From ac705603645e544d11461f879d4c593819c790f1 Mon Sep 17 00:00:00 2001 From: "lyuxiang.lx" Date: Mon, 16 Dec 2024 09:54:24 +0800 Subject: [PATCH] fix lint --- cosyvoice/cli/cosyvoice.py | 3 ++- cosyvoice/cli/frontend.py | 4 ++-- cosyvoice/cli/model.py | 16 ++++++++-------- cosyvoice/flow/decoder.py | 22 ++++++++++++---------- cosyvoice/flow/flow.py | 5 ++--- cosyvoice/tokenizer/tokenizer.py | 2 +- cosyvoice/transformer/upsample_encoder.py | 11 +++++------ cosyvoice/utils/common.py | 2 +- 8 files changed, 33 insertions(+), 32 deletions(-) diff --git a/cosyvoice/cli/cosyvoice.py b/cosyvoice/cli/cosyvoice.py index 7c2531d..a6dc7a6 100644 --- a/cosyvoice/cli/cosyvoice.py +++ b/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): @@ -153,4 +154,4 @@ class CosyVoice2(CosyVoice): self.model.load_onnx('{}/flow.decoder.estimator.fp32.onnx'.format(model_dir)) if load_trt: self.model.load_trt('{}/flow.decoder.estimator.fp16.Volta.plan'.format(model_dir)) - del configs \ No newline at end of file + del configs diff --git a/cosyvoice/cli/frontend.py b/cosyvoice/cli/frontend.py index 7b09c59..1281971 100644 --- a/cosyvoice/cli/frontend.py +++ b/cosyvoice/cli/frontend.py @@ -123,7 +123,7 @@ class CosyVoiceFrontEnd: text = remove_bracket(text) text = re.sub(r'[，,、]+$', '。', text) texts = list(split_paragraph(text, partial(self.tokenizer.encode, allowed_special=self.allowed_special), "zh", token_max_n=80, - token_min_n=60, merge_len=20, comma_split=False)) + token_min_n=60, merge_len=20, comma_split=False)) else: if self.use_ttsfrd: texts = [i["text"] for i in json.loads(self.frd.do_voicegen_frd(text))["sentences"]] @@ -132,7 +132,7 @@ class CosyVoiceFrontEnd: text = self.en_tn_model.normalize(text) text = spell_out_number(text, self.inflect_parser) texts = list(split_paragraph(text, partial(self.tokenizer.encode, allowed_special=self.allowed_special), "en", token_max_n=80, - token_min_n=60, merge_len=20, comma_split=False)) + token_min_n=60, merge_len=20, comma_split=False)) if split is False: return text return texts diff --git a/cosyvoice/cli/model.py b/cosyvoice/cli/model.py index a66b463..bf962e9 100644 --- a/cosyvoice/cli/model.py +++ b/cosyvoice/cli/model.py @@ -330,13 +330,13 @@ class CosyVoice2Model: def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, token_offset, finalize=False, speed=1.0): tts_mel, _ = self.flow.inference(token=token.to(self.device), - token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device), - prompt_token=prompt_token.to(self.device), - prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device), - prompt_feat=prompt_feat.to(self.device), - prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device), - embedding=embedding.to(self.device), - finalize=finalize) + token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device), + prompt_token=prompt_token.to(self.device), + prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device), + prompt_feat=prompt_feat.to(self.device), + prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device), + embedding=embedding.to(self.device), + finalize=finalize) tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio:] # append hift cache if self.hift_cache_dict[uuid] is not None: @@ -418,4 +418,4 @@ class CosyVoice2Model: yield {'tts_speech': this_tts_speech.cpu()} with self.lock: self.tts_speech_token_dict.pop(this_uuid) - self.llm_end_dict.pop(this_uuid) \ No newline at end of file + self.llm_end_dict.pop(this_uuid) diff --git a/cosyvoice/flow/decoder.py b/cosyvoice/flow/decoder.py index dfb3c07..6167a11 100644 --- a/cosyvoice/flow/decoder.py +++ b/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) @@ -70,12 +70,11 @@ class CausalConv1d(torch.nn.Conv1d): dtype=None ) -> None: super(CausalConv1d, self).__init__(in_channels, out_channels, - kernel_size, stride, - padding=0, dilation=dilation, - groups=groups, bias=bias, - padding_mode=padding_mode, - device=device, dtype=dtype - ) + kernel_size, stride, + padding=0, dilation=dilation, + groups=groups, bias=bias, + padding_mode=padding_mode, + 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( [ diff --git a/cosyvoice/flow/flow.py b/cosyvoice/flow/flow.py index d99c495..c69ded6 100644 --- a/cosyvoice/flow/flow.py +++ b/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 @@ -211,7 +210,7 @@ class CausalMaskedDiffWithXvec(torch.nn.Module): h, h_lengths = self.encoder(token, token_len) if finalize is False: h = h[:, :-self.pre_lookahead_len * self.token_mel_ratio] - mel_len1, mel_len2 = prompt_feat.shape[1], h.shape[1] - prompt_feat.shape[1] + mel_len1, mel_len2 = prompt_feat.shape[1], h.shape[1] - prompt_feat.shape[1] h = self.encoder_proj(h) # get conditions @@ -229,4 +228,4 @@ class CausalMaskedDiffWithXvec(torch.nn.Module): ) feat = feat[:, :, mel_len1:] assert feat.shape[2] == mel_len2 - return feat, None \ No newline at end of file + return feat, None diff --git a/cosyvoice/tokenizer/tokenizer.py b/cosyvoice/tokenizer/tokenizer.py index fbe78ff..8113891 100644 --- a/cosyvoice/tokenizer/tokenizer.py +++ b/cosyvoice/tokenizer/tokenizer.py @@ -274,4 +274,4 @@ def get_qwen_tokenizer( token_path: str, skip_special_tokens: bool ) -> QwenTokenizer: - return QwenTokenizer(token_path=token_path, skip_special_tokens=skip_special_tokens) \ No newline at end of file + return QwenTokenizer(token_path=token_path, skip_special_tokens=skip_special_tokens) diff --git a/cosyvoice/transformer/upsample_encoder.py b/cosyvoice/transformer/upsample_encoder.py index 7c64726..05a99cc 100644 --- a/cosyvoice/transformer/upsample_encoder.py +++ b/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( @@ -315,8 +314,8 @@ class UpsampleConformerEncoder(torch.nn.Module): return xs def forward_up_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor, - pos_emb: torch.Tensor, - mask_pad: torch.Tensor) -> torch.Tensor: + pos_emb: torch.Tensor, + mask_pad: torch.Tensor) -> torch.Tensor: for layer in self.up_encoders: xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad) return xs diff --git a/cosyvoice/utils/common.py b/cosyvoice/utils/common.py index 2e12ad2..b356f0c 100644 --- a/cosyvoice/utils/common.py +++ b/cosyvoice/utils/common.py @@ -163,4 +163,4 @@ def mask_to_bias(mask: torch.Tensor, dtype: torch.dtype) -> torch.Tensor: # NOTE(Mddct): torch.finfo jit issues # chunk_masks = (1.0 - chunk_masks) * torch.finfo(dtype).min mask = (1.0 - mask) * torch.finfo(dtype).min - return mask \ No newline at end of file + return mask