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Running on A100
Running on A100
File size: 3,962 Bytes
2c4c098 | 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 | # Copyright 2025 Xiaomi Corporation.
from transformers import PretrainedConfig
class MiMoAudioTokenizerConfig(PretrainedConfig):
model_type = "mimo_audio_tokenizer"
def __init__(
self,
max_audio_seconds: int = 1800,
stride_size: int = 2,
avg_pooler: int = 1,
d_model: int = 768,
scale_embedding: bool = True,
kernel_size: int = 3,
activation_function: str = "gelu",
encoder_layers: int = 8,
encoder_skip_layer_id: int = None,
encoder_attention_heads: int = 12,
encoder_ffn_dim: int = 3072,
encoder_causal: bool = False,
encoder_attn_window_size: list[int] = None,
decoder_layers: int = 8,
decoder_attention_heads: int = 12,
decoder_ffn_dim: int = 3072,
decoder_kernel_size: int = 3,
decoder_stride_size: int = 2,
decoder_causal: bool = True,
decoder_attn_window_size: list[int] = None,
nfft: int = 1024,
vocoder_dim: int = 512,
vocoder_intermediate_dim: int = 4096,
vocoder_num_layers: int = 30,
n_mels: int = 80,
sampling_rate: int = 24000,
hop_length: int = 240,
window_size: int = 1024,
vocoder_padding: str = "same",
fmin: int = 0,
fmax: int = None,
num_quantizers: int = 12,
codebook_size: list[int] = None,
threshold_ema_dead_code: int = 10,
position_embedding_type: str = "rope",
rope_theta: int = 10000,
rope_type: str = "default",
ln_type: str = "LayerNorm",
vocoder_attention_heads: int = 4,
vocoder_attn_window_size: list[int] = None,
**kwargs,
):
super().__init__(**kwargs)
self.max_audio_seconds = max_audio_seconds
self.stride_size = stride_size
self.avg_pooler = avg_pooler
self.d_model = d_model
self.scale_embedding = scale_embedding
self.kernel_size = kernel_size
self.activation_function = activation_function
self.encoder_layers = encoder_layers
self.encoder_skip_layer_id = encoder_skip_layer_id
self.encoder_attention_heads = encoder_attention_heads
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_causal = encoder_causal
self.encoder_attn_window_size = (
encoder_attn_window_size
if encoder_attn_window_size is not None
else [-1, -1]
)
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_kernel_size = decoder_kernel_size
self.decoder_stride_size = decoder_stride_size
self.decoder_causal = decoder_causal
self.decoder_attn_window_size = (
decoder_attn_window_size
if decoder_attn_window_size is not None
else [-1, -1]
)
self.nfft = nfft
self.vocoder_dim = vocoder_dim
self.vocoder_intermediate_dim = vocoder_intermediate_dim
self.vocoder_num_layers = vocoder_num_layers
self.n_mels = n_mels
self.sampling_rate = sampling_rate
self.hop_length = hop_length
self.window_size = window_size
self.vocoder_padding = vocoder_padding
self.fmin = fmin
self.fmax = fmax
self.num_quantizers = num_quantizers
self.codebook_size = codebook_size if codebook_size is not None else [1024]
self.threshold_ema_dead_code = threshold_ema_dead_code
self.position_embedding_type = position_embedding_type
self.rope_theta = rope_theta
self.rope_type = rope_type
self.ln_type = ln_type
self.vocoder_attention_heads = vocoder_attention_heads
self.vocoder_attn_window_size = (
vocoder_attn_window_size
if vocoder_attn_window_size is not None
else [40, 10]
)
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