Update README.md

README.md

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 ---
-library_name: transformers
-tags: []
+tags:
+- audio
+- audio-language
+- multimodal
+- reasoning
+- auditory-semantics
+- supervised-fine-tuning
+- sft
+- qwen
+- audsem
+- question-answering
+- qa
+language: en
+license: apache-2.0
+datasets:
+- GLJS/AudSem
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
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-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
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-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+# AudSemThinker-QA
+
+## Model Description
+`AudSemThinker-QA` is a specialized variant of `AudSemThinker`, meticulously fine-tuned for audio-based question answering tasks. It integrates a structured reasoning process based on auditory semantics, explicitly analyzing sound-generating agents, physical sound sources, generation mechanisms, and contextual cues.
+
+This model is built upon the `Qwen2.5-Omni-7B` multimodal foundation model and is fine-tuned *exclusively* on the question answering subset of the novel `AudSem` dataset using Supervised Fine-Tuning (SFT). `AudSemThinker-QA` generates responses in a three-phase structure: a detailed `<thinking>` process, a listing of `<semantic_elements>`, and a concise `<answer>`.
+
+## How to Use
+To use `AudSemThinker-QA` for audio question answering, you can load it using the `transformers` library. Ensure you have `torch`, `torchaudio`, and `soundfile` installed.
+
+```python
+from transformers import AutoProcessor, AutoModelForCausalLM
+import torch
+import torchaudio
+import soundfile as sf
+
+# Load processor and model
+processor = Qwen2_5OmniProcessor.from_pretrained("GLJS/audsemthinker-qa", trust_remote_code=True)
+model = Qwen2_5OmniThinkerForConditionalGeneration.from_pretrained(
+    "GLJS/audsemthinker-qa",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True,
+    low_cpu_mem_usage=True,
+)
+
+# Example audio file (replace with your audio path)
+audio_file = "path/to/your/audio.wav"
+
+audio_input, sampling_rate = torchaudio.load(audio_file)
+if sampling_rate != processor.feature_extractor.sampling_rate:
+    audio_input = torchaudio.transforms.Resample(orig_freq=sampling_rate, new_freq=processor.feature_extractor.sampling_rate)(audio_input)
+audio_input = audio_input.squeeze().numpy() # Ensure mono and numpy array
+
+# Example question
+question = "What type of sound is present in the audio?"
+user_prompt_text = f"You are given a question and an audio clip. Your task is to answer the question based on the audio clip. First, think about the question and the audio clip and put your thoughts in <think> and </think> tags. Then reason about the semantic elements involved in the audio clip and put your reasoning in <semantic_elements> and </semantic_elements> tags. Then answer the question based on the audio clip, put your answer in <answer> and </answer> tags.\nQuestion: {question}"
+
+# Construct messages in conversation format, similar to training
+messages = [
+    {"role": "system", "content": [{"type": "text", "text": "You 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."}]},
+    {
+        "role": "user",
+        "content": [
+            {"type": "audio", "audio": audio_input},
+            {"type": "text", "text": user_prompt_text}
+        ]
+    }
+]
+
+# Apply chat template
+text_from_chat_template = processor.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+# Prepare inputs for the model
+inputs = processor(
+    text=text_from_chat_template,
+    audio=[audio_input], # Pass audio as a list of numpy arrays
+    return_tensors="pt"
+).to(model.device)
+
+# Generate response
+output_ids = model.generate(**inputs, max_new_tokens=512)
+response = processor.batch_decode(output_ids, skip_special_tokens=True)[0]
+
+print(response)
+# Expected output format for QA:
+# <think>...detailed reasoning about the audio scene and question...</think>
+# <semantic_elements>...list of identified semantic descriptors...</semantic_elements>
+# <answer>...answer to the question...</answer>
+```
+
+## Training Data
+`AudSemThinker-QA` is fine-tuned on the **Question Answering (QA) subset** of the `AudSem` dataset. This subset primarily consists of audio-based multiple-choice and open-ended question answering examples, which were synthetically generated from YouTube closed captions through a robust multi-stage pipeline. The `AudSem` dataset is designed to minimize overlap with existing benchmarks.
+
+## Training Procedure
+*   **Base Model:** Qwen2.5-Omni-7B.
+*   **Fine-tuning Paradigm:** Supervised Fine-Tuning (SFT).
+*   **Parameter-Efficient Fine-tuning:** LoRA (Low-Rank Adaptation) applied to projection layers.
+*   **Optimizer:** AdamW.
+*   **Learning Rate:** 2e-04.
+*   **Epochs:** 1.
+*   **Precision:** bf16.
+*   **Batch Size:** 4.
+*   **Hardware:** Trained on a single H100 GPU.
+*   **Training Time:** Approximately 6 hours for the QA subset.
+*   **Output Format:** Trained to generate structured XML-like output with `<think>`, `<semantic_elements>`, and `<answer>` tags. The loss is computed only on the model completion part (assistant's response).
+
+## Evaluation Results
+`AudSemThinker-QA` demonstrates strong performance in question-answering tasks across various audio domains, often outperforming general audio-language models on specific QA benchmarks.
+
+## Limitations and Bias
+*   **Generalization:** While strong in QA tasks, `AudSemThinker-QA` may exhibit trade-offs in performance on other task types (e.g., general audio captioning) compared to models trained on broader datasets.
+*   **Speech Understanding:** Similar to the base `AudSemThinker`, its speech understanding capabilities are not a primary focus and may be less developed.
+*   **Data Contamination:** While `AudSem` is designed to minimize overlap, the underlying `Qwen2.5-Omni` pretrained model might have encountered data present in test sets during its initial pretraining.
+
+## Ethical Considerations
+*   **Data Sourcing:** The `AudSem` dataset is primarily sourced from YouTube closed captions. While systematic checks for harmful content (e.g., child abuse, hate speech, sexual content, harassment) were performed and YouTube's community guidelines provide a safeguard, inherent biases or problematic content from the original video sources could potentially be present.
+*   **Societal Impact:** `AudSemThinker-QA` can contribute to positive societal impacts by enhancing audio-language understanding, particularly in question-answering scenarios. This could aid in various applications requiring precise information extraction from audio.
+
+## Citation
+```bibtex
+@article{wijngaard2025audsemthinker,
+  title={AudSemThinker: Enhancing Audio-Language Models through Reasoning over Semantics of Sound},
+  author={Wijngaard, Gijs and Formisano, Elia and Esposito, Michele and Dumontier, Michel},
+  journal={NeurIPS},
+  year={2025},
+  url={https://github.com/GLJS/AudSemThinker}
+}
+```