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Architecture:
log-mel input_features -> HF WhisperEncoder
-> 4x time merge (B, T, 1024) -> (B, T/4, 4096)
-> VQAdaptor (4096 -> 1024)
-> masked_scatter into text embeddings
-> Qwen3-0.6B decoder -> logits
"""
from __future__ import annotations
from typing import Optional
import torch
from torch import nn
from transformers import GenerationMixin, PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.models.qwen3.modeling_qwen3 import Qwen3Model
from transformers.models.whisper.modeling_whisper import WhisperEncoder
from transformers.utils import torch_compilable_check
from .configuration_moss_transcribe_diarize import MossTranscribeDiarizeConfig
class VQAdaptor(nn.Module):
"""Projects merged Whisper features to LM hidden dim.
``Linear(in → hidden) → SiLU → Linear(hidden → hidden) → LayerNorm``
"""
def __init__(self, input_dim: int, hidden_size: int, norm_eps: float = 1e-6):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(input_dim, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True),
nn.LayerNorm(hidden_size, eps=norm_eps, bias=True),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.layers(x)
class MossTranscribeDiarizePreTrainedModel(PreTrainedModel):
config_class = MossTranscribeDiarizeConfig
base_model_prefix = "model"
input_modalities = ("audio", "text")
_no_split_modules = ["Qwen3DecoderLayer", "WhisperEncoderLayer"]
_skip_keys_device_placement = "past_key_values"
supports_gradient_checkpointing = True
_supports_sdpa = True
_supports_attention_backend = True
class MossTranscribeDiarizeModel(MossTranscribeDiarizePreTrainedModel):
base_model_prefix = "model"
"""Single-stream multimodal backbone: Whisper-Medium encoder + Qwen3-0.6B.
Audio features are injected into text embeddings via ``masked_scatter`` at
positions marked by ``audio_token_id`` in ``input_ids``.
"""
def __init__(self, config: MossTranscribeDiarizeConfig):
super().__init__(config)
self.language_model: nn.Module = Qwen3Model(config.text_config)
self.whisper_encoder: nn.Module = WhisperEncoder(config.audio_config)
self.vq_adaptor: VQAdaptor = VQAdaptor(
input_dim=config.adaptor_input_dim,
hidden_size=config.text_config.hidden_size,
norm_eps=config.text_config.rms_norm_eps,
)
self.post_init()
def get_input_embeddings(self):
return self.language_model.embed_tokens
def set_input_embeddings(self, value):
self.language_model.embed_tokens = value
# ---- 4x time merge ---------------------------------------------------
def time_merge(self, features: torch.Tensor) -> torch.Tensor:
"""``(B, T, D) -> (B, T//M, D*M)`` where M is ``audio_merge_size``."""
B, T, D = features.shape
merge_size = int(self.config.audio_merge_size)
T_trim = (T // merge_size) * merge_size
return features[:, :T_trim, :].reshape(B, T_trim // merge_size, D * merge_size)
# ---- audio feature extraction -----------------------------------------
def get_audio_features(
self,
input_features: torch.Tensor,
audio_feature_lengths: torch.LongTensor,
audio_chunk_mapping: Optional[torch.LongTensor] = None,
) -> list[torch.Tensor]:
"""Whisper encoder -> 4x time merge -> VQAdaptor.
Returns list of ``(1, N_tokens, hidden_size)`` tensors.
"""
if input_features is None:
raise ValueError("input_features must be provided for audio feature extraction.")
if audio_feature_lengths is None:
raise ValueError("audio_feature_lengths must be provided with input_features.")
device = next(self.whisper_encoder.parameters()).device
encoder_dtype = next(self.whisper_encoder.parameters()).dtype
input_features = input_features.to(device=device, dtype=encoder_dtype)
audio_feature_lengths = audio_feature_lengths.to(device=device)
if audio_feature_lengths.numel() != input_features.shape[0]:
raise ValueError(
"audio_feature_lengths must contain one length per input_features chunk: "
f"got {audio_feature_lengths.numel()} lengths for {input_features.shape[0]} chunks."
)
whisper_features = self.whisper_encoder(input_features, return_dict=True).last_hidden_state
chunk_mapping = (
audio_chunk_mapping.to(device=device)
if audio_chunk_mapping is not None
else torch.zeros(input_features.shape[0], dtype=torch.long, device=device)
)
if chunk_mapping.numel() != input_features.shape[0]:
raise ValueError(
"audio_chunk_mapping must contain one sample index per input_features chunk: "
f"got {chunk_mapping.numel()} indices for {input_features.shape[0]} chunks."
)
num_audios = int(chunk_mapping.max().item()) + 1 if chunk_mapping.numel() else 0
per_audio_chunks = [[] for _ in range(num_audios)]
for chunk_idx, token_len in enumerate(audio_feature_lengths.tolist()):
sample_idx = int(chunk_mapping[chunk_idx].item())
per_audio_chunks[sample_idx].append(
whisper_features[chunk_idx : chunk_idx + 1, : int(token_len) * 4]
)
adapted = []
for parts in per_audio_chunks:
feat = torch.cat(parts, dim=1)
feat = feat.to(self.dtype)
merged = self.time_merge(feat)
adapted.append(self.vq_adaptor(merged))
return adapted
# ---- inject audio into text embeddings --------------------------------
def get_placeholder_mask(
self,
input_ids: Optional[torch.LongTensor],
inputs_embeds: torch.FloatTensor,
audio_features: torch.Tensor,
) -> torch.BoolTensor:
"""Return the expanded audio placeholder mask and validate feature count."""
if input_ids is None:
special_audio_mask = inputs_embeds == self.get_input_embeddings()(
torch.tensor(self.config.audio_token_id, dtype=torch.long, device=inputs_embeds.device)
)
special_audio_mask = special_audio_mask.all(-1)
else:
special_audio_mask = input_ids.to(device=inputs_embeds.device) == self.config.audio_token_id
if special_audio_mask.shape != inputs_embeds.shape[:2]:
raise ValueError(
"input_ids shape must match the first two dimensions of inputs_embeds: "
f"got {tuple(special_audio_mask.shape)} and {tuple(inputs_embeds.shape[:2])}."
)
n_audio_tokens = special_audio_mask.sum()
special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
torch_compilable_check(
inputs_embeds[special_audio_mask].numel() == audio_features.numel(),
(
f"Audio features and audio tokens do not match: "
f"tokens: {n_audio_tokens}, features {audio_features.shape[0]}"
),
)
return special_audio_mask
def inject_audio_features(
self,
input_ids,
inputs_embeds,
input_features,
audio_feature_lengths,
audio_chunk_mapping,
):
"""Replace audio placeholder positions with projected audio features."""
if input_features is None:
return inputs_embeds
audio_features = self.get_audio_features(
input_features=input_features,
audio_feature_lengths=audio_feature_lengths,
audio_chunk_mapping=audio_chunk_mapping,
)
audio_embeds = torch.cat([f.squeeze(0) for f in audio_features], dim=0)
audio_embeds = audio_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
audio_mask = self.get_placeholder_mask(input_ids, inputs_embeds, audio_embeds)
return inputs_embeds.masked_scatter(audio_mask, audio_embeds)
# ---- forward ----------------------------------------------------------
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values=None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
input_features: Optional[torch.FloatTensor] = None,
audio_feature_lengths: Optional[torch.LongTensor] = None,
audio_chunk_mapping: Optional[torch.LongTensor] = None,
**kwargs,
):
return_dict = True if return_dict is None else return_dict
if input_ids is None and inputs_embeds is None:
raise ValueError("You must specify one of input_ids or inputs_embeds.")
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You must specify only one of input_ids or inputs_embeds.")
if inputs_embeds is None:
inputs_embeds = self.get_input_embeddings()(input_ids)
inputs_embeds = self.inject_audio_features(
input_ids=input_ids,
inputs_embeds=inputs_embeds,
input_features=input_features,
audio_feature_lengths=audio_feature_lengths,
audio_chunk_mapping=audio_chunk_mapping,
)
if output_attentions is not None:
kwargs["output_attentions"] = output_attentions
if output_hidden_states is not None:
kwargs["output_hidden_states"] = output_hidden_states
outputs = self.language_model(
input_ids=None, attention_mask=attention_mask, position_ids=position_ids,
past_key_values=past_key_values, inputs_embeds=inputs_embeds,
use_cache=use_cache, **kwargs,
)
if not return_dict:
return outputs.to_tuple()
return outputs
class MossTranscribeDiarizeForConditionalGeneration(MossTranscribeDiarizePreTrainedModel, GenerationMixin):
_tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"}
def __init__(self, config: MossTranscribeDiarizeConfig):
super().__init__(config)
self.model = MossTranscribeDiarizeModel(config)
self.vocab_size = config.text_config.vocab_size
self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
self.post_init()
def get_input_embeddings(self):
return self.model.get_input_embeddings()
def set_input_embeddings(self, value):
self.model.set_input_embeddings(value)
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def get_audio_features(
self,
input_features: torch.Tensor,
audio_feature_lengths: torch.LongTensor,
audio_chunk_mapping: Optional[torch.LongTensor] = None,
) -> list[torch.Tensor]:
return self.model.get_audio_features(
input_features=input_features,
audio_feature_lengths=audio_feature_lengths,
audio_chunk_mapping=audio_chunk_mapping,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values=None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
input_features: Optional[torch.FloatTensor] = None,
audio_feature_lengths: Optional[torch.LongTensor] = None,
audio_chunk_mapping: Optional[torch.LongTensor] = None,
logits_to_keep: int | torch.Tensor = 0,
**kwargs,
):
return_dict = True if return_dict is None else return_dict
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=True,
input_features=input_features,
audio_feature_lengths=audio_feature_lengths,
audio_chunk_mapping=audio_chunk_mapping,
**kwargs,
)
hidden_states = outputs.last_hidden_state
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.lm_head(hidden_states[:, slice_indices, :])
loss = None
if labels is not None:
loss = self.loss_function(
logits=logits,
labels=labels,
vocab_size=self.config.text_config.vocab_size,
**kwargs,
)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss, logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# ---- generation support -----------------------------------------------
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
input_features=None,
audio_feature_lengths=None,
audio_chunk_mapping=None,
is_first_iteration=False,
use_cache=True,
**kwargs,
):
model_inputs = super().prepare_inputs_for_generation(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask, inputs_embeds=inputs_embeds,
is_first_iteration=is_first_iteration, use_cache=use_cache, **kwargs,
)
if input_features is not None and (is_first_iteration or not use_cache):
model_inputs["input_features"] = input_features
model_inputs["audio_feature_lengths"] = audio_feature_lengths
model_inputs["audio_chunk_mapping"] = audio_chunk_mapping
return model_inputs
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