Adds FunAudioChat multimodal audio model support (#2) (#33058)

Signed-off-by: ramos <49182011+nemoramo@users.noreply.github.com>
Signed-off-by: mayufeng <mayufeng@example.com>
Co-authored-by: mayufeng <mayufeng@example.com>

examples/offline_inference/audio_language.py

@@ -117,6 +117,31 @@ def run_glmasr(question: str, audio_count: int) -> ModelRequestData:
     )
 
 
+# FunAudioChat
+def run_funaudiochat(question: str, audio_count: int) -> ModelRequestData:
+    # NOTE: FunAudioChat is not available on the HuggingFace Hub at the time of
+    # writing. Pass a local model path via `--model`.
+    model_name = "funaudiochat"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=4096,
+        max_num_seqs=2,
+        limit_mm_per_prompt={"audio": audio_count},
+        enforce_eager=True,
+    )
+
+    audio_in_prompt = "".join(
+        ["<|audio_bos|><|AUDIO|><|audio_eos|>\n" for _ in range(audio_count)]
+    )
+    prompt = f"{audio_in_prompt}{question}"
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompt,
+    )
+
+
 # Granite Speech
 def run_granite_speech(question: str, audio_count: int) -> ModelRequestData:
     # NOTE - the setting in this example are somewhat different from what is
@@ -410,6 +435,7 @@ model_example_map = {
     "audioflamingo3": run_audioflamingo3,
     "gemma3n": run_gemma3n,
     "glmasr": run_glmasr,
+    "funaudiochat": run_funaudiochat,
     "granite_speech": run_granite_speech,
     "midashenglm": run_midashenglm,
     "minicpmo": run_minicpmo,
@@ -435,6 +461,12 @@ def parse_args():
         choices=model_example_map.keys(),
         help='Huggingface "model_type".',
     )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default=None,
+        help="Model ID or local path override. Required for funaudiochat.",
+    )
     parser.add_argument(
         "--num-prompts", type=int, default=1, help="Number of prompts to run."
     )
@@ -467,6 +499,9 @@ def main(args):
     if model not in model_example_map:
         raise ValueError(f"Model type {model} is not supported.")
 
+    if model == "funaudiochat" and not args.model:
+        raise ValueError("--model is required when --model-type=funaudiochat")
+
     if args.tensor_parallel_size is not None and args.tensor_parallel_size < 1:
         raise ValueError(
             f"tensor_parallel_size must be a positive integer, "
@@ -477,6 +512,8 @@ def main(args):
     req_data = model_example_map[model](
         question_per_audio_count[audio_count], audio_count
     )
+    if model == "funaudiochat":
+        req_data.engine_args.model = args.model
 
     # Disable other modalities to save memory
     default_limits = {"image": 0, "video": 0, "audio": 0}

tests/models/registry.py

@@ -692,6 +692,9 @@ _MULTIMODAL_EXAMPLE_MODELS = {
         "baidu/ERNIE-4.5-VL-28B-A3B-PT",
         trust_remote_code=True,
     ),
+    "FunAudioChatForConditionalGeneration": _HfExamplesInfo(
+        "funaudiochat", is_available_online=False
+    ),
     "FuyuForCausalLM": _HfExamplesInfo("adept/fuyu-8b"),
     "Gemma3ForConditionalGeneration": _HfExamplesInfo("google/gemma-3-4b-it"),
     "Gemma3nForConditionalGeneration": _HfExamplesInfo("google/gemma-3n-E2B-it"),

