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from typing import Optional, Union
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 StepAudio2TextConfig(PretrainedConfig):
model_type = "step_audio_2_text"
def __init__(
self,
vocab_size=64012,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=48,
num_attention_heads=32,
num_attention_groups=4,
num_key_value_heads=4,
hidden_act="silu",
max_position_embeddings=8192,
initializer_range=0.02,
rms_norm_eps=1e-6,
rope_theta=1000000.0,
rope_scaling=None,
eos_token_id=None,
**kwargs
):
if eos_token_id is not None:
if isinstance(eos_token_id, list):
eos_token_id = list(set([151643, 151645, 151665] + eos_token_id))
else:
eos_token_id = [151643, 151645, 151665, eos_token_id]
else:
eos_token_id = [151643, 151645, 151665]
super().__init__(
eos_token_id=eos_token_id,
**kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_attention_groups = num_attention_groups
self.num_key_value_heads = num_key_value_heads
assert self.num_attention_groups == self.num_key_value_heads, "num_attention_groups must be equal to num_key_value_heads"
self.hidden_act = hidden_act
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.text_config = Qwen2Config(
vocab_size=vocab_size,
hidden_size=hidden_size,
intermediate_size=intermediate_size,
num_hidden_layers=num_hidden_layers,
num_attention_heads=num_attention_heads,
num_key_value_heads=num_key_value_heads,
hidden_act=hidden_act,
max_position_embeddings=max_position_embeddings,
initializer_range=initializer_range,
rms_norm_eps=rms_norm_eps,
rope_theta=rope_theta,
rope_scaling=rope_scaling,
architectures=["Qwen2ForCausalLM"],
torch_dtype=getattr(self, "torch_dtype", "bfloat16"),
)
class StepAudio2Config(PretrainedConfig):
model_type = "step_audio_2"
architectures = ["StepAudio2ForCausalLM"]
def __init__(
self,
audio_encoder_config :Optional[Union[dict, StepAudio2EncoderConfig]] = None,
text_config: Optional[Union[dict, StepAudio2TextConfig]] = 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)
if text_config is None:
text_config = StepAudio2TextConfig().text_config
elif isinstance(text_config, dict):
text_config = StepAudio2TextConfig(**text_config).text_config
self.text_config = text_config
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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