuffle based on epoch
g = torch.Generator()
g.manual_seed(self.epoch)
indices = []
if self.shuffle:
for bucket in self.buckets:
indices.append(torch.randperm(len(bucket), generator=g).tolist())
else:
for bucket in self.buckets:
indices.append(list(range(len(bucket))))
batches = []
for i in range(len(self.buckets)):
bucket = self.buckets[i]
len_bucket = len(bucket)
ids_bucket = indices[i]
num_samples_bucket = self.num_samples_per_bucket[i]
# add extra samples to make it evenly divisible
rem = num_samples_bucket - len_bucket
ids_bucket = (
ids_bucket
+ ids_bucket * (rem // len_bucket)
+ ids_bucket[: (rem % len_bucket)]
)
# subsample
ids_bucket = ids_bucket[self.rank :: self.num_replicas]
# batching
for j in range(len(ids_bucket) // self.batch_size):
batch = [
bucket[idx]
for idx in ids_bucket[
j * self.batch_size : (j + 1) * self.batch_size
]
]
batches.append(batch)
if self.shuffle:
batch_ids = torch.randperm(len(batches), generator=g).tolist()
batches = [batches[i] for i in batch_ids]
self.batches = batches
assert len(self.batches) * self.batch_size == self.num_samples
return iter(self.batches)
def _bisect(self, x, lo=0, hi=None):
if hi is None:
hi = len(self.boundaries) - 1
if hi > lo:
mid = (hi + lo) // 2
if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
return mid
elif x <= self.boundaries[mid]:
return self._bisect(x, lo, mid)
else:
return self._bisect(x, mid + 1, hi)
else:
return -1
def __len__(self):
return self.num_samples // self.batch_size
voice_conversion/train/losses.py
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import torch
from torch.nn import functional as F
def feature_loss(fmap_r, fmap_g):
loss = 0
for dr, dg in zip(fmap_r, fmap_g):
for rl, gl in zip(dr, dg):
rl = rl.float().detach()
gl = gl.float()
loss += torch.mean(torch.abs(rl - gl))
return loss * 2
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
loss = 0
r_losses = []
g_losses = []
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
dr = dr.float()
dg = dg.float()
r_loss = torch.mean((1 - dr) ** 2)
g_loss = torch.mean(dg**2)
loss += r_loss + g_loss
r_losses.append(r_loss.item())
g_losses.append(g_loss.item())
return loss, r_losses, g_losses
def generator_loss(disc_outputs):
loss = 0
gen_losses = []
for dg in disc_outputs:
dg = dg.float()
l = torch.mean((1 - dg) ** 2)
gen_losses.append(l)
loss += l
return loss, gen_losses
def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
"""
z_p, logs_q: [b, h, t_t]
m_p, logs_p: [b, h, t_t]
"""
z_p = z_p.float()
logs_q = logs_q.float()
m_p = m_p.float()
logs_p = logs_p.float()
z_mask = z_mask.float()
kl = logs_p - logs_q - 0.5
kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)
kl = torch.sum(kl * z_mask)
l = kl / torch.sum(z_mask)
return l
voice_conversion/train/mel_processing.py
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import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
MAX_WAV_VALUE = 32768.0
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
"""
PARAMS
------
C: compression factor
"""
return torch.log(torch.clamp(x, min=clip_val) * C)
def dynamic_range_decompression_torch(x, C=1):
"""
PARAMS
------
C: compression factor used to compress
"""
return torch.exp(x) / C
def spectral_normalize_torch(magnitudes):
return dynamic_range_compression_torch(magnitudes)
def spectral_de_normalize_torch(magnitudes):
return dynamic_range_decompression_torch(magnitudes)
# Reusable banks
mel_basis = {}
hann_window = {}
def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
"""Convert waveform into Linear-frequency Linear-amplitude spectrogram.
