MERaLiON-3-ASR

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A high-level ASR wrapper around MERaLiON/MERaLiON-3-3B-ASR.

The package wraps the model with a vLLM backend and pre-wires the transcription prompt, decoding configuration, and 30 s audio chunking on both the offline path and the served path. Callers only provide audio.

Install

pip install meralion-3-asr

Requires Python 3.10+ and a CUDA GPU. vLLM and the FastAPI sidecar dependencies are installed automatically. vLLM is the recommended backend. A pure transformers backend is also available (see Transformers backend below).

Quick start

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 audio is split into 30 s non-overlapping chunks and the per-chunk transcripts are concatenated.

Transformers backend

vLLM is the recommended backend. A pure transformers backend is also available — it loads the model in-process with AutoModelForSpeechSeq2Seq, which is handy for debugging or environments without vLLM:

from meralion_3_asr import Meralion3ASR

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

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

The same prompt, decoding configuration, and 30 s chunking are applied on both backends. See docs/backends.md for a vLLM-vs-transformers comparison.

Serving (OpenAI-compatible HTTP)

meralion-3-asr serve starts a FastAPI sidecar in front of a private vllm serve process and exposes a single OpenAI-compatible route, POST /v1/audio/transcriptions.

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

Common flags:

Any unknown --key value pairs are forwarded to the internal vllm serve.

Call it with the 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)

or raw HTTP:

curl -F file=@audio.wav -F model=MERaLiON/MERaLiON-3-3B-ASR \
    http://localhost:8000/v1/audio/transcriptions

Development

pip install -e ".[dev]"
pytest -q

License

MERaLiON-3-Public-Licence