File size: 8,954 Bytes
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from dataclasses import dataclass
import math
import torch
import torchaudio
from comfy.ldm.mmaudio.vae.distributions import DiagonalGaussianDistribution
from comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier
from comfy.ldm.lightricks.vae.causal_audio_autoencoder import (
CausalityAxis,
CausalAudioAutoencoder,
)
from comfy.ldm.lightricks.vocoders.vocoder import Vocoder, VocoderWithBWE
LATENT_DOWNSAMPLE_FACTOR = 4
@dataclass(frozen=True)
class AudioVAEComponentConfig:
"""Container for model component configuration extracted from metadata."""
autoencoder: dict
vocoder: dict
@classmethod
def from_metadata(cls, metadata: dict) -> "AudioVAEComponentConfig":
assert metadata is not None and "config" in metadata, "Metadata is required for audio VAE"
raw_config = metadata["config"]
if isinstance(raw_config, str):
parsed_config = json.loads(raw_config)
else:
parsed_config = raw_config
audio_config = parsed_config.get("audio_vae")
vocoder_config = parsed_config.get("vocoder")
assert audio_config is not None, "Audio VAE config is required for audio VAE"
assert vocoder_config is not None, "Vocoder config is required for audio VAE"
return cls(autoencoder=audio_config, vocoder=vocoder_config)
class AudioLatentNormalizer:
"""Applies per-channel statistics in patch space and restores original layout."""
def __init__(self, patchfier: AudioPatchifier, statistics_processor: torch.nn.Module):
self.patchifier = patchfier
self.statistics = statistics_processor
def normalize(self, latents: torch.Tensor) -> torch.Tensor:
channels = latents.shape[1]
freq = latents.shape[3]
patched, _ = self.patchifier.patchify(latents)
normalized = self.statistics.normalize(patched)
return self.patchifier.unpatchify(normalized, channels=channels, freq=freq)
def denormalize(self, latents: torch.Tensor) -> torch.Tensor:
channels = latents.shape[1]
freq = latents.shape[3]
patched, _ = self.patchifier.patchify(latents)
denormalized = self.statistics.un_normalize(patched)
return self.patchifier.unpatchify(denormalized, channels=channels, freq=freq)
class AudioPreprocessor:
"""Prepares raw waveforms for the autoencoder by matching training conditions."""
def __init__(self, target_sample_rate: int, mel_bins: int, mel_hop_length: int, n_fft: int):
self.target_sample_rate = target_sample_rate
self.mel_bins = mel_bins
self.mel_hop_length = mel_hop_length
self.n_fft = n_fft
def resample(self, waveform: torch.Tensor, source_rate: int) -> torch.Tensor:
if source_rate == self.target_sample_rate:
return waveform
return torchaudio.functional.resample(waveform, source_rate, self.target_sample_rate)
def waveform_to_mel(
self, waveform: torch.Tensor, waveform_sample_rate: int, device
) -> torch.Tensor:
waveform = self.resample(waveform, waveform_sample_rate)
mel_transform = torchaudio.transforms.MelSpectrogram(
sample_rate=self.target_sample_rate,
n_fft=self.n_fft,
win_length=self.n_fft,
hop_length=self.mel_hop_length,
f_min=0.0,
f_max=self.target_sample_rate / 2.0,
n_mels=self.mel_bins,
window_fn=torch.hann_window,
center=True,
pad_mode="reflect",
power=1.0,
mel_scale="slaney",
norm="slaney",
).to(device)
mel = mel_transform(waveform)
mel = torch.log(torch.clamp(mel, min=1e-5))
return mel.permute(0, 1, 3, 2).contiguous()
class AudioVAE(torch.nn.Module):
"""High-level Audio VAE wrapper exposing encode and decode entry points."""
