Repository: huggingface/transformers
Pull request: Fix qwen-omni processor text only mode

# What does this PR do?

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Hi @BakerBunker and @zucchini-nlp,
This PR enables the Qwen2.5-Omni-Processor to handle text-only inputs, without requiring corresponding audio, video, or image data.
This feature is a prerequisite for the VLLM implementation, when uses multimodal embeddings as input.

```
from transformers import Qwen2_5OmniProcessor
text = "
system\nYou are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech.
\n
user\n


<prompt>
\n
assistant\n"
processor = Qwen2_5OmniProcessor.from_pretrained("Qwen/Qwen2.5-Omni-7B")
inputs = processor(text=text)
print(inputs)
```

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Models:

- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier

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- flax: @gante and @Rocketknight1
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Changed files:
filename: src/transformers/models/qwen2_5_omni/processing_qwen2_5_omni.py
patch: @@ -195,16 +195,17 @@ def __call__(
         if not isinstance(text, list):
             text = [text]
 
-        text = self.replace_multimodal_special_tokens(
-            text,
-            audio_lengths,
-            image_grid_thw,
-            video_grid_thw,
-            video_second_per_grid=video_second_per_grid,
-            use_audio_in_video=use_audio_in_video,
-            position_id_per_seconds=position_id_per_seconds,
-            seconds_per_chunk=seconds_per_chunk,
-        )
+        if images is not None or videos is not None or audio is not None:
+            text = self.replace_multimodal_special_tokens(
+                text,
+                audio_lengths,
+                image_grid_thw,
+                video_grid_thw,
+                video_second_per_grid=video_second_per_grid,
+                use_audio_in_video=use_audio_in_video,
+                position_id_per_seconds=position_id_per_seconds,
+                seconds_per_chunk=seconds_per_chunk,
+            )
 
         texts_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])