Args:
y :: (B, T) - Audio waveforms
n_fft
sampling_rate
hop_size
win_size
center
Returns:
:: (B, Freq, Frame) - Linear-frequency Linear-amplitude spectrogram
"""
# Validation
if torch.min(y) < -1.07:
print("min value is ", torch.min(y))
if torch.max(y) > 1.07:
print("max value is ", torch.max(y))
# Window - Cache if needed
global hann_window
dtype_device = str(y.dtype) + "_" + str(y.device)
wnsize_dtype_device = str(win_size) + "_" + dtype_device
if wnsize_dtype_device not in hann_window:
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
dtype=y.dtype, device=y.device
)
# Padding
y = torch.nn.functional.pad(
y.unsqueeze(1),
(int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
mode="reflect",
)
y = y.squeeze(1)
# Complex Spectrogram :: (B, T) -> (B, Freq, Frame, RealComplex=2)
spec = torch.stft(
y,
n_fft,
hop_length=hop_size,
win_length=win_size,
window=hann_window[wnsize_dtype_device],
center=center,
pad_mode="reflect",
normalized=False,
onesided=True,
return_complex=False,
)
# Linear-frequency Linear-amplitude spectrogram :: (B, Freq, Frame, RealComplex=2) -> (B, Freq, Frame)
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
return spec
def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
# MelBasis - Cache if needed
global mel_basis
dtype_device = str(spec.dtype) + "_" + str(spec.device)
fmax_dtype_device = str(fmax) + "_" + dtype_device
if fmax_dtype_device not in mel_basis:
mel = librosa_mel_fn(
sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
)
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
dtype=spec.dtype, device=spec.device
)
# Mel-frequency Log-amplitude spectrogram :: (B, Freq=num_mels, Frame)
melspec = torch.matmul(mel_basis[fmax_dtype_device], spec)
melspec = spectral_normalize_torch(melspec)
return melspec
def mel_spectrogram_torch(
y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False
):
"""Convert waveform into Mel-frequency Log-amplitude spectrogram.
Args:
y :: (B, T) - Waveforms
Returns:
melspec :: (B, Freq, Frame) - Mel-frequency Log-amplitude spectrogram
"""
# Linear-frequency Linear-amplitude spectrogram :: (B, T) -> (B, Freq, Frame)
spec = spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center)
# Mel-frequency Log-amplitude spectrogram :: (B, Freq, Frame) -> (B, Freq=num_mels, Frame)
melspec = spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax)
return melspec
voice_conversion/train/process_ckpt.py
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import torch, traceback, os, pdb, sys
now_dir = os.getcwd()
sys.path.append(now_dir)
from collections import OrderedDict
from i18n import I18nAuto
i18n = I18nAuto()
def savee(ckpt, sr, if_f0, name, epoch, version, hps):
try:
opt = OrderedDict()
opt["weight"] = {}
for key in ckpt.keys():
if "enc_q" in key:
continue
opt["weight"][key] = ckpt[key].half()
opt["config"] = [
hps.data.filter_length // 2 + 1,
32,
hps.model.inter_channels,
hps.model.hidden_channels,
hps.model.filter_channels,
hps.model.n_heads,
hps.model.n_layers,
hps.model.kernel_size,
hps.model.p_dropout,
hps.model.resblock,
hps.model.resblock_kernel_sizes,
hps.model.resblock_dilation_sizes,
hps.model.upsample_rates,
hps.model.upsample_initial_channel,
hps.model.upsample_kernel_sizes,
hps.model.spk_embed_dim,
hps.model.gin_channels,
hps.data.sampling_rate,
]
opt["info"] = "%sepoch" % epoch
opt["sr"] = sr
opt["f0"] = if_f0
opt["version"] = version
torch.save(opt, "weights/%s.pth" % name)
return "Success."
