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[Bugfix] Fix qwen3-omni audio truncation issue #26815

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Merged
DarkLight1337 merged 4 commits into from
Oct 15, 2025
+16 −2
Merged

[Bugfix] Fix qwen3-omni audio truncation issue #26815

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70532f6
fix qwen3-omni audio truncation issue
Isotr0py Oct 14, 2025
9281723
enforce truncation=False
Isotr0py Oct 14, 2025
bd61ae5
ooops
Isotr0py Oct 14, 2025
7fd991a
add comment and correct truncation case
Isotr0py Oct 14, 2025
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18 changes: 16 additions & 2 deletions vllm/model_executor/models/qwen3_omni_moe_thinker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,9 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
from packaging.version import Version
from transformers import PretrainedConfig
from transformers import __version__ as TRANSFORMERS_VERSION
from transformers.feature_extraction_utils import BatchFeature
from transformers.models.qwen3_omni_moe.configuration_qwen3_omni_moe import (
Qwen3OmniMoeConfig,
Expand Down Expand Up @@ -711,11 +713,12 @@ def pad_to_hop_length(x: np.ndarray, hop_length: int) -> np.ndarray:
return x

# NOTE: WhisperFeatureExtractor cannot handle empty list of audios
feature_extractor = self.info.get_feature_extractor()
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@DarkLight1337 DarkLight1337 Oct 14, 2025

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Can you add a comment so we can revert once it's fixed on transformers side?

hop_length = feature_extractor.hop_length
if audios:
# NOTE: Qwen3-Omni processor accept "audio"
# To make sure the cache works with padding=True, we pre-padded
# the audio to multiple of hop_length.
hop_length = self.info.get_feature_extractor().hop_length
mm_data["audio"] = [
pad_to_hop_length(audio, hop_length)
if isinstance(audio, np.ndarray)
Expand All @@ -725,6 +728,14 @@ def pad_to_hop_length(x: np.ndarray, hop_length: int) -> np.ndarray:
mm_kwargs = dict(
**mm_kwargs,
)
# TODO(Isotr0py): Remove this patch after upstream fix PR
# released and Transformers version update:
# https://github.com/huggingface/transformers/pull/41473
if (
Version(TRANSFORMERS_VERSION) < Version("4.58.0")
and "truncation" not in mm_kwargs
):
mm_kwargs["truncation"] = False

hf_inputs = super()._call_hf_processor(
prompt=prompt,
Expand All @@ -738,7 +749,6 @@ def pad_to_hop_length(x: np.ndarray, hop_length: int) -> np.ndarray:
and "feature_attention_mask" in hf_inputs
and (audios := mm_data.get("audio", []))
):
hop_length = self.info.get_feature_extractor().hop_length
audio_num_frames = []
for _, audio in enumerate(audios):
audio_length = len(audio[0]) if isinstance(audio, tuple) else len(audio)
Expand All @@ -747,6 +757,10 @@ def pad_to_hop_length(x: np.ndarray, hop_length: int) -> np.ndarray:
if audio_length % hop_length == 0
else (audio_length // hop_length - 1)
)
if mm_kwargs.get("truncation", False):
num_frame = min(
num_frame, feature_extractor.n_samples // hop_length
)
audio_num_frames.append(num_frame)
hf_inputs["feature_attention_mask"] = [
torch.ones(num_frame) for num_frame in audio_num_frames
Expand Down