vllm/model_executor/models/funaudiochat.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""Inference-only FunAudioChat model compatible with HuggingFace weights.
+
+FunAudioChat is a Qwen3 text model augmented with:
+  - a continuous audio encoder (Whisper-mel frontend + transformer)
+  - a discrete audio encoder (speech tokenizer + projector)
+
+In the HF implementation, audio features are scattered into `<|AUDIO|>` token
+positions via `inputs_embeds`, while `position_ids` (RoPE) remains standard 1D.
+"""
+
+from __future__ import annotations
+
+import os
+from collections.abc import Iterable, Mapping, Sequence
+from functools import cached_property
+from typing import Any
+
+import numpy as np
+import torch
+import torch.nn as nn
+from transformers import PreTrainedTokenizerFast, WhisperFeatureExtractor
+from transformers.activations import get_activation
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.modeling_outputs import BaseModelOutput
+
+from vllm.config import VllmConfig
+from vllm.config.multimodal import BaseDummyOptions
+from vllm.model_executor.layers.attention.mm_encoder_attention import MMEncoderAttention
+from vllm.model_executor.layers.linear import QKVParallelLinear, RowParallelLinear
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (
+    MultiModalDataDict,
+    MultiModalFieldConfig,
+    MultiModalKwargsItems,
+)
+from vllm.multimodal.parse import (
+    AudioProcessorItems,
+    MultiModalDataItems,
+    MultiModalDataParser,
+)
+from vllm.multimodal.processing import (
+    BaseDummyInputsBuilder,
+    BaseMultiModalProcessor,
+    BaseProcessingInfo,
+    PromptReplacement,
+    PromptUpdate,
+    PromptUpdateDetails,
+)
+from vllm.sequence import IntermediateTensors
+from vllm.utils.import_utils import _has_module
+
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .utils import AutoWeightsLoader, init_vllm_registered_model, maybe_prefix
+
+
+class _SinusoidsPositionEmbedding(nn.Module):
+    def __init__(self, length: int, channels: int, max_timescale: float = 10000.0):
+        super().__init__()
+        if channels % 2 != 0:
+            raise ValueError("SinusoidsPositionEmbedding needs even channels input")
+
+        log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
+        inv_timescales = torch.exp(
+            -log_timescale_increment * torch.arange(channels // 2).float()
+        )
+        scaled_time = (
+            torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+        )
+        self.register_buffer(
+            "positional_embedding",
+            torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1),
+            persistent=False,
+        )
+
+
+class FunAudioChatAudioAttention(nn.Module):
+    """Multi-headed attention used inside the continuous audio tower."""
+
+    def __init__(self, config: Any):
+        super().__init__()
+        self.embed_dim = int(config.d_model)
+        self.total_num_heads = int(config.encoder_attention_heads)
+        self.dropout = float(getattr(config, "attention_dropout", 0.0))
+        self.head_dim = self.embed_dim // self.total_num_heads
+        self.num_key_value_groups = 1  # needed for eager attention
+        self.config = config
+
+        if self.head_dim * self.total_num_heads != self.embed_dim:
+            raise ValueError(
+                "embed_dim must be divisible by num_heads "
+                f"(got embed_dim={self.embed_dim}, "
+                f"num_heads={self.total_num_heads})."
+            )
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = 0.0
+        self.is_decoder = False
+        self.is_causal = False
+
+        self.qkv_proj = QKVParallelLinear(
+            self.embed_dim,
+            self.head_dim,
+            self.total_num_heads,
+            bias=True,
+        )
+        self.num_heads = self.qkv_proj.num_heads
+        self.num_kv_heads = self.qkv_proj.num_kv_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+
+        self.attn = MMEncoderAttention(
+            num_heads=self.num_heads,
+            head_size=self.head_dim,
+            scale=self.scaling,
+            num_kv_heads=self.num_kv_heads,
+            prefix="funaudiochat_audio_tower.attn",
+        )
+        self.out_proj = RowParallelLinear(
+            self.embed_dim,
+            self.embed_dim,
+            bias=True,
+        )
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+
+        params_dict = dict(self.named_parameters())
+        with torch.no_grad():
+            if self.qkv_proj.bias is not None:
+                # HF FunAudioChat uses bias=False for k_proj. Ensure the missing
+                # shard starts as zeros, while allowing q/v shards to load.
+                self.qkv_proj.bias.zero_()
+
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            for param_name, shard_name, shard_id in stacked_params_mapping:
+                if shard_name not in name:
+                    continue
+                name = name.replace(shard_name, param_name)
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+
+            loaded_params.add(name)
+
+        return loaded_params
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        **kwargs: object,
+    ) -> torch.Tensor:
+        del kwargs
+        del attention_mask
+        seq_length, _ = hidden_states.size()
+
+        qkv, _ = self.qkv_proj(hidden_states)
+        query_states, key_states, value_states = qkv.split(
+            [self.q_size, self.kv_size, self.kv_size], dim=-1
+        )
+
+        max_seqlen: torch.Tensor | None = None
+        if cu_seqlens is not None:
+            max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
+
+        attn_output = self.attn(
+            query_states.reshape(1, seq_length, self.q_size),
+            key_states.reshape(1, seq_length, self.kv_size),
+            value_states.reshape(1, seq_length, self.kv_size),
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+        ).reshape(seq_length, -1)
+
+        output, _ = self.out_proj(attn_output)
+        return output
+
+
+class FunAudioChatAudioEncoderLayer(nn.Module):
+    def __init__(self, config: Any):
+        super().__init__()
+        self.embed_dim = int(config.d_model)
+        self.self_attn = FunAudioChatAudioAttention(config)
+        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+        self.dropout = float(config.dropout)
+        self.activation_fn = get_activation(str(config.activation_function))
+        self.activation_dropout = float(config.activation_dropout)
+        self.fc1 = nn.Linear(self.embed_dim, int(config.encoder_ffn_dim))
+        self.fc2 = nn.Linear(int(config.encoder_ffn_dim), self.embed_dim)
+        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        **kwargs: object,
