Merge pull request RVC-Project#1769 from RVC-Project/formatter-dev

chore(format): run black on dev

gui_v1.py

@@ -877,11 +877,11 @@ def audio_callback(
                     self.input_wav_denoise[-self.block_frame - 2 * self.zc :]
                 )[160:]
             else:
-                self.input_wav_res[
-                    -160 * (indata.shape[0] // self.zc + 1) :
-                ] = self.resampler(self.input_wav[-indata.shape[0] - 2 * self.zc :])[
-                    160:
-                ]
+                self.input_wav_res[-160 * (indata.shape[0] // self.zc + 1) :] = (
+                    self.resampler(self.input_wav[-indata.shape[0] - 2 * self.zc :])[
+                        160:
+                    ]
+                )
             # infer
             if self.function == "vc":
                 infer_wav = self.rvc.infer(

infer/lib/infer_pack/models.py

@@ -400,13 +400,17 @@ def forward(self, f0: torch.Tensor, upp: int):
                 f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
                     idx + 2
                 )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
+            rad_values = (
+                f0_buf / self.sampling_rate
+            ) % 1  ###%1意味着n_har的乘积无法后处理优化
             rand_ini = torch.rand(
                 f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
             )
             rand_ini[:, 0] = 0
             rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
+            tmp_over_one = torch.cumsum(
+                rad_values, 1
+            )  # % 1  #####%1意味着后面的cumsum无法再优化
             tmp_over_one *= upp
             tmp_over_one = F.interpolate(
                 tmp_over_one.transpose(2, 1),

infer/lib/infer_pack/models_onnx.py

@@ -333,13 +333,17 @@ def forward(self, f0, upp):
                 f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
                     idx + 2
                 )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
+            rad_values = (
+                f0_buf / self.sampling_rate
+            ) % 1  ###%1意味着n_har的乘积无法后处理优化
             rand_ini = torch.rand(
                 f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
             )
             rand_ini[:, 0] = 0
             rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
+            tmp_over_one = torch.cumsum(
+                rad_values, 1
+            )  # % 1  #####%1意味着后面的cumsum无法再优化
             tmp_over_one *= upp
             tmp_over_one = F.interpolate(
                 tmp_over_one.transpose(2, 1),

