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Remove useless code. (#10223)

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comfyanonymous 2025-10-05 12:41:19 -07:00 committed by GitHub
parent 187f43696d
commit 195e0b0639
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1 changed files with 0 additions and 11 deletions
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comfy/ldm/ace/vae/music_dcae_pipeline.py
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@ -23,8 +23,6 @@ class MusicDCAE(torch.nn.Module):
else: else:
self.source_sample_rate = source_sample_rate self.source_sample_rate = source_sample_rate
# self.resampler = torchaudio.transforms.Resample(source_sample_rate, 44100)
self.transform = transforms.Compose([ self.transform = transforms.Compose([
transforms.Normalize(0.5, 0.5), transforms.Normalize(0.5, 0.5),
]) ])
@ -37,10 +35,6 @@ class MusicDCAE(torch.nn.Module):
self.scale_factor = 0.1786 self.scale_factor = 0.1786
self.shift_factor = -1.9091 self.shift_factor = -1.9091
def load_audio(self, audio_path):
audio, sr = torchaudio.load(audio_path)
return audio, sr
def forward_mel(self, audios): def forward_mel(self, audios):
mels = [] mels = []
for i in range(len(audios)): for i in range(len(audios)):
@ -73,10 +67,8 @@ class MusicDCAE(torch.nn.Module):
latent = self.dcae.encoder(mel.unsqueeze(0)) latent = self.dcae.encoder(mel.unsqueeze(0))
latents.append(latent) latents.append(latent)
latents = torch.cat(latents, dim=0) latents = torch.cat(latents, dim=0)
# latent_lengths = (audio_lengths / sr * 44100 / 512 / self.time_dimention_multiple).long()
latents = (latents - self.shift_factor) * self.scale_factor latents = (latents - self.shift_factor) * self.scale_factor
return latents return latents
# return latents, latent_lengths
@torch.no_grad() @torch.no_grad()
def decode(self, latents, audio_lengths=None, sr=None): def decode(self, latents, audio_lengths=None, sr=None):
@ -91,9 +83,7 @@ class MusicDCAE(torch.nn.Module):
wav = self.vocoder.decode(mels[0]).squeeze(1) wav = self.vocoder.decode(mels[0]).squeeze(1)
if sr is not None: if sr is not None:
# resampler = torchaudio.transforms.Resample(44100, sr).to(latents.device).to(latents.dtype)
wav = torchaudio.functional.resample(wav, 44100, sr) wav = torchaudio.functional.resample(wav, 44100, sr)
# wav = resampler(wav)
else: else:
sr = 44100 sr = 44100
pred_wavs.append(wav) pred_wavs.append(wav)
@ -101,7 +91,6 @@ class MusicDCAE(torch.nn.Module):
if audio_lengths is not None: if audio_lengths is not None:
pred_wavs = [wav[:, :length].cpu() for wav, length in zip(pred_wavs, audio_lengths)] pred_wavs = [wav[:, :length].cpu() for wav, length in zip(pred_wavs, audio_lengths)]
return torch.stack(pred_wavs) return torch.stack(pred_wavs)
# return sr, pred_wavs
def forward(self, audios, audio_lengths=None, sr=None): def forward(self, audios, audio_lengths=None, sr=None):
latents, latent_lengths = self.encode(audios=audios, audio_lengths=audio_lengths, sr=sr) latents, latent_lengths = self.encode(audios=audios, audio_lengths=audio_lengths, sr=sr)