Parakeet TDT 0.6B v3 vs Qwen3-ASR-0.6B

Parakeet TDT 0.6B v3

A small, fast transcription cousin for 25 European languages that detects the language and returns punctuated text with word timestamps. It fits in under 1 GB as NVIDIA's GGUF and runs in Transformers, NeMo or NVIDIA's C++ runtime, under CC BY 4.0. Few other runtimes load it, and it takes no language hint.

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.

Parakeet TDT 0.6B v3

1. For the smallest setup, hf download nvidia/parakeet-tdt-0.6b-v3 parakeet-tdt-0.6b-v3.q8_0.gguf --local-dir models, then nemo-speech transcribe speech.wav --model models/parakeet-tdt-0.6b-v3.q8_0.gguf with NeMo-Speech.cpp
2. In Python, pipeline('automatic-speech-recognition', model='nvidia/parakeet-tdt-0.6b-v3') works on Transformers 5.10.0 or later, despite the card's note to install from source
3. Resample to 16 kHz mono first; it takes wav or flac
4. Ask for timestamps with timestamps=True in NeMo, or pass durations=output.durations to processor.decode in Transformers
5. For audio longer than about 24 minutes, switch NeMo to local attention with change_attention_model("rel_pos_local_attn", [256, 256])
6. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions

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