add qwen3-asr

vllm/model_executor/models/registry.py

@@ -329,6 +329,10 @@ _MULTIMODAL_MODELS = {
         "granite_speech",
         "GraniteSpeechForConditionalGeneration",
     ),
+    "Qwen3ASRForConditionalGeneration": (
+        "qwen3_asr",
+        "Qwen3ASRForConditionalGeneration",
+    ),
     "H2OVLChatModel": ("h2ovl", "H2OVLChatModel"),
     "HunYuanVLForConditionalGeneration": (
         "hunyuan_vision",

vllm/transformers_utils/configs/qwen3_asr.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright 2026 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class Qwen3ASRAudioEncoderConfig(PretrainedConfig):
+    model_type = "qwen3_asr_audio_encoder"
+
+    def __init__(
+        self,
+        num_mel_bins=128,
+        encoder_layers=32,
+        encoder_attention_heads=20,
+        encoder_ffn_dim=5120,
+        d_model=1280,
+        dropout=0,
+        attention_dropout=0,
+        activation_function="gelu",
+        activation_dropout=0,
+        scale_embedding=False,
+        initializer_range=0.02,
+        max_source_positions=1500,
+        n_window=100,
+        output_dim=3584,
+        n_window_infer=400,
+        conv_chunksize=500,
+        downsample_hidden_size=480,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        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.n_window_infer = n_window_infer
+        self.conv_chunksize = conv_chunksize
+        self.downsample_hidden_size = downsample_hidden_size
+
+
+class Qwen3ASRTextConfig(PretrainedConfig):
+    model_type = "qwen3_asr_text"
+    base_config_key = "text_config"
+
+    def __init__(
+        self,
+        vocab_size=151936,
+        hidden_size=4096,
+        intermediate_size=22016,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=32,
+        head_dim=128,
+        hidden_act="silu",
+        max_position_embeddings=128000,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=5000000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        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_key_value_heads = (
+            num_attention_heads if num_key_value_heads is None else num_key_value_heads
+        )
+        self.head_dim = head_dim
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+class Qwen3ASRThinkerConfig(PretrainedConfig):
+    model_type = "qwen3_asr_thinker"
+    attribute_map = {}
+    sub_configs = {
+        "audio_config": Qwen3ASRAudioEncoderConfig,
+        "text_config": Qwen3ASRTextConfig,
+    }
+
+    def __init__(
+        self,
+        audio_config=None,
+        text_config=None,
+        audio_token_id=151646,
+        audio_start_token_id=151647,
+        user_token_id=872,
+        initializer_range=0.02,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.user_token_id = user_token_id
+        self.audio_start_token_id = audio_start_token_id
+        self.initializer_range = initializer_range
+
+        if isinstance(audio_config, dict):
+            audio_config = Qwen3ASRAudioEncoderConfig(**audio_config)
+        elif audio_config is None:
+            audio_config = Qwen3ASRAudioEncoderConfig()
+        self.audio_config = audio_config
+
+        if isinstance(text_config, dict):
+            text_config = Qwen3ASRTextConfig(**text_config)
+        elif text_config is None:
+            text_config = Qwen3ASRTextConfig()
+        self.text_config = text_config
+        self.audio_token_id = audio_token_id
+
+
+class Qwen3ASRConfig(PretrainedConfig):
+    model_type = "qwen3_asr"
+    sub_configs = {
+        "thinker_config": Qwen3ASRThinkerConfig,
+    }
+
+    def __init__(
+        self,
+        thinker_config=None,
+        support_languages=None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        if thinker_config is None:
+            thinker_config = {}
+        self.thinker_config = Qwen3ASRThinkerConfig(**thinker_config)
+        self.support_languages = support_languages
+
+    def get_text_config(self, decoder=False) -> "PretrainedConfig":
+        return self.thinker_config.get_text_config()
+
+
+__all__ = [
+    "Qwen3ASRConfig",
+    "Qwen3ASRThinkerConfig",
+    "Qwen3ASRAudioEncoderConfig",
+]
+

vllm/transformers_utils/processors/__init__.py

@@ -14,6 +14,7 @@ from vllm.transformers_utils.processors.hunyuan_vl import HunYuanVLProcessor
 from vllm.transformers_utils.processors.hunyuan_vl_image import HunYuanVLImageProcessor
 from vllm.transformers_utils.processors.ovis import OvisProcessor
 from vllm.transformers_utils.processors.ovis2_5 import Ovis2_5Processor
+from vllm.transformers_utils.processors.qwen3_asr import Qwen3ASRProcessor
 