infer/modules/ipex/attention.py

@@ -62,12 +62,12 @@ def torch_bmm(input, mat2, *, out=None):
                 ):  # pylint: disable=invalid-name
                     start_idx_2 = i2 * split_2_slice_size
                     end_idx_2 = (i2 + 1) * split_2_slice_size
-                    hidden_states[
-                        start_idx:end_idx, start_idx_2:end_idx_2
-                    ] = original_torch_bmm(
-                        input[start_idx:end_idx, start_idx_2:end_idx_2],
-                        mat2[start_idx:end_idx, start_idx_2:end_idx_2],
-                        out=out,
+                    hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = (
+                        original_torch_bmm(
+                            input[start_idx:end_idx, start_idx_2:end_idx_2],
+                            mat2[start_idx:end_idx, start_idx_2:end_idx_2],
+                            out=out,
+                        )
                     )
             else:
                 hidden_states[start_idx:end_idx] = original_torch_bmm(
@@ -138,61 +138,67 @@ def scaled_dot_product_attention(
                     start_idx_2 = i2 * split_2_slice_size
                     end_idx_2 = (i2 + 1) * split_2_slice_size
                     if no_shape_one:
-                        hidden_states[
-                            start_idx:end_idx, start_idx_2:end_idx_2
-                        ] = original_scaled_dot_product_attention(
-                            query[start_idx:end_idx, start_idx_2:end_idx_2],
-                            key[start_idx:end_idx, start_idx_2:end_idx_2],
-                            value[start_idx:end_idx, start_idx_2:end_idx_2],
-                            attn_mask=attn_mask[
-                                start_idx:end_idx, start_idx_2:end_idx_2
-                            ]
-                            if attn_mask is not None
-                            else attn_mask,
-                            dropout_p=dropout_p,
-                            is_causal=is_causal,
+                        hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = (
+                            original_scaled_dot_product_attention(
+                                query[start_idx:end_idx, start_idx_2:end_idx_2],
+                                key[start_idx:end_idx, start_idx_2:end_idx_2],
+                                value[start_idx:end_idx, start_idx_2:end_idx_2],
+                                attn_mask=(
+                                    attn_mask[start_idx:end_idx, start_idx_2:end_idx_2]
+                                    if attn_mask is not None
+                                    else attn_mask
+                                ),
+                                dropout_p=dropout_p,
+                                is_causal=is_causal,
+                            )
                         )
                     else:
-                        hidden_states[
-                            :, start_idx:end_idx, start_idx_2:end_idx_2
-                        ] = original_scaled_dot_product_attention(
-                            query[:, start_idx:end_idx, start_idx_2:end_idx_2],
-                            key[:, start_idx:end_idx, start_idx_2:end_idx_2],
-                            value[:, start_idx:end_idx, start_idx_2:end_idx_2],
-                            attn_mask=attn_mask[
-                                :, start_idx:end_idx, start_idx_2:end_idx_2
-                            ]
-                            if attn_mask is not None
-                            else attn_mask,
-                            dropout_p=dropout_p,
-                            is_causal=is_causal,
+                        hidden_states[:, start_idx:end_idx, start_idx_2:end_idx_2] = (
+                            original_scaled_dot_product_attention(
+                                query[:, start_idx:end_idx, start_idx_2:end_idx_2],
+                                key[:, start_idx:end_idx, start_idx_2:end_idx_2],
+                                value[:, start_idx:end_idx, start_idx_2:end_idx_2],
+                                attn_mask=(
+                                    attn_mask[
+                                        :, start_idx:end_idx, start_idx_2:end_idx_2
+                                    ]
+                                    if attn_mask is not None
+                                    else attn_mask
+                                ),
+                                dropout_p=dropout_p,
+                                is_causal=is_causal,
+                            )
                         )
             else:
                 if no_shape_one:
-                    hidden_states[
-                        start_idx:end_idx
-                    ] = original_scaled_dot_product_attention(
-                        query[start_idx:end_idx],
-                        key[start_idx:end_idx],
-                        value[start_idx:end_idx],
-                        attn_mask=attn_mask[start_idx:end_idx]
-                        if attn_mask is not None
-                        else attn_mask,
-                        dropout_p=dropout_p,
-                        is_causal=is_causal,
+                    hidden_states[start_idx:end_idx] = (
+                        original_scaled_dot_product_attention(
+                            query[start_idx:end_idx],
+                            key[start_idx:end_idx],
+                            value[start_idx:end_idx],
+                            attn_mask=(
+                                attn_mask[start_idx:end_idx]
+                                if attn_mask is not None
+                                else attn_mask
+                            ),
+                            dropout_p=dropout_p,
+                            is_causal=is_causal,
+                        )
                     )
                 else:
-                    hidden_states[
-                        :, start_idx:end_idx
-                    ] = original_scaled_dot_product_attention(
-                        query[:, start_idx:end_idx],
-                        key[:, start_idx:end_idx],
-                        value[:, start_idx:end_idx],
-                        attn_mask=attn_mask[:, start_idx:end_idx]
-                        if attn_mask is not None
-                        else attn_mask,
-                        dropout_p=dropout_p,
-                        is_causal=is_causal,
+                    hidden_states[:, start_idx:end_idx] = (
+                        original_scaled_dot_product_attention(
+                            query[:, start_idx:end_idx],
+                            key[:, start_idx:end_idx],
+                            value[:, start_idx:end_idx],
+                            attn_mask=(
+                                attn_mask[:, start_idx:end_idx]
+                                if attn_mask is not None
+                                else attn_mask
+                            ),
+                            dropout_p=dropout_p,
+                            is_causal=is_causal,
+                        )
                     )
     else:
         return original_scaled_dot_product_attention(

infer/modules/ipex/hijacks.py

@@ -104,11 +104,11 @@ def return_xpu(device):
     return (
         f"xpu:{device[-1]}"
         if isinstance(device, str) and ":" in device
-        else f"xpu:{device}"
-        if isinstance(device, int)
-        else torch.device("xpu")
-        if isinstance(device, torch.device)
-        else "xpu"
+        else (
+            f"xpu:{device}"
+            if isinstance(device, int)
+            else torch.device("xpu") if isinstance(device, torch.device) else "xpu"
+        )
     )
 
 
@@ -271,12 +271,16 @@ def ipex_hijacks():
         "torch.batch_norm",
         lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(
             input,
-            weight
-            if weight is not None
-            else torch.ones(input.size()[1], device=input.device),
-            bias
-            if bias is not None
-            else torch.zeros(input.size()[1], device=input.device),
+            (
+                weight
+                if weight is not None
+                else torch.ones(input.size()[1], device=input.device)
+            ),
+            (
+                bias
+                if bias is not None
+                else torch.zeros(input.size()[1], device=input.device)
+            ),
             *args,
             **kwargs,
         ),
@@ -286,12 +290,16 @@ def ipex_hijacks():
         "torch.instance_norm",
         lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(
             input,
-            weight
-            if weight is not None
-            else torch.ones(input.size()[1], device=input.device),
-            bias
-            if bias is not None
-            else torch.zeros(input.size()[1], device=input.device),
+            (
+                weight
+                if weight is not None
+                else torch.ones(input.size()[1], device=input.device)
+            ),
+            (
+                bias
+                if bias is not None
+                else torch.zeros(input.size()[1], device=input.device)
+            ),
             *args,
             **kwargs,
         ),

