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Dasheng Audio Tokenizer Configuration
"""
from transformers import PretrainedConfig
class DashengTokenizerConfig(PretrainedConfig):
"""
Configuration class for DashEng Audio Tokenizer.
This configuration is used to initialize the DashEng model with the same
parameters as the original implementation in models.py.
Args:
target_nmels (int): Number of Mel bins for the frontend. Default: 100
decoder_embed_dim (int): Decoder embedding dimension. Default: 768
decoder_depth (int): Number of decoder layers. Default: 8
decoder_intermediate_size (int): Decoder intermediate size. Default: 1536
istft_n_fft (int): ISTFT n_fft parameter. Default: 1280
istft_hop (int): ISTFT hop parameter. Default: 640
upsample_tokens (int): Upsample factor for tokens. Default: 1
n_mels_patch (int): Number of Mel bins for patch embedding. Default: 100
hop_length (int): Hop length for Mel spectrogram. Default: 160
"""
model_type = "dashengtokenizer"
def __init__(
self,
embed_dim: int = 1280,
depth:int = 32,
num_heads: int = 16,
decoder_embed_dim: int = 1280,
decoder_depth: int = 12,
decoder_intermediate_size: int = 5120,
istft_n_fft: int = 1280,
istft_hop: int = 320, # 20ms
upsample_tokens: int = 2,
n_mels_patch: int = 128, # acoustic nmel
hop_length: int = 160, # acoustic hop
**kwargs,
):
super().__init__(**kwargs)
self.embed_dim = embed_dim
self.depth = depth
self.num_heads = num_heads
self.decoder_embed_dim = decoder_embed_dim
self.decoder_depth = decoder_depth
self.decoder_intermediate_size = decoder_intermediate_size
self.istft_n_fft = istft_n_fft
self.istft_hop = istft_hop
self.upsample_tokens = upsample_tokens
self.n_mels_patch = n_mels_patch
self.hop_length = hop_length
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