MERaLiON / MERaLiON-3-3B-ASR

Model Details of MERaLiON-3-3B-ASR

A note on the code-switching benchmark

The code-switching section aggregates 13 datasets of natural Southeast Asian conversational speech, totalling 5,430 samples / 35.4 hours, in which English is mixed organically with another SEA language. Coverage by direction:

The benchmark is intended to capture real-world SEA usage patterns rather than synthetic alternation between scripts. Of the constituent corpora, only IMDA Part 4 (NSC) is publicly available; the remaining datasets are proprietary or curated in-house. Per-sample normalisation and scoring scripts for the whole evaluation are available at SEA-SpeechBench .

Model Description

The model is trained as an ASR-optimised fine-tune over a multilingual mixture of curated Southeast Asian speech data, with particular emphasis on Singapore English, regional dialects (Cantonese, Hokkien), and natural English-X code-switching.

How to Use

The recommended way to run MERaLiON-3-3B-ASR is through the meralion-3-asr Python package, which wraps a vLLM backend tuned for this model. Install directly from source:

pip install "meralion-3-asr[vllm] @ git+https://github.com/YingxuH/MERaLiON-3-ASR.git"

The package pre-wires the transcription prompt, decoding configuration, no-repeat-ngram guard, and 30 s audio chunking — on both the offline path and the served path. Callers only provide audio.

1. Offline batch (in-process vLLM)

from meralion_3_asr import Meralion3ASR

model = Meralion3ASR.from_pretrained("MERaLiON/MERaLiON-3-3B-ASR", backend="vllm")

text = model.transcribe("audio.wav")                          # str
texts = model.transcribe_batch(["a.wav", "b.wav", "c.wav"])   # List[str]

Inputs may be local file paths, https:// URLs, base64 data URLs, or (numpy_array, sample_rate) tuples. Audio is automatically resampled to mono 16 kHz; long files are chunked transparently.

2. Serving via the bundled sidecar

meralion-3-asr serve starts a FastAPI sidecar fronting an internal vllm serve process. The sidecar exposes a single OpenAI-compatible endpoint, /v1/audio/transcriptions , applies 30 s non-overlapping chunking server-side, and forwards each chunk to the internal vLLM. Clients send audio only — no prompt, no decoding flags.

meralion-3-asr serve --model MERaLiON/MERaLiON-3-3B-ASR --port 8000

OpenAI Python SDK:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

with open("audio.wav", "rb") as f:
    resp = client.audio.transcriptions.create(
        model="MERaLiON/MERaLiON-3-3B-ASR",
        file=f,
    )
print(resp.text)

curl:

curl -s -X POST http://localhost:8000/v1/audio/transcriptions \
    -F "model=MERaLiON/MERaLiON-3-3B-ASR" \
    -F "[email protected]" \
  | jq -r .text

A transformers backend is also available for debugging and small-scale use, but is currently considered experimental — the vLLM backend is the only fully supported and benchmarked path.

Hardware & Infrastructure

MERaLiON-3 was trained on the ASPIRE 2A+ supercomputer cluster at the National Supercomputing Centre (NSCC), Singapore, hosted by A*STAR I2R:

• GPUs : 128 Nvidia H100 GPUs (16 nodes × 8 H100)
• Memory : 2 TB RAM per node
• Storage : 30 TB NVMe per node + 2.5 PB SSD-based Lustre filesystem
• Interconnect : 400 Gb/s NDR InfiniBand (full fat-tree topology)

Related Resources

• meralion-3-asr Python package (GitHub)
• SEA-SpeechBench evaluation harness
• Related models:
  • MERaLiON-2-10B-ASR
  • MERaLiON-2-3B
  • MERaLiON-2-10B

License

License: TBD.

Disclaimer

⚠️ MERaLiON-3-3B-ASR has not been specifically aligned for safety and may transcribe content that is inappropriate, offensive, or harmful. Developers and users are responsible for performing their own safety fine-tuning and implementing necessary security measures. The authors shall not be held liable for any claims, damages, or other liabilities arising from the use of the released models, weights, or code.

Citation

If you use MERaLiON-3-3B-ASR in your work, please cite the foundational MERaLiON references:

@misc{he2024meralionaudiollmtechnicalreport,
      title={MERaLiON-AudioLLM: Bridging Audio and Language with Large Language Models},
      author={{MERaLiON Team}},
      year={2024},
      eprint={2412.09818},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.09818},
}

@article{wang2024audiobench,
    title={AudioBench: A Universal Benchmark for Audio Large Language Models},
    author={Wang, Bin and Zou, Xunlong and Lin, Geyu and Sun, Shuo and Liu, Zhuohan and Zhang, Wenyu and Liu, Zhengyuan and Aw, AiTi and Chen, Nancy F},
    journal={NAACL},
    year={2025}
}

@article{wang2025advancing,
    title={Advancing Singlish Understanding: Bridging the Gap with Datasets and Multimodal Models},
    author={Wang, Bin and Zou, Xunlong and Sun, Shuo and Zhang, Wenyu and He, Yingxu and Liu, Zhuohan and Wei, Chengwei and Chen, Nancy F and Aw, AiTi},
    journal={arXiv preprint arXiv:2501.01034},
    year={2025}
}

@article{zhang2024mowe,
    title={MoWE-Audio: Multitask AudioLLMs with Mixture of Weak Encoders},
    author={Zhang, Wenyu and Sun, Shuo and Wang, Bin and Zou, Xunlong and Liu, Zhuohan and He, Yingxu and Lin, Geyu and Chen, Nancy F and Aw, Ai Ti},
    journal={ICASSP},
    year={2025}
}