Update README.md

README.md

@@ -3,55 +3,53 @@ language:
 - ar
 license: apache-2.0
 base_model: openai/whisper-small
-tags:
-- generated_from_trainer
 datasets:
 - mozilla-foundation/common_voice_11_0
 model-index:
-- name: Arabic-Whisper Small
+- name: Whisper-small-ar
   results: []
+library_name: transformers
+pipeline_tag: automatic-speech-recognition
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # Arabic-Whisper Small
 
-This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_11_0 