 __all__ = [
     "BagelProcessor",
@@ -22,4 +23,5 @@ __all__ = [
     "HunYuanVLImageProcessor",
     "OvisProcessor",
     "Ovis2_5Processor",
+    "Qwen3ASRProcessor",
 ]

vllm/transformers_utils/processors/qwen3_asr.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright 2026 The Qwen team, Alibaba Group and the HuggingFace Inc. team.
+
+import re
+from typing import Iterable
+
+import numpy as np
+from transformers import AutoProcessor
+from transformers.audio_utils import AudioInput
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.processing_utils import ProcessingKwargs, ProcessorMixin
+from transformers.tokenization_utils_base import TextInput
+
+
+class Qwen3ASRProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+            "padding_side": "left",
+        },
+        "audio_kwargs": {
+            "sampling_rate": 16000,
+            "padding": True,
+            "return_attention_mask": True,
+        },
+    }
+
+
+def _get_feat_extract_output_lengths(input_lengths):
+    """
+    Computes the output length of the convolutional layers and the output
+    length of the audio encoder.
+    """
+    input_lengths_leave = input_lengths % 100
+    feat_lengths = (input_lengths_leave - 1) // 2 + 1
+    output_lengths = ((feat_lengths - 1) // 2 + 1 - 1) // 2 + 1 + (
+        input_lengths // 100
+    ) * 13
+    return output_lengths
+
+
+class Qwen3ASRProcessor(ProcessorMixin):
+    """
+    Composite processor for Qwen3-ASR:
+    - WhisperFeatureExtractor for audio
+    - Qwen2 tokenizer family for text
+    """
+
+    attributes = ["feature_extractor", "tokenizer"]
+    feature_extractor_class = "WhisperFeatureExtractor"
+    tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
+
+    def __init__(self, feature_extractor=None, tokenizer=None, chat_template=None):
+        super().__init__(feature_extractor, tokenizer, chat_template=chat_template)
+        self.audio_token = self.tokenizer.audio_token
+        self.audio_bos_token = self.tokenizer.audio_bos_token
+        self.audio_eos_token = self.tokenizer.audio_eos_token
+
+    def __call__(
+        self,
+        text: TextInput = None,
+        audio: AudioInput = None,
+        **kwargs,
+    ) -> BatchFeature:
+        if text is None:
+            raise ValueError("You need to specify either a `text` input to process.")
+
+        output_kwargs = self._merge_kwargs(
+            Qwen3ASRProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        if audio is not None:
+            output_kwargs["audio_kwargs"]["padding"] = True
+            output_kwargs["audio_kwargs"]["truncation"] = False
+            audio_inputs = self.feature_extractor(audio, **output_kwargs["audio_kwargs"])
+            audio_inputs["feature_attention_mask"] = audio_inputs.pop("attention_mask")
+            audio_lengths = iter(
+                _get_feat_extract_output_lengths(
+                    audio_inputs["feature_attention_mask"].sum(-1)
+                )
+            )
+        else:
+            audio_inputs = {}
+            audio_lengths = iter([])
+
+        if not isinstance(text, list):
+            text = [text]
+
+        text = self.replace_multimodal_special_tokens(text, audio_lengths)
+        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+
+        return BatchFeature(
+            data={**text_inputs, **audio_inputs},
+            tensor_type=kwargs.get("return_tensors"),
+        )
+
+    def replace_multimodal_special_tokens(
+        self, text: list[str], audio_lengths: Iterable[int]
+    ) -> list[str]:
+        processed_text: list[str] = []
+        for sample in text:
+            pattern = re.escape(self.audio_token)
+            positions = sorted(
+                (match.start(), match.group())
+                for match in re.finditer(pattern, sample)
+            )
+            for _, special_token in positions:
+                if special_token == self.audio_token:
+                    sample = sample.replace(
+                        self.audio_token,
+                        "<|audio_placeholder|>" * int(next(audio_lengths)),
+                        1,
+                    )
+            sample = sample.replace("<|audio_placeholder|>", self.audio_token)
+            processed_text.append(sample)
+        return processed_text
+
+    def get_chunked_index(
+        self, token_indices: np.ndarray, tokens_per_chunk: int
+    ) -> list[tuple[int, int]]:
+        def _iter():
+            i, start_idx = 0, 0
+            current_chunk = 1
+            while i < len(token_indices):
+                if token_indices[i] >= current_chunk * tokens_per_chunk:
+                    yield (start_idx, i)
+                    start_idx = i
+                    current_chunk += 1
+                i += 1
+            yield (start_idx, len(token_indices))
+
+        return list(_iter())
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        feature_extractor_input_names = self.feature_extractor.model_input_names
+        return list(
+            dict.fromkeys(
+                tokenizer_input_names
+                + feature_extractor_input_names
+                + ["feature_attention_mask"]
+            )
+        )
+
+
+AutoProcessor.register("Qwen3ASRProcessor", Qwen3ASRProcessor)
+
+__all__ = ["Qwen3ASRProcessor"]
+