except:
return traceback.format_exc()
def show_info(path):
try:
a = torch.load(path, map_location="cpu")
return "模型信息:%s\n采样率:%s\n模型是否输入音高引导:%s\n版本:%s" % (
a.get("info", "None"),
a.get("sr", "None"),
a.get("f0", "None"),
a.get("version", "None"),
)
except:
return traceback.format_exc()
def extract_small_model(path, name, sr, if_f0, info, version):
try:
ckpt = torch.load(path, map_location="cpu")
if "model" in ckpt:
ckpt = ckpt["model"]
opt = OrderedDict()
opt["weight"] = {}
for key in ckpt.keys():
if "enc_q" in key:
continue
opt["weight"][key] = ckpt[key].half()
if sr == "40k":
opt["config"] = [
1025,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[10, 10, 2, 2],
512,
[16, 16, 4, 4],
109,
256,
40000,
]
elif sr == "48k":
if(version=="v1"):
opt["config"] = [
1025,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[10, 6, 2, 2, 2],
512,
[16, 16, 4, 4, 4],
109,
256,
48000,
]
else:
opt["config"] = [
1025,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[12,10,2,2],
512,
[24,20,4,4],
109,
256,
48000,
]
elif sr == "32k":
if(version=="v1"):
opt["config"] = [
513,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[10, 4, 2, 2, 2],
512,
[16, 16, 4, 4, 4],
109,
256,
32000,
]
else:
opt["config"] = [
513,
32,
192,
192,
768,
2,
6,
3,
0,
"1",
[3, 7, 11],
[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
[10,8,2,2],
512,
[20,16,4,4],
109,
256,
32000,
]
if info == "":
info = "Extracted model."
opt["info"] = info
opt["version"] = version
opt["sr"] = sr
opt["f0"] = int(if_f0)
torch.save(opt, "weights/%s.pth" % name)
return "Success."
except:
return traceback.format_exc()
def change_info(path, info, name):
try:
ckpt = torch.load(path, map_location="cpu")
ckpt["info"] = info
if name == "":
name = os.path.basename(path)
torch.save(ckpt, "weights/%s" % name)
return "Success."
except:
return traceback.format_exc()
def merge(path1, path2, alpha1, sr, f0, info, name, version):
try:
def extract(ckpt):
a = ckpt["model"]
opt = OrderedDict()
opt["weight"] = {}
for key in a.keys():
if "enc_q" in key:
continue
opt["weight"][key] = a[key]
return opt
ckpt1 = torch.load(path1, map_location="cpu")
ckpt2 = torch.load(path2, map_location="cpu")
cfg = ckpt1["config"]
if "model" in ckpt1:
ckpt1 = extract(ckpt1)
else:
ckpt1 = ckpt1["weight"]
if "model" in ckpt2:
ckpt2 = extract(ckpt2)
else:
ckpt2 = ckpt2["weight"]
if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())):
return "Fail to merge the models. The model architectures are not the same."
opt = OrderedDict()
opt["weight"] = {}
for key in ckpt1.keys():
# try:
if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape:
min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0])
opt["weight"][key] = (
alpha1 * (ckpt1[key][:min_shape0].float())
+ (1 - alpha1) * (ckpt2[key][:min_shape0].float())
).half()
else:
opt["weight"][key] = (
alpha1 * (ckpt1[key].float()) + (1 - alpha1) * (ckpt2[key].float())
).half()
# except:
# pdb.set_trace()
opt["config"] = cfg
"""
if(sr=="40k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 10, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 40000]
elif(sr=="48k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10,6,2,2,2], 512, [16, 16, 4, 4], 109, 256, 48000]
elif(sr=="32k"):opt["config"] = [513, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 4, 2, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 32000]
"""
opt["sr"] = sr
opt["f0"] = 1 if f0 == i18n("是") else 0
opt["version"] = version
opt["info"] = info
torch.save(opt, "weights/%s.pth" % name)
return "Success."
