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Zero
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# Original from: https://github.com/ace-step/ACE-Step/blob/main/music_dcae/music_dcae_pipeline.py
import torch
from .autoencoder_dc import AutoencoderDC
import logging
try:
import torchaudio
except:
logging.warning("torchaudio missing, ACE model will be broken")
import torchvision.transforms as transforms
from .music_vocoder import ADaMoSHiFiGANV1
class MusicDCAE(torch.nn.Module):
def __init__(self, source_sample_rate=None, dcae_config={}, vocoder_config={}):
super(MusicDCAE, self).__init__()
self.dcae = AutoencoderDC(**dcae_config)
self.vocoder = ADaMoSHiFiGANV1(**vocoder_config)
if source_sample_rate is None:
self.source_sample_rate = 48000
else:
self.source_sample_rate = source_sample_rate
# self.resampler = torchaudio.transforms.Resample(source_sample_rate, 44100)
self.transform = transforms.Compose([
transforms.Normalize(0.5, 0.5),
])
self.min_mel_value = -11.0
self.max_mel_value = 3.0
self.audio_chunk_size = int(round((1024 * 512 / 44100 * 48000)))
self.mel_chunk_size = 1024
self.time_dimention_multiple = 8
self.latent_chunk_size = self.mel_chunk_size // self.time_dimention_multiple
self.scale_factor = 0.1786
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):
mels = []
for i in range(len(audios)):
image = self.vocoder.mel_transform(audios[i])
mels.append(image)
mels = torch.stack(mels)
return mels
@torch.no_grad()
def encode(self, audios, audio_lengths=None, sr=None):
if audio_lengths is None:
audio_lengths = torch.tensor([audios.shape[2]] * audios.shape[0])
audio_lengths = audio_lengths.to(audios.device)
if sr is None:
sr = self.source_sample_rate
if sr != 44100:
audios = torchaudio.functional.resample(audios, sr, 44100)
max_audio_len = audios.shape[-1]
if max_audio_len % (8 * 512) != 0:
audios = torch.nn.functional.pad(audios, (0, 8 * 512 - max_audio_len % (8 * 512)))
mels = self.forward_mel(audios)
mels = (mels - self.min_mel_value) / (self.max_mel_value - self.min_mel_value)
mels = self.transform(mels)
latents = []
for mel in mels:
latent = self.dcae.encoder(mel.unsqueeze(0))
latents.append(latent)
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
return latents
# return latents, latent_lengths
@torch.no_grad()
def decode(self, latents, audio_lengths=None, sr=None):
latents = latents / self.scale_factor + self.shift_factor
pred_wavs = []
for latent in latents:
mels = self.dcae.decoder(latent.unsqueeze(0))
mels = mels * 0.5 + 0.5
mels = mels * (self.max_mel_value - self.min_mel_value) + self.min_mel_value
wav = self.vocoder.decode(mels[0]).squeeze(1)
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 = resampler(wav)
else:
sr = 44100
pred_wavs.append(wav)
if audio_lengths is not None:
pred_wavs = [wav[:, :length].cpu() for wav, length in zip(pred_wavs, audio_lengths)]
return torch.stack(pred_wavs)
# return sr, pred_wavs
def forward(self, audios, audio_lengths=None, sr=None):
latents, latent_lengths = self.encode(audios=audios, audio_lengths=audio_lengths, sr=sr)
sr, pred_wavs = self.decode(latents=latents, audio_lengths=audio_lengths, sr=sr)
return sr, pred_wavs, latents, latent_lengths
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