itshamdi404

Egy_Arabic_whisper-small

README

License: apache-2.0

Model Details

Table with columns: Property, Value
Property	Value
Base Model	`openai/whisper-small`
Task	Automatic Speech Recognition (Speech-to-Text)
Language	Arabic (Egyptian Dialect)
Framework	Hugging Face Transformers
Architecture	Transformer Encoder-Decoder (Whisper)

Training Overview

The model was fine-tuned on a dataset containing Egyptian Arabic speech and corresponding transcripts.

Training pipeline included:

Audio resampling to 16kHz
Text normalization
Filtering invalid samples
Whisper processor feature extraction
Fine-tuning using Hugging Face Transformers Trainer
GPU training with PyTorch

The training process aimed to improve transcription accuracy on dialectal Egyptian Arabic speech.

Intended Use

This model is intended for:

Egyptian Arabic speech transcription
Arabic speech recognition research
Voice AI applications
Speech dataset analysis
ASR benchmarking for Arabic dialects

Example use cases include:

Voice assistants
Transcription tools
Speech dataset labeling
Arabic voice interfaces

Limitations

The model is specialized for Egyptian Arabic and may perform worse on other Arabic dialects.
Performance may degrade on:
- Noisy audio
- Overlapping speech
- Low-quality recordings
Like most ASR systems, rare words or names may be misrecognized.

Example Usage

python

import torchimport librosafrom transformers import WhisperProcessor, WhisperForConditionalGeneration model_id = "itshamdi404/Egy_Arabic_whisper-small" processor = WhisperProcessor.from_pretrained(model_id, language="ar", task="transcribe")model = WhisperForConditionalGeneration.from_pretrained(model_id) audio, sr = librosa.load("test.wav", sr=16000) inputs = processor(    audio,    sampling_rate=16000,    return_tensors="pt") with torch.no_grad():    predicted_ids = model.generate(inputs["input_features"]) transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] print(transcription)

Model Evaluation

The model was evaluated using standard ASR metrics:

Table with columns: Metric, Description
Metric	Description
WER	Word Error Rate
CER	Character Error Rate

Evaluation was performed on unseen Egyptian Arabic speech samples.

Dataset

The model was fine-tuned using Egyptian Arabic speech data consisting of paired:

Audio recordings
Text transcripts

The dataset includes a variety of speakers and natural spoken language.

Future Improvements

Possible future improvements include:

Training on larger Egyptian Arabic datasets
Adding more speaker diversity
Improving robustness to noisy environments
Evaluating across multiple Arabic dialects

Author

Hamdi Mohamed — AI Engineer specializing in:

Large Language Models (LLMs)
Speech AI
Computer Vision

Citation

If you use this model in your research or project, please cite:

bibtex

@misc{hamdi2026whisper,  author = {Hamdi Mohamed},  title  = {Whisper Small Egyptian Arabic ASR Model},  year   = {2026},  url    = {https://huggingface.co/itshamdi404}}

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Model Details

Model Provider

itshamdi404

Model Tree

Base

openai/whisper-small

Fine-tuned

this model

Input Modalities

Audio

Output Modalities

Text

Supported Functionality

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README

License: apache-2.0

Model Details

Table with columns: Property, Value
Property	Value
Base Model	`openai/whisper-small`
Task	Automatic Speech Recognition (Speech-to-Text)
Language	Arabic (Egyptian Dialect)
Framework	Hugging Face Transformers
Architecture	Transformer Encoder-Decoder (Whisper)

Training Overview

The model was fine-tuned on a dataset containing Egyptian Arabic speech and corresponding transcripts.

Training pipeline included:

Audio resampling to 16kHz
Text normalization
Filtering invalid samples
Whisper processor feature extraction
Fine-tuning using Hugging Face Transformers Trainer
GPU training with PyTorch

The training process aimed to improve transcription accuracy on dialectal Egyptian Arabic speech.

Intended Use

This model is intended for:

Egyptian Arabic speech transcription
Arabic speech recognition research
Voice AI applications
Speech dataset analysis
ASR benchmarking for Arabic dialects

Example use cases include:

Voice assistants
Transcription tools
Speech dataset labeling
Arabic voice interfaces

Limitations

The model is specialized for Egyptian Arabic and may perform worse on other Arabic dialects.
Performance may degrade on:
- Noisy audio
- Overlapping speech
- Low-quality recordings
Like most ASR systems, rare words or names may be misrecognized.

Example Usage

python

import torchimport librosafrom transformers import WhisperProcessor, WhisperForConditionalGeneration model_id = "itshamdi404/Egy_Arabic_whisper-small" processor = WhisperProcessor.from_pretrained(model_id, language="ar", task="transcribe")model = WhisperForConditionalGeneration.from_pretrained(model_id) audio, sr = librosa.load("test.wav", sr=16000) inputs = processor(    audio,    sampling_rate=16000,    return_tensors="pt") with torch.no_grad():    predicted_ids = model.generate(inputs["input_features"]) transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] print(transcription)

import torchimport librosafrom transformers import WhisperProcessor, WhisperForConditionalGeneration model_id = "itshamdi404/Egy_Arabic_whisper-small" processor = WhisperProcessor.from_pretrained(model_id, language="ar", task="transcribe")model = WhisperForConditionalGeneration.from_pretrained(model_id) audio, sr = librosa.load("test.wav", sr=16000) inputs = processor(    audio,    sampling_rate=16000,    return_tensors="pt") with torch.no_grad():    predicted_ids = model.generate(inputs["input_features"]) transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] print(transcription)

Model Evaluation

The model was evaluated using standard ASR metrics:

Table with columns: Metric, Description
Metric	Description
WER	Word Error Rate
CER	Character Error Rate

Evaluation was performed on unseen Egyptian Arabic speech samples.

Dataset

The model was fine-tuned using Egyptian Arabic speech data consisting of paired:

Audio recordings
Text transcripts

The dataset includes a variety of speakers and natural spoken language.

Future Improvements

Possible future improvements include:

Training on larger Egyptian Arabic datasets
Adding more speaker diversity
Improving robustness to noisy environments
Evaluating across multiple Arabic dialects

Author

Hamdi Mohamed — AI Engineer specializing in:

Large Language Models (LLMs)
Speech AI
Computer Vision

Citation

If you use this model in your research or project, please cite:

bibtex

@misc{hamdi2026whisper,  author = {Hamdi Mohamed},  title  = {Whisper Small Egyptian Arabic ASR Model},  year   = {2026},  url    = {https://huggingface.co/itshamdi404}}

@misc{hamdi2026whisper,  author = {Hamdi Mohamed},  title  = {Whisper Small Egyptian Arabic ASR Model},  year   = {2026},  url    = {https://huggingface.co/itshamdi404}}