def __init__(self, metadata: dict):
super().__init__()
component_config = AudioVAEComponentConfig.from_metadata(metadata)
self.autoencoder = CausalAudioAutoencoder(config=component_config.autoencoder)
if "bwe" in component_config.vocoder:
self.vocoder = VocoderWithBWE(config=component_config.vocoder)
else:
self.vocoder = Vocoder(config=component_config.vocoder)
autoencoder_config = self.autoencoder.get_config()
self.normalizer = AudioLatentNormalizer(
AudioPatchifier(
patch_size=1,
audio_latent_downsample_factor=LATENT_DOWNSAMPLE_FACTOR,
sample_rate=autoencoder_config["sampling_rate"],
hop_length=autoencoder_config["mel_hop_length"],
is_causal=autoencoder_config["is_causal"],
),
self.autoencoder.per_channel_statistics,
)
self.preprocessor = AudioPreprocessor(
target_sample_rate=autoencoder_config["sampling_rate"],
mel_bins=autoencoder_config["mel_bins"],
mel_hop_length=autoencoder_config["mel_hop_length"],
n_fft=autoencoder_config["n_fft"],
)
def encode(self, audio, sample_rate=44100) -> torch.Tensor:
"""Encode a waveform dictionary into normalized latent tensors."""
waveform = audio
waveform_sample_rate = sample_rate
input_device = waveform.device
expected_channels = self.autoencoder.encoder.in_channels
if waveform.shape[1] != expected_channels:
if waveform.shape[1] == 1:
waveform = waveform.expand(-1, expected_channels, *waveform.shape[2:])
else:
raise ValueError(
f"Input audio must have {expected_channels} channels, got {waveform.shape[1]}"
)
mel_spec = self.preprocessor.waveform_to_mel(
waveform, waveform_sample_rate, device=waveform.device
)
latents = self.autoencoder.encode(mel_spec)
posterior = DiagonalGaussianDistribution(latents)
latent_mode = posterior.mode()
normalized = self.normalizer.normalize(latent_mode)
return normalized.to(input_device)
def decode(self, latents: torch.Tensor) -> torch.Tensor:
"""Decode normalized latent tensors into an audio waveform."""
original_shape = latents.shape
latents = self.normalizer.denormalize(latents)
target_shape = self.target_shape_from_latents(original_shape)
mel_spec = self.autoencoder.decode(latents, target_shape=target_shape)
waveform = self.run_vocoder(mel_spec)
return waveform
def target_shape_from_latents(self, latents_shape):
batch, _, time, _ = latents_shape
target_length = time * LATENT_DOWNSAMPLE_FACTOR
if self.autoencoder.causality_axis != CausalityAxis.NONE:
target_length -= LATENT_DOWNSAMPLE_FACTOR - 1
return (
batch,
self.autoencoder.decoder.out_ch,
target_length,
self.autoencoder.mel_bins,
)
def num_of_latents_from_frames(self, frames_number: int, frame_rate: float) -> int:
return math.ceil((float(frames_number) / frame_rate) * self.latents_per_second)
def run_vocoder(self, mel_spec: torch.Tensor) -> torch.Tensor:
audio_channels = self.autoencoder.decoder.out_ch
vocoder_input = mel_spec.transpose(2, 3)
if audio_channels == 1:
vocoder_input = vocoder_input.squeeze(1)
elif audio_channels != 2:
raise ValueError(f"Unsupported audio_channels: {audio_channels}")
return self.vocoder(vocoder_input)
@property
def sample_rate(self) -> int:
return int(self.autoencoder.sampling_rate)
@property
def mel_hop_length(self) -> int:
return int(self.autoencoder.mel_hop_length)
@property
def mel_bins(self) -> int:
return int(self.autoencoder.mel_bins)
@property
def latent_channels(self) -> int:
return int(self.autoencoder.decoder.z_channels)
@property
def latent_frequency_bins(self) -> int:
return int(self.mel_bins // LATENT_DOWNSAMPLE_FACTOR)
@property
def latents_per_second(self) -> float:
return self.sample_rate / self.mel_hop_length / LATENT_DOWNSAMPLE_FACTOR
@property
def output_sample_rate(self) -> int:
output_rate = getattr(self.vocoder, "output_sample_rate", None)
if output_rate is not None:
return int(output_rate)
upsample_factor = getattr(self.vocoder, "upsample_factor", None)
if upsample_factor is None:
raise AttributeError(
"Vocoder is missing upsample_factor; cannot infer output sample rate"
)
return int(self.sample_rate * upsample_factor / self.mel_hop_length)
def memory_required(self, input_shape):
return self.device_manager.patcher.model_size()
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