cleaning

.github/workflows/code_formatter.yml

@@ -0,0 +1,51 @@
+name: Code Formatter
+
+on:
+ push:
+ branches:
+ - main
+
+jobs:
+ push_format:
+ runs-on: ubuntu-latest
+
+ permissions:
+ contents: write
+ pull-requests: write
+
+ steps:
+ - uses: actions/checkout@v3
+ with:
+ ref: ${{github.ref_name}}
+
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v4
+ with:
+ python-version: ${{ matrix.python-version }}
+
+ - name: Install Black
+ run: pip install "black[jupyter]"
+
+ - name: Run Black
+ # run: black $(git ls-files '*.py')
+ run: black .
+
+ - name: Commit Back
+ continue-on-error: true
+ id: commitback
+ run: |
+ git config --local user.email "github-actions[bot]@users.noreply.github.com"
+ git config --local user.name "github-actions[bot]"
+ git add --all
+ git commit -m "chore(format): run black on ${{github.ref_name}}"
+
+ - name: Create Pull Request
+ if: steps.commitback.outcome == 'success'
+ continue-on-error: true
+ uses: peter-evans/create-pull-request@v5
+ with:
+ delete-branch: true
+ body: "Automatically apply code formatter change"
+ title: "chore(format): run black on ${{github.ref_name}}"
+ commit-message: "chore(format): run black on ${{github.ref_name}}"
+ branch: formatter-${{github.ref_name}}

modules/server/model.py

@@ -17,8 +17,13 @@
 
 from lib.voras.models import Synthesizer
 from modules.cmd_opts import opts
-from modules.models import (EMBEDDINGS_LIST, MODELS_DIR, get_embedder,
- get_vc_model, update_state_dict)
+from modules.models import (
+ EMBEDDINGS_LIST,
+ MODELS_DIR,
+ get_embedder,
+ get_vc_model,
+ update_state_dict,
+)
 from modules.shared import ROOT_DIR, device, is_half
 
 MODELS_DIR = opts.models_dir or os.path.join(ROOT_DIR, "models")
@@ -95,20 +100,30 @@ def __init__(self, rvc_model_file: str) -> None:
  self.device = device
  self.is_half = is_half
 
- def __call__(
- self,
- audio: np.ndarray,
- sr: int,
- sid: int
- ):
+ def __call__(self, audio: np.ndarray, sr: int, sid: int):
  # bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
  # audio = signal.filtfilt(bh, ah, audio)
  print(sr, audio.shape)
  if sr != self.sr:
- audio = torchaudio.functional.resample(torch.from_numpy(audio), sr, self.sr, rolloff=0.99).detach().cpu().numpy()
+ audio = (
+ torchaudio.functional.resample(
+ torch.from_numpy(audio), sr, self.sr, rolloff=0.99
+ )
+ .detach()
+ .cpu()
+ .numpy()
+ )
  print(sr, audio.shape)
- audio = (audio / np.maximum(np.max(np.abs(audio), keepdims=True), 1e-7) * (.95 * .8)) + 0.2 * audio
- audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect" if audio.shape[0] > self.window // 2 else "constant")
+ audio = (
+ audio
+ / np.maximum(np.max(np.abs(audio), keepdims=True), 1e-7)
+ * (0.95 * 0.8)
+ ) + 0.2 * audio
+ audio_pad = np.pad(
+ audio,
+ (self.window // 2, self.window // 2),
+ mode="reflect" if audio.shape[0] > self.window // 2 else "constant",
+ )
  print(audio_pad.shape)
 
  opt_ts = []
@@ -143,7 +158,6 @@ def __call__(
  torch.cuda.empty_cache()
  return audio_opt
 
-
  def _convert(
  self,
  sid: int,
@@ -193,4 +207,4 @@ def _convert(
  del feats, padding_mask
  if torch.cuda.is_available():
  torch.cuda.empty_cache()
- return audio1
+ return audio1