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Refactor: Format Code with Ruff and Update Deprecated G2PW Link (#2255)

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* ruff check --fix

* ruff format --line-length 120 --target-version py39

* Change the link for G2PW Model

* update pytorch version and colab
This commit is contained in:
XXXXRT666
2025-04-07 09:42:47 +01:00
committed by GitHub
parent 9da7e17efe
commit 53cac93589
132 changed files with 8185 additions and 6648 deletions
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66
GPT_SoVITS/BigVGAN/meldataset.py
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@@ -86,9 +86,7 @@ def mel_spectrogram(
key = f"{n_fft}_{num_mels}_{sampling_rate}_{hop_size}_{win_size}_{fmin}_{fmax}_{device}"
if key not in mel_basis_cache:
mel = librosa_mel_fn(
sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
)
mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
mel_basis_cache[key] = torch.from_numpy(mel).float().to(device)
hann_window_cache[key] = torch.hann_window(win_size).to(device)
@@ -96,9 +94,7 @@ def mel_spectrogram(
hann_window = hann_window_cache[key]
padding = (n_fft - hop_size) // 2
y = torch.nn.functional.pad(
y.unsqueeze(1), (padding, padding), mode="reflect"
).squeeze(1)
y = torch.nn.functional.pad(y.unsqueeze(1), (padding, padding), mode="reflect").squeeze(1)
spec = torch.stft(
y,
@@ -150,17 +146,13 @@ def get_dataset_filelist(a):
with open(a.input_training_file, "r", encoding="utf-8") as fi:
training_files = [
os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav")
for x in fi.read().split("\n")
if len(x) > 0
os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav") for x in fi.read().split("\n") if len(x) > 0
]
print(f"first training file: {training_files[0]}")
with open(a.input_validation_file, "r", encoding="utf-8") as fi:
validation_files = [
os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav")
for x in fi.read().split("\n")
if len(x) > 0
os.path.join(a.input_wavs_dir, x.split("|")[0] + ".wav") for x in fi.read().split("\n") if len(x) > 0
]
print(f"first validation file: {validation_files[0]}")
@@ -171,9 +163,7 @@ def get_dataset_filelist(a):
for x in fi.read().split("\n")
if len(x) > 0
]
print(
f"first unseen {i}th validation fileset: {unseen_validation_files[0]}"
)
print(f"first unseen {i}th validation fileset: {unseen_validation_files[0]}")
list_unseen_validation_files.append(unseen_validation_files)
return training_files, validation_files, list_unseen_validation_files
@@ -227,13 +217,9 @@ class MelDataset(torch.utils.data.Dataset):
print("[INFO] checking dataset integrity...")
for i in tqdm(range(len(self.audio_files))):
assert os.path.exists(
self.audio_files[i]
), f"{self.audio_files[i]} not found"
assert os.path.exists(self.audio_files[i]), f"{self.audio_files[i]} not found"
def __getitem__(
self, index: int
) -> Tuple[torch.Tensor, torch.Tensor, str, torch.Tensor]:
def __getitem__(self, index: int) -> Tuple[torch.Tensor, torch.Tensor, str, torch.Tensor]:
try:
filename = self.audio_files[index]
@@ -248,17 +234,12 @@ class MelDataset(torch.utils.data.Dataset):
# Obtain randomized audio chunk
if source_sampling_rate != self.sampling_rate:
# Adjust segment size to crop if the source sr is different
target_segment_size = math.ceil(
self.segment_size
* (source_sampling_rate / self.sampling_rate)
)
target_segment_size = math.ceil(self.segment_size * (source_sampling_rate / self.sampling_rate))
else:
target_segment_size = self.segment_size
# Compute upper bound index for the random chunk
random_chunk_upper_bound = max(
0, audio.shape[0] - target_segment_size
)
random_chunk_upper_bound = max(0, audio.shape[0] - target_segment_size)
# Crop or pad audio to obtain random chunk with target_segment_size
if audio.shape[0] >= target_segment_size:
@@ -318,9 +299,9 @@ class MelDataset(torch.utils.data.Dataset):
else:
# For fine-tuning, assert that the waveform is in the defined sampling_rate
# Fine-tuning won't support on-the-fly resampling to be fool-proof (the dataset should have been prepared properly)
assert (
source_sampling_rate == self.sampling_rate
), f"For fine_tuning, waveform must be in the spcified sampling rate {self.sampling_rate}, got {source_sampling_rate}"
assert source_sampling_rate == self.sampling_rate, (
f"For fine_tuning, waveform must be in the spcified sampling rate {self.sampling_rate}, got {source_sampling_rate}"
)
# Cast ndarray to torch tensor
audio = torch.FloatTensor(audio)
@@ -346,20 +327,14 @@ class MelDataset(torch.utils.data.Dataset):
mel = mel[:, :, mel_start : mel_start + frames_per_seg]
audio = audio[
:,
mel_start
* self.hop_size : (mel_start + frames_per_seg)
* self.hop_size,
mel_start * self.hop_size : (mel_start + frames_per_seg) * self.hop_size,
]
# Pad pre-computed mel and audio to match length to ensuring fine-tuning without error.
# NOTE: this may introduce a single-frame misalignment of the <pre-computed mel, audio>
# To remove possible misalignment, it is recommended to prepare the <pre-computed mel, audio> pair where the audio length is the integer multiple of self.hop_size
mel = torch.nn.functional.pad(
mel, (0, frames_per_seg - mel.size(2)), "constant"
)
audio = torch.nn.functional.pad(
audio, (0, self.segment_size - audio.size(1)), "constant"
)
mel = torch.nn.functional.pad(mel, (0, frames_per_seg - mel.size(2)), "constant")
audio = torch.nn.functional.pad(audio, (0, self.segment_size - audio.size(1)), "constant")
# Compute mel_loss used by spectral regression objective. Uses self.fmax_loss instead (usually None)
mel_loss = mel_spectrogram(
@@ -376,9 +351,10 @@ class MelDataset(torch.utils.data.Dataset):
# Shape sanity checks
assert (
audio.shape[1] == mel.shape[2] * self.hop_size
and audio.shape[1] == mel_loss.shape[2] * self.hop_size
), f"Audio length must be mel frame length * hop_size. Got audio shape {audio.shape} mel shape {mel.shape} mel_loss shape {mel_loss.shape}"
audio.shape[1] == mel.shape[2] * self.hop_size and audio.shape[1] == mel_loss.shape[2] * self.hop_size
), (
f"Audio length must be mel frame length * hop_size. Got audio shape {audio.shape} mel shape {mel.shape} mel_loss shape {mel_loss.shape}"
)
return (mel.squeeze(), audio.squeeze(0), filename, mel_loss.squeeze())
@@ -387,9 +363,7 @@ class MelDataset(torch.utils.data.Dataset):
if self.fine_tuning:
raise e # Terminate training if it is fine-tuning. The dataset should have been prepared properly.
else:
print(
f"[WARNING] Failed to load waveform, skipping! filename: {filename} Error: {e}"
)
print(f"[WARNING] Failed to load waveform, skipping! filename: {filename} Error: {e}")
return self[random.randrange(len(self))]
def __len__(self):