chore(format): run black on dev (#94)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

infer/lib/audio.py

@@ -28,6 +28,7 @@ def float_to_int16(audio: np.ndarray) -> np.ndarray:
     am = 32767 * 32768 // am
     return np.multiply(audio, am).astype(np.int16)
 
+
 def float_np_array_to_wav_buf(wav: np.ndarray, sr: int, f32=False) -> BytesIO:
     buf = BytesIO()
     if f32:
@@ -41,10 +42,12 @@ def float_np_array_to_wav_buf(wav: np.ndarray, sr: int, f32=False) -> BytesIO:
     buf.seek(0, 0)
     return buf
 
+
 def save_audio(path: str, audio: np.ndarray, sr: int, f32=False):
     with open(path, "wb") as f:
         f.write(float_np_array_to_wav_buf(audio, sr, f32).getbuffer())
 
+
 def wav2(i: BytesIO, o: BufferedWriter, format: str):
     inp = av.open(i, "r")
     format = video_format_dict.get(format, format)
@@ -65,12 +68,14 @@ def wav2(i: BytesIO, o: BufferedWriter, format: str):
 
 
 def load_audio(
-        file: Union[str, BytesIO, Path],
-        sr: Optional[int]=None,
-        format: Optional[str]=None,
-        mono=True
-    ) -> Union[np.ndarray, Tuple[np.ndarray, int]]:
-    if (isinstance(file, str) and not Path(file).exists()) or (isinstance(file, Path) and not file.exists()):
+    file: Union[str, BytesIO, Path],
+    sr: Optional[int] = None,
+    format: Optional[str] = None,
+    mono=True,
+) -> Union[np.ndarray, Tuple[np.ndarray, int]]:
+    if (isinstance(file, str) and not Path(file).exists()) or (
+        isinstance(file, Path) and not file.exists()
+    ):
         raise FileNotFoundError(f"File not found: {file}")
     rate = 0
 
@@ -78,12 +83,25 @@ def load_audio(
     audio_stream = next(s for s in container.streams if s.type == "audio")
     channels = 1 if audio_stream.layout == "mono" else 2
     container.seek(0)
-    resampler = AudioResampler(format="fltp", layout=audio_stream.layout, rate=sr) if sr is not None else None
+    resampler = (
+        AudioResampler(format="fltp", layout=audio_stream.layout, rate=sr)
+        if sr is not None
+        else None
+    )
 
     # Estimated maximum total number of samples to pre-allocate the array
     # AV stores length in microseconds by default
-    estimated_total_samples = int(container.duration * sr // 1_000_000) if sr is not None else 48000
-    decoded_audio = np.zeros(estimated_total_samples + 1 if channels == 1 else (channels, estimated_total_samples + 1), dtype=np.float32)
+    estimated_total_samples = (
+        int(container.duration * sr // 1_000_000) if sr is not None else 48000
+    )
+    decoded_audio = np.zeros(
+        (
+            estimated_total_samples + 1
+            if channels == 1
+            else (channels, estimated_total_samples + 1)
+        ),
+        dtype=np.float32,
+    )
 
     offset = 0
 
@@ -92,7 +110,9 @@ def load_audio(
         rate = 0
         for frame in packet:
             frame.pts = None  # 清除时间戳，避免重新采样问题
-            resampled_frames = resampler.resample(frame) if resampler is not None else [frame]
+            resampled_frames = (
+                resampler.resample(frame) if resampler is not None else [frame]
+            )
             for resampled_frame in resampled_frames:
                 frame_data = resampled_frame.to_ndarray()
                 rate = resampled_frame.rate
@@ -104,13 +124,16 @@ def load_audio(
             yield p.decode()
 
     for r, frames_data in map(process_packet, frame_iter(container)):
-        if not rate: rate = r
+        if not rate:
+            rate = r
         for frame_data in frames_data:
             end_index = offset + len(frame_data[0])
 
             # 检查 decoded_audio 是否有足够的空间，并在必要时调整大小
             if end_index > decoded_audio.shape[1]:
-                decoded_audio = np.resize(decoded_audio, (decoded_audio.shape[0], end_index*4))
+                decoded_audio = np.resize(
+                    decoded_audio, (decoded_audio.shape[0], end_index * 4)
+                )
 
             np.copyto(decoded_audio[..., offset:end_index], frame_data)
             offset += len(frame_data[0])
@@ -126,7 +149,9 @@ def load_audio(
     return decoded_audio, rate
 
