FermionResearch/Phonon-1-Micro

Phonon-1 Micro

Phonon-1 Micro is an open speech recognition model for English, the smallest build of the Phonon-1 family. It downloads in 285 MB, runs on a laptop or a datacenter GPU, and beats Moonshine base on all eight benchmarks below. It was trained at 2.4 bits per weight from the start.

Benchmarks

Benchmark	Phonon-1 (415 MB)	Phonon-1 Micro (285 MB)	Parakeet-0.6B 4-bit (637 MB)	Moonshine base (248 MB)	Whisper large-v3-turbo (1,619 MB)	Whisper small (967 MB)	wav2vec2-large (1,262 MB)	Qwen3-ASR teacher (1,569 MB)
LibriSpeech test-clean	2.640	3.002	2.186	3.417	2.10	3.4†	2.8†	2.235
LibriSpeech test-other	5.699	6.511	3.937	8.262	4.07	7.6†	6.3†	4.618
TED-LIUM	3.421	3.878	2.829	5.272	—	—	—	2.889
SPGISpeech	4.163	4.858	4.104	5.731	2.79†	—	13.31†	3.074
VoxPopuli	8.394	9.177	6.345	10.470	11.22†	—	—	7.151
GigaSpeech	11.396	11.882	9.614	12.114	8.52†	—	—	9.321
Earnings-22	12.571	14.771	11.190	17.872	11.07†	—	36.28†	11.188
AMI	13.084	14.094	12.723	17.790	15.16†	—	—	12.560
Macro (eight benchmarks)	7.67	8.52	6.62	10.1	—	—	—	6.63

Word error rate, lower is better. Unmarked cells: measured by us — full test sets, Whisper English text normalizer, greedy decoding. † = published figure (model card, paper, or the Open ASR Leaderboard). Dash = no comparable measurement.

Run it

pip install fermion-research

fermion transcribe --model FermionResearch/Phonon-1-Micro recording.wav

Or serve an OpenAI-compatible endpoint:

fermion serve --model FermionResearch/Phonon-1-Micro

curl -s http://127.0.0.1:8000/v1/audio/transcriptions \
  -F "file=@recording.wav" \
  -F "model=FermionResearch/Phonon-1-Micro"

The same weights run on a Mac (via MLX) or an NVIDIA GPU; the CUDA runtime and Docker image are in the GitHub repo.

License

Apache License 2.0 for the weights and the command line. Base model: Qwen/Qwen3-ASR-0.6B, Apache-2.0.