| from transformers.configuration_utils import PretrainedConfig |
| from typing import List |
|
|
|
|
| class HeartCodecConfig(PretrainedConfig): |
| model_type = "heartcodec" |
|
|
| def __init__( |
| self, |
| |
| dim: int = 512, |
| codebook_size: int = 8192, |
| decay: float = 0.9, |
| commitment_weight: float = 1.0, |
| threshold_ema_dead_code: int = 2, |
| use_cosine_sim: bool = False, |
| codebook_dim: int = 32, |
| num_quantizers: int = 8, |
| |
| attention_head_dim: int = 64, |
| in_channels: int = 1024, |
| norm_type: str = "ada_norm_single", |
| num_attention_heads: int = 24, |
| num_layers: int = 24, |
| num_layers_2: int = 6, |
| out_channels: int = 256, |
| |
| num_bands: int = 1, |
| sample_rate: int = 48000, |
| causal: bool = True, |
| num_samples: int = 2, |
| downsample_factors: List[int] = [3, 4, 4, 4, 5], |
| downsample_kernel_sizes: List[int] = [6, 8, 8, 8, 10], |
| upsample_factors: List[int] = [5, 4, 4, 4, 3], |
| upsample_kernel_sizes: List[int] = [10, 8, 8, 8, 6], |
| latent_hidden_dim: int = 128, |
| default_kernel_size: int = 7, |
| delay_kernel_size: int = 5, |
| init_channel: int = 64, |
| res_kernel_size: int = 7, |
| **kwargs |
| ): |
| super().__init__(**kwargs) |
| self.dim = dim |
| self.codebook_size = codebook_size |
| self.decay = decay |
| self.commitment_weight = commitment_weight |
| self.threshold_ema_dead_code = threshold_ema_dead_code |
| self.use_cosine_sim = use_cosine_sim |
| self.codebook_dim = codebook_dim |
| self.num_quantizers = num_quantizers |
|
|
| self.attention_head_dim = attention_head_dim |
| self.in_channels = in_channels |
| self.norm_type = norm_type |
| self.num_attention_heads = num_attention_heads |
| self.num_layers = num_layers |
| self.num_layers_2 = num_layers_2 |
| self.out_channels = out_channels |
|
|
| self.num_bands = num_bands |
| self.sample_rate = sample_rate |
| self.causal = causal |
| self.num_samples = num_samples |
| self.downsample_factors = downsample_factors |
| self.downsample_kernel_sizes = downsample_kernel_sizes |
| self.upsample_factors = upsample_factors |
| self.upsample_kernel_sizes = upsample_kernel_sizes |
| self.latent_hidden_dim = latent_hidden_dim |
| self.default_kernel_size = default_kernel_size |
| self.delay_kernel_size = delay_kernel_size |
| self.init_channel = init_channel |
| self.res_kernel_size = res_kernel_size |
|
|
