update lint

.github/workflows/lint.yml

@@ -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

cosyvoice/bin/average_model.py

@@ -16,7 +16,6 @@
 import os
 import argparse
 import glob
-import sys
 
 import yaml
 import torch

cosyvoice/bin/train.py

@@ -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)

cosyvoice/dataset/processor.py

@@ -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:

cosyvoice/hifigan/discriminator.py

@@ -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

cosyvoice/hifigan/hifigan.py

@@ -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}

cosyvoice/utils/executor.py

@@ -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
         '''
 

cosyvoice/utils/losses.py

@@ -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