fix lint

cosyvoice/cli/cosyvoice.py

@@ -120,6 +120,7 @@ class CosyVoice:
             yield model_output
             start_time = time.time()
 
+
 class CosyVoice2(CosyVoice):
 
     def __init__(self, model_dir, load_jit=False, load_onnx=False, load_trt=False):

cosyvoice/flow/decoder.py

@@ -49,7 +49,7 @@ class CausalBlock1D(Block1D):
 
 
 class CausalResnetBlock1D(ResnetBlock1D):
-    def __init__(self, dim: int, dim_out: int, time_emb_dim: int, groups: int=8):
+    def __init__(self, dim: int, dim_out: int, time_emb_dim: int, groups: int = 8):
         super(CausalResnetBlock1D, self).__init__(dim, dim_out, time_emb_dim, groups)
         self.block1 = CausalBlock1D(dim, dim_out)
         self.block2 = CausalBlock1D(dim_out, dim_out)
@@ -74,8 +74,7 @@ class CausalConv1d(torch.nn.Conv1d):
                                            padding=0, dilation=dilation,
                                            groups=groups, bias=bias,
                                            padding_mode=padding_mode,
-            device=device, dtype=dtype
+                                           device=device, dtype=dtype)
-        )
         assert stride == 1
         self.causal_padding = (kernel_size - 1, 0)
 
@@ -124,7 +123,8 @@ class ConditionalDecoder(nn.Module):
             input_channel = output_channel
             output_channel = channels[i]
             is_last = i == len(channels) - 1
-            resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) if self.causal else ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+            resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) if self.causal \
+                     else ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
             transformer_blocks = nn.ModuleList(
                 [
                     BasicTransformerBlock(
@@ -138,14 +138,16 @@ class ConditionalDecoder(nn.Module):
                 ]
             )
             downsample = (
-                Downsample1D(output_channel) if not is_last else CausalConv1d(output_channel, output_channel, 3) if self.causal else nn.Conv1d(output_channel, output_channel, 3, padding=1)
+                Downsample1D(output_channel) if not is_last else \
+                CausalConv1d(output_channel, output_channel, 3) if self.causal else nn.Conv1d(output_channel, output_channel, 3, padding=1)
             )
             self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample]))
 
         for _ in range(num_mid_blocks):
             input_channel = channels[-1]
             out_channels = channels[-1]
-            resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) if self.causal else ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+            resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) if self.causal else \
+                     ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
 
             transformer_blocks = nn.ModuleList(
                 [

cosyvoice/flow/flow.py

@@ -202,7 +202,6 @@ class CausalMaskedDiffWithXvec(torch.nn.Module):
         embedding = self.spk_embed_affine_layer(embedding)
 
         # concat text and prompt_text
-        token_len1, token_len2 = prompt_token.shape[1], token.shape[1]
         token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len
         mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
         token = self.input_embedding(torch.clamp(token, min=0)) * mask

cosyvoice/transformer/upsample_encoder.py

@@ -19,7 +19,6 @@ from typing import Tuple
 
 import torch
 from torch import nn
-import torch.utils.checkpoint as ckpt
 from torch.nn import functional as F
 
 from cosyvoice.transformer.convolution import ConvolutionModule
@@ -49,14 +48,14 @@ class Upsample1D(nn.Module):
             number of output channels. Defaults to `channels`.
     """
 
-    def __init__(self, channels: int, out_channels: int, stride: int=2):
+    def __init__(self, channels: int, out_channels: int, stride: int = 2):
         super().__init__()
         self.channels = channels
         self.out_channels = out_channels
         self.stride = stride
         # In this mode, first repeat interpolate, than conv with stride=1
         self.conv = nn.Conv1d(
-            self.channels, self.out_channels, stride*2+1, stride=1,
+            self.channels, self.out_channels, stride * 2 + 1, stride = 1,
             padding=0,
         )
 
@@ -74,7 +73,7 @@ class PreLookaheadLayer(nn.Module):
         self.pre_lookahead_len = pre_lookahead_len
         self.conv1 = nn.Conv1d(
             channels, channels,
-            kernel_size=pre_lookahead_len+1,
+            kernel_size=pre_lookahead_len + 1,
             stride=1, padding=0,
         )
         self.conv2 = nn.Conv1d(