Ming-3.0-audio-flash vs Qwen3-ASR-1.7B

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-1.7B

A small local transcription cousin for 30 languages and 22 Chinese dialects that identifies the language, takes context to get names right and streams through vLLM, under plain Apache 2.0. Its BF16 files need about 5 GB and run in released vLLM, SGLang, llama.cpp and Transformers. The main risk is upkeep, with 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-1.7B

1. Start it with vllm serve Qwen/Qwen3-ASR-1.7B (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. Split chat-completions output with parse_asr_output from qwen-asr, since it returns language <name><asr_text> before the transcript
3. For Transformers, load Qwen/Qwen3-ASR-1.7B-hf with Transformers 5.13.0 or later and use processor.apply_transcription_request(audio, language=..., prompt=...), then decode(..., return_format="transcription_only")
4. Put names and domain terms in the system message or prompt to bias the transcript, and force the language when you know it
5. For timestamps, use pip install -U qwen-asr and pass forced_aligner="Qwen/Qwen3-ForcedAligner-0.6B" with return_time_stamps=True
6. Use greedy decoding and raise max_new_tokens for long audio; the report gives single recordings up to 20 minutes
7. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions