Ming-3.0-asr-tiny vs Qwen3-ASR-0.6B

Ming-3.0-asr-tiny

A local transcription cousin for Chinese, English and Chinese dialects that takes hotwords or dialogue context to get names right, under MIT. It needs one CUDA GPU with about 21 GB and runs only through the publisher's remote code or SGLang-Omni's main branch. The main risk is running the publisher's Python on load, with no safety results.

Qwen3-ASR-0.6B

A very small local transcription cousin for 30 languages and 22 Chinese dialects, built for throughput and realtime streaming through vLLM, under plain Apache 2.0. Its BF16 files need about 2 GB and run in released vLLM, SGLang, llama.cpp and Transformers. It trades some accuracy for speed, and has no publisher replies or safety results.

Ming-3.0-asr-tiny

1. Start it with hf download inclusionAI/Ming-3.0-asr-tiny --local-dir Ming-3.0-asr-tiny, then pip install -r requirements.txt in Python 3.12 with CUDA (Transformers 5.12.1, fla-core 0.5.2, PyTorch 2.11.0), then python examples/transcribe.py --model . --audio speech.wav --language-hint en
2. To serve it, sgl-omni serve --model-path inclusionAI/Ming-3.0-asr-tiny from SGLang-Omni's main branch, then POST the WAV to /v1/audio/transcriptions with response_format json and read the text field
3. Parse raw Transformers output with decode_transcript from asr_request.py, which strips the language label before the tab
4. Pass language zh or en when known, anything else is rejected, and append a topic or hotwords to the base instruction with no newline
5. Load with dtype torch.bfloat16 rather than casting the model, use greedy decoding, max_new_tokens 256 and no_repeat_ngram_size 10, and don't apply the chat template
6. Split long recordings yourself or use --audio-chunk-frames, since input and output share 8,192 tokens
7. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions

Qwen3-ASR-0.6B

1. Start it with vllm serve Qwen/Qwen3-ASR-0.6B (vLLM v0.32.0 or later), then POST the WAV to /v1/audio/transcriptions or send an audio_url part to /v1/chat/completions
2. For vLLM's realtime streaming, start it with --hf-overrides '{"architectures":["Qwen3ASRRealtimeGeneration"]}', which vLLM doesn't pick from config.json
3. Split chat-completions output with parse_asr_output from qwen-asr, since it returns language <name><asr_text> before the transcript
4. For Transformers, load Qwen/Qwen3-ASR-0.6B-hf with Transformers 5.13.0 or later and use processor.apply_transcription_request(audio, language=..., prompt=...)
5. Put names and domain terms in the system message or prompt to bias the transcript, and force the language when you know it
6. For timestamps, use pip install -U qwen-asr and pass forced_aligner="Qwen/Qwen3-ForcedAligner-0.6B" with return_time_stamps=True
7. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions