Ming-3.0-audio-flash vs Qwen3-ASR-0.6B

Ming-3.0-audio-flash

Ming-3.0-audio-flash is a cousin for Chinese and English transcription, questions about long audio and spoken replies in built-in or cloned voices, under MIT. It needs about 260 GB on a multi-GPU server, with inclusionAI's own runtime branches or Transformers remote code. The main risks are running repository code to load it and no published 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-audio-flash

1. Start it with inclusionAI's recipe, hf download inclusionAI/Ming-3.0-audio-flash --local-dir inclusionAI/Ming-3.0-audio-flash && export MODEL_PATH="$PWD/inclusionAI/Ming-3.0-audio-flash" && pip install -r requirements-vllm-omni.txt && MING_OUTPUT_MODE=asr ./scripts/serve_vllm_omni.sh "$MODEL_PATH", run from a clone of github.com/inclusionAI/Ming-3.0-audio-flash
2. The 128K profile uses tensor parallelism over eight 80 GB H800 GPUs, the SGLang profile four and SGLang-Omni's cookbook two. It doesn't fit the 107.4 GB reference machine
3. Use MING_OUTPUT_MODE=asr with the validated prompt and greedy decoding for transcripts, and split the language label from the text at the tab
4. Use MING_OUTPUT_MODE=text for audio questions and the Talker profile with modalities=["text", "audio"] for spoken replies
5. Send audio as a base64 data URL in an audio_url item for vLLM-Omni, or as input_audio for SGLang
6. Pick a voice with audio.voice and an id, or clone one with ref_audio and its exact prompt_text, never both
7. Turn thinking on per request with chat_template_kwargs {"enable_thinking": true}, it's off by default and the reasoning comes back in think tags
8. Tool calls follow the template's <tool_call> with <arg_key> and <arg_value> pairs, and no parser is named, so parse them yourself
9. Review the repository's Python and the talker/ code, .pyc files included, before loading with trust_remote_code

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