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

Automatic Speech Recognition
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audio
hf-asr-leaderboard
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faster-whisper-ivrit-ai-whisper-large-v2-tuned
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---
language:
- en
- zh
- de
- es
- ru
- ko
- fr
- ja
- pt
- tr
- pl
- ca
- nl
- ar
- sv
- it
- id
- hi
- fi
- vi
- he
- uk
- el
- ms
- cs
- ro
- da
- hu
- ta
- 'no'
- th
- ur
- hr
- bg
- lt
- la
- mi
- ml
- cy
- sk
- te
- fa
- lv
- bn
- sr
- az
- sl
- kn
- et
- mk
- br
- eu
- is
- hy
- ne
- mn
- bs
- kk
- sq
- sw
- gl
- mr
- pa
- si
- km
- sn
- yo
- so
- af
- oc
- ka
- be
- tg
- sd
- gu
- am
- yi
- lo
- uz
- fo
- ht
- ps
- tk
- nn
- mt
- sa
- lb
- my
- bo
- tl
- mg
- as
- tt
- haw
- ln
- ha
- ba
- jw
- su
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
widget:
- example_title: Librispeech sample 1
  src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
  src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
pipeline_tag: automatic-speech-recognition
license: apache-2.0
datasets:
- ivrit-ai/whisper-training
---

# NOTE: THIS IS A CT-2 (Faster-Whisper) version of the model
the original model can be found [here](https://huggingface.co/ivrit-ai/whisper-large-v2-tuned)

# Whisper

Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation.
More details about it are available [here](https://huggingface.co/openai/whisper-large-v2).

**whisper-large-v2-tuned** is a version of whisper-large-v2, fine-tuned by [ivrit.ai](https://www.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

```python
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](https://github.com/yairl/ivrit.ai/blob/master/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**
```bibtex
@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**
```bibtex
@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}
}
```