---
language:
  - en
  - zh
license: other
library_name: transformers
base_model: MERaLiON/MERaLiON-2-3B
tags:
  - audio
  - speech-recognition
  - code-switching
  - dpo
  - meralion
datasets:
  - custom
metrics:
  - mer
pipeline_tag: automatic-speech-recognition
---

# MERaLiON-2-3B-DPO-CodeSwitch

A fine-tuned version of [MERaLiON/MERaLiON-2-3B](https://huggingface.co/MERaLiON/MERaLiON-2-3B) 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** | 0.3238 | **0.3175** | **-2.0%** |
| **SEAME-MAN** | 0.2579 | **0.2561** | **-0.7%** |
| **EMILIA** | 0.3201 | **0.3041** | **-5.0%** |
| **CS-Dialogue** | 0.2541 | **0.2258** | **-11.1%** |

### 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)

## Examples

Below are examples showing improvements from baseline to DPO-trained model:

### Example 1: Hallucination Fixed
| | Transcription |
|---|---|
| **Ground Truth** | 你们 是 一首 歌 也是 教 一个 session [啊] [哦] [嗯] |
| **Baseline** | 你们是一首歌也是教一个 session (oh) 我们也是 session 那个 sessional practice 的... *(hallucinated extra content)* |
| **This Model** | 你们是一首歌也是教一个 session (啊) (哦) |
| **MER** | 2.20 → **0.07** |

### Example 2: Code-Switching Preserved (Maid)
| | Transcription |
|---|---|
| **Ground Truth** | [啊] 然后 因为 我们 家里 有 一个 maid 的 [吗] 我 妈妈 有请 一个 maid [mah] 那个 是 打扫 屋子 的 东西 这样 之类 [吗] that is why 可以 [咯] 因为 |
| **Baseline** | (ah) 然后因为我们家里有一个 maid 的 (mah) 妈妈就请一个 maid 的 (mah) (mah) (mah)... *(repeated filler words)* |
| **This Model** | (啊) 然后因为我们家里有一个 maid 的 (mah) 我妈妈就请一个 maid (mah) 那个是打扫屋子的东西这样子 (leh) (mah) that's why 可以 (loh) 因为 |
| **MER** | 1.02 → **0.17** |

### Example 3: English Location Preserved (Temasek Poly)
| | Transcription |
|---|---|
| **Ground Truth** | 我 住 temasek poly 那边 |
| **Baseline** | 我住达马士科波利那边 *(transliterated to Chinese)* |
| **This Model** | 我住 tamasek poly 那边 |
| **MER** | 1.00 → **0.17** |

### Example 4: Code-Switching Preserved (Exam)
| | Transcription |
|---|---|
| **Ground Truth** | 考 得 很 考 得 like shit |
| **Baseline** | 课程很课程很 like shit *(wrong Chinese characters)* |
| **This Model** | 考得很 考得 like shit |
| **MER** | 0.71 → **0.00** |

### Example 5: Mixed Language Preserved (Youth)
| | Transcription |
|---|---|
| **Ground Truth** | not really youth [lah] 还是 youth 了 三十岁 |
| **Baseline** | not really you (lah) 还是 you (lah) 三十岁 (oh) *(lost "youth")* |
| **This Model** | not really youth (lah) 还是 youth 了三十岁 |
| **MER** | 0.36 → **0.00** |

## Training Configuration

### Model Architecture
| Parameter | Value |
|-----------|-------|
| Base Model | [MERaLiON/MERaLiON-2-3B](https://huggingface.co/MERaLiON/MERaLiON-2-3B) |
| Training Type | Full Fine-Tuning |
| Total Parameters | ~3.47B |
| Trainable Parameters | ~3.47B |

### 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 |
| Global Batch Size | 256 (32 GPUs x 1 x 8) |
| Precision | BF16 |
| Max Sequence Length | 2048 |
| Weight Decay | 0.01 |
| Max Gradient Norm | 1.0 |
| Training Steps | 300 |
| FSDP | Full Shard |


## Usage

```python
from meralion2_model.modeling_meralion2 import MERaLiON2ForConditionalGeneration
from meralion2_model.processing_meralion2 import MERaLiON2Processor
import torch
import librosa

# Load model
model = MERaLiON2ForConditionalGeneration.from_pretrained(
    "myaccountfor/MERaLiON-2-3B-DPO-CodeSwitch",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)
processor = MERaLiON2Processor.from_pretrained(
    "myaccountfor/MERaLiON-2-3B-DPO-CodeSwitch",
    trust_remote_code=True
)

model.eval()

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

# Build prompt
prompt = "Please transcribe this speech."
input_text = f"Instruction: {prompt} \nFollow the text instruction based on the following audio: <SpeechHere>"

# Apply chat template
conversation = [{"role": "user", "content": input_text}]
chat_prompt = "<bos><start_of_turn>user\n" + input_text + "<end_of_turn>\n<start_of_turn>model\n"

# Process inputs
inputs = processor(text=[chat_prompt], audios=[audio])
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,
        do_sample=False,
        use_cache=True,
    )
    generated_ids = outputs[:, inputs['input_ids'].size(1):]
    transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

print(transcription)
```

## Files

```
├── README.md                      # This file
├── config.json                    # Model configuration
├── pytorch_model.bin              # Model weights (~8.1 GB)
├── 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 [MERaLiON-2-3B](https://huggingface.co/MERaLiON/MERaLiON-2-3B) model.