 
-def downsample_audio(input_path: str, output_path: str, format: str, br=128_000) -> None:
+def downsample_audio(
+    input_path: str, output_path: str, format: str, br=128_000
+) -> None:
     """
     default to 128kb/s (equivalent to -q:a 2)
     """

infer/lib/slicer2.py

@@ -244,11 +244,15 @@ def main():
     for i, chunk in enumerate(chunks):
         if len(chunk.shape) > 1:
             chunk = chunk.T
-        save_audio(os.path.join(
-            out,
-            f"%s_%d.wav"
-            % (os.path.basename(args.audio).rsplit(".", maxsplit=1)[0], i),
-        ), chunk, sr)
+        save_audio(
+            os.path.join(
+                out,
+                f"%s_%d.wav"
+                % (os.path.basename(args.audio).rsplit(".", maxsplit=1)[0], i),
+            ),
+            chunk,
+            sr,
+        )
 
 
 if __name__ == "__main__":

infer/modules/train/preprocess.py

@@ -62,15 +62,24 @@ class PreProcess:
         tmp_audio = (tmp_audio / tmp_max * (self.max * self.alpha)) + (
             1 - self.alpha
         ) * tmp_audio
-        save_audio("%s/%s_%s.wav" % (self.gt_wavs_dir, idx0, idx1), tmp_audio, self.sr, f32=True)  
+        save_audio(
+            "%s/%s_%s.wav" % (self.gt_wavs_dir, idx0, idx1),
+            tmp_audio,
+            self.sr,
+            f32=True,
+        )
         with open("%s/%s_%s.wav" % (self.wavs16k_dir, idx0, idx1), "wb") as f:
-            f.write(float_np_array_to_wav_buf(
-                load_audio(
-                    float_np_array_to_wav_buf(tmp_audio, self.sr, f32=True),
-                    sr=16000,
-                    format="wav",
-                )
-            , 16000, True).getbuffer())
+            f.write(
+                float_np_array_to_wav_buf(
+                    load_audio(
+                        float_np_array_to_wav_buf(tmp_audio, self.sr, f32=True),
+                        sr=16000,
+                        format="wav",
+                    ),
+                    16000,
+                    True,
+                ).getbuffer()
+            )
 
     def pipeline(self, path, idx0):
         try:

infer/modules/train/train.py

@@ -133,11 +133,21 @@ def run(rank, n_gpus, hps: utils.HParams, logger: logging.Logger):
 
     try:
         dist.init_process_group(
-            backend="gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl", init_method="env://", world_size=n_gpus, rank=rank
+            backend=(
+                "gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl"
+            ),
+            init_method="env://",
+            world_size=n_gpus,
+            rank=rank,
         )
     except:
         dist.init_process_group(
-            backend="gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl", init_method="env://?use_libuv=False", world_size=n_gpus, rank=rank
+            backend=(
+                "gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl"
+            ),
+            init_method="env://?use_libuv=False",
+            world_size=n_gpus,
+            rank=rank,
         )
     torch.manual_seed(hps.train.seed)
     if torch.cuda.is_available():
@@ -243,13 +253,17 @@ def run(rank, n_gpus, hps: utils.HParams, logger: logging.Logger):
             if hasattr(net_g, "module"):
                 logger.info(
                     net_g.module.load_state_dict(
-                        torch.load(hps.pretrainG, map_location="cpu", weights_only=True)["model"]
+                        torch.load(
+                            hps.pretrainG, map_location="cpu", weights_only=True
+                        )["model"]
                     )
                 )  ##测试不加载优化器
             else:
                 logger.info(
                     net_g.load_state_dict(
-                        torch.load(hps.pretrainG, map_location="cpu", weights_only=True)["model"]
+                        torch.load(
+                            hps.pretrainG, map_location="cpu", weights_only=True
+                        )["model"]
                     )
                 )  ##测试不加载优化器
         if hps.pretrainD != "":
@@ -258,13 +272,17 @@ def run(rank, n_gpus, hps: utils.HParams, logger: logging.Logger):
             if hasattr(net_d, "module"):
                 logger.info(
                     net_d.module.load_state_dict(
-                        torch.load(hps.pretrainD, map_location="cpu", weights_only=True)["model"]
+                        torch.load(
+                            hps.pretrainD, map_location="cpu", weights_only=True
+                        )["model"]
                     )
                 )
             else:
                 logger.info(
                     net_d.load_state_dict(
-                        torch.load(hps.pretrainD, map_location="cpu", weights_only=True)["model"]
+                        torch.load(
+                            hps.pretrainD, map_location="cpu", weights_only=True
+                        )["model"]
                     )
                 )
 

infer/modules/uvr5/mdxnet.py

@@ -208,8 +208,12 @@ class Predictor:
         sources = self.demix(mix.T)
         opt = sources[0].T
         if format in ["wav", "flac"]:
-            save_audio("%s/vocal_%s.%s" % (vocal_root, basename, format), mix - opt, rate)
-            save_audio("%s/instrument_%s.%s" % (others_root, basename, format), opt, rate)
+            save_audio(
+                "%s/vocal_%s.%s" % (vocal_root, basename, format), mix - opt, rate
+            )
+            save_audio(
+                "%s/instrument_%s.%s" % (others_root, basename, format), opt, rate
+            )
         else:
             path_vocal = "%s/vocal_%s.wav" % (vocal_root, basename)
             path_other = "%s/instrument_%s.wav" % (others_root, basename)

infer/modules/uvr5/vr.py

@@ -48,9 +48,7 @@ class AudioPre:
         self.mp = mp
         self.model = model
 