+    ) -> tuple[torch.Tensor]:
+        residual = hidden_states
+        hidden_states = self.self_attn_layer_norm(hidden_states)
+        hidden_states = self.self_attn(
+            hidden_states=hidden_states,
+            cu_seqlens=cu_seqlens,
+            attention_mask=attention_mask,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.final_layer_norm(hidden_states)
+        hidden_states = self.activation_fn(self.fc1(hidden_states))
+        hidden_states = nn.functional.dropout(
+            hidden_states, p=self.activation_dropout, training=self.training
+        )
+        hidden_states = self.fc2(hidden_states)
+        hidden_states = nn.functional.dropout(
+            hidden_states, p=self.dropout, training=self.training
+        )
+        hidden_states = residual + hidden_states
+
+        return (hidden_states,)
+
+
+class FunAudioChatAudioEncoder(nn.Module):
+    """Continuous audio tower."""
+
+    def __init__(self, config: Any):
+        super().__init__()
+        self.config = config
+
+        embed_dim = int(config.d_model)
+        self.num_mel_bins = int(config.num_mel_bins)
+        self.max_source_positions = int(config.max_source_positions)
+        self.embed_scale = (embed_dim**0.5) if bool(config.scale_embedding) else 1.0
+        self.n_window = int(config.n_window)
+
+        self.conv1 = nn.Conv1d(self.num_mel_bins, embed_dim, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv1d(embed_dim, embed_dim, kernel_size=3, stride=2, padding=1)
+        self.layers = nn.ModuleList(
+            [
+                FunAudioChatAudioEncoderLayer(config)
+                for _ in range(int(config.encoder_layers))
+            ]
+        )
+        self.ln_post = nn.LayerNorm(embed_dim)
+        self.avg_pooler = nn.AvgPool1d(2, stride=2)
+        self.proj = nn.Linear(embed_dim, int(config.output_dim))
+        self.positional_embedding = _SinusoidsPositionEmbedding(
+            self.max_source_positions, embed_dim
+        )
+
+        # Present in HF weights even if unused during S2T.
+        self.audio_bos_eos_token = nn.Embedding(2, int(config.output_dim))
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.conv1.weight.dtype
+
+    def _prepare_attention_mask(
+        self, inputs_tensor: torch.Tensor, cu_seqlens: torch.Tensor
+    ) -> torch.Tensor | None:
+        if getattr(self.config, "_attn_implementation", "eager") == "flash_attention_2":
+            return None
+
+        seq_length = inputs_tensor.shape[0]
+        attention_mask = torch.full(
+            (1, 1, seq_length, seq_length),
+            torch.finfo(inputs_tensor.dtype).min,
+            device=inputs_tensor.device,
+            dtype=inputs_tensor.dtype,
+        )
+        for i in range(1, len(cu_seqlens)):
+            start = int(cu_seqlens[i - 1].item())
+            end = int(cu_seqlens[i].item())
+            attention_mask[..., start:end, start:end] = 0
+        return attention_mask
+
+    def forward(
+        self,
+        input_features: torch.Tensor,
+        feature_lens: torch.Tensor,
+        aftercnn_lens: torch.Tensor,
+        speech_maxlen: int,
+        **kwargs: object,
+    ) -> BaseModelOutput:
+        # For max-length audio (300s => ~7500 speech frames at 25Hz), the
+        # Torch SDPA path can be prohibitively memory hungry (~O(n^2) inside the
+        # longest chunks). Require FlashAttention for such inputs to avoid OOM
+        # and performance cliffs.
+        if int(speech_maxlen) >= 7500:
+            if not _has_module("flash_attn"):
+                raise RuntimeError(
+                    "FunAudioChat long audio (~300s) requires FlashAttention-2 "
+                    "for the continuous audio tower, but `flash_attn` is not "
+                    "installed in the runtime environment."
+                )
+            if not getattr(
+                self.layers[0].self_attn.attn, "is_flash_attn_backend", False
+            ):
+                raise RuntimeError(
+                    "FunAudioChat long audio (~300s) requires FlashAttention for the "
+                    "continuous audio tower, but the selected MM encoder attention "
+                    "backend is not FlashAttention."
+                )
+
+        # Handle empty / invalid items (feature_lens == 0) without crashing.
+        original_batch_size = int(feature_lens.size(0))
+        device = input_features.device
+
+        valid_mask = feature_lens > 0
+        valid_indices = torch.where(valid_mask)[0]
+
+        if valid_indices.numel() == 0:
+            output_dim = int(self.proj.out_features)
+            return BaseModelOutput(
+                last_hidden_state=torch.zeros(
+                    (original_batch_size, speech_maxlen, output_dim),
+                    device=device,
+                    dtype=self.proj.weight.dtype,
+                )
+            )
+
+        input_features_list = input_features.split(feature_lens.tolist(), dim=1)
+        valid_input_features_list = [input_features_list[int(i)] for i in valid_indices]
+        valid_input_features = torch.cat(valid_input_features_list, dim=1)
+
+        valid_feature_lens = feature_lens[valid_mask]
+        valid_aftercnn_lens = aftercnn_lens[valid_mask]
+
+        chunk_num = torch.ceil(valid_feature_lens / (self.n_window * 2)).long()
+
+        chunk_lengths_list: list[int] = []
+        full_chunk_len = self.n_window * 2
+        for i, length in enumerate(valid_feature_lens):
+            num_chunks_for_sample = int(chunk_num[i].item())
+            if num_chunks_for_sample == 0:
+                continue
+            chunk_lengths_list.extend([full_chunk_len] * (num_chunks_for_sample - 1))
+            last_chunk_len = int(length.item()) % full_chunk_len
+            if last_chunk_len == 0:
+                last_chunk_len = full_chunk_len
+            chunk_lengths_list.append(last_chunk_len)
+
+        chunk_lengths = torch.tensor(
+            chunk_lengths_list, dtype=torch.long, device=device
+        )
+
+        chunk_list = valid_input_features.split(chunk_lengths.tolist(), dim=1)
+        padded_feature, padded_mask, padded_mask_after_cnn = (
+            self.padded_and_mask_function(
+                chunk_list, chunk_lengths, padding_value=0, padding_side="right"
+            )
+        )
+
+        padded_embed = nn.functional.gelu(self.conv1(padded_feature)) * padded_mask
+        padded_embed = nn.functional.gelu(self.conv2(padded_embed)).transpose(1, 2)
+
+        padded_embed = padded_embed + self.positional_embedding.positional_embedding[
+            : padded_embed.shape[1], :
+        ].unsqueeze(0).to(padded_embed.dtype)
+
+        hidden_states = padded_embed[padded_mask_after_cnn]
