| | import math |
| |
|
| | import torch |
| | from torch import nn |
| |
|
| | from fish_speech.models.vqgan.modules.fsq import DownsampleFiniteScalarQuantize |
| | from fish_speech.models.vqgan.modules.wavenet import WaveNet |
| | from fish_speech.models.vqgan.utils import sequence_mask |
| | from fish_speech.utils.spectrogram import LogMelSpectrogram |
| |
|
| |
|
| | class VQEncoder(nn.Module): |
| | def __init__( |
| | self, |
| | ): |
| | super().__init__() |
| |
|
| | self.encoder = WaveNet( |
| | input_channels=128, |
| | residual_channels=768, |
| | residual_layers=20, |
| | dilation_cycle=4, |
| | ) |
| |
|
| | self.quantizer = DownsampleFiniteScalarQuantize( |
| | input_dim=768, n_codebooks=1, n_groups=2, levels=[8, 5, 5, 5] |
| | ) |
| |
|
| | self.spec = LogMelSpectrogram( |
| | sample_rate=44100, |
| | n_fft=2048, |
| | win_length=2048, |
| | hop_length=512, |
| | n_mels=128, |
| | f_min=0.0, |
| | f_max=8000.0, |
| | ) |
| |
|
| | self.eval() |
| | e = self.load_state_dict( |
| | torch.load("checkpoints/vq-gan-group-fsq-2x1024.pth", map_location="cpu"), |
| | strict=False, |
| | ) |
| |
|
| | assert len(e.missing_keys) == 0, e.missing_keys |
| | assert all( |
| | k.startswith("decoder.") |
| | or k.startswith("quality_projection.") |
| | or k.startswith("discriminator.") |
| | for k in e.unexpected_keys |
| | ), e.unexpected_keys |
| |
|
| | @torch.no_grad() |
| | def forward(self, audios, audio_lengths, sr=None): |
| | mel_spec = self.spec(audios, sample_rate=sr) |
| |
|
| | if sr is not None: |
| | audio_lengths = audio_lengths * 44100 // sr |
| |
|
| | mel_lengths = audio_lengths // self.spec.hop_length |
| | mel_masks = ( |
| | torch.arange(mel_spec.shape[2], device=mel_spec.device) |
| | < mel_lengths[:, None] |
| | ) |
| | mel_masks_float_conv = mel_masks[:, None, :].float() |
| | mels = mel_spec * mel_masks_float_conv |
| |
|
| | |
| | encoded_features = self.encoder(mels) * mel_masks_float_conv |
| | encoded_features = self.quantizer(encoded_features).z * mel_masks_float_conv |
| |
|
| | return encoded_features |
| |
|
| | @torch.no_grad() |
| | def indicies_to_vq_features( |
| | self, |
| | indices, |
| | feature_lengths, |
| | ): |
| | factor = math.prod(self.quantizer.downsample_factor) |
| | mel_masks = sequence_mask(feature_lengths * factor, indices.shape[2] * factor) |
| | mel_masks_float_conv = mel_masks[:, None, :].float() |
| | z = self.quantizer.decode(indices) * mel_masks_float_conv |
| |
|
| | return z |
| |
|
| | @torch.no_grad() |
| | def encode(self, audios, audio_lengths, sr=None): |
| | audios = audios.float() |
| |
|
| | mels = self.spec(audios, sample_rate=sr) |
| | mel_lengths = audio_lengths // self.spec.hop_length |
| | mel_masks = sequence_mask(mel_lengths, mels.shape[2]) |
| | mel_masks_float_conv = mel_masks[:, None, :].float() |
| | mels = mels * mel_masks_float_conv |
| |
|
| | |
| | encoded_features = self.encoder(mels) * mel_masks_float_conv |
| | feature_lengths = mel_lengths // math.prod(self.quantizer.downsample_factor) |
| |
|
| | return self.quantizer.encode(encoded_features), feature_lengths |
| |
|
