transformers pytorch whisper automatic-speech-recognition ar dataset:mozilla-foundation/common_voice_11_0 base_model:openai/whisper-small base_model:finetune:openai/whisper-small license:apache-2.0 endpoints_compatible region:us
---
base_model: openai/whisper-small
datasets:
- mozilla-foundation/common_voice_11_0
language:
- ar
library_name: transformers
license: apache-2.0
pipeline_tag: automatic-speech-recognition
model-index:
- name: Whisper-small-ar
  results: []
---

# Arabic-Whisper Small

## Description

Whisper-small-ar is an Automatic Speech Recognition (ASR) model fine-tuned specifically for the Arabic language using the Whisper model architecture. ASR models are designed to convert spoken language into written text. This model has been fine-tuned on the Mozilla Common Voice dataset (version 11.0) to transcribe spoken Arabic speech into textual form.

### Key Features

- **Arabic Language Support:** Whisper-small-ar is optimized for recognizing and transcribing the Arabic language accurately. It can handle various Arabic dialects and accents.

- **Transformer Architecture:** The model is built on a powerful Transformer-based encoder-decoder architecture, which has demonstrated state-of-the-art performance in various natural language processing tasks, including ASR.

- **Fine-tuned for Arabic ASR:** The model has undergone a fine-tuning process on a substantial amount of Arabic speech data, making it well-suited for a wide range of ASR applications in Arabic, such as transcription of podcasts, call center recordings, and more.

- **Open-Source:** Whisper-small-ar is open-source and available for use by the research and developer community, facilitating the advancement of ASR technology for the Arabic language.

- **Compatible with Hugging Face Transformers:** You can easily integrate and utilize this model in your ASR projects using the Hugging Face Transformers library.

### Use Cases

Whisper-small-ar can be employed in a variety of ASR use cases, including:

- **Transcription Services:** Convert spoken Arabic content, such as audio recordings, podcasts, or videos, into written text for indexing, search, or translation purposes.

- **Voice Assistants:** Enhance voice-activated systems and virtual assistants with accurate Arabic speech recognition capabilities.

- **Language Processing Applications:** Integrate the model into applications involving Arabic language processing, such as sentiment analysis, keyword extraction, and more.

- **Multilingual ASR:** Combine Whisper-small-ar with other multilingual ASR models for applications requiring recognition of multiple languages.

## Usage
```python
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("automatic-speech-recognition", model="ayoubkirouane/whisper-small-ar")

def transcribe(audio):
    text = pipe(audio)["text"]
    return text
```