+        cu_seqlens = torch.cat(
+            (
+                torch.zeros(1, device=padded_mask_after_cnn.device, dtype=torch.int32),
+                padded_mask_after_cnn.sum(1).cumsum(0),
+            )
+        ).to(torch.int32)
+
+        for encoder_layer in self.layers:
+            (hidden_states,) = encoder_layer(
+                hidden_states,
+                cu_seqlens=cu_seqlens,
+                **kwargs,
+            )
+
+        hidden_states_list = hidden_states.split(valid_aftercnn_lens.tolist(), dim=0)
+
+        pooled_list: list[torch.Tensor] = []
+        pooled_lengths: list[int] = []
+        for each_audio_states in hidden_states_list:
+            seq_len = int(each_audio_states.shape[0])
+            if seq_len >= 2:
+                pooled = nn.functional.avg_pool1d(
+                    each_audio_states.transpose(0, 1), kernel_size=2, stride=2
+                ).transpose(0, 1)
+            else:
+                pooled = each_audio_states
+            pooled_list.append(pooled)
+            pooled_lengths.append(int(pooled.shape[0]))
+
+        pooled_concat = torch.cat(pooled_list, dim=0)
+        processed_concat = self.proj(self.ln_post(pooled_concat))
+        processed_audio_list = list(processed_concat.split(pooled_lengths, dim=0))
+
+        output_dim = (
+            int(processed_audio_list[0].shape[-1])
+            if processed_audio_list
+            else int(self.proj.out_features)
+        )
+        output_hidden_states = torch.zeros(
+            (original_batch_size, speech_maxlen, output_dim),
+            dtype=processed_audio_list[0].dtype
+            if processed_audio_list
+            else self.proj.weight.dtype,
+            device=device,
+        )
+
+        for valid_idx, processed in zip(valid_indices, processed_audio_list):
+            seq_len = min(int(processed.shape[0]), int(speech_maxlen))
+            output_hidden_states[int(valid_idx), :seq_len] = processed[:seq_len]
+
+        return BaseModelOutput(last_hidden_state=output_hidden_states)
+
+    def padded_and_mask_function(
+        self,
+        tensor_list: Sequence[torch.Tensor],
+        tensor_len: torch.Tensor,
+        padding_value: float = 0.0,
+        padding_side: str = "right",
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        max_len = int(tensor_len.max().item())
+        dim = int(tensor_list[0].shape[0])
+        padded_tensor = torch.full(
+            size=(len(tensor_list), dim, max_len),
+            fill_value=padding_value,
+            dtype=self.dtype,
+            device=tensor_list[0].device,
+        )
+
+        batch_mask = torch.zeros(
+            (len(tensor_len), max_len), dtype=torch.long, device=padded_tensor.device
+        )
+        for i, length in enumerate(tensor_len):
+            length_val = int(length.item())
+            batch_mask[i, :length_val] = 1
+            padded_tensor[i, :, :length_val] = tensor_list[i]
+
+        feature_lens_after_cnn = (tensor_len - 1) // 2 + 1
+        max_len_after_cnn = int(feature_lens_after_cnn.max().item())
+        batch_mask_after_cnn = torch.zeros(
+            (len(tensor_len), max_len_after_cnn),
+            dtype=torch.long,
+            device=padded_tensor.device,
+        )
+        for i, length in enumerate(feature_lens_after_cnn):
+            batch_mask_after_cnn[i, : int(length.item())] = 1
+
+        if padding_side != "right":
+            raise NotImplementedError("Only right padding is supported.")
+
+        return (
+            padded_tensor,
+            batch_mask.unsqueeze(1).to(padded_tensor.dtype),
+            batch_mask_after_cnn.bool(),
+        )
+
+    # From the HF FunAudioChat implementation.
+    def _get_feat_extract_output_lengths(
+        self, input_lengths: torch.LongTensor
+    ) -> tuple[torch.LongTensor, torch.LongTensor]:
+        input_lengths = (input_lengths - 1) // 2 + 1
+        output_lengths = (input_lengths - 2) // 2 + 1
+        return input_lengths, output_lengths
+
+
+class FunAudioChatDiscreteEncoder(nn.Module):
+    """Discrete audio encoder (speech tokenizer -> grouped embeddings)."""
+
+    def __init__(self, config: Any):
+        super().__init__()
+        self.padding_idx = int(config.pad_token_id)
+        self.group_size = int(config.group_size)
+        self.hidden_size = int(config.output_dim)
+        self.continuous_features_mode = getattr(
+            config, "continuous_features_mode", "add"
+        )
+        self.embed_tokens = nn.Embedding(
+            int(config.codebook_size), self.hidden_size, self.padding_idx
+        )
+        self.output_matching = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
+        self.continual_output_matching = nn.Linear(
+            self.hidden_size, self.hidden_size, bias=False
+        )
+
+    def forward(
+        self,
+        audio_ids: torch.Tensor,
+        continuous_audio_features: torch.Tensor | None = None,
+        continuous_audio_output_lengths: torch.Tensor | None = None,
+        feature_exist_mask: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        del continuous_audio_output_lengths
+
+        inputs_embeds = self.embed_tokens(audio_ids)
+        hidden_states = inputs_embeds.reshape(
+            inputs_embeds.shape[0], -1, self.group_size * self.hidden_size
+        )
+        hidden_states = hidden_states.reshape(
+            hidden_states.shape[0], -1, self.group_size, self.hidden_size
+        ).mean(dim=2)
+        hidden_states = self.output_matching(hidden_states)
+
+        if continuous_audio_features is not None:
+            continuous_audio_features = continuous_audio_features.reshape(
+                continuous_audio_features.shape[0],
+                -1,
+                self.group_size,
+                self.hidden_size,
+            ).mean(dim=2)
+            continuous_audio_hidden_states = self.continual_output_matching(
+                continuous_audio_features
+            )
+
+            if feature_exist_mask is None:
+                feature_exist_mask = torch.ones(
+                    (hidden_states.shape[0],),
+                    dtype=torch.bool,
+                    device=hidden_states.device,
+                )
+            if self.continuous_features_mode == "add":
+                hidden_states[feature_exist_mask] += continuous_audio_hidden_states
+            else:
+                hidden_states[feature_exist_mask] = continuous_audio_hidden_states
+
+        return hidden_states
+
+    def _get_feat_extract_output_lengths(
+        self, input_lengths: torch.LongTensor
+    ) -> tuple[torch.LongTensor, torch.LongTensor]:
+        output_lengths = (input_lengths + self.group_size - 1) // self.group_size
+        return input_lengths, output_lengths
