Ming-3.0-asr-tiny vs Parakeet TDT 0.6B v3

Ming-3.0-asr-tiny

A local transcription cousin for Chinese, English and Chinese dialects that takes hotwords or dialogue context to get names right, under MIT. It needs one CUDA GPU with about 21 GB and runs only through the publisher's remote code or SGLang-Omni's main branch. The main risk is running the publisher's Python on load, with no safety results.

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.

Ming-3.0-asr-tiny

1. Start it with hf download inclusionAI/Ming-3.0-asr-tiny --local-dir Ming-3.0-asr-tiny, then pip install -r requirements.txt in Python 3.12 with CUDA (Transformers 5.12.1, fla-core 0.5.2, PyTorch 2.11.0), then python examples/transcribe.py --model . --audio speech.wav --language-hint en
2. To serve it, sgl-omni serve --model-path inclusionAI/Ming-3.0-asr-tiny from SGLang-Omni's main branch, then POST the WAV to /v1/audio/transcriptions with response_format json and read the text field
3. Parse raw Transformers output with decode_transcript from asr_request.py, which strips the language label before the tab
4. Pass language zh or en when known, anything else is rejected, and append a topic or hotwords to the base instruction with no newline
5. Load with dtype torch.bfloat16 rather than casting the model, use greedy decoding, max_new_tokens 256 and no_repeat_ngram_size 10, and don't apply the chat template
6. Split long recordings yourself or use --audio-chunk-frames, since input and output share 8,192 tokens
7. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions

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