Qwen3-ASR-0.6B vs Qwen3-ASR-1.7B

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.

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.

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

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