sivan22/faster-whisper-ivrit-ai-whisper-large-v2-tuned

NOTE: THIS IS A CT-2 (Faster-Whisper) version of the model

the original model can be found here

Whisper

Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. More details about it are available here.

whisper-large-v2-tuned is a version of whisper-large-v2, fine-tuned by ivrit.ai to improve Hebrew ASR using crowd-sourced labeling.

Model details

This model comes as a single checkpoint, whisper-large-v2-tuned. It is a 1550M parameters multi-lingual ASR solution.

Usage

from faster_whisper import WhisperModel

model = WhisperModel("sivan22/faster-whisper-ivrit-ai-whisper-large-v2-tuned")

segments, info = model.transcribe("audio.mp3")
for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))

Evaluation

You can use the evaluate_model.py reference on GitHub to evalute the model's quality.

BibTeX entry and citation info

ivrit.ai: A Comprehensive Dataset of Hebrew Speech for AI Research and Development

@misc{marmor2023ivritai,
      title={ivrit.ai: A Comprehensive Dataset of Hebrew Speech for AI Research and Development}, 
      author={Yanir Marmor and Kinneret Misgav and Yair Lifshitz},
      year={2023},
      eprint={2307.08720},
      archivePrefix={arXiv},
      primaryClass={eess.AS}
}

Whisper: Robust Speech Recognition via Large-Scale Weak Supervision

@misc{radford2022whisper,
  doi = {10.48550/ARXIV.2212.04356},
  url = {https://arxiv.org/abs/2212.04356},
  author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
  title = {Robust Speech Recognition via Large-Scale Weak Supervision},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}