File size: 9,860 Bytes
29792f4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
# 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.

"""
All the functions to build the relevant models and modules
from the Hydra config.
"""

import typing as tp

import audiocraft
import omegaconf
import torch

from .encodec import CompressionModel, EncodecModel
from .lm import LMModel
from ..modules.codebooks_patterns import (
    CodebooksPatternProvider,
    DelayedPatternProvider,
    MusicLMPattern,
    ParallelPatternProvider,
    UnrolledPatternProvider,
    VALLEPattern,
)
from ..modules.conditioners import (
    BaseConditioner,
    ChromaStemConditioner,
    CLAPEmbeddingConditioner,
    ConditionFuser,
    ConditioningProvider,
    LUTConditioner,
    T5Conditioner,
)
from .unet import DiffusionUnet
from .. import quantization as qt
from ..utils.utils import dict_from_config
from ..modules.diffusion_schedule import MultiBandProcessor, SampleProcessor


def get_quantizer(quantizer: str, cfg: omegaconf.DictConfig, dimension: int) -> qt.BaseQuantizer:
    klass = {
        'no_quant': qt.DummyQuantizer,
        'rvq': qt.ResidualVectorQuantizer
    }[quantizer]
    kwargs = dict_from_config(getattr(cfg, quantizer))
    if quantizer != 'no_quant':
        kwargs['dimension'] = dimension
    return klass(**kwargs)


def get_encodec_autoencoder(encoder_name: str, cfg: omegaconf.DictConfig):
    if encoder_name == 'seanet':
        kwargs = dict_from_config(getattr(cfg, 'seanet'))
        encoder_override_kwargs = kwargs.pop('encoder')
        decoder_override_kwargs = kwargs.pop('decoder')
        encoder_kwargs = {**kwargs, **encoder_override_kwargs}
        decoder_kwargs = {**kwargs, **decoder_override_kwargs}
        encoder = audiocraft.modules.SEANetEncoder(**encoder_kwargs)
        decoder = audiocraft.modules.SEANetDecoder(**decoder_kwargs)
        return encoder, decoder
    else:
        raise KeyError(f"Unexpected compression model {cfg.compression_model}")


def get_compression_model(cfg: omegaconf.DictConfig) -> CompressionModel:
    """Instantiate a compression model."""
    if cfg.compression_model == 'encodec':
        kwargs = dict_from_config(getattr(cfg, 'encodec'))
        encoder_name = kwargs.pop('autoencoder')
        quantizer_name = kwargs.pop('quantizer')
        encoder, decoder = get_encodec_autoencoder(encoder_name, cfg)
        quantizer = get_quantizer(quantizer_name, cfg, encoder.dimension)
        frame_rate = kwargs['sample_rate'] // encoder.hop_length
        renormalize = kwargs.pop('renormalize', False)
        # deprecated params
        kwargs.pop('renorm', None)
        return EncodecModel(encoder, decoder, quantizer,
                            frame_rate=frame_rate, renormalize=renormalize, **kwargs).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 == 'transformer_lm':
        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)
        return LMModel(
            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)
        elif model_type == 'lut':
            conditioners[str(cond)] = LUTConditioner(output_dim=output_dim, **model_args)
        elif model_type == 'chroma_stem':
            conditioners[str(cond)] = ChromaStemConditioner(
                output_dim=output_dim,
                duration=duration,
                device=device,
                **model_args
            )
        elif model_type == 'clap':
            conditioners[str(cond)] = CLAPEmbeddingConditioner(
                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: int, cfg: omegaconf.DictConfig) -> CodebooksPatternProvider:
    """Instantiate a codebooks pattern provider object."""
    pattern_providers = {
        'parallel': ParallelPatternProvider,
        'delay': DelayedPatternProvider,
        'unroll': UnrolledPatternProvider,
        'valle': VALLEPattern,
        'musiclm': MusicLMPattern,
    }
    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_debug_compression_model(device='cpu', sample_rate: int = 32000):
    """Instantiate a debug compression model to be used for unit tests."""
    assert sample_rate in [16000, 32000], "unsupported sample rate for debug compression model"
    model_ratios = {
        16000: [10, 8, 8],  # 25 Hz at 16kHz
        32000: [10, 8, 16]  # 25 Hz at 32kHz
    }
    ratios: tp.List[int] = model_ratios[sample_rate]
    frame_rate = 25
    seanet_kwargs: dict = {
        'n_filters': 4,
        'n_residual_layers': 1,
        'dimension': 32,
        'ratios': ratios,
    }
    print(seanet_kwargs)
    encoder = audiocraft.modules.SEANetEncoder(**seanet_kwargs)
    decoder = audiocraft.modules.SEANetDecoder(**seanet_kwargs)
    quantizer = qt.ResidualVectorQuantizer(dimension=32, bins=400, n_q=4)
    init_x = torch.randn(8, 32, 128)
    quantizer(init_x, 1)  # initialize kmeans etc.
    compression_model = EncodecModel(
        encoder, decoder, quantizer,
        frame_rate=frame_rate, sample_rate=sample_rate, channels=1).to(device)
    return compression_model.eval()


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


def get_debug_lm_model(device='cpu'):
    """Instantiate a debug LM to be used for unit tests."""
    pattern = DelayedPatternProvider(n_q=4)
    dim = 16
    providers = {
        'description': LUTConditioner(n_bins=128, dim=dim, output_dim=dim, tokenizer="whitespace"),
    }
    condition_provider = ConditioningProvider(providers)
    fuser = ConditionFuser(
        {'cross': ['description'], 'prepend': [],
         'sum': [], 'input_interpolate': []})
    lm = LMModel(
        pattern, condition_provider, fuser,
        n_q=4, card=400, dim=dim, num_heads=4, custom=True, num_layers=2,
        cross_attention=True, causal=True)
    return lm.to(device).eval()


def get_wrapped_compression_model(
        compression_model: CompressionModel,
        cfg: omegaconf.DictConfig) -> CompressionModel:
    # more to come.
    return compression_model