File size: 1,810 Bytes
69690cd | 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 | from typing import Optional
from transformers import Qwen2Config
from transformers.configuration_utils import PretrainedConfig
class StepAudio2EncoderConfig(PretrainedConfig):
model_type = "step_audio_2_encoder"
def __init__(
self,
n_mels=128,
n_audio_ctx=1500,
n_audio_state=512,
n_audio_head=8,
n_audio_layer=6,
llm_dim=4096,
kernel_size=3,
adapter_stride=2,
**kwargs,
):
self.n_mels = n_mels
self.n_audio_ctx = n_audio_ctx
self.n_audio_state = n_audio_state
self.n_audio_head = n_audio_head
self.n_audio_layer = n_audio_layer
self.llm_dim = llm_dim
self.kernel_size = kernel_size
self.adapter_stride = adapter_stride
super().__init__(**kwargs)
class StepAudio2Config(PretrainedConfig):
model_type = "step_audio_2"
architectures = ["StepAudio2ForCausalLM"]
def __init__(
self,
audio_encoder_config=None,
use_sliding_window: bool = False,
sliding_window: Optional[int] = 2048,
max_window_layers: Optional[int] = None,
**kwargs
):
kwargs.setdefault("use_sliding_window", use_sliding_window)
kwargs.setdefault("sliding_window", sliding_window)
if max_window_layers is None:
max_window_layers = kwargs.get("num_hidden_layers", None)
kwargs.setdefault("max_window_layers", max_window_layers)
super().__init__(**kwargs)
self.text_config = Qwen2Config(**kwargs)
if audio_encoder_config is None:
self.audio_encoder_config = StepAudio2EncoderConfig()
elif isinstance(audio_encoder_config, dict):
self.audio_encoder_config = StepAudio2EncoderConfig(**audio_encoder_config)
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