except:
return traceback.format_exc()
voice_conversion/train/utils.py
-486
View File
@@ -1,486 +0,0 @@
import os, traceback
import glob
import sys
import argparse
import logging
import json
import subprocess
import numpy as np
from scipy.io.wavfile import read
import torch
MATPLOTLIB_FLAG = False
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
logger = logging
def load_checkpoint_d(checkpoint_path, combd, sbd, optimizer=None, load_opt=1):
assert os.path.isfile(checkpoint_path)
checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
##################
def go(model, bkey):
saved_state_dict = checkpoint_dict[bkey]
if hasattr(model, "module"):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
new_state_dict = {}
for k, v in state_dict.items(): # 模型需要的shape
try:
new_state_dict[k] = saved_state_dict[k]
if saved_state_dict[k].shape != state_dict[k].shape:
print(
"shape-%s-mismatch|need-%s|get-%s"
% (k, state_dict[k].shape, saved_state_dict[k].shape)
) #
raise KeyError
except:
# logger.info(traceback.format_exc())
logger.info("%s is not in the checkpoint" % k) # pretrain缺失的
new_state_dict[k] = v # 模型自带的随机值
if hasattr(model, "module"):
model.module.load_state_dict(new_state_dict, strict=False)
else:
model.load_state_dict(new_state_dict, strict=False)
go(combd, "combd")
go(sbd, "sbd")
#############
logger.info("Loaded model weights")
iteration = checkpoint_dict["iteration"]
learning_rate = checkpoint_dict["learning_rate"]
if (
optimizer is not None and load_opt == 1
): ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
# try:
optimizer.load_state_dict(checkpoint_dict["optimizer"])
# except:
# traceback.print_exc()
logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
return model, optimizer, learning_rate, iteration
# def load_checkpoint(checkpoint_path, model, optimizer=None):
# assert os.path.isfile(checkpoint_path)
# checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
# iteration = checkpoint_dict['iteration']
# learning_rate = checkpoint_dict['learning_rate']
# if optimizer is not None:
# optimizer.load_state_dict(checkpoint_dict['optimizer'])
# # print(1111)
# saved_state_dict = checkpoint_dict['model']
# # print(1111)
#
# if hasattr(model, 'module'):
# state_dict = model.module.state_dict()
# else:
# state_dict = model.state_dict()
# new_state_dict= {}
# for k, v in state_dict.items():
# try:
# new_state_dict[k] = saved_state_dict[k]
# except:
# logger.info("%s is not in the checkpoint" % k)
# new_state_dict[k] = v
# if hasattr(model, 'module'):
# model.module.load_state_dict(new_state_dict)
# else:
# model.load_state_dict(new_state_dict)
# logger.info("Loaded checkpoint '{}' (epoch {})" .format(
# checkpoint_path, iteration))
# return model, optimizer, learning_rate, iteration
def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1):
assert os.path.isfile(checkpoint_path)
checkpoint_dict = torch.load(checkpoint_path, map_location="cpu")
saved_state_dict = checkpoint_dict["model"]
if hasattr(model, "module"):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
new_state_dict = {}
for k, v in state_dict.items(): # 模型需要的shape
try:
new_state_dict[k] = saved_state_dict[k]
if saved_state_dict[k].shape != state_dict[k].shape:
print(
"shape-%s-mismatch|need-%s|get-%s"
% (k, state_dict[k].shape, saved_state_dict[k].shape)
) #
raise KeyError
except:
# logger.info(traceback.format_exc())
logger.info("%s is not in the checkpoint" % k) # pretrain缺失的
new_state_dict[k] = v # 模型自带的随机值
if hasattr(model, "module"):
model.module.load_state_dict(new_state_dict, strict=False)
else:
model.load_state_dict(new_state_dict, strict=False)
logger.info("Loaded model weights")
iteration = checkpoint_dict["iteration"]
learning_rate = checkpoint_dict["learning_rate"]
if (
optimizer is not None and load_opt == 1
): ###加载不了，如果是空的的话，重新初始化，可能还会影响lr时间表的更新，因此在train文件最外围catch
# try:
optimizer.load_state_dict(checkpoint_dict["optimizer"])
# except:
# traceback.print_exc()
logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, iteration))
return model, optimizer, learning_rate, iteration
