fix: qwen2.5 omni apply_chat_template system content check
Author: weedge
Pull request: yes

# What does this PR do?

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Fixes # (issue)

fix: qwen2.5 omni apply_chat_template system content check

test case:
```python
    def thinker_inference_stream(
        messages,
        use_audio_in_video=False,
        speaker=DEFAULT_SPEAKER,
    ):
        text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
        # image_inputs, video_inputs = process_vision_info([messages])
        audios, images, videos = process_mm_info(messages, use_audio_in_video=use_audio_in_video)
        inputs = processor(
            text=text,
            audio=audios,
            images=images,
            videos=videos,
            return_tensors="pt",
            padding=True,
            use_audio_in_video=use_audio_in_video,
        )
        inputs = inputs.to(model.device).to(model.dtype)

        streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)

        generation_kwargs = dict(
            **inputs,
            streamer=streamer,
            use_audio_in_video=use_audio_in_video,
            return_audio=False,
            speaker=speaker,
            thinker_do_sample=True,
            # do_sample=True,
            top_k=10,
            top_p=0.9,
            temperature=0.95,
            repetition_penalty=1.1,
            min_new_tokens=0,
            max_new_tokens=2048,
        )
        thread = Thread(target=model.generate, kwargs=generation_kwargs)
        thread.start()

        generated_text = ""
        times = []
        start_time = perf_counter()
        for new_text in streamer:
            times.append(perf_counter() - start_time)
            start_time = perf_counter()
            generated_text += new_text
            yield new_text
        print(
            f"generate [{generated_text}] first token cost time: {times[0]} s, {len(times)} tokens cost time: {sum(times)} s"
        )
        torch.cuda.empty_cache()


def asr_stream():
    for case in [
        {
            "audio_path": "1272-128104-0000.flac",
            "prompt": "Listen to the provided English speech and produce a translation in Chinese text.",
            "sys_prompt": "You are a speech translation model.",
        },
        {
            "audio_path": "BAC009S0764W0121.wav",
            "prompt": "请将这段中文语音转换为纯文本，去掉标点符号。",
            "sys_prompt": [{"type": "text", "text": "You are a speech recognition model."}],
        },
    ]:
        audio_path = os.path.join(ASSETS_DIR, case["audio_path"])
        messages = [
            {"role": "system", "content": case["sys_prompt"]},
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": case["prompt"]},
                    {"type": "audio", "audio": audio_path},
                ],
            },
        ]
        text_streamer = thinker_inference_stream(messages, use_audio_in_video=True)
        for text in text_streamer:
            print(text)
```

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Models:

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- chat templates: @Rocketknight1

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