audiocraft/builders.py

# 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.

import typing as tp
import omegaconf
import torch
from .encodec import EncodecModel
from .lm import LMModel
from .seanet import SEANetDecoder
from .codebooks_patterns import DelayedPatternProvider
from .conditioners import (
    BaseConditioner,
    ConditionFuser,
    ConditioningProvider,
    T5Conditioner,
)
from .unet import DiffusionUnet
from .vq import ResidualVectorQuantizer
from .utils.utils import dict_from_config
from .diffusion_schedule import MultiBandProcessor, SampleProcessor


def get_quantizer(quantizer, cfg, dimension):
    klass = {
        'no_quant': None,
        'rvq': ResidualVectorQuantizer
    }[quantizer]
    kwargs = dict_from_config(getattr(cfg, quantizer))
    if quantizer != 'no_quant':
        kwargs['dimension'] = dimension
    return klass(**kwargs)


def get_encodec_autoencoder(cfg):
    kwargs = dict_from_config(getattr(cfg, 'seanet'))
    _ = kwargs.pop('encoder')
    decoder_override_kwargs = kwargs.pop('decoder')
    decoder_kwargs = {**kwargs, **decoder_override_kwargs}
    decoder = SEANetDecoder(**decoder_kwargs)
    return decoder
    


def get_compression_model(cfg):
    """Instantiate a compression model."""
    if cfg.compression_model == 'encodec':
        kwargs = dict_from_config(getattr(cfg, 'encodec'))
        quantizer_name = kwargs.pop('quantizer')
        decoder = get_encodec_autoencoder(cfg)
        quantizer = get_quantizer(quantizer_name, cfg, 128)
        renormalize = kwargs.pop('renormalize', False)
        # deprecated params
        # print(f'{frame_rate=} {encoder.dimension=}')  frame_rate=50 encoder.dimension=128
        kwargs.pop('renorm', None)
        # print('\n______!____________\n', kwargs, '\n______!____________\n')
        #     ______!____________
        #     {'autoencoder': 'seanet', 'sample_rate': 16000, 'channels': 1, 'causal': False} 
        #     ______!____________

        return EncodecModel(decoder=decoder,
                            quantizer=quantizer,
                            frame_rate=50,
                            renormalize=renormalize, 
                            sample_rate=16000,
                            channels=1,
                            causal=False
                            ).to(cfg.device)
    else:
        raise KeyError(f"Unexpected compression model {cfg.compression_model}")


def get_lm_model(cfg: omegaconf.DictConfig) -> LMModel:
    """Instantiate a transformer LM."""
    if cfg.lm_model in ['transformer_lm', 'transformer_lm_magnet']:
        kwargs = dict_from_config(getattr(cfg, 'transformer_lm'))
        n_q = kwargs['n_q']
        q_modeling = kwargs.pop('q_modeling', None)
        codebooks_pattern_cfg = getattr(cfg, 'codebooks_pattern')
        attribute_dropout = dict_from_config(getattr(cfg, 'attribute_dropout'))
        cls_free_guidance = dict_from_config(getattr(cfg, 'classifier_free_guidance'))
        cfg_prob, cfg_coef = cls_free_guidance['training_dropout'], cls_free_guidance['inference_coef']
        fuser = get_condition_fuser(cfg)
        condition_provider = get_conditioner_provider(kwargs["dim"], cfg).to(cfg.device)
        if len(fuser.fuse2cond['cross']) > 0:  # enforce cross-att programmatically
            kwargs['cross_attention'] = True
        if codebooks_pattern_cfg.modeling is None:
            assert q_modeling is not None, \
                "LM model should either have a codebook pattern defined or transformer_lm.q_modeling"
            codebooks_pattern_cfg = omegaconf.OmegaConf.create(
                {'modeling': q_modeling, 'delay': {'delays': list(range(n_q))}}
            )

        pattern_provider = get_codebooks_pattern_provider(n_q, codebooks_pattern_cfg)
        # lm_class = MagnetLMModel if cfg.lm_model == 'transformer_lm_magnet' else LMModel
        lm_class = LMModel # hard coded D
        print(f'{lm_class=}\n\n\n\n=====================')
        return lm_class(
            pattern_provider=pattern_provider,
            condition_provider=condition_provider,
            fuser=fuser,
            cfg_dropout=cfg_prob,
            cfg_coef=cfg_coef,
            attribute_dropout=attribute_dropout,
            dtype=getattr(torch, cfg.dtype),
            device=cfg.device,
            **kwargs
        ).to(cfg.device)
    else:
        raise KeyError(f"Unexpected LM model {cfg.lm_model}")


def get_conditioner_provider(output_dim: int, cfg: omegaconf.DictConfig) -> ConditioningProvider:
    """Instantiate a conditioning model."""
    device = cfg.device
    duration = cfg.dataset.segment_duration
    cfg = getattr(cfg, 'conditioners')
    dict_cfg = {} if cfg is None else dict_from_config(cfg)
    conditioners: tp.Dict[str, BaseConditioner] = {}
    condition_provider_args = dict_cfg.pop('args', {})
    condition_provider_args.pop('merge_text_conditions_p', None)
    condition_provider_args.pop('drop_desc_p', None)

    for cond, cond_cfg in dict_cfg.items():
        model_type = cond_cfg['model']
        model_args = cond_cfg[model_type]
        if model_type == 't5':
            conditioners[str(cond)] = T5Conditioner(output_dim=output_dim, device=device, **model_args)
        else:
            raise ValueError(f"Unrecognized conditioning model: {model_type}")
    conditioner = ConditioningProvider(conditioners, device=device, **condition_provider_args)
    return conditioner


def get_condition_fuser(cfg: omegaconf.DictConfig) -> ConditionFuser:
    """Instantiate a condition fuser object."""
    fuser_cfg = getattr(cfg, 'fuser')
    fuser_methods = ['sum', 'cross', 'prepend', 'input_interpolate']
    fuse2cond = {k: fuser_cfg[k] for k in fuser_methods}
    kwargs = {k: v for k, v in fuser_cfg.items() if k not in fuser_methods}
    fuser = ConditionFuser(fuse2cond=fuse2cond, **kwargs)
    return fuser


def get_codebooks_pattern_provider(n_q, cfg):
    pattern_providers = {
        'delay': DelayedPatternProvider,  # THIS
    }
    name = cfg.modeling
    kwargs = dict_from_config(cfg.get(name)) if hasattr(cfg, name) else {}
    
    klass = pattern_providers[name]
    return klass(n_q, **kwargs)





def get_diffusion_model(cfg: omegaconf.DictConfig):
    # TODO Find a way to infer the channels from dset
    channels = cfg.channels
    num_steps = cfg.schedule.num_steps
    return DiffusionUnet(
            chin=channels, num_steps=num_steps, **cfg.diffusion_unet)


def get_processor(cfg, sample_rate: int = 24000):
    sample_processor = SampleProcessor()
    if cfg.use:
        kw = dict(cfg)
        kw.pop('use')
        kw.pop('name')
        if cfg.name == "multi_band_processor":
            sample_processor = MultiBandProcessor(sample_rate=sample_rate, **kw)
    return sample_processor