# 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. """ Main model for using MusicGen. This will combine all the required components and provide easy access to the generation API. """ import os import typing as tp import torch from .encodec import CompressionModel from .lm import LMModel from .builders import get_debug_compression_model, get_debug_lm_model from .loaders import load_compression_model, load_lm_model, HF_MODEL_CHECKPOINTS_MAP from ..data.audio_utils import convert_audio from ..modules.conditioners import ConditioningAttributes, WavCondition from ..utils.autocast import TorchAutocast MelodyList = tp.List[tp.Optional[torch.Tensor]] MelodyType = tp.Union[torch.Tensor, MelodyList] class MusicGen: """MusicGen main model with convenient generation API. Args: name (str): name of the model. compression_model (CompressionModel): Compression model used to map audio to invertible discrete representations. lm (LMModel): Language model over discrete representations. """ def __init__(self, name: str, compression_model: CompressionModel, lm: LMModel): self.name = name self.compression_model = compression_model self.lm = lm self.device = next(iter(lm.parameters())).device self.generation_params: dict = {} self.set_generation_params(duration=15) # 15 seconds by default if self.device.type == 'cpu': self.autocast = TorchAutocast(enabled=False) else: self.autocast = TorchAutocast( enabled=True, device_type=self.device.type, dtype=torch.float16) @property def frame_rate(self) -> int: """Roughly the number of AR steps per seconds.""" return self.compression_model.frame_rate @property def sample_rate(self) -> int: """Sample rate of the generated audio.""" return self.compression_model.sample_rate @property def audio_channels(self) -> int: """Audio channels of the generated audio.""" return self.compression_model.channels @staticmethod def get_pretrained(name: str = 'melody', device='cuda'): """Return pretrained model, we provide four models: - small (300M), text to music, # see: https://huggingface.co/facebook/musicgen-small - medium (1.5B), text to music, # see: https://huggingface.co/facebook/musicgen-medium - melody (1.5B) text to music and text+melody to music, # see: https://huggingface.co/facebook/musicgen-melody - large (3.3B), text to music, # see: https://huggingface.co/facebook/musicgen-large """ if name == 'debug': # used only for unit tests compression_model = get_debug_compression_model(device) lm = get_debug_lm_model(device) return MusicGen(name, compression_model, lm) if name not in HF_MODEL_CHECKPOINTS_MAP: raise ValueError( f"{name} is not a valid checkpoint name. " f"Choose one of {', '.join(HF_MODEL_CHECKPOINTS_MAP.keys())}" ) cache_dir = os.environ.get('MUSICGEN_ROOT', None) compression_model = load_compression_model(name, device=device, cache_dir=cache_dir) lm = load_lm_model(name, device=device, cache_dir=cache_dir) return MusicGen(name, compression_model, lm) def set_generation_params(self, use_sampling: bool = True, top_k: int = 250, top_p: float = 0.0, temperature: float = 1.0, duration: float = 30.0, cfg_coef: float = 3.0, two_step_cfg: bool = False): """Set the generation parameters for MusicGen. Args: use_sampling (bool, optional): Use sampling if True, else do argmax decoding. Defaults to True. top_k (int, optional): top_k used for sampling. Defaults to 250. top_p (float, optional): top_p used for sampling, when set to 0 top_k is used. Defaults to 0.0. temperature (float, optional): Softmax temperature parameter. Defaults to 1.0. duration (float, optional): Duration of the generated waveform. Defaults to 30.0. cfg_coef (float, optional): Coefficient used for classifier free guidance. Defaults to 3.0. two_step_cfg (bool, optional): If True, performs 2 forward for Classifier Free Guidance, instead of batching together the two. This has some impact on how things are padded but seems to have little impact in practice. """ assert duration <= 30, "The MusicGen cannot generate more than 30 seconds" self.generation_params = { 'max_gen_len': int(duration * self.frame_rate), 'use_sampling': use_sampling, 'temp': temperature, 'top_k': top_k, 'top_p': top_p, 'cfg_coef': cfg_coef, 'two_step_cfg': two_step_cfg, } def generate_unconditional(self, num_samples: int, progress: bool = False) -> torch.Tensor: """Generate samples in an unconditional manner. Args: num_samples (int): Number of samples to be generated. progress (bool, optional): Flag to display progress of the generation process. Defaults to False. """ descriptions: tp.List[tp.Optional[str]] = [None] * num_samples attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None) return self._generate_tokens(attributes, prompt_tokens, progress) def generate(self, descriptions: tp.List[str], progress: bool = False) -> torch.Tensor: """Generate samples conditioned on text. Args: descriptions (tp.List[str]): A list of strings used as text conditioning. progress (bool, optional): Flag to display progress of the generation process. Defaults to False. """ attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None) assert prompt_tokens is None return self._generate_tokens(attributes, prompt_tokens, progress) def generate_with_chroma(self, descriptions: tp.List[str], melody_wavs: MelodyType, melody_sample_rate: int, progress: bool = False) -> torch.Tensor: """Generate samples conditioned on text and melody. Args: descriptions (tp.List[str]): A list of strings used as text conditioning. melody_wavs: (torch.Tensor or list of Tensor): A batch of waveforms used as melody conditioning. Should have shape [B, C, T] with B matching the description length, C=1 or 2. It can be [C, T] if there is a single description. It can also be a list of [C, T] tensors. melody_sample_rate: (int): Sample rate of the melody waveforms. progress (bool, optional): Flag to display progress of the generation process. Defaults to False. """ if isinstance(melody_wavs, torch.Tensor): if melody_wavs.dim() == 2: melody_wavs = melody_wavs[None] if melody_wavs.dim() != 3: raise ValueError("Melody wavs should have a shape [B, C, T].") melody_wavs = list(melody_wavs) else: for melody in melody_wavs: if melody is not None: assert melody.dim() == 2, "One melody in the list has the wrong number of dims." melody_wavs = [ convert_audio(wav, melody_sample_rate, self.sample_rate, self.audio_channels) if wav is not None else None for wav in melody_wavs] attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions=descriptions, prompt=None, melody_wavs=melody_wavs) assert prompt_tokens is None return self._generate_tokens(attributes, prompt_tokens, progress) def generate_continuation(self, prompt: torch.Tensor, prompt_sample_rate: int, descriptions: tp.Optional[tp.List[tp.Optional[str]]] = None, progress: bool = False) -> torch.Tensor: """Generate samples conditioned on audio prompts. Args: prompt (torch.Tensor): A batch of waveforms used for continuation. Prompt should be [B, C, T], or [C, T] if only one sample is generated. prompt_sample_rate (int): Sampling rate of the given audio waveforms. descriptions (tp.List[str], optional): A list of strings used as text conditioning. Defaults to None. progress (bool, optional): Flag to display progress of the generation process. Defaults to False. """ if prompt.dim() == 2: prompt = prompt[None] if prompt.dim() != 3: raise ValueError("prompt should have 3 dimensions: [B, C, T] (C = 1).") prompt = convert_audio(prompt, prompt_sample_rate, self.sample_rate, self.audio_channels) if descriptions is None: descriptions = [None] * len(prompt) attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, prompt) assert prompt_tokens is not None return self._generate_tokens(attributes, prompt_tokens, progress) @torch.no_grad() def _prepare_tokens_and_attributes( self, descriptions: tp.Sequence[tp.Optional[str]], prompt: tp.Optional[torch.Tensor], melody_wavs: tp.Optional[MelodyList] = None, ) -> tp.Tuple[tp.List[ConditioningAttributes], tp.Optional[torch.Tensor]]: """Prepare model inputs. Args: descriptions (tp.List[str]): A list of strings used as text conditioning. prompt (torch.Tensor): A batch of waveforms used for continuation. melody_wavs (tp.Optional[torch.Tensor], optional): A batch of waveforms used as melody conditioning. Defaults to None. """ attributes = [ ConditioningAttributes(text={'description': description}) for description in descriptions] if melody_wavs is None: for attr in attributes: attr.wav['self_wav'] = WavCondition( torch.zeros((1, 1), device=self.device), torch.tensor([0], device=self.device), path='null_wav') # type: ignore else: if self.name != "melody": raise RuntimeError("This model doesn't support melody conditioning. " "Use the `melody` model.") assert len(melody_wavs) == len(descriptions), \ f"number of melody wavs must match number of descriptions! " \ f"got melody len={len(melody_wavs)}, and descriptions len={len(descriptions)}" for attr, melody in zip(attributes, melody_wavs): if melody is None: attr.wav['self_wav'] = WavCondition( torch.zeros((1, 1), device=self.device), torch.tensor([0], device=self.device), path='null_wav') # type: ignore else: attr.wav['self_wav'] = WavCondition( melody.to(device=self.device), torch.tensor([melody.shape[-1]], device=self.device)) if prompt is not None: if descriptions is not None: assert len(descriptions) == len(prompt), "Prompt and nb. descriptions doesn't match" prompt = prompt.to(self.device) prompt_tokens, scale = self.compression_model.encode(prompt) assert scale is None else: prompt_tokens = None return attributes, prompt_tokens def _generate_tokens(self, attributes: tp.List[ConditioningAttributes], prompt_tokens: tp.Optional[torch.Tensor], progress: bool = False) -> torch.Tensor: """Generate discrete audio tokens given audio prompt and/or conditions. Args: attributes (tp.List[ConditioningAttributes]): Conditions used for generation (text/melody). prompt_tokens (tp.Optional[torch.Tensor]): Audio prompt used for continuation. progress (bool, optional): Flag to display progress of the generation process. Defaults to False. Returns: torch.Tensor: Generated audio, of shape [B, C, T], T is defined by the generation params. """ def _progress_callback(generated_tokens: int, tokens_to_generate: int): print(f'{generated_tokens: 6d} / {tokens_to_generate: 6d}', end='\r') if prompt_tokens is not None: assert self.generation_params['max_gen_len'] > prompt_tokens.shape[-1], \ "Prompt is longer than audio to generate" callback = None if progress: callback = _progress_callback # generate by sampling from LM with self.autocast: gen_tokens = self.lm.generate(prompt_tokens, attributes, callback=callback, **self.generation_params) # generate audio assert gen_tokens.dim() == 3 with torch.no_grad(): gen_audio = self.compression_model.decode(gen_tokens, None) return gen_audio