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Hebrew Speech Recognition with Wav2Vec2

Usage

Without package installation (using transformers library)

from transformers import (
    AutomaticSpeechRecognitionPipeline,
    AutoFeatureExtractor,
    Wav2Vec2ForCTC,
    AutoTokenizer
)

pretrained_model_name_or_path = "imvladikon/wav2vec2-xls-r-300m-hebrew"
asr = AutomaticSpeechRecognitionPipeline(
    feature_extractor=AutoFeatureExtractor.from_pretrained(
        pretrained_model_name_or_path
    ),
    model=Wav2Vec2ForCTC.from_pretrained(
        pretrained_model_name_or_path
    ),
    tokenizer=AutoTokenizer.from_pretrained(
        pretrained_model_name_or_path
    ))
filename = "audio.wav"
print(asr(filename))

Chunking file into smaller chunks is not implemented yet.

With package installation

pip install git+https://github.com/imvladikon/wav2vec2-hebrew

Speech recognition

from wav2vec2_hebrew import HebrewSpeechRecognitionPipeline

asr = HebrewSpeechRecognitionPipeline()
filename = "./samples/bereshit011.wav"
output = asr(filename)
print(output)
# [{'text': 'בראשית ברא אלוהים את השמייים ואת הארץ'}]

Alignment

import torchaudio
from wav2vec2_hebrew import HebrewWav2Vec2Aligner

filename = "./samples/bereshit011.wav"
text = "בראשית ברא אלוהים את השמיים ואת הארץ"
aligner = HebrewWav2Vec2Aligner(input_sample_rate=16000, use_cuda=True)
# aligning segments to text (sentences)
first_sentence = aligner.align_data(filename, text)[0]
# {'sentence': 'בראשית ברא אלוהים את השמיים ואת הארץ', 
#  'segments': [Segment(label='בראשית', start=6750.516853932584, end=18644.284644194755, score=0.16025335497152965)...]}

# showing in IPython (notebook)
waveform, sample_rate = torchaudio.load(filename)
aligner.show_segments(waveform, first_sentence)
# showing segments using IPython.display.Audio

Training process

Training logs and details are available in the train folder.

Datasets

Weights