def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
logger.info(
"Saving model and optimizer state at epoch {} to {}".format(
iteration, checkpoint_path
)
)
if hasattr(model, "module"):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
torch.save(
{
"model": state_dict,
"iteration": iteration,
"optimizer": optimizer.state_dict(),
"learning_rate": learning_rate,
},
checkpoint_path,
)
def save_checkpoint_d(combd, sbd, optimizer, learning_rate, iteration, checkpoint_path):
logger.info(
"Saving model and optimizer state at epoch {} to {}".format(
iteration, checkpoint_path
)
)
if hasattr(combd, "module"):
state_dict_combd = combd.module.state_dict()
else:
state_dict_combd = combd.state_dict()
if hasattr(sbd, "module"):
state_dict_sbd = sbd.module.state_dict()
else:
state_dict_sbd = sbd.state_dict()
torch.save(
{
"combd": state_dict_combd,
"sbd": state_dict_sbd,
"iteration": iteration,
"optimizer": optimizer.state_dict(),
"learning_rate": learning_rate,
},
checkpoint_path,
)
def summarize(
writer,
global_step,
scalars={},
histograms={},
images={},
audios={},
audio_sampling_rate=22050,
):
for k, v in scalars.items():
writer.add_scalar(k, v, global_step)
for k, v in histograms.items():
writer.add_histogram(k, v, global_step)
for k, v in images.items():
writer.add_image(k, v, global_step, dataformats="HWC")
for k, v in audios.items():
writer.add_audio(k, v, global_step, audio_sampling_rate)
def latest_checkpoint_path(dir_path, regex="G_*.pth"):
f_list = glob.glob(os.path.join(dir_path, regex))
f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
x = f_list[-1]
print(x)
return x
def plot_spectrogram_to_numpy(spectrogram):
global MATPLOTLIB_FLAG
if not MATPLOTLIB_FLAG:
import matplotlib
matplotlib.use("Agg")
MATPLOTLIB_FLAG = True
mpl_logger = logging.getLogger("matplotlib")
mpl_logger.setLevel(logging.WARNING)
import matplotlib.pylab as plt
import numpy as np
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
plt.colorbar(im, ax=ax)
plt.xlabel("Frames")
plt.ylabel("Channels")
plt.tight_layout()
fig.canvas.draw()
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
plt.close()
return data
def plot_alignment_to_numpy(alignment, info=None):
global MATPLOTLIB_FLAG
if not MATPLOTLIB_FLAG:
import matplotlib
matplotlib.use("Agg")
MATPLOTLIB_FLAG = True
mpl_logger = logging.getLogger("matplotlib")
mpl_logger.setLevel(logging.WARNING)
import matplotlib.pylab as plt
import numpy as np
fig, ax = plt.subplots(figsize=(6, 4))
im = ax.imshow(
alignment.transpose(), aspect="auto", origin="lower", interpolation="none"
)
fig.colorbar(im, ax=ax)
xlabel = "Decoder timestep"
if info is not None:
xlabel += "\n\n" + info
plt.xlabel(xlabel)
plt.ylabel("Encoder timestep")
plt.tight_layout()
fig.canvas.draw()
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
plt.close()
return data
def load_wav_to_torch(full_path):
sampling_rate, data = read(full_path)
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
def load_filepaths_and_text(filename, split="|"):
with open(filename, encoding="utf-8") as f:
filepaths_and_text = [line.strip().split(split) for line in f]
return filepaths_and_text
def get_hparams(init=True):
"""
todo:
结尾七人组：
保存频率、总epoch done
bs done
pretrainG、pretrainD done
卡号：os.en["CUDA_VISIBLE_DEVICES"] done
if_latest done
模型：if_f0 done
采样率：自动选择config done
是否缓存数据集进GPU:if_cache_data_in_gpu done
-m:
自动决定training_files路径,改掉train_nsf_load_pretrain.py里的hps.data.training_files done
-c不要了
"""
parser = argparse.ArgumentParser()
# parser.add_argument('-c', '--config', type=str, default="configs/40k.json",help='JSON file for configuration')
parser.add_argument(
"-se",
"--save_every_epoch",
type=int,
required=True,
help="checkpoint save frequency (epoch)",
)
parser.add_argument(
"-te", "--total_epoch", type=int, required=True, help="total_epoch"
)
parser.add_argument(
"-pg", "--pretrainG", type=str, default="", help="Pretrained Discriminator path"
)
parser.add_argument(
"-pd", "--pretrainD", type=str, default="", help="Pretrained Generator path"
)
parser.add_argument("-g", "--gpus", type=str, default="0", help="split by -")
parser.add_argument(
"-bs", "--batch_size", type=int, required=True, help="batch size"
)
parser.add_argument(
"-e", "--experiment_dir", type=str, required=True, help="experiment dir"
) # -m
parser.add_argument(
"-sr", "--sample_rate", type=str, required=True, help="sample rate, 32k/40k/48k"
)
parser.add_argument(
"-sw",
"--save_every_weights",
type=str,
default="0",
help="save the extracted model in weights directory when saving checkpoints",
)
parser.add_argument(
"-v", "--version", type=str, required=True, help="model version"
)
parser.add_argument(
"-f0",
"--if_f0",
type=int,
required=True,
help="use f0 as one of the inputs of the model, 1 or 0",
)
parser.add_argument(
"-l",
"--if_latest",
type=int,
required=True,
help="if only save the latest G/D pth file, 1 or 0",
)
parser.add_argument(
"-c",
"--if_cache_data_in_gpu",
type=int,
required=True,
help="if caching the dataset in GPU memory, 1 or 0",
)
args = parser.parse_args()
name = args.experiment_dir
experiment_dir = os.path.join("./logs", args.experiment_dir)
if not os.path.exists(experiment_dir):
os.makedirs(experiment_dir)
if args.version == "v1" or args.sample_rate == "40k":
config_path = "configs/%s.json" % args.sample_rate
else:
config_path = "configs/%s_v2.json" % args.sample_rate
config_save_path = os.path.join(experiment_dir, "config.json")
if init:
with open(config_path, "r") as f:
data = f.read()
with open(config_save_path, "w") as f:
f.write(data)
else:
with open(config_save_path, "r") as f:
data = f.read()
config = json.loads(data)
hparams = HParams(**config)
hparams.model_dir = hparams.experiment_dir = experiment_dir
hparams.save_every_epoch = args.save_every_epoch
hparams.name = name
hparams.total_epoch = args.total_epoch
hparams.pretrainG = args.pretrainG
hparams.pretrainD = args.pretrainD
hparams.version = args.version
hparams.gpus = args.gpus
hparams.train.batch_size = args.batch_size
hparams.sample_rate = args.sample_rate
hparams.if_f0 = args.if_f0
hparams.if_latest = args.if_latest
hparams.save_every_weights = args.save_every_weights
hparams.if_cache_data_in_gpu = args.if_cache_data_in_gpu
hparams.data.training_files = "%s/filelist.txt" % experiment_dir
return hparams
def get_hparams_from_dir(model_dir):
config_save_path = os.path.join(model_dir, "config.json")
with open(config_save_path, "r") as f:
data = f.read()
config = json.loads(data)
hparams = HParams(**config)
hparams.model_dir = model_dir
return hparams
def get_hparams_from_file(config_path):
with open(config_path, "r") as f:
data = f.read()
config = json.loads(data)
hparams = HParams(**config)
return hparams
def check_git_hash(model_dir):
source_dir = os.path.dirname(os.path.realpath(__file__))
if not os.path.exists(os.path.join(source_dir, ".git")):
logger.warn(
"{} is not a git repository, therefore hash value comparison will be ignored.".format(
source_dir
)
)
return
cur_hash = subprocess.getoutput("git rev-parse HEAD")
path = os.path.join(model_dir, "githash")
if os.path.exists(path):
saved_hash = open(path).read()
if saved_hash != cur_hash:
logger.warn(
"git hash values are different. {}(saved) != {}(current)".format(
saved_hash[:8], cur_hash[:8]
)
)
else:
open(path, "w").write(cur_hash)
def get_logger(model_dir, filename="train.log"):
global logger
logger = logging.getLogger(os.path.basename(model_dir))
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
if not os.path.exists(model_dir):
os.makedirs(model_dir)
h = logging.FileHandler(os.path.join(model_dir, filename))
h.setLevel(logging.DEBUG)
h.setFormatter(formatter)
logger.addHandler(h)
return logger
class HParams:
def __init__(self, **kwargs):
for k, v in kwargs.items():
if type(v) == dict:
v = HParams(**v)
self[k] = v
def keys(self):
return self.__dict__.keys()
def items(self):
return self.__dict__.items()
def values(self):
return self.__dict__.values()
def __len__(self):
return len(self.__dict__)
def __getitem__(self, key):
return getattr(self, key)
def __setitem__(self, key, value):
return setattr(self, key, value)
def __contains__(self, key):
return key in self.__dict__
def __repr__(self):
return self.__dict__.__repr__()