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vllm/transformers_utils/processors/qwen3_asr.py
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vllm/transformers_utils/processors/qwen3_asr.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# ruff: noqa
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# mypy: ignore-errors
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# coding=utf-8
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# Copyright 2026 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import regex as re
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import numpy as np
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from transformers import AutoProcessor
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from transformers.audio_utils import AudioInput
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from transformers.feature_extraction_utils import BatchFeature
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from transformers.processing_utils import ProcessingKwargs, ProcessorMixin
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from transformers.tokenization_utils_base import TextInput
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class Qwen3ASRProcessorKwargs(ProcessingKwargs, total=False):
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_defaults = {
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"text_kwargs": {
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"padding": False,
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"padding_side": "left",
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},
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"audio_kwargs": {
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"sampling_rate": 16000,
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"padding": True,
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"return_attention_mask": True,
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},
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}
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def _get_feat_extract_output_lengths(input_lengths):
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"""
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Computes the output length of the convolutional layers and the output length of the audio encoder
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"""
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input_lengths_leave = input_lengths % 100
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feat_lengths = (input_lengths_leave - 1) // 2 + 1
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output_lengths = (
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((feat_lengths - 1) // 2 + 1 - 1) // 2 + 1 + (input_lengths // 100) * 13
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)
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return output_lengths
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class Qwen3ASRProcessor(ProcessorMixin):
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r"""
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Constructs a Qwen3ASR processor.
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[`Qwen3ASRProcessor`] offers all the functionalities of [`WhisperFeatureExtractor`], and [`Qwen2TokenizerFast`]. See the
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[`~Qwen3ASRProcessor.__call__`] and [`~Qwen3ASRProcessor.decode`] for more information.
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Args:
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feature_extractor ([`WhisperFeatureExtractor`], *optional*):
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The audio feature extractor.
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tokenizer ([`Qwen2TokenizerFast`], *optional*):
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The text tokenizer.
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chat_template (`Optional[str]`, *optional*):
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The Jinja template to use for formatting the conversation. If not provided, the default chat template is used.
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"""
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attributes = ["feature_extractor", "tokenizer"]
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feature_extractor_class = "WhisperFeatureExtractor"
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tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
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def __init__(self, feature_extractor=None, tokenizer=None, chat_template=None):
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super().__init__(feature_extractor, tokenizer, chat_template=chat_template)
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self.audio_token = self.tokenizer.audio_token
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self.audio_bos_token = self.tokenizer.audio_bos_token
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self.audio_eos_token = self.tokenizer.audio_eos_token
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def __call__(
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self,
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text: TextInput = None,
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audio: AudioInput = None,
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**kwargs,
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) -> BatchFeature:
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"""
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Main method to prepare for the model one or several sequences(s) and audio(s). This method forwards the `text`
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and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
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the text. To prepare the audio(s), this method forwards the `audio` and `kwargs` arguments to
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WhisperFeatureExtractor's [`~WhisperFeatureExtractor.__call__`] if `audio` is not `None`. Please refer to the doctsring
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of the above two methods for more information.
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Args:
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text (`str`, `List[str]`, `List[List[str]]`):
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The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
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(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
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`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
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audio (`np.ndarray`, `List[np.ndarray]`):
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The audio or batch of audio to be prepared. Each audio can be a NumPy array.
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"""
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if text is None:
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raise ValueError("You need to specify either a `text` input to process.")
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output_kwargs = self._merge_kwargs(
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Qwen3ASRProcessorKwargs,
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tokenizer_init_kwargs=self.tokenizer.init_kwargs,
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**kwargs,
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)
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if audio is not None:
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output_kwargs["audio_kwargs"]["padding"] = True
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output_kwargs["audio_kwargs"]["truncation"] = False
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audio_inputs = self.feature_extractor(
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audio, **output_kwargs["audio_kwargs"]
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)
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audio_inputs["feature_attention_mask"] = audio_inputs.pop(
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"attention_mask"
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) # rename feature_attention_mask to prevent conflicts later on
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audio_inputs["input_features"] = audio_inputs.pop(
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"input_features"
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) # rename input_features to prevent conflicts later on
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audio_lengths = iter(
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_get_feat_extract_output_lengths(
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audio_inputs["feature_attention_mask"].sum(-1)
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)
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)
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else:
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audio_inputs = {}
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audio_lengths = iter([])
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if not isinstance(text, list):
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text = [text]
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text = self.replace_multimodal_special_tokens(
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text,
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audio_lengths,
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)
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texts_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
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return BatchFeature(
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data={**texts_inputs, **audio_inputs},
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tensor_type=kwargs.get("return_tensors"),
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)
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def replace_multimodal_special_tokens(
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self,
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text,
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audio_lengths,
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):
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processed_text = []
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for sample in text:
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positions = []
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special_tokens = [re.escape(tok) for tok in [self.audio_token]]
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pattern = "|".join(special_tokens)
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positions = sorted(
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[
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(match.start(), match.group())
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for match in re.finditer(pattern, sample)
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]
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)
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positions.sort(key=lambda x: x[0])
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for _, special_token in positions:
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if special_token == self.audio_token:
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sample = sample.replace(
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self.audio_token,
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"<|audio_placeholder|>" * next(audio_lengths),
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1,
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)
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sample = sample.replace("<|audio_placeholder|>", self.audio_token)
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processed_text.append(sample)
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return processed_text
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def get_chunked_index(
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self, token_indices: np.ndarray, tokens_per_chunk: int
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) -> list[tuple[int, int]]:
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"""
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Splits token index list into chunks based on token value ranges.
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Given a list of token indices, returns a list of (start, end) index tuples representing
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slices of the list where the token values fall within successive ranges of `tokens_per_chunk`.
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For example, if `tokens_per_chunk` is 1000, the function will create chunks such that:
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- the first chunk contains token values < 1000,
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- the second chunk contains values >= 1000 and < 2000, and so on.
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Parameters:
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token_indices (`np.ndarray`): A monotonically increasing list of token index values.
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tokens_per_chunk (`int`): Number of tokens per chunk (used as the chunk size threshold).
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Returns:
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`list[tuple[int, int]]`: A list of tuples, each representing the start (inclusive)
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and end (exclusive) indices of a chunk in `token_indices`.
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"""
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def _iter():
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i, start_idx = 0, 0 # skip bos token
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current_chunk = 1
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while i < len(token_indices): # skip eos token
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if token_indices[i] >= current_chunk * tokens_per_chunk:
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yield (start_idx, i)
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start_idx = i
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current_chunk += 1
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i += 1
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yield (start_idx, len(token_indices))
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return list(_iter())
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def apply_chat_template(self, conversations, chat_template=None, **kwargs):
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kwargs["return_dict"] = False
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return super().apply_chat_template(conversations, chat_template, **kwargs)
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@property
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def model_input_names(self):
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tokenizer_input_names = self.tokenizer.model_input_names
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feature_extractor_input_names = self.feature_extractor.model_input_names
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return list(
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dict.fromkeys(
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tokenizer_input_names
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+ feature_extractor_input_names
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+ ["feature_attention_mask"]
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)
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)
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AutoProcessor.register("Qwen3ASRProcessor", Qwen3ASRProcessor)
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