Instructions to use Kryton-Protocol/Kryton-Scribe-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kryton-Protocol/Kryton-Scribe-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Kryton-Protocol/Kryton-Scribe-v1")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Kryton-Protocol/Kryton-Scribe-v1") model = AutoModelForSpeechSeq2Seq.from_pretrained("Kryton-Protocol/Kryton-Scribe-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Kryton Scribe v1
Kryton Scribe v1 merges the compatible encoders of two Apache-2.0 Whisper Small checkpoints: 90% multilingual and 10% English. The multilingual decoder, tokenizer, and generation configuration are retained because the source vocabularies differ by one token.
The Transformers directory passed a CPU load and transcription test against the included Kryton Voice smoke sample, returning Hello world. Broader multilingual word-error-rate, translation, timestamp, and hallucination evaluation is still recommended.
Exact source revisions, verified hashes, blend weights, tensor counts, support-file hashes, and the output hash are in kryton-merge-manifest.json. build_model.py reproduces the merge.
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