# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. """ Multi Band Diffusion models as described in "From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion" (paper link). """ import typing as tp import torch import julius from .unet import DiffusionUnet from ..modules.diffusion_schedule import NoiseSchedule from .encodec import CompressionModel from ..solvers.compression import CompressionSolver from .loaders import load_compression_model, load_diffusion_models class DiffusionProcess: """Sampling for a diffusion Model. Args: model (DiffusionUnet): Diffusion U-Net model. noise_schedule (NoiseSchedule): Noise schedule for diffusion process. """ def __init__(self, model: DiffusionUnet, noise_schedule: NoiseSchedule) -> None: """ """ self.model = model self.schedule = noise_schedule def generate(self, condition: torch.Tensor, initial_noise: torch.Tensor, step_list: tp.Optional[tp.List[int]] = None): """Perform one diffusion process to generate one of the bands. Args: condition (tensor): The embeddings form the compression model. initial_noise (tensor): The initial noise to start the process/ """ return self.schedule.generate_subsampled(model=self.model, initial=initial_noise, step_list=step_list, condition=condition) class MultiBandDiffusion: """Sample from multiple diffusion models. Args: DPs (list of DiffusionProcess): Diffusion processes. codec_model (CompressionModel): Underlying compression model used to obtain discrete tokens. """ def __init__(self, DPs: tp.List[DiffusionProcess], codec_model: CompressionModel) -> None: self.DPs = DPs self.codec_model = codec_model self.device = next(self.codec_model.parameters()).device @property def sample_rate(self) -> int: return self.codec_model.sample_rate @staticmethod def get_mbd_musicgen(device=None): """Load our diffusion models trained for MusicGen.""" if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' path = 'facebook/multiband-diffusion' filename = 'mbd_musicgen_32khz.th' name = 'facebook/musicgen-small' codec_model = load_compression_model(name, device=device) models, processors, cfgs = load_diffusion_models(path, filename=filename, device=device) DPs = [] for i in range(len(models)): schedule = NoiseSchedule(**cfgs[i].schedule, sample_processor=processors[i], device=device) DPs.append(DiffusionProcess(model=models[i], noise_schedule=schedule)) return MultiBandDiffusion(DPs=DPs, codec_model=codec_model) @staticmethod def get_mbd_24khz(bw: float = 3.0, pretrained: bool = True, device: tp.Optional[tp.Union[torch.device, str]] = None, n_q: tp.Optional[int] = None): """Get the pretrained Models for MultibandDiffusion. Args: bw (float): Bandwidth of the compression model. pretrained (bool): Whether to use / download if necessary the models. device (torch.device or str, optional): Device on which the models are loaded. n_q (int, optional): Number of quantizers to use within the compression model. """ if device is None: device = 'cuda' if torch.cuda.is_available() else 'cpu' assert bw in [1.5, 3.0, 6.0], f"bandwidth {bw} not available" if n_q is not None: assert n_q in [2, 4, 8] assert {1.5: 2, 3.0: 4, 6.0: 8}[bw] == n_q, \ f"bandwidth and number of codebooks missmatch to use n_q = {n_q} bw should be {n_q * (1.5 / 2)}" n_q = {1.5: 2, 3.0: 4, 6.0: 8}[bw] codec_model = CompressionSolver.model_from_checkpoint( '//pretrained/facebook/encodec_24khz', device=device) codec_model.set_num_codebooks(n_q) codec_model = codec_model.to(device) path = 'facebook/multiband-diffusion' filename = f'mbd_comp_{n_q}.pt' models, processors, cfgs = load_diffusion_models(path, filename=filename, device=device) DPs = [] for i in range(len(models)): schedule = NoiseSchedule(**cfgs[i].schedule, sample_processor=processors[i], device=device) DPs.append(DiffusionProcess(model=models[i], noise_schedule=schedule)) return MultiBandDiffusion(DPs=DPs, codec_model=codec_model) return MultiBandDiffusion(DPs, codec_model) @torch.no_grad() def get_condition(self, wav: torch.Tensor, sample_rate: int) -> torch.Tensor: """Get the conditioning (i.e. latent reprentatios of the compression model) from a waveform. Args: wav (torch.Tensor): The audio that we want to extract the conditioning from sample_rate (int): sample rate of the audio""" if sample_rate != self.sample_rate: wav = julius.resample_frac(wav, sample_rate, self.sample_rate) codes, scale = self.codec_model.encode(wav) assert scale is None, "Scaled compression models not supported." emb = self.get_emb(codes) return emb @torch.no_grad() def get_emb(self, codes: torch.Tensor): """Get latent representation from the discrete codes Argrs: codes (torch.Tensor): discrete tokens""" emb = self.codec_model.decode_latent(codes) return emb def generate(self, emb: torch.Tensor, size: tp.Optional[torch.Size] = None, step_list: tp.Optional[tp.List[int]] = None): """Generate Wavform audio from the latent embeddings of the compression model Args: emb (torch.Tensor): Conditioning embeddinds size (none torch.Size): size of the output if None this is computed from the typical upsampling of the model step_list (optional list[int]): list of Markov chain steps, defaults to 50 linearly spaced step. """ if size is None: upsampling = int(self.codec_model.sample_rate / self.codec_model.frame_rate) size = torch.Size([emb.size(0), self.codec_model.channels, emb.size(-1) * upsampling]) assert size[0] == emb.size(0) out = torch.zeros(size).to(self.device) for DP in self.DPs: out += DP.generate(condition=emb, step_list=step_list, initial_noise=torch.randn_like(out)) return out def re_eq(self, wav: torch.Tensor, ref: torch.Tensor, n_bands: int = 32, strictness: float = 1): """match the eq to the encodec output by matching the standard deviation of some frequency bands Args: wav (torch.Tensor): audio to equalize ref (torch.Tensor):refenrence audio from which we match the spectrogram. n_bands (int): number of bands of the eq strictness (float): how strict the the matching. 0 is no matching, 1 is exact matching. """ split = julius.SplitBands(n_bands=n_bands, sample_rate=self.codec_model.sample_rate).to(wav.device) bands = split(wav) bands_ref = split(ref) out = torch.zeros_like(ref) for i in range(n_bands): out += bands[i] * (bands_ref[i].std() / bands[i].std()) ** strictness return out def regenerate(self, wav: torch.Tensor, sample_rate: int): """Regenerate a wavform through compression and diffusion regeneration. Args: wav (torch.Tensor): Original 'ground truth' audio sample_rate (int): sample rate of the input (and output) wav """ if sample_rate != self.codec_model.sample_rate: wav = julius.resample_frac(wav, sample_rate, self.codec_model.sample_rate) emb = self.get_condition(wav, sample_rate=self.codec_model.sample_rate) size = wav.size() out = self.generate(emb, size=size) if sample_rate != self.codec_model.sample_rate: out = julius.resample_frac(out, self.codec_model.sample_rate, sample_rate) return out def tokens_to_wav(self, tokens: torch.Tensor, n_bands: int = 32): """Generate Waveform audio with diffusion from the discrete codes. Args: tokens (torch.Tensor): discrete codes n_bands (int): bands for the eq matching. """ wav_encodec = self.codec_model.decode(tokens) condition = self.get_emb(tokens) wav_diffusion = self.generate(emb=condition, size=wav_encodec.size()) return self.re_eq(wav=wav_diffusion, ref=wav_encodec, n_bands=n_bands)