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# Copyright (c) 2024 Alibaba Inc
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import random
from typing import Dict, Optional
import torch
import torch.nn as nn
from torch.nn import functional as F
from omegaconf import DictConfig
from inspiremusic.utils.mask import make_pad_mask
from inspiremusic.music_tokenizer.vqvae import VQVAE
class MaskedDiff(torch.nn.Module):
def __init__(self,
input_size: int = 512,
output_size: int = 128,
output_type: str = "mel",
vocab_size: int = 4096,
input_frame_rate: int = 50,
only_mask_loss: bool = True,
encoder: torch.nn.Module = None,
length_regulator: torch.nn.Module = None,
decoder: torch.nn.Module = None,
decoder_conf: Dict = {'in_channels': 240, 'out_channel': 80,
'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine',
'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}),
'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64,
'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}},
mel_feat_conf: Dict = {'n_fft': 1024, 'num_mels': 128, 'sampling_rate': 48000,
'hop_size': 256, 'win_size': 1024, 'fmin': 0, 'fmax': 48000},
generator_model_dir: str = "pretrained_models/InspireMusic-Base/music_tokenizer",
num_codebooks: int = 4
):
super().__init__()
self.input_size = input_size
self.output_size = output_size
self.decoder_conf = decoder_conf
self.mel_feat_conf = mel_feat_conf
self.vocab_size = vocab_size
self.output_type = output_type
self.input_frame_rate = input_frame_rate
logging.info(f"input frame rate={self.input_frame_rate}")
self.input_embedding = nn.Embedding(vocab_size, input_size)
self.encoder = encoder
self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size)
self.decoder = decoder
self.length_regulator = length_regulator
self.only_mask_loss = only_mask_loss
self.quantizer = VQVAE( f'{generator_model_dir}/config.json',
f'{generator_model_dir}/model.pt',with_encoder=True).quantizer
self.quantizer.eval()
self.num_codebooks = num_codebooks
self.cond = None
self.interpolate = False
def forward(
self,
batch: dict,
device: torch.device,
) -> Dict[str, Optional[torch.Tensor]]:
audio_token = batch['acoustic_token'].to(device)
audio_token_len = batch['acoustic_token_len'].to(device)
audio_token = audio_token.view(audio_token.size(0),-1,self.num_codebooks)
if "semantic_token" not in batch:
token = audio_token[:,:,0]
token_len = (audio_token_len/self.num_codebooks).long()
else:
token = batch['semantic_token'].to(device)
token_len = batch['semantic_token_len'].to(device)
with torch.no_grad():
feat = self.quantizer.embed(audio_token)
feat_len = (audio_token_len/self.num_codebooks).long()
token = self.input_embedding(token)
h, h_lengths = self.encoder(token, token_len)
h, h_lengths = self.length_regulator(h, feat_len)
# get conditions
if self.cond:
conds = torch.zeros(feat.shape, device=token.device)
for i, j in enumerate(feat_len):
if random.random() < 0.5:
continue
index = random.randint(0, int(0.3 * j))
conds[i, :index] = feat[i, :index]
conds = conds.transpose(1, 2)
else:
conds = None
mask = (~make_pad_mask(feat_len)).to(h)
loss, _ = self.decoder.compute_loss(
feat,
mask.unsqueeze(1),
h.transpose(1, 2).contiguous(),
None,
cond=conds
)
return {'loss': loss}
@torch.inference_mode()
def inference(self,
token,
token_len,
sample_rate):
assert token.shape[0] == 1
token = self.input_embedding(torch.clamp(token, min=0))
h, h_lengths = self.encoder(token, token_len)
if sample_rate == 48000:
token_len = 2 * token_len
h, h_lengths = self.length_regulator(h, token_len)
# get conditions
conds = None
mask = (~make_pad_mask(token_len)).to(h)
feat = self.decoder(
mu=h.transpose(1, 2).contiguous(),
mask=mask.unsqueeze(1),
spks=None,
cond=conds,
n_timesteps=10
)
return feat |