-    def _path_audio_(
-        self, music_file, ins_root=None, vocal_root=None, format="flac"
-    ):
+    def _path_audio_(self, music_file, ins_root=None, vocal_root=None, format="flac"):
         if ins_root is None and vocal_root is None:
             return "No save root."
         name = os.path.basename(music_file)
@@ -134,10 +132,14 @@ class AudioPre:
             else:
                 head = "instrument_"
             if format in ["wav", "flac"]:
-                save_audio(os.path.join(
+                save_audio(
+                    os.path.join(
                         ins_root,
                         head + "{}_{}.{}".format(name, self.data["agg"], format),
-                    ), wav_instrument, self.mp.param["sr"])
+                    ),
+                    wav_instrument,
+                    self.mp.param["sr"],
+                )
             else:
                 path = os.path.join(
                     ins_root, head + "{}_{}.wav".format(name, self.data["agg"])
@@ -162,10 +164,14 @@ class AudioPre:
                 wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, self.mp)
             logger.info("%s vocals done" % name)
             if format in ["wav", "flac"]:
-                save_audio(os.path.join(
+                save_audio(
+                    os.path.join(
                         vocal_root,
                         head + "{}_{}.{}".format(name, self.data["agg"], format),
-                    ), wav_vocals, self.mp.param["sr"])
+                    ),
+                    wav_vocals,
+                    self.mp.param["sr"],
+                )
             else:
                 path = os.path.join(
                     vocal_root, head + "{}_{}.wav".format(name, self.data["agg"])

infer/modules/vc/modules.py

@@ -252,8 +252,12 @@ class VC:
                     try:
                         tgt_sr, audio_opt = opt
                         if format1 in ["wav", "flac"]:
-                            save_audio("%s/%s.%s"
-                                % (opt_root, os.path.basename(path), format1), audio_opt, tgt_sr)
+                            save_audio(
+                                "%s/%s.%s"
+                                % (opt_root, os.path.basename(path), format1),
+                                audio_opt,
+                                tgt_sr,
+                            )
                         else:
                             path = "%s/%s.%s" % (
                                 opt_root,
@@ -261,7 +265,11 @@ class VC:
                                 format1,
                             )
                             with open(path, "wb") as outf:
-                                wav2(float_np_array_to_wav_buf(audio_opt, tgt_sr), outf, format1)
+                                wav2(
+                                    float_np_array_to_wav_buf(audio_opt, tgt_sr),
+                                    outf,
+                                    format1,
+                                )
                     except:
                         info += traceback.format_exc()
                 infos.append("%s->%s" % (os.path.basename(path), info))

rvc/layers/generators.py

@@ -166,9 +166,11 @@ class SineGenerator(torch.nn.Module):
         rad = f0 / self.sampling_rate * a
         rad2 = torch.fmod(rad[:, :-1, -1:].float() + 0.5, 1.0) - 0.5
         rad_acc = rad2.cumsum(dim=1).fmod(1.0).to(f0)
-        rad += F.pad(rad_acc, (0, 0, 1, 0), mode='constant')
+        rad += F.pad(rad_acc, (0, 0, 1, 0), mode="constant")
         rad = rad.reshape(f0.shape[0], -1, 1)
-        b = torch.arange(1, self.dim + 1, dtype=f0.dtype, device=f0.device).reshape(1, 1, -1)
+        b = torch.arange(1, self.dim + 1, dtype=f0.dtype, device=f0.device).reshape(
+            1, 1, -1
+        )
         rad *= b
         rand_ini = torch.rand(1, 1, self.dim, device=f0.device)
         rand_ini[..., 0] = 0

tools/cmd/onnx/infer.py

@@ -20,4 +20,4 @@ wav, sr = librosa.load(wav_path, sr=sampling_rate)
 
 audio = model.infer(wav, sr, sampling_rate, sid, f0_method, f0_up_key)
 
-save_audio(out_path, audio, sampling_rate)
+save_audio(out_path, audio, sampling_rate)

web.py

@@ -144,12 +144,14 @@ outside_index_root = os.getenv("outside_index_root")
 names = [""]
 index_paths = [""]
 
+
 def lookup_names(weight_root):
     global names
     for name in os.listdir(weight_root):
         if name.endswith(".pth"):
             names.append(name)
 
+
 def lookup_indices(index_root):
     global index_paths
     for root, _, files in os.walk(index_root, topdown=False):
@@ -157,6 +159,7 @@ def lookup_indices(index_root):
             if name.endswith(".index") and "trained" not in name:
                 index_paths.append(str(pathlib.Path(root, name)))
 
+
 lookup_names(weight_root)
 lookup_indices(index_root)
 lookup_indices(outside_index_root)