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