+
+
+class FunAudioChatProcessingInfo(BaseProcessingInfo):
+    token_fps: int = 25
+
+    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
+        return {"audio": None}
+
+    def get_target_channels(self) -> int:
+        return 1
+
+    def get_mm_max_tokens_per_item(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> Mapping[str, int] | None:
+        # The discrete audio encoder downsamples 25Hz frames with group_size=5,
+        # so for a 300s clip the max number of `<|AUDIO|>` placeholders is 1500.
+        cfg = self.get_hf_config()
+        audio_cfg = getattr(cfg, "audio_config", None)
+        max_audio_tokens = int(getattr(audio_cfg, "max_source_positions", 1500))
+        return {"audio": max_audio_tokens}
+
+    @cached_property
+    def feature_extractor(self) -> WhisperFeatureExtractor:
+        return WhisperFeatureExtractor.from_pretrained(self.model_id)
+
+    @cached_property
+    def speech_tokenizer(self) -> PreTrainedTokenizerFast:
+        return PreTrainedTokenizerFast.from_pretrained(
+            self.model_id, subfolder="speech_tokenizer"
+        )
+
+    def get_feature_extractor(self) -> WhisperFeatureExtractor:
+        return self.feature_extractor
+
+    def get_speech_tokenizer(self) -> PreTrainedTokenizerFast:
+        return self.speech_tokenizer
+
+    def get_audio_group_size(self) -> int:
+        cfg = self.get_hf_config()
+        audio_cfg = getattr(cfg, "audio_config", None)
+        return int(getattr(audio_cfg, "group_size", 5))
+
+
+class FunAudioChatDummyInputsBuilder(
+    BaseDummyInputsBuilder[FunAudioChatProcessingInfo]
+):
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_audios = mm_counts.get("audio", 0)
+        return "<|audio_bos|><|AUDIO|><|audio_eos|>" * int(num_audios)
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+        mm_options: Mapping[str, BaseDummyOptions] | None = None,
+    ) -> MultiModalDataDict:
+        feature_extractor = self.info.get_feature_extractor()
+        sampling_rate = int(feature_extractor.sampling_rate)
+
+        # Dummy inputs are used for profiling; construct the worst-case audio
+        # length that maximizes the number of encoder tokens.
+        cfg = self.info.get_hf_config()
+        audio_cfg = getattr(cfg, "audio_config", None)
+        max_audio_tokens = int(getattr(audio_cfg, "max_source_positions", 1500))
+        group_size = self.info.get_audio_group_size()
+        token_fps = int(getattr(self.info, "token_fps", 25))
+        target_num_frames = max(1, max_audio_tokens) * max(1, group_size)
+        audio_len = max(
+            1,
+            (target_num_frames * sampling_rate + token_fps - 1) // token_fps,
+        )
+        num_audios = int(mm_counts.get("audio", 0))
+
+        audio_overrides = mm_options.get("audio") if mm_options else None
+        return {
+            "audio": self._get_dummy_audios(
+                length=audio_len,
+                num_audios=num_audios,
+                overrides=audio_overrides,
+            )
+        }
+
+
+class FunAudioChatMultiModalProcessor(
+    BaseMultiModalProcessor[FunAudioChatProcessingInfo]
+):
+    def _get_data_parser(self) -> MultiModalDataParser:
+        feature_extractor = self.info.get_feature_extractor()
+        return MultiModalDataParser(
+            target_sr=int(feature_extractor.sampling_rate),
+            target_channels=self.info.get_target_channels(),
+        )
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        tokenizer = self.info.get_tokenizer()
+        input_ids = torch.tensor([tokenizer.encode(prompt, **tok_kwargs)])
+
+        audios = mm_data.get("audios", [])
+        if not audios:
+            return BatchFeature({"input_ids": input_ids})
+
+        feature_extractor = self.info.get_feature_extractor()
+        sr = int(feature_extractor.sampling_rate)
+        min_samples = int(getattr(feature_extractor, "n_fft", 400) or 400)
+
+        wavs: list[np.ndarray] = []
+        speech_strs: list[str] = []
+
+        speech_tokenizer = self.info.get_speech_tokenizer()
+        pad_token = speech_tokenizer.pad_token or "<|audio_pad|>"
+        for audio in audios:
+            if isinstance(audio, torch.Tensor):
+                audio = audio.detach().cpu().numpy()
+            audio_np = np.asarray(audio, dtype=np.float32)
+
+            if min_samples > 0 and audio_np.shape[0] < min_samples:
+                audio_np = np.pad(
+                    audio_np, (0, min_samples - audio_np.shape[0]), mode="constant"
+                )
+
+            wavs.append(audio_np)
+            num_frames = int(
+                (float(audio_np.shape[0]) / float(sr)) * float(self.info.token_fps)
+            )
+            speech_strs.append(pad_token * max(1, int(num_frames)))
+
+        audio_group_size = self.info.get_audio_group_size()
+        speech_inputs = speech_tokenizer(
+            speech_strs,
+            return_attention_mask=True,
+            return_token_type_ids=False,
+            padding=True,
+            pad_to_multiple_of=audio_group_size,
+            return_tensors="pt",
+        )
+
+        wav_inputs = feature_extractor(
+            wavs,
+            sampling_rate=sr,
+            return_attention_mask=True,
+            padding="max_length",
+            return_tensors="pt",
+        )
+
+        mm_inputs: dict[str, torch.Tensor] = {
+            "speech_ids": speech_inputs["input_ids"],
+            "speech_attention_mask": speech_inputs["attention_mask"],
+            "input_features": wav_inputs["input_features"],
+            "feature_attention_mask": wav_inputs["attention_mask"],
+            "feature_exist_mask": torch.ones((len(wavs),), dtype=torch.bool),
+        }
+
+        return BatchFeature({"input_ids": input_ids, **mm_inputs})
+
+    def _hf_processor_applies_updates(
+        self,
+        prompt_text: str,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        tokenization_kwargs: Mapping[str, object],
+    ) -> bool:
+        return False
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return {
+            "speech_ids": MultiModalFieldConfig.batched("audio"),
+            "speech_attention_mask": MultiModalFieldConfig.batched("audio"),
+            "input_features": MultiModalFieldConfig.batched("audio"),
+            "feature_attention_mask": MultiModalFieldConfig.batched("audio"),
+            "feature_exist_mask": MultiModalFieldConfig.batched("audio"),
+        }
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargsItems,
+    ) -> Sequence[PromptUpdate]:
+        tokenizer = self.info.get_tokenizer()
+        vocab = tokenizer.get_vocab()
+
+        audio_token = "<|AUDIO|>"
+        audio_token_id = vocab[audio_token]
+
