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