infer/modules/train/extract_feature_print.py

@@ -116,9 +116,11 @@ def readwave(wav_path, normalize=False):
                 feats = readwave(wav_path, normalize=saved_cfg.task.normalize)
                 padding_mask = torch.BoolTensor(feats.shape).fill_(False)
                 inputs = {
-                    "source": feats.half().to(device)
-                    if is_half and device not in ["mps", "cpu"]
-                    else feats.to(device),
+                    "source": (
+                        feats.half().to(device)
+                        if is_half and device not in ["mps", "cpu"]
+                        else feats.to(device)
+                    ),
                     "padding_mask": padding_mask.to(device),
                     "output_layer": 9 if version == "v1" else 12,  # layer 9
                 }

infer/modules/vc/modules.py

@@ -38,26 +38,28 @@ def get_vc(self, sid, *to_return_protect):
 
         to_return_protect0 = {
             "visible": self.if_f0 != 0,
-            "value": to_return_protect[0]
-            if self.if_f0 != 0 and to_return_protect
-            else 0.5,
+            "value": (
+                to_return_protect[0] if self.if_f0 != 0 and to_return_protect else 0.5
+            ),
             "__type__": "update",
         }
         to_return_protect1 = {
             "visible": self.if_f0 != 0,
-            "value": to_return_protect[1]
-            if self.if_f0 != 0 and to_return_protect
-            else 0.33,
+            "value": (
+                to_return_protect[1] if self.if_f0 != 0 and to_return_protect else 0.33
+            ),
             "__type__": "update",
         }
 
         if sid == "" or sid == []:
-            if self.hubert_model is not None:  # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
+            if (
+                self.hubert_model is not None
+            ):  # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
                 logger.info("Clean model cache")
                 del (self.net_g, self.n_spk, self.hubert_model, self.tgt_sr)  # ,cpt
-                self.hubert_model = (
-                    self.net_g
-                ) = self.n_spk = self.hubert_model = self.tgt_sr = None
+                self.hubert_model = self.net_g = self.n_spk = self.hubert_model = (
+                    self.tgt_sr
+                ) = None
                 if torch.cuda.is_available():
                     torch.cuda.empty_cache()
                 ###楼下不这么折腾清理不干净

tools/app.py

@@ -59,20 +59,28 @@
                 )
             sid.change(fn=vc.get_vc, inputs=[sid], outputs=[spk_item])
             gr.Markdown(
-                value=i18n("男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ")
+                value=i18n(
+                    "男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. "
+                )
             )
             vc_input3 = gr.Audio(label="上传音频（长度小于90秒）")
-            vc_transform0 = gr.Number(label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0)
+            vc_transform0 = gr.Number(
+                label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0
+            )
             f0method0 = gr.Radio(
-                label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"),
+                label=i18n(
+                    "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU"
+                ),
                 choices=["pm", "harvest", "crepe", "rmvpe"],
                 value="pm",
                 interactive=True,
             )
             filter_radius0 = gr.Slider(
                 minimum=0,
                 maximum=7,
-                label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
+                label=i18n(
+                    ">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"
+                ),
                 value=3,
                 step=1,
                 interactive=True,
@@ -107,19 +115,25 @@
             rms_mix_rate0 = gr.Slider(
                 minimum=0,
                 maximum=1,
-                label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
+                label=i18n(
+                    "输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"
+                ),
                 value=1,
                 interactive=True,
             )
             protect0 = gr.Slider(
                 minimum=0,
                 maximum=0.5,
-                label=i18n("保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"),
+                label=i18n(
+                    "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"
+                ),
                 value=0.33,
                 step=0.01,
                 interactive=True,
             )
-            f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"))
+            f0_file = gr.File(
+                label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调")
+            )
             but0 = gr.Button(i18n("转换"), variant="primary")
             vc_output1 = gr.Textbox(label=i18n("输出信息"))
             vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)"))

tools/infer/infer-pm-index256.py

@@ -2,6 +2,7 @@
 
 对源特征进行检索
 """
+
 import os
 import logging
 

tools/infer/train-index-v2.py

@@ -1,6 +1,7 @@
 """
 格式：直接cid为自带的index位；aid放不下了，通过字典来查，反正就5w个
 """
+
 import os
 import traceback
 import logging

tools/infer/train-index.py

@@ -1,6 +1,7 @@
 """
 格式：直接cid为自带的index位；aid放不下了，通过字典来查，反正就5w个
 """
+
 import os
 import logging