metadata

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 model is NOT the latest model released by ivrit.ai.

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

To transcribe audio samples, the model has to be used alongside a WhisperProcessor.

import torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration

SAMPLING_RATE = 16000

has_cuda = torch.cuda.is_available()
model_path = 'ivrit-ai/whisper-large-v2-tuned'

model = WhisperForConditionalGeneration.from_pretrained(model_path)
if has_cuda:
    model.to('cuda:0')

processor = WhisperProcessor.from_pretrained(model_path)

# audio_resample based on entry being part of an existing dataset.
# Alternatively, this can be loaded from an audio file.
audio_resample = librosa.resample(entry['audio']['array'], orig_sr=entry['audio']['sampling_rate'], target_sr=SAMPLING_RATE)

input_features = processor(audio_resample, sampling_rate=SAMPLING_RATE, return_tensors="pt").input_features
if has_cuda:
  input_features = input_features.to('cuda:0')

predicted_ids = model.generate(input_features, language='he', num_beams=5)
transcript = processor.batch_decode(predicted_ids, skip_special_tokens=True)

print(f'Transcript: {transcription[0]}')

Evaluation

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

Long-Form Transcription

The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers pipeline method. Chunking is enabled by setting chunk_length_s=30 when instantiating the pipeline. With chunking enabled, the pipeline can be run with batched inference. It can also be extended to predict sequence level timestamps by passing return_timestamps=True:

>>> import torch
>>> from transformers import pipeline
>>> from datasets import load_dataset

>>> device = "cuda:0" if torch.cuda.is_available() else "cpu"

>>> pipe = pipeline(
>>>   "automatic-speech-recognition",
>>>   model="ivrit-ai/whisper-large-v2-tuned",
>>>   chunk_length_s=30,
>>>   device=device,
>>> )

>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> sample = ds[0]["audio"]

>>> prediction = pipe(sample.copy(), batch_size=8)["text"]
" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel."

>>> # we can also return timestamps for the predictions
>>> prediction = pipe(sample.copy(), batch_size=8, return_timestamps=True)["chunks"]
[{'text': ' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.',
  'timestamp': (0.0, 5.44)}]

Refer to the blog post ASR Chunking for more details on the chunking algorithm.

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}
}