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| # 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 MAGNeT. This will combine all the required components | |
| and provide easy access to the generation API. | |
| """ | |
| import typing as tp | |
| import torch | |
| from .genmodel import BaseGenModel | |
| from .loaders import load_compression_model, load_lm_model_magnet | |
| class MAGNeT(BaseGenModel): | |
| """MAGNeT main model with convenient generation API. | |
| Args: | |
| See MusicGen class. | |
| """ | |
| def __init__(self, **kwargs): | |
| super().__init__(**kwargs) | |
| # MAGNeT operates over a fixed sequence length defined in it's config. | |
| self.duration = self.lm.cfg.dataset.segment_duration | |
| self.set_generation_params() | |
| def get_pretrained(name: str = 'facebook/magnet-small-10secs', device=None): | |
| """Return pretrained model, we provide six models: | |
| - facebook/magnet-small-10secs (300M), text to music, 10-second audio samples. | |
| # see: https://huggingface.co/facebook/magnet-small-10secs | |
| - facebook/magnet-medium-10secs (1.5B), text to music, 10-second audio samples. | |
| # see: https://huggingface.co/facebook/magnet-medium-10secs | |
| - facebook/magnet-small-30secs (300M), text to music, 30-second audio samples. | |
| # see: https://huggingface.co/facebook/magnet-small-30secs | |
| - facebook/magnet-medium-30secs (1.5B), text to music, 30-second audio samples. | |
| # see: https://huggingface.co/facebook/magnet-medium-30secs | |
| - facebook/audio-magnet-small (300M), text to sound-effect (10-second samples). | |
| # see: https://huggingface.co/facebook/audio-magnet-small | |
| - facebook/audio-magnet-medium (1.5B), text to sound-effect (10-second samples). | |
| # see: https://huggingface.co/facebook/audio-magnet-medium | |
| """ | |
| if device is None: | |
| if torch.cuda.device_count(): | |
| device = 'cuda' | |
| else: | |
| device = 'cpu' | |
| compression_model = load_compression_model(name, device=device) | |
| lm = load_lm_model_magnet(name, compression_model_frame_rate=int(compression_model.frame_rate), device=device) | |
| if 'self_wav' in lm.condition_provider.conditioners: | |
| lm.condition_provider.conditioners['self_wav'].match_len_on_eval = True | |
| kwargs = {'name': name, 'compression_model': compression_model, 'lm': lm} | |
| return MAGNeT(**kwargs) | |
| def set_generation_params(self, use_sampling: bool = True, top_k: int = 0, | |
| top_p: float = 0.9, temperature: float = 3.0, | |
| max_cfg_coef: float = 10.0, min_cfg_coef: float = 1.0, | |
| decoding_steps: tp.List[int] = [20, 10, 10, 10], | |
| span_arrangement: str = 'nonoverlap'): | |
| """Set the generation parameters for MAGNeT. | |
| 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 0. | |
| top_p (float, optional): top_p used for sampling, when set to 0 top_k is used. Defaults to 0.9. | |
| temperature (float, optional): Initial softmax temperature parameter. Defaults to 3.0. | |
| max_cfg_coef (float, optional): Coefficient used for classifier free guidance. Defaults to 10.0. | |
| min_cfg_coef (float, optional): End coefficient of classifier free guidance annealing. Defaults to 1.0. | |
| decoding_steps (list of n_q ints, optional): The number of iterative decoding steps, | |
| for each of the n_q RVQ codebooks. | |
| span_arrangement (str, optional): Use either non-overlapping spans ('nonoverlap') | |
| or overlapping spans ('stride1') in the masking scheme. | |
| """ | |
| self.generation_params = { | |
| 'use_sampling': use_sampling, | |
| 'temp': temperature, | |
| 'top_k': top_k, | |
| 'top_p': top_p, | |
| 'max_cfg_coef': max_cfg_coef, | |
| 'min_cfg_coef': min_cfg_coef, | |
| 'decoding_steps': [int(s) for s in decoding_steps], | |
| 'span_arrangement': span_arrangement | |
| } | |