Instructions to use ga642381/Mercury-GTFT-M0v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ga642381/Mercury-GTFT-M0v2 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ga642381/Mercury-GTFT-M0v2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Mercury-GTFT-M0v2
A full-duplex speech-to-speech model fine-tuned for time-aware conversation. Released under a codename while the associated paper is under anonymous review.
What this checkpoint is
See the fd-tico ablation series.
Trained for 2 epochs on 4,440 self-generated full-duplex dialogues whose word timestamps come from forced alignment of the rendered audio (not from the model's own decision frames).
Reported measurements
Text/audio consistency on 100 held-out dialogues โ does the produced audio actually say what the text-decision stream decided? Both sides come from the model's own artifacts; no reference transcript is involved.
| this checkpoint | untuned base | |
|---|---|---|
| character-level consistency (mean / median) | โ | 92.6% / 96.2% |
| word-level recall | โ | 79.1% |
| severe mismatch (<50%) | โ | 6/100 |
| text emission rate | โ tok/s | 2.5 tok/s |
Frames are 80 ms (12.5 fps), audio is 24 kHz.
Attribution and licence
Derived from a publicly available full-duplex speech model released under CC-BY-NC-4.0. This checkpoint inherits that licence: non-commercial use only. The base model's name and full attribution are withheld only for the duration of anonymous review and will be added here once the associated paper is de-anonymised.
Not included
Training code, data, and evaluation scripts are withheld during anonymous review.
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