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.

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}
}