Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-ASR-1.7B

Nemotron 3.5 ASR Streaming 0.6B

A small streaming transcription cousin for live audio in about 32 languages, with language detection and 80 ms to 1.12 s latency. It runs in under 8 GB in Transformers or as a 0.74 GB GGUF in NeMo-Speech.cpp, and several providers host it. The main gaps are no documented timestamps and weak accuracy outside the top 19 locales.

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.

Nemotron 3.5 ASR Streaming 0.6B

1. Quickest local run, pip install "transformers>=5.13.0", then pipeline("automatic-speech-recognition", model="nvidia/nemotron-3.5-asr-streaming-0.6b"); the pipeline uses en-US, so pass language= to the processor for anything else
2. With NeMo-Speech.cpp, nemo-speech transcribe audio.wav --model nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --language auto
3. Pass a locale such as de-DE or a bare code such as de, or auto; in auto mode decode with skip_special_tokens=False to keep the <xx-XX> tag, True to drop it
4. Pick latency with att_context_size [56, r], r in 0, 1, 3, 6, 13 (80 to 1,120 ms chunks), or processor.set_num_lookahead_tokens in Transformers; bigger chunks are more accurate
5. Use nvidia/nemotron-speech-streaming-en-0.6b instead for English only, as NVIDIA recommends
6. Prefer model.safetensors or the GGUF over the .nemo file, which is a pickle
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