+        out_mm_data = out_mm_kwargs.get_data()
+        speech_attention_mask = out_mm_data.get("speech_attention_mask")
+        if speech_attention_mask is None:
+            audio_output_lengths: list[int] = []
+        else:
+            assert isinstance(speech_attention_mask, torch.Tensor)
+            speech_lengths = speech_attention_mask.sum(-1)
+            group_size = self.info.get_audio_group_size()
+            audio_output_lengths = (
+                (speech_lengths + group_size - 1) // group_size
+            ).tolist()
+
+        def get_replacement_funaudiochat(item_idx: int):
+            num_features = (
+                int(audio_output_lengths[item_idx]) if audio_output_lengths else 1
+            )
+            if num_features <= 0:
+                audios = mm_items.get_items("audio", AudioProcessorItems)
+                audio_len = audios.get_audio_length(item_idx)
+                raise ValueError(
+                    f"The audio (len={audio_len}) is too short to be "
+                    "represented inside the model"
+                )
+
+            audio_tokens = [audio_token_id] * num_features
+            return PromptUpdateDetails.select_token_id(
+                audio_tokens,
+                embed_token_id=audio_token_id,
+            )
+
+        return [
+            PromptReplacement(
+                modality="audio",
+                target=audio_token,
+                replacement=get_replacement_funaudiochat,
+            )
+        ]
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    FunAudioChatMultiModalProcessor,
+    info=FunAudioChatProcessingInfo,
+    dummy_inputs=FunAudioChatDummyInputsBuilder,
+)
+class FunAudioChatForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
+        if modality.startswith("audio"):
+            return "<|audio_bos|><|AUDIO|><|audio_eos|>"
+
+        raise ValueError("Only audio modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+        self.config = config
+        self.multimodal_config = multimodal_config
+        self.quant_config = quant_config
+
+        with self._mark_tower_model(vllm_config, "audio"):
+            self.continuous_audio_tower = FunAudioChatAudioEncoder(config.audio_config)
+            self.audio_tower = FunAudioChatDiscreteEncoder(config.audio_config)
+
+        with self._mark_language_model(vllm_config):
+            self.language_model = init_vllm_registered_model(
+                vllm_config=vllm_config,
+                hf_config=config.text_config,
+                prefix=maybe_prefix(prefix, "language_model"),
+                architectures=["Qwen3ForCausalLM"],
+            )
+
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors
+        )
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def _get_continuous_audio_features(
+        self,
+        input_features: torch.Tensor,
+        feature_attention_mask: torch.Tensor,
+        speech_maxlen: int,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Align mask and features to avoid indexing errors when padding differs.
+        if (
+            input_features.dim() == 3
+            and feature_attention_mask.shape[1] != input_features.shape[-1]
+        ):
+            min_len = min(
+                int(feature_attention_mask.shape[1]), int(input_features.shape[-1])
+            )
+            feature_attention_mask = feature_attention_mask[:, :min_len]
+            input_features = input_features[:, :, :min_len]
+
+        feature_lens = torch.sum(feature_attention_mask, dim=1)
+
+        flat_features = input_features.permute(0, 2, 1)[
+            feature_attention_mask.bool()
+        ].permute(1, 0)
+
+        audio_feat_lengths, audio_output_lengths = (
+            self.continuous_audio_tower._get_feat_extract_output_lengths(feature_lens)
+        )
+
+        audio_outputs = self.continuous_audio_tower(
+            flat_features,
+            feature_lens=feature_lens,
+            aftercnn_lens=audio_feat_lengths,
+            speech_maxlen=speech_maxlen,
+        )
+        return audio_outputs.last_hidden_state, audio_output_lengths
+
+    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
+        speech_ids = kwargs.get("speech_ids")
+        speech_attention_mask = kwargs.get("speech_attention_mask")
+        input_features = kwargs.get("input_features")
+        feature_attention_mask = kwargs.get("feature_attention_mask")
+        feature_exist_mask = kwargs.get("feature_exist_mask")
+
+        if speech_ids is None:
+            return []
+
+        pad_id = int(getattr(self.audio_tower, "padding_idx", 0))
+
+        if not isinstance(speech_ids, torch.Tensor):
+            if (
+                isinstance(speech_ids, (list, tuple))
+                and len(speech_ids) > 0
+                and all(isinstance(t, torch.Tensor) for t in speech_ids)
+            ):
+                speech_ids_tensors = []
+                for t in speech_ids:
+                    if t.dim() == 2 and t.shape[0] == 1:
+                        t = t.squeeze(0)
+                    if t.dim() != 1:
+                        raise TypeError(
+                            "FunAudioChat speech_ids must be a 1D tensor per item "
+                            f"(got shape={tuple(t.shape)})"
+                        )
+                    speech_ids_tensors.append(t)
+                speech_ids = nn.utils.rnn.pad_sequence(
+                    speech_ids_tensors,
+                    batch_first=True,
+                    padding_value=pad_id,
+                )
+            else:
+                raise TypeError(
+                    "FunAudioChat speech_ids must be a Tensor or a sequence of Tensors "
+                    f"(got {type(speech_ids)})"
+                )
+
+        if speech_attention_mask is None:
+            speech_attention_mask = speech_ids.ne(pad_id).to(dtype=torch.int64)
+
+        if not isinstance(speech_attention_mask, torch.Tensor):
+            if (
+                isinstance(speech_attention_mask, (list, tuple))
+                and len(speech_attention_mask) > 0
+                and all(isinstance(t, torch.Tensor) for t in speech_attention_mask)
+            ):
+                mask_tensors = []
+                for t in speech_attention_mask:
+                    if t.dim() == 2 and t.shape[0] == 1:
+                        t = t.squeeze(0)
+                    if t.dim() != 1:
+                        raise TypeError(
+                            "FunAudioChat speech_attention_mask must be a 1D tensor "
+                            f"per item (got shape={tuple(t.shape)})"
+                        )
+                    mask_tensors.append(t)
+                speech_attention_mask = nn.utils.rnn.pad_sequence(
+                    mask_tensors,
+                    batch_first=True,
+                    padding_value=0,
+                )
+            else:
+                raise TypeError(
+                    "FunAudioChat speech_attention_mask must be a Tensor or a "
+                    f"sequence of Tensors (got {type(speech_attention_mask)})"
+                )
+
+        debug = os.getenv("VLLM_FUN_AUDIOCHAT_DEBUG", "") == "1"
+        if debug:
+            print(
+                f"[FunAudioChat] embed_multimodal speech_ids={tuple(speech_ids.shape)} "
+                f"speech_attention_mask={tuple(speech_attention_mask.shape)}",
+                flush=True,
+            )
+            attn_impl = getattr(
+                self.continuous_audio_tower.config, "_attn_implementation", None
+            )
+            print(
+                f"[FunAudioChat] audio_attn_impl={attn_impl}",
+                flush=True,
+            )
+            if hasattr(self.continuous_audio_tower, "conv1"):
+                conv1_w = self.continuous_audio_tower.conv1.weight
+                print(
+                    f"[FunAudioChat] conv1_w_norm={float(conv1_w.norm().item()):.6g}",
+                    flush=True,
+                )
+            try:
+                attn0 = self.continuous_audio_tower.layers[0].self_attn
+                q_norm = float(attn0.q_proj.weight.norm().item())
+                k_norm = float(attn0.k_proj.weight.norm().item())
+                v_norm = float(attn0.v_proj.weight.norm().item())
+                o_norm = float(attn0.out_proj.weight.norm().item())
+                print(
+                    f"[FunAudioChat] attn0_q_norm={q_norm:.6g} "
+                    f"k_norm={k_norm:.6g} "
+                    f"v_norm={v_norm:.6g} "
+                    f"o_norm={o_norm:.6g}",
+                    flush=True,
+                )
+            except Exception:
+                pass
+            if isinstance(input_features, torch.Tensor):
+                print(
+                    f"[FunAudioChat] input_features={tuple(input_features.shape)}",
+                    flush=True,
+                )
+            if isinstance(feature_attention_mask, torch.Tensor):
+                print(
+                    "[FunAudioChat] feature_attention_mask="
+                    f"{tuple(feature_attention_mask.shape)}",
+                    flush=True,
+                )
+
+        group_size = int(self.audio_tower.group_size)
+        speech_maxlen = int(speech_ids.shape[-1])
+
+        # Ensure token length is divisible by group_size.
+        target_len = ((speech_maxlen + group_size - 1) // group_size) * group_size
+        if target_len > speech_maxlen:
+            pad_id = int(self.audio_tower.padding_idx)
+            pad_len = target_len - speech_maxlen
+            speech_ids = nn.functional.pad(speech_ids, (0, pad_len), value=pad_id)
+            speech_attention_mask = nn.functional.pad(
+                speech_attention_mask, (0, pad_len), value=0
+            )
+            speech_maxlen = int(speech_ids.shape[-1])
+
+        continuous_audio_features = None
+        continuous_audio_output_lengths = None
+        if input_features is not None and feature_attention_mask is not None:
+            assert isinstance(input_features, torch.Tensor)
+            assert isinstance(feature_attention_mask, torch.Tensor)
+            continuous_audio_features, continuous_audio_output_lengths = (
+                self._get_continuous_audio_features(
+                    input_features=input_features,
+                    feature_attention_mask=feature_attention_mask,
+                    speech_maxlen=speech_maxlen,
+                )
+            )
+
+        if feature_exist_mask is None:
+            feature_exist_mask = torch.ones(
+                (speech_ids.shape[0],), dtype=torch.bool, device=speech_ids.device
+            )
+        assert isinstance(feature_exist_mask, torch.Tensor)
+
+        audio_features = self.audio_tower(
+            speech_ids,
+            continuous_audio_features=continuous_audio_features,
+            continuous_audio_output_lengths=continuous_audio_output_lengths,
+            feature_exist_mask=feature_exist_mask,
+        )
+
+        _, audio_output_lengths = self.audio_tower._get_feat_extract_output_lengths(
+            speech_attention_mask.sum(-1)
+        )
+        lengths = audio_output_lengths.tolist()
+
+        embeds = tuple(
+            audio_features[i, : int(length)] for i, length in enumerate(lengths)
+        )
+        if debug:
+            embed_lens = [int(t.shape[0]) for t in embeds]
+            print(f"[FunAudioChat] embed_multimodal out_lens={embed_lens}", flush=True)
+            if embeds:
+                t0 = embeds[0]
+                print(
+                    f"[FunAudioChat] embed0 dtype={t0.dtype} device={t0.device} "
+                    f"nan={bool(torch.isnan(t0).any())} "
+                    f"norm={float(t0.norm().item()):.6g}",
+                    flush=True,
+                )
+            dump_path = os.getenv("VLLM_FUN_AUDIOCHAT_DUMP_PATH", "")
+            if (
+                dump_path
+                and speech_ids.shape[0] == 1
+                and len(embeds) == 1
+                and embed_lens[0] > 10
+            ):
+                if not os.path.exists(dump_path):
+                    np.save(dump_path, embeds[0].detach().float().cpu().numpy())
+                    print(f"[FunAudioChat] dumped embeds to {dump_path}", flush=True)
+                cont_path = dump_path.replace(".npy", "_cont.npy")
+                if continuous_audio_features is not None and not os.path.exists(
+                    cont_path
+                ):
+                    np.save(
+                        cont_path,
+                        continuous_audio_features.detach().float().cpu().numpy(),
+                    )
+                    print(
+                        f"[FunAudioChat] dumped continuous to {cont_path}", flush=True
+                    )
+        return embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: IntermediateTensors | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        **kwargs: object,
+    ) -> torch.Tensor | IntermediateTensors:
+        del kwargs
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        return self.language_model.model(
+            input_ids,
+            positions,
+            intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+
+    def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor | None:
+        return self.language_model.compute_logits(hidden_states)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self, skip_prefixes=["audio_invert_tower."])
+        return loader.load_weights(weights)

vllm/model_executor/models/registry.py

@@ -312,6 +312,10 @@ _MULTIMODAL_MODELS = {
         "ernie45_vl",
         "Ernie4_5_VLMoeForConditionalGeneration",
     ),
+    "FunAudioChatForConditionalGeneration": (
+        "funaudiochat",
+        "FunAudioChatForConditionalGeneration",
+    ),
     "FuyuForCausalLM": ("fuyu", "FuyuForCausalLM"),
     "Gemma3ForConditionalGeneration": ("gemma3_mm", "Gemma3ForConditionalGeneration"),  # noqa: E501
     "Gemma3nForConditionalGeneration": (

vllm/multimodal/audio.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import math
 from dataclasses import dataclass
 from enum import Enum
 from typing import Literal
@@ -195,6 +196,13 @@ class AudioResampler:
             raise RuntimeError(
                 "Audio resampling is not supported when `target_sr` is not provided"
             )
+        if math.isclose(
+            float(orig_sr),
+            float(self.target_sr),
+            rel_tol=0.0,
+            abs_tol=1e-6,
+        ):
+            return audio
         if self.method == "librosa":
             return resample_audio_librosa(
                 audio, orig_sr=orig_sr, target_sr=self.target_sr

vllm/transformers_utils/config.py

@@ -77,6 +77,7 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
     deepseek_vl_v2="DeepseekVLV2Config",
     deepseek_v32="DeepseekV3Config",
     flex_olmo="FlexOlmoConfig",
+    funaudiochat="FunAudioChatConfig",
     hunyuan_vl="HunYuanVLConfig",
     isaac="IsaacConfig",
     kimi_linear="KimiLinearConfig",

vllm/transformers_utils/configs/__init__.py

@@ -22,6 +22,8 @@ _CLASS_TO_MODULE: dict[str, str] = {
     "DotsOCRConfig": "vllm.transformers_utils.configs.dotsocr",
     "EAGLEConfig": "vllm.transformers_utils.configs.eagle",
     "FlexOlmoConfig": "vllm.transformers_utils.configs.flex_olmo",
+    "FunAudioChatConfig": "vllm.transformers_utils.configs.funaudiochat",
+    "FunAudioChatAudioEncoderConfig": "vllm.transformers_utils.configs.funaudiochat",
     "HunYuanVLConfig": "vllm.transformers_utils.configs.hunyuan_vl",
     "HunYuanVLTextConfig": "vllm.transformers_utils.configs.hunyuan_vl",
     "HunYuanVLVisionConfig": "vllm.transformers_utils.configs.hunyuan_vl",
@@ -65,6 +67,8 @@ __all__ = [
     "DotsOCRConfig",
     "EAGLEConfig",
     "FlexOlmoConfig",
+    "FunAudioChatConfig",
+    "FunAudioChatAudioEncoderConfig",
     "HunYuanVLConfig",
     "HunYuanVLTextConfig",
     "HunYuanVLVisionConfig",

vllm/transformers_utils/configs/funaudiochat.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+from transformers import PretrainedConfig
+
+# NOTE: Temporary shim for FunAudioChat checkpoints.
+# These checkpoints use `model_type="funaudiochat"`, which is not currently
+# recognized by released Transformers, and the public checkpoint does not
+# provide an `auto_map` to enable `trust_remote_code=True`.
+# Remove this file once Transformers adds native support (or the checkpoint
+# provides an `auto_map`) and vLLM can rely on `AutoConfig.from_pretrained()`.
+
+
+class FunAudioChatAudioEncoderConfig(PretrainedConfig):
+    model_type = "funaudiochat_audio_encoder"
+
+    def __init__(
+        self,
+        _attn_implementation: str | None = None,
+        num_mel_bins: int = 128,
+        encoder_layers: int = 32,
+        encoder_attention_heads: int = 20,
+        encoder_ffn_dim: int = 5120,
+        d_model: int = 1280,
+        dropout: float = 0.0,
+        attention_dropout: float = 0.0,
+        activation_function: str = "gelu",
+        activation_dropout: float = 0.0,
+        scale_embedding: bool = False,
+        initializer_range: float = 0.02,
+        max_source_positions: int = 1500,
+        n_window: int = 100,
+        output_dim: int = 3584,
+        bos_token_id: int | None = None,
+        codebook_size: int | None = None,
+        continuous_features_mode: str = "replace",
+        crq_transformer_config: dict | None = None,
+        eos_token_id: int | None = None,
+        group_size: int = 5,
+        enable_audio_invert_tower: bool = True,
+        pad_token_id: int | None = None,
+        **kwargs,
+    ) -> None:
+        attn_impl = kwargs.pop("_attn_implementation", None) or _attn_implementation
+        super().__init__(**kwargs)
+        # Match HF default for attention implementation selection.
+        self._attn_implementation = attn_impl or "sdpa"
+
+        self.num_mel_bins = num_mel_bins
+        self.d_model = d_model
+        self.encoder_layers = encoder_layers
+        self.encoder_attention_heads = encoder_attention_heads
+        self.encoder_ffn_dim = encoder_ffn_dim
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.activation_function = activation_function
+        self.activation_dropout = activation_dropout
+        self.num_hidden_layers = encoder_layers
+        self.initializer_range = initializer_range
+        self.scale_embedding = scale_embedding
+        self.max_source_positions = max_source_positions
+        self.n_window = n_window
+        self.output_dim = output_dim
+
+        self.bos_token_id = bos_token_id
+        self.codebook_size = codebook_size
+        self.continuous_features_mode = continuous_features_mode
+        self.crq_transformer_config = crq_transformer_config
+        self.eos_token_id = eos_token_id
+        self.group_size = group_size
+        self.enable_audio_invert_tower = enable_audio_invert_tower
+        self.pad_token_id = pad_token_id
+
+
+class FunAudioChatConfig(PretrainedConfig):
+    model_type = "funaudiochat"
+    attribute_map = {
+        "audio_token_id": "audio_token_index",
+    }
+
+    def __init__(
+        self,
+        audio_config: PretrainedConfig | dict | None = None,
+        text_config: PretrainedConfig | dict | None = None,
+        audio_token_index: int = 151646,
+        ignore_index: int = -100,
+        hidden_size: int | None = None,
+        **kwargs,
+    ) -> None:
+        self.audio_token_index = audio_token_index
+        self.ignore_index = ignore_index
+
+        if isinstance(audio_config, dict):
+            audio_config.setdefault(
+                "model_type", FunAudioChatAudioEncoderConfig.model_type
+            )
+            audio_config = FunAudioChatAudioEncoderConfig(**audio_config)
+        elif audio_config is None:
+            audio_config = FunAudioChatAudioEncoderConfig()
+        self.audio_config = audio_config
+
+        if isinstance(text_config, dict):
+            # Default to qwen2 for backwards compatibility; FunAudioChat uses
+            # qwen3 in practice for recent checkpoints.
+            text_config.setdefault("model_type", "qwen2")
+            import transformers
+
+            text_cls = transformers.CONFIG_MAPPING[text_config["model_type"]]
+            text_config = text_cls(**text_config)
+        elif text_config is None:
+            import transformers
+
+            text_config = transformers.CONFIG_MAPPING["qwen2"]()
+        self.text_config = text_config
+
+        self.hidden_size = (
+            int(self.text_config.hidden_size)
+            if hidden_size is None
+            else int(hidden_size)
+        )
+
+        super().__init__(**kwargs)