---
license: apache-2.0
base_model: Qwen/Qwen3-ASR-0.6B
base_model_relation: quantized
language:
- en
library_name: mlx
pipeline_tag: automatic-speech-recognition
tags:
- mlx
- apple-silicon
- speech-to-text
- asr
- stt
- low-bit
- ternary
- quantization-aware-training
- on-device
- streaming
metrics:
- wer
---

# Phonon-1

Phonon-1 is an open speech recognition model for English. It downloads in
415 MB, runs on a laptop or a datacenter GPU, and transcribes an hour of audio
in about two and a half minutes. It was trained at 2.4 bits per weight from
the start, and it is the second model in the lab's low-bit lane after
Neutrino-1.

## Benchmarks

| Dataset | 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.

Median 23.9× realtime across nine corpora on a base M5 MacBook Air.

## Run it

```bash
pip install fermion-research
fermion transcribe recording.wav
```

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

The same weights run on a Mac (via MLX) or an NVIDIA GPU; the CUDA runtime and
Docker image are in the [GitHub repo](https://github.com/fermionresearch/phonon).

## License

**Apache License 2.0** for the weights and the [command line](https://pypi.org/project/fermion-research/). Base model: [`Qwen/Qwen3-ASR-0.6B`](https://huggingface.co/Qwen/Qwen3-ASR-0.6B), Apache-2.0.