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ebc90bc | 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 | """AudioDiT model configuration"""
# transformers >= 5.0 renamed PretrainedConfig → PreTrainedConfig; tolerate both.
try:
from transformers import PreTrainedConfig
except ImportError:
from transformers import PretrainedConfig as PreTrainedConfig
from transformers import logging
from transformers.models.umt5.configuration_umt5 import UMT5Config
logger = logging.get_logger(__name__)
class AudioDiTVaeConfig(PreTrainedConfig):
r"""
Configuration class for the AudioDiT WAV-VAE audio autoencoder.
Args:
in_channels (`int`, *optional*, defaults to 1):
Number of input audio channels (mono=1).
channels (`int`, *optional*, defaults to 128):
Base channel count for encoder/decoder.
c_mults (`list[int]`, *optional*, defaults to `[1, 2, 4, 8, 16]`):
Channel multipliers for each encoder/decoder stage.
strides (`list[int]`, *optional*, defaults to `[2, 4, 4, 8, 8]`):
Downsampling strides for each encoder stage.
latent_dim (`int`, *optional*, defaults to 64):
Dimensionality of the latent space (after VAE bottleneck: encoder outputs 128, split to mean+scale → 64).
encoder_latent_dim (`int`, *optional*, defaults to 128):
Dimensionality of the encoder output before VAE bottleneck.
use_snake (`bool`, *optional*, defaults to `True`):
Whether to use Snake activation instead of ELU.
downsample_shortcut (`str`, *optional*, defaults to `"averaging"`):
Shortcut type for encoder downsampling blocks.
upsample_shortcut (`str`, *optional*, defaults to `"duplicating"`):
Shortcut type for decoder upsampling blocks.
out_shortcut (`str`, *optional*, defaults to `"averaging"`):
Shortcut type for encoder output projection.
in_shortcut (`str`, *optional*, defaults to `"duplicating"`):
Shortcut type for decoder input projection.
final_tanh (`bool`, *optional*, defaults to `False`):
Whether to apply tanh to the decoder output.
downsampling_ratio (`int`, *optional*, defaults to 2048):
Total downsampling ratio from audio samples to latent frames.
sample_rate (`int`, *optional*, defaults to 24000):
Audio sample rate.
scale (`float`, *optional*, defaults to 0.71):
Scale factor for the latent space.
"""
model_type = "audiodit_vae"
def __init__(
self,
in_channels: int = 1,
channels: int = 128,
c_mults: list[int] | None = None,
strides: list[int] | None = None,
latent_dim: int = 64,
encoder_latent_dim: int = 128,
use_snake: bool = True,
downsample_shortcut: str = "averaging",
upsample_shortcut: str = "duplicating",
out_shortcut: str = "averaging",
in_shortcut: str = "duplicating",
final_tanh: bool = False,
downsampling_ratio: int = 2048,
sample_rate: int = 24000,
scale: float = 0.71,
**kwargs,
):
super().__init__(**kwargs)
self.in_channels = in_channels
self.channels = channels
self.c_mults = c_mults if c_mults is not None else [1, 2, 4, 8, 16]
self.strides = strides if strides is not None else [2, 4, 4, 8, 8]
self.latent_dim = latent_dim
self.encoder_latent_dim = encoder_latent_dim
self.use_snake = use_snake
self.downsample_shortcut = downsample_shortcut
self.upsample_shortcut = upsample_shortcut
self.out_shortcut = out_shortcut
self.in_shortcut = in_shortcut
self.final_tanh = final_tanh
self.downsampling_ratio = downsampling_ratio
self.sample_rate = sample_rate
self.scale = scale
class AudioDiTConfig(PreTrainedConfig):
r"""
Configuration class for AudioDiT, a Conditional Flow Matching TTS model based on DiT architecture.
Args:
dit_dim (`int`, *optional*, defaults to 1536):
Hidden dimension of the DiT transformer.
dit_depth (`int`, *optional*, defaults to 24):
Number of transformer layers.
dit_heads (`int`, *optional*, defaults to 24):
Number of attention heads.
dit_ff_mult (`float`, *optional*, defaults to 4.0):
Feed-forward network multiplier.
dit_text_dim (`int`, *optional*, defaults to 768):
Dimension of the text encoder output (UMT5-base).
dit_dropout (`float`, *optional*, defaults to 0.0):
Dropout rate.
dit_bias (`bool`, *optional*, defaults to `True`):
Whether to use bias in linear layers.
dit_cross_attn (`bool`, *optional*, defaults to `True`):
Whether to use cross-attention layers.
dit_adaln_type (`str`, *optional*, defaults to `"global"`):
Type of adaptive layer norm (`"global"` or `"local"`).
dit_adaln_use_text_cond (`bool`, *optional*, defaults to `True`):
Whether to condition AdaLN on text embeddings.
dit_long_skip (`bool`, *optional*, defaults to `True`):
Whether to use long skip connection (input added to output).
dit_text_conv (`bool`, *optional*, defaults to `True`):
Whether to apply ConvNeXt blocks on text embeddings.
dit_qk_norm (`bool`, *optional*, defaults to `True`):
Whether to apply RMS normalization to Q and K.
dit_cross_attn_norm (`bool`, *optional*, defaults to `False`):
Whether to apply layer normalization in cross-attention.
dit_eps (`float`, *optional*, defaults to 1e-6):
Epsilon for normalization layers.
dit_use_latent_condition (`bool`, *optional*, defaults to `True`):
Whether to use latent conditioning (for prompt audio).
repa_dit_layer (`int`, *optional*, defaults to 8):
Layer index for representation alignment.
latent_dim (`int`, *optional*, defaults to 64):
Dimensionality of the audio latent space.
sigma (`float`, *optional*, defaults to 0.0):
Noise level for conditional flow matching.
sampling_rate (`int`, *optional*, defaults to 24000):
Audio sample rate.
latent_hop (`int`, *optional*, defaults to 2048):
Hop size in audio samples per latent frame.
max_wav_duration (`float`, *optional*, defaults to 30.0):
Maximum audio duration in seconds.
text_encoder_model (`str`, *optional*, defaults to `"google/umt5-base"`):
HuggingFace model identifier for the text encoder.
text_add_embed (`bool`, *optional*, defaults to `True`):
Whether to add the first hidden state to the last hidden state in text encoding.
text_norm_feat (`bool`, *optional*, defaults to `True`):
Whether to apply layer normalization to text features.
vae_config (`AudioDiTVaeConfig` or `dict`, *optional*):
Configuration for the WAV-VAE audio autoencoder.
Example:
```python
>>> from transformers import AudioDiTConfig, AudioDiTModel
>>> configuration = AudioDiTConfig()
>>> model = AudioDiTModel(configuration)
>>> configuration = model.config
```
"""
model_type = "audiodit"
sub_configs = {"vae_config": AudioDiTVaeConfig, "text_encoder_config": UMT5Config}
def __init__(
self,
dit_dim: int = 1536,
dit_depth: int = 24,
dit_heads: int = 24,
dit_ff_mult: float = 4.0,
dit_text_dim: int = 768,
dit_dropout: float = 0.0,
dit_bias: bool = True,
dit_cross_attn: bool = True,
dit_adaln_type: str = "global",
dit_adaln_use_text_cond: bool = True,
dit_long_skip: bool = True,
dit_text_conv: bool = True,
dit_qk_norm: bool = True,
dit_cross_attn_norm: bool = False,
dit_eps: float = 1e-6,
dit_use_latent_condition: bool = True,
repa_dit_layer: int = 8,
latent_dim: int = 64,
sigma: float = 0.0,
sampling_rate: int = 24000,
latent_hop: int = 2048,
max_wav_duration: float = 30.0,
text_encoder_model: str = "google/umt5-base",
text_add_embed: bool = True,
text_norm_feat: bool = True,
vae_config: AudioDiTVaeConfig | dict | None = None,
text_encoder_config: UMT5Config | dict | None = None,
**kwargs,
):
super().__init__(**kwargs)
self.dit_dim = dit_dim
self.dit_depth = dit_depth
self.dit_heads = dit_heads
self.dit_ff_mult = dit_ff_mult
self.dit_text_dim = dit_text_dim
self.dit_dropout = dit_dropout
self.dit_bias = dit_bias
self.dit_cross_attn = dit_cross_attn
self.dit_adaln_type = dit_adaln_type
self.dit_adaln_use_text_cond = dit_adaln_use_text_cond
self.dit_long_skip = dit_long_skip
self.dit_text_conv = dit_text_conv
self.dit_qk_norm = dit_qk_norm
self.dit_cross_attn_norm = dit_cross_attn_norm
self.dit_eps = dit_eps
self.dit_use_latent_condition = dit_use_latent_condition
self.repa_dit_layer = repa_dit_layer
self.latent_dim = latent_dim
self.sigma = sigma
self.sampling_rate = sampling_rate
self.latent_hop = latent_hop
self.max_wav_duration = max_wav_duration
self.text_encoder_model = text_encoder_model
self.text_add_embed = text_add_embed
self.text_norm_feat = text_norm_feat
if isinstance(vae_config, dict):
vae_config = AudioDiTVaeConfig(**vae_config)
self.vae_config = vae_config if vae_config is not None else AudioDiTVaeConfig()
if isinstance(text_encoder_config, dict):
text_encoder_config = UMT5Config(**text_encoder_config)
self.text_encoder_config = text_encoder_config
__all__ = ["AudioDiTConfig", "AudioDiTVaeConfig"]
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