Introduce Docker and Windows CI Workflow, Pre-commit Formatting, and Language Resource Auto-Download (#2351)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

GPT_SoVITS/module/mel_processing.py

@@ -39,24 +39,36 @@ hann_window = {}
 
 def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
     if torch.min(y) < -1.2:
-        print('min value is ', torch.min(y))
+        print("min value is ", torch.min(y))
     if torch.max(y) > 1.2:
-        print('max value is ', torch.max(y))
+        print("max value is ", torch.max(y))
 
     global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
+    dtype_device = str(y.dtype) + "_" + str(y.device)
     # wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    key = "%s-%s-%s-%s-%s" %(dtype_device,n_fft, sampling_rate, hop_size, win_size)
+    key = "%s-%s-%s-%s-%s" % (dtype_device, n_fft, sampling_rate, hop_size, win_size)
     # if wnsize_dtype_device not in hann_window:
     if key not in hann_window:
         # hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
         hann_window[key] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
 
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
+    y = torch.nn.functional.pad(
+        y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect"
+    )
     y = y.squeeze(1)
     # spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[key],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
+    spec = torch.stft(
+        y,
+        n_fft,
+        hop_length=hop_size,
+        win_length=win_size,
+        window=hann_window[key],
+        center=center,
+        pad_mode="reflect",
+        normalized=False,
+        onesided=True,
+        return_complex=False,
+    )
 
     spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-8)
     return spec
@@ -64,9 +76,9 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
 
 def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
     global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
+    dtype_device = str(spec.dtype) + "_" + str(spec.device)
     # fmax_dtype_device = str(fmax) + '_' + dtype_device
-    key = "%s-%s-%s-%s-%s-%s"%(dtype_device,n_fft, num_mels, sampling_rate, fmin, fmax)
+    key = "%s-%s-%s-%s-%s-%s" % (dtype_device, n_fft, num_mels, sampling_rate, fmin, fmax)
     # if fmax_dtype_device not in mel_basis:
     if key not in mel_basis:
         mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
@@ -78,17 +90,25 @@ def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
     return spec
 
 
-
 def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
     if torch.min(y) < -1.2:
-        print('min value is ', torch.min(y))
+        print("min value is ", torch.min(y))
     if torch.max(y) > 1.2:
-        print('max value is ', torch.max(y))
+        print("max value is ", torch.max(y))
 
     global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
+    dtype_device = str(y.dtype) + "_" + str(y.device)
     # fmax_dtype_device = str(fmax) + '_' + dtype_device
-    fmax_dtype_device = "%s-%s-%s-%s-%s-%s-%s-%s"%(dtype_device,n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax)
+    fmax_dtype_device = "%s-%s-%s-%s-%s-%s-%s-%s" % (
+        dtype_device,
+        n_fft,
+        num_mels,
+        sampling_rate,
+        hop_size,
+        win_size,
+        fmin,
+        fmax,
+    )
     # wnsize_dtype_device = str(win_size) + '_' + dtype_device
     wnsize_dtype_device = fmax_dtype_device
     if fmax_dtype_device not in mel_basis:
@@ -97,11 +117,23 @@ def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size,
     if wnsize_dtype_device not in hann_window:
         hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
 
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
+    y = torch.nn.functional.pad(
+        y.unsqueeze(1), (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), mode="reflect"
+    )
     y = y.squeeze(1)
 
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
+    spec = torch.stft(
+        y,
+        n_fft,
+        hop_length=hop_size,
+        win_length=win_size,
+        window=hann_window[wnsize_dtype_device],
+        center=center,
+        pad_mode="reflect",
+        normalized=False,
+        onesided=True,
+        return_complex=False,
+    )
 
     spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-8)