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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
# Encode
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
# Encode
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
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