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

Ming-3.0-audio-tiny

A small MIT-licensed audio-language model that transcribes Chinese and English, answers questions about audio and replies in a built-in or cloned voice. It needs about 25 GB and SGLang-Omni from main or inclusionAI's own vLLM-Omni and SGLang branches. The main risk is that every loading path runs repository code, with nothing published on safety.

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-tiny

1. Fastest start is SGLang-Omni from main, sgl-omni serve --model-path inclusionAI/Ming-3.0-audio-tiny --talker.gpu 0, which exposes OpenAI-style /v1/audio/transcriptions, /v1/audio/speech and /v1/chat/completions
2. For transcripts use greedy decoding with 10-gram blocking and the validated ASR prompt, and expect a language label, a tab, then the text
3. Parse tool calls yourself from <tool_call>name with <arg_key> and <arg_value> pairs, since no runtime parser is named
4. Thinking is off by default, turn it on with chat_template_kwargs enable_thinking true and strip the <think> block from the reply yourself
5. In vLLM-Omni pick a voice with audio.voice id from docs/voices.md, and don't combine it with a reference recording
6. Review the repository's Python before passing trust_remote_code, and consider deleting the __pycache__ folders under talker/ so only readable source runs

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