File size: 8,256 Bytes
679c1f9 |
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 |
# Copyright (c) 2025 SparkAudio
# 2025 Xinsheng Wang ([email protected])
#
# 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 torch
import torch.nn as nn
from pathlib import Path
from typing import Dict, Any
from omegaconf import DictConfig
from safetensors.torch import load_file
from sparktts.utils.file import load_config
from sparktts.modules.speaker.speaker_encoder import SpeakerEncoder
from sparktts.modules.encoder_decoder.feat_encoder import Encoder
from sparktts.modules.encoder_decoder.feat_decoder import Decoder
from sparktts.modules.encoder_decoder.wave_generator import WaveGenerator
from sparktts.modules.vq.factorized_vector_quantize import FactorizedVectorQuantize
class BiCodec(nn.Module):
"""
BiCodec model for speech synthesis, incorporating a speaker encoder, feature encoder/decoder,
quantizer, and wave generator.
"""
def __init__(
self,
mel_params: Dict[str, Any],
encoder: nn.Module,
decoder: nn.Module,
quantizer: nn.Module,
speaker_encoder: nn.Module,
prenet: nn.Module,
postnet: nn.Module,
**kwargs
) -> None:
"""
Initializes the BiCodec model with the required components.
Args:
mel_params (dict): Parameters for the mel-spectrogram transformer.
encoder (nn.Module): Encoder module.
decoder (nn.Module): Decoder module.
quantizer (nn.Module): Quantizer module.
speaker_encoder (nn.Module): Speaker encoder module.
prenet (nn.Module): Prenet network.
postnet (nn.Module): Postnet network.
"""
super().__init__()
self.encoder = encoder
self.decoder = decoder
self.quantizer = quantizer
self.speaker_encoder = speaker_encoder
self.prenet = prenet
self.postnet = postnet
self.init_mel_transformer(mel_params)
@classmethod
def load_from_checkpoint(cls, model_dir: Path, **kwargs) -> "BiCodec":
"""
Loads the model from a checkpoint.
Args:
model_dir (Path): Path to the model directory containing checkpoint and config.
Returns:
BiCodec: The initialized BiCodec model.
"""
ckpt_path = f'{model_dir}/model.safetensors'
config = load_config(f'{model_dir}/config.yaml')['audio_tokenizer']
mel_params = config["mel_params"]
encoder = Encoder(**config["encoder"])
quantizer = FactorizedVectorQuantize(**config["quantizer"])
prenet = Decoder(**config["prenet"])
postnet = Decoder(**config["postnet"])
decoder = WaveGenerator(**config["decoder"])
speaker_encoder = SpeakerEncoder(**config["speaker_encoder"])
model = cls(
mel_params=mel_params,
encoder=encoder,
decoder=decoder,
quantizer=quantizer,
speaker_encoder=speaker_encoder,
prenet=prenet,
postnet=postnet,
)
state_dict = load_file(ckpt_path)
missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
for key in missing_keys:
print(f"Missing tensor: {key}")
for key in unexpected_keys:
print(f"Unexpected tensor: {key}")
model.eval()
model.remove_weight_norm()
return model
def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]:
"""
Performs a forward pass through the model.
Args:
batch (dict): A dictionary containing features, reference waveform, and target waveform.
Returns:
dict: A dictionary containing the reconstruction, features, and other metrics.
"""
feat = batch["feat"]
mel = self.mel_transformer(batch["ref_wav"]).squeeze(1)
z = self.encoder(feat.transpose(1, 2))
vq_outputs = self.quantizer(z)
x_vector, d_vector = self.speaker_encoder(mel.transpose(1, 2))
conditions = d_vector
with_speaker_loss = False
x = self.prenet(vq_outputs["z_q"], conditions)
pred_feat = self.postnet(x)
x = x + conditions.unsqueeze(-1)
wav_recon = self.decoder(x)
return {
"vq_loss": vq_outputs["vq_loss"],
"perplexity": vq_outputs["perplexity"],
"cluster_size": vq_outputs["active_num"],
"recons": wav_recon,
"pred_feat": pred_feat,
"x_vector": x_vector,
"d_vector": d_vector,
"audios": batch["wav"].unsqueeze(1),
"with_speaker_loss": with_speaker_loss,
}
@torch.no_grad()
def tokenize(self, batch: Dict[str, Any]):
"""
Tokenizes the input audio into semantic and global tokens.
Args:
batch (dict): The input audio features and reference waveform.
Returns:
tuple: Semantic tokens and global tokens.
"""
feat = batch["feat"]
mel = self.mel_transformer(batch["ref_wav"]).squeeze(1)
z = self.encoder(feat.transpose(1, 2))
semantic_tokens = self.quantizer.tokenize(z)
global_tokens = self.speaker_encoder.tokenize(mel.transpose(1, 2))
return semantic_tokens, global_tokens
@torch.no_grad()
def detokenize(self, semantic_tokens, global_tokens):
"""
Detokenizes the semantic and global tokens into a waveform.
Args:
semantic_tokens (tensor): Semantic tokens.
global_tokens (tensor): Global tokens.
Returns:
tensor: Reconstructed waveform.
"""
z_q = self.quantizer.detokenize(semantic_tokens)
d_vector = self.speaker_encoder.detokenize(global_tokens)
x = self.prenet(z_q, d_vector)
x = x + d_vector.unsqueeze(-1)
wav_recon = self.decoder(x)
return wav_recon
def init_mel_transformer(self, config: Dict[str, Any]):
"""
Initializes the MelSpectrogram transformer based on the provided configuration.
Args:
config (dict): Configuration parameters for MelSpectrogram.
"""
import torchaudio.transforms as TT
self.mel_transformer = TT.MelSpectrogram(
config["sample_rate"],
config["n_fft"],
config["win_length"],
config["hop_length"],
config["mel_fmin"],
config["mel_fmax"],
n_mels=config["num_mels"],
power=1,
norm="slaney",
mel_scale="slaney",
)
def remove_weight_norm(self):
"""Removes weight normalization from all layers."""
def _remove_weight_norm(m):
try:
torch.nn.utils.remove_weight_norm(m)
except ValueError:
pass # The module didn't have weight norm
self.apply(_remove_weight_norm)
# Test the model
if __name__ == "__main__":
config = load_config("pretrained_models/SparkTTS-0.5B/BiCodec/config.yaml")
model = BiCodec.load_from_checkpoint(
model_dir="pretrained_models/SparkTTS-0.5B/BiCodec",
)
# Generate random inputs for testing
duration = 0.96
x = torch.randn(20, 1, int(duration * 16000))
feat = torch.randn(20, int(duration * 50), 1024)
inputs = {"feat": feat, "wav": x, "ref_wav": x}
# Forward pass
outputs = model(inputs)
semantic_tokens, global_tokens = model.tokenize(inputs)
wav_recon = model.detokenize(semantic_tokens, global_tokens)
# Verify if the reconstruction matches
if torch.allclose(outputs["recons"].detach(), wav_recon):
print("Test successful")
else:
print("Test failed")
|