Arabic Speech Syllables Recognition Using Wav2vec2

🚀 Arabic syllables recognition with tashkeel

This is a fine - tuned wav2vec2 model designed to recognize Arabic syllables from speech, trained on a Modern Standard Arabic dataset. A 5 - gram language model is also available with the model.

🚀 Quick Start

Prerequisites

First, install the necessary libraries:

!pip install datasets transformers
!pip install https://github.com/kpu/kenlm/archive/master.zip pyctcdecode

Load the model and processor

from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from transformers import Wav2Vec2ProcessorWithLM
processor = Wav2Vec2ProcessorWithLM.from_pretrained('IbrahimSalah/Syllables_final_Large')
model = Wav2Vec2ForCTC.from_pretrained("IbrahimSalah/Syllables_final_Large")

Prepare the dataset

import pandas as pd
dftest = pd.DataFrame(columns=['audio'])
import datasets
from datasets import Dataset
path ='/content/908-33.wav'
dftest['audio']=[path]  ## audio path
dataset = Dataset.from_pandas(dftest)

Process the audio

import torch
import torchaudio
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["audio"])
    print(sampling_rate)
    resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) # The original data was with 48,000 sampling rate. You can change it according to your input.
    batch["audio"] = resampler(speech_array).squeeze().numpy()
    return batch

Make predictions

import numpy as np
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["audio"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
    logits = model(inputs.input_values).logits
    print(logits.numpy().shape)

transcription = processor.batch_decode(logits.numpy()).text
print("Prediction:",transcription[0])

💻 Usage Examples

Basic Usage

# Prerequisites
!pip install datasets transformers
!pip install https://github.com/kpu/kenlm/archive/master.zip pyctcdecode

# Load the model and processor
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from transformers import Wav2Vec2ProcessorWithLM
processor = Wav2Vec2ProcessorWithLM.from_pretrained('IbrahimSalah/Syllables_final_Large')
model = Wav2Vec2ForCTC.from_pretrained("IbrahimSalah/Syllables_final_Large")

# Prepare the dataset
import pandas as pd
dftest = pd.DataFrame(columns=['audio'])
import datasets
from datasets import Dataset
path ='/content/908-33.wav'
dftest['audio']=[path]  ## audio path
dataset = Dataset.from_pandas(dftest)

# Process the audio
import torch
import torchaudio
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["audio"])
    print(sampling_rate)
    resampler = torchaudio.transforms.Resample(sampling_rate, 16_000) # The original data was with 48,000 sampling rate. You can change it according to your input.
    batch["audio"] = resampler(speech_array).squeeze().numpy()
    return batch

# Make predictions
import numpy as np
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["audio"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
    logits = model(inputs.input_values).logits
    print(logits.numpy().shape)

transcription = processor.batch_decode(logits.numpy()).text
print("Prediction:",transcription[0])

Advanced Usage

You can then convert the syllables to full word using our fine - tuned mT5 model [IbrahimSalah/Arabic_Syllables_to_text_Converter_Using_MT5]

📚 Documentation

Datasets

• IbrahimSalah/The_Arabic_News_speech_Corpus_Dataset

Tags

• Arabic
• MSA
• Speech
• Syllables
• Wav2vec
• ASR

Paper DOI

https://doi.org/10.60161/2521-001-001-006

📄 License

Citation

BibTeX:

@article{2024SyllableBasedAS,
  title={Syllable-Based Arabic Speech Recognition Using Wav2Vec},
  author={إبراهيم عبدالعال and مصطفى الشافعي and محمد عبدالواحد},
  journal={مجلة اللغات الحاسوبية والمعالجة الآلية للغة العربية},
  year={2024},
  url={https://api.semanticscholar.org/CorpusID:269151543}
}