---
language:
  - en
  - zh
license: other
library_name: transformers
base_model: Qwen/Qwen2-Audio-7B-Instruct
tags:
  - audio
  - speech-recognition
  - code-switching
  - dpo
  - qwen2-audio
datasets:
  - custom
metrics:
  - mer
pipeline_tag: automatic-speech-recognition
---

# Qwen2-Audio-7B-DPO-CodeSwitch

A fine-tuned version of [Qwen/Qwen2-Audio-7B-Instruct](https://huggingface.co/Qwen/Qwen2-Audio-7B-Instruct) trained with DPO (Direct Preference Optimization) on code-switching speech transcription data.

## Evaluation Results (MER - Mixed Error Rate, lower is better)

| Benchmark | Baseline | This Model | Improvement |
|-----------|----------|------------|-------------|
| **SEAME-SGE** | 0.9511 | **0.8552** | **-10.1%** |
| **SEAME-MAN** | 0.7289 | **0.5830** | **-20.0%** |
| **EMILIA** | 0.4470 | **0.4208** | **-5.9%** |
| **CS-Dialogue** | 0.3891 | **0.3140** | **-19.3%** |

### Benchmark Descriptions
- **SEAME-SGE**: SEAME dev set (Singapore English focused) - 3,222 samples ([AudioLLMs/seame_dev_sge](https://huggingface.co/datasets/AudioLLMs/seame_dev_sge))
- **SEAME-MAN**: SEAME dev set (Mandarin focused) - 2,610 samples ([AudioLLMs/seame_dev_man](https://huggingface.co/datasets/AudioLLMs/seame_dev_man))
- **EMILIA**: Synthetic code-switching evaluation set (1,000 samples)
- **CS-Dialogue**: Code-switching dialogue evaluation set (359 samples)

## Training Configuration

### Model Architecture
| Parameter | Value |
|-----------|-------|
| Base Model | [Qwen/Qwen2-Audio-7B-Instruct](https://huggingface.co/Qwen/Qwen2-Audio-7B-Instruct) |
| Training Type | Full Fine-Tuning |
| Total Parameters | ~7B |

### Training Hyperparameters
| Parameter | Value |
|-----------|-------|
| Training Method | DPO (Direct Preference Optimization) |
| DPO Beta | 0.5 |
| Learning Rate | 1e-6 |
| LR Scheduler | Cosine |
| Warmup Ratio | 0.1 |
| Batch Size (per device) | 1 |
| Gradient Accumulation Steps | 8 |
| Precision | BF16 |
| Max Sequence Length | 2048 |
| Weight Decay | 0.01 |
| Max Gradient Norm | 1.0 |
| FSDP | Full Shard |

## Usage

```python
from transformers import Qwen2AudioForConditionalGeneration, AutoProcessor
import torch
import librosa

# Load model
model = Qwen2AudioForConditionalGeneration.from_pretrained(
    "myaccountfor/Qwen2-Audio-7B-DPO-CodeSwitch",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)
processor = AutoProcessor.from_pretrained(
    "myaccountfor/Qwen2-Audio-7B-DPO-CodeSwitch",
    trust_remote_code=True
)

model.eval()

# Load audio
audio, sr = librosa.load("path/to/audio.wav", sr=16000)

# Process inputs
conversation = [
    {"role": "user", "content": [
        {"type": "audio", "audio_url": "path/to/audio.wav"},
        {"type": "text", "text": "Please transcribe this audio."}
    ]}
]

text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
audios = [audio]

inputs = processor(text=text, audios=audios, return_tensors="pt", padding=True)
inputs = {k: v.to(model.device) for k, v in inputs.items()}

# Generate
with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=256)
    transcription = processor.batch_decode(outputs, skip_special_tokens=True)[0]

print(transcription)
```

## Files

```
├── README.md                      # This file
├── config.json                    # Model configuration
├── model weights                  # Model weights
├── tokenizer files                # Tokenizer assets
└── eval_results/
    ├── baseline_seame_sge.json    # Baseline results on SEAME-SGE
    ├── baseline_seame_man.json    # Baseline results on SEAME-MAN
    ├── baseline_emilia.json       # Baseline results on EMILIA
    ├── baseline_cs_dialogue.json  # Baseline results on CS-Dialogue
    ├── trained_seame_sge.json     # This model's results on SEAME-SGE
    ├── trained_seame_man.json     # This model's results on SEAME-MAN
    ├── trained_emilia.json        # This model's results on EMILIA
    └── trained_cs_dialogue.json   # This model's results on CS-Dialogue
```

## License

This model inherits the license of the base [Qwen2-Audio-7B-Instruct](https://huggingface.co/Qwen/Qwen2-Audio-7B-Instruct) model.