deepgeek/CosyVoice
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update lint

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lyuxiang.lx
2024-10-16 13:06:31 +08:00
parent 555efd0301
commit 29507bc77a
8 changed files with 21 additions and 13 deletions
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2
.github/workflows/lint.yml vendored
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@@ -52,5 +52,5 @@ jobs:
set -eux
pip install flake8==3.8.2 flake8-bugbear flake8-comprehensions flake8-executable flake8-pyi==20.5.0 mccabe pycodestyle==2.6.0 pyflakes==2.2.0
flake8 --version
flake8 --max-line-length 150 --ignore B006,B008,B905,C408,E402,E741,W503,W504 --exclude ./third_party/,./runtime/python/grpc/cosyvoice_pb2*py
flake8 --max-line-length 150 --ignore B006,B008,B905,C408,E402,E731,E741,W503,W504 --exclude ./third_party/,./runtime/python/grpc/cosyvoice_pb2*py
if [ $? != 0 ]; then exit 1; fi

1
cosyvoice/bin/average_model.py
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@@ -16,7 +16,6 @@
import os
import argparse
import glob
import sys
import yaml
import torch

3
cosyvoice/bin/train.py
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@@ -138,7 +138,8 @@ def main():
dist.barrier()
group_join = dist.new_group(backend="gloo", timeout=datetime.timedelta(seconds=args.timeout))
if gan is True:
executor.train_one_epoc_gan(model, optimizer, scheduler, optimizer_d, scheduler_d, train_data_loader, cv_data_loader, writer, info_dict, group_join)
executor.train_one_epoc_gan(model, optimizer, scheduler, optimizer_d, scheduler_d, train_data_loader, cv_data_loader,
writer, info_dict, group_join)
else:
executor.train_one_epoc(model, optimizer, scheduler, train_data_loader, cv_data_loader, writer, info_dict, group_join)
dist.destroy_process_group(group_join)

5
cosyvoice/dataset/processor.py
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@@ -177,6 +177,7 @@ def compute_fbank(data,
sample['speech_feat'] = mat
yield sample
def compute_f0(data, pitch_extractor, mode='train'):
""" Extract f0
@@ -404,8 +405,8 @@ def padding(data, use_spk_embedding, mode='train', gan=False):
pitch_feat = [sample[i]['pitch_feat'] for i in order]
pitch_feat_len = torch.tensor([i.size(0) for i in pitch_feat], dtype=torch.int32)
pitch_feat = pad_sequence(pitch_feat,
batch_first=True,
padding_value=0)
batch_first=True,
padding_value=0)
batch["pitch_feat"] = pitch_feat
batch["pitch_feat_len"] = pitch_feat_len
else:

7
cosyvoice/hifigan/discriminator.py
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@@ -1,4 +1,3 @@
from typing import List
import torch
import torch.nn as nn
from torch.nn.utils import weight_norm
@@ -6,6 +5,7 @@ from typing import List, Optional, Tuple
from einops import rearrange
from torchaudio.transforms import Spectrogram
class MultipleDiscriminator(nn.Module):
def __init__(
self, mpd: nn.Module, mrd: nn.Module
@@ -28,6 +28,7 @@ class MultipleDiscriminator(nn.Module):
fmap_gs += this_fmap_gs
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
class MultiResolutionDiscriminator(nn.Module):
def __init__(
self,
@@ -112,7 +113,7 @@ class DiscriminatorR(nn.Module):
x = torch.view_as_real(x)
x = rearrange(x, "b f t c -> b c t f")
# Split into bands
x_bands = [x[..., b[0] : b[1]] for b in self.bands]
x_bands = [x[..., b[0]: b[1]] for b in self.bands]
return x_bands
def forward(self, x: torch.Tensor, cond_embedding_id: torch.Tensor = None):
@@ -136,4 +137,4 @@ class DiscriminatorR(nn.Module):
fmap.append(x)
x += h
return x, fmap
return x, fmap

7
cosyvoice/hifigan/hifigan.py
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@@ -5,6 +5,7 @@ import torch.nn.functional as F
from matcha.hifigan.models import feature_loss, generator_loss, discriminator_loss
from cosyvoice.utils.losses import tpr_loss, mel_loss
class HiFiGan(nn.Module):
def __init__(self, generator, discriminator, mel_spec_transform,
multi_mel_spectral_recon_loss_weight=45, feat_match_loss_weight=2.0,
@@ -44,7 +45,9 @@ class HiFiGan(nn.Module):
else:
loss_tpr = torch.zeros(1).to(device)
loss_f0 = F.l1_loss(generated_f0, pitch_feat)
loss = loss_gen + self.feat_match_loss_weight * loss_fm + self.multi_mel_spectral_recon_loss_weight * loss_mel + self.tpr_loss_weight * loss_tpr + loss_f0
loss = loss_gen + self.feat_match_loss_weight * loss_fm + \
self.multi_mel_spectral_recon_loss_weight * loss_mel + \
self.tpr_loss_weight * loss_tpr + loss_f0
return {'loss': loss, 'loss_gen': loss_gen, 'loss_fm': loss_fm, 'loss_mel': loss_mel, 'loss_tpr': loss_tpr, 'loss_f0': loss_f0}
def forward_discriminator(self, batch, device):
@@ -63,4 +66,4 @@ class HiFiGan(nn.Module):
loss_tpr = torch.zeros(1).to(device)
loss_f0 = F.l1_loss(generated_f0, pitch_feat)
loss = loss_disc + self.tpr_loss_weight * loss_tpr + loss_f0
return {'loss': loss, 'loss_disc': loss_disc, 'loss_tpr': loss_tpr, 'loss_f0': loss_f0}
return {'loss': loss, 'loss_disc': loss_disc, 'loss_tpr': loss_tpr, 'loss_f0': loss_f0}

5
cosyvoice/utils/executor.py
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@@ -25,7 +25,7 @@ from cosyvoice.utils.train_utils import update_parameter_and_lr, log_per_step, l
class Executor:
def __init__(self, gan: bool=False):
def __init__(self, gan: bool = False):
self.gan = gan
self.step = 0
self.epoch = 0
@@ -81,7 +81,8 @@ class Executor:
dist.barrier()
self.cv(model, cv_data_loader, writer, info_dict, on_batch_end=True)
def train_one_epoc_gan(self, model, optimizer, scheduler, optimizer_d, scheduler_d, train_data_loader, cv_data_loader, writer, info_dict, group_join):
def train_one_epoc_gan(self, model, optimizer, scheduler, optimizer_d, scheduler_d, train_data_loader, cv_data_loader,
writer, info_dict, group_join):
''' Train one epoch
'''

4
cosyvoice/utils/losses.py
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@@ -1,6 +1,7 @@
import torch
import torch.nn.functional as F
def tpr_loss(disc_real_outputs, disc_generated_outputs, tau):
loss = 0
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
@@ -9,10 +10,11 @@ def tpr_loss(disc_real_outputs, disc_generated_outputs, tau):
loss += tau - F.relu(tau - L_rel)
return loss
def mel_loss(real_speech, generated_speech, mel_transforms):
loss = 0
for transform in mel_transforms:
mel_r = transform(real_speech)
mel_g = transform(generated_speech)
loss += F.l1_loss(mel_g, mel_r)
return loss
return loss