inclusionAI/Ming-3.0-asr-tiny

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GitHub code and cookbook · [Quick Start](#quick-start) · [Evaluation](#evaluation)

Ming-3.0-asr-tiny combines the Ling-tiny-v3 language backbone with an audio encoder. It transcribes single-turn Chinese and English speech, covers regional dialects, and can use hotwords or preceding dialogue to improve recognition of domain-specific terms. This repository includes the model weights, tokenizer, custom Transformers implementation, and runnable inference example.

Capabilities

• Chinese and English ASR: speech-to-text transcription through one model interface.
• Dialect recognition: evaluated on 15 regional dialect test sets, including Cantonese, Minnan, and Shanghainese.
• Context ASR: accepts relevant hotwords or dialogue context as part of the transcription prompt.
• Challenging speech: evaluated on noisy speech and five application domains.

Architecture

Quick Start

Download and install

Use Python 3.12 on a Linux machine with a compatible CUDA GPU. Download the complete checkpoint and install its dependencies, including PyTorch and torchaudio:

python -m pip install "huggingface_hub[cli]"
hf download inclusionAI/Ming-3.0-asr-tiny --local-dir Ming-3.0-asr-tiny
cd Ming-3.0-asr-tiny
python -m pip install -r requirements.txt

Run the PyTorch / Transformers example

python examples/transcribe.py \
--model . \
--audio /path/to/speech.wav

Add --language-hint zh or --language-hint en when the language is known. The example uses CUDA, BF16, SDPA, greedy decoding, and up to 256 new tokens; it loads the bundled model code with trust_remote_code=True. The dependency versions are listed in requirements.txt, including Transformers 5.12.1, fla-core 0.5.2, and PyTorch 2.11.0. For another CUDA version, first install matching PyTorch and torchaudio 2.11.0 wheels using the official PyTorch instructions, then run pip install -r requirements.txt.

PyTorch / Transformers Python API

Run this directly from the downloaded model directory with your own speech.wav. It is the same inference path as the bundled [executable example](./examples/transcribe.py):

import soundfile as sf
import torch
from transformers import AutoModelForCausalLM, AutoProcessor
from asr_request import decode_transcript, prepare_asr_request

processor = AutoProcessor.from_pretrained(".", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
".", trust_remote_code=True, dtype=torch.bfloat16,
device_map="cuda:0", attn_implementation="sdpa",
).eval()
waveform, rate = sf.read("speech.wav", dtype="float32", always_2d=True)
inputs = prepare_asr_request(
processor, (waveform.T, rate), return_tensors="pt",
).to(model.device)
eos = model.config.text_config.eos_token_id
with torch.inference_mode():
output = model.generate(
**inputs, max_new_tokens=256, do_sample=False,
eos_token_id=eos, pad_token_id=eos, no_repeat_ngram_size=10,
)
print(decode_transcript(
processor.tokenizer, output[0, inputs.input_ids.shape[1]:].tolist(),
))

Context ASR

Ordinary transcription and Context ASR use the same input format. Append the topic and hotwords directly to the base instruction, without a newline. For known-language clips up to 60 seconds, --context-chunks selects 3000 mel frames up to 30 seconds and 6000 beyond 30 seconds:

python examples/transcribe.py --model . --audio /path/to/speech.wav \
--language-hint zh --context-chunks \
--prompt $'Please recognize the language of this speech and transcribe it, placing a `\t` between the identified language and the transcribe.This is an audio about 电影蜥蜴.This audio may contains the following words or phrases:蜥蜴,姜知垠,曹承佑,姜惠贞,八音盒.'

For English, use --language-hint en. The topic and hotwords above are illustrative. Dialogue context can also be included in the prompt text. For Python calls, pass the full prompt through prepare_asr_request(..., prompt=...).

SGLang-Omni serving

SGLang-Omni serves this checkpoint on one GPU:

sgl-omni serve --model-path inclusionAI/Ming-3.0-asr-tiny

Once the server is running, transcribe a WAV file through the OpenAI-compatible audio endpoint:

curl -X POST http://127.0.0.1:8000/v1/audio/transcriptions \
-F model=inclusionAI/Ming-3.0-asr-tiny \
-F file=@/path/to/speech.wav \
-F response_format=json

The response contains a text field. language accepts en or zh, prompt appends one line of topic or hotword context, and stream=true returns the transcript as server-sent events. See the SGLang-Omni cookbook for response formats, streaming, and long-audio handling.

Evaluation

ASR Overview

Watch the Ming-3.0-asr-tiny release film

Chinese and English ASR

Dialects and Domains

Context ASR

Usage Notes

• Output is transcription text, without word-level timestamps or speaker diarization.
• The included example does not support streaming input; image and video inputs are unsupported.
• Long audio can be encoded in chunks with --audio-chunk-frames, subject to the model context limit.
• Hotwords are hints, not a guarantee that a term will appear in the transcript.

For local Transformers inference, run the bundled [PyTorch example](./examples/transcribe.py).

License

The model weights and original project code are released under the [MIT License](./LICENSE), following the Ming project. Bundled Apache-2.0 source files retain their own headers and [license text](./LICENSE-APACHE-2.0). modeling_whisper_encoder.py retains the MIT notice for code adapted from OpenAI Whisper.

Citation

If you use Ming-3.0-asr-tiny, cite the GitHub repository. Citation metadata is also available in [CITATION.cff](./CITATION.cff).

@misc{ming_3_0_asr_tiny_2026,
author = {{inclusionAI}},
title = {Ming-3.0-asr-tiny: Context-Aware Speech Recognition},
year = {2026},
url = {https://github.com/inclusionAI/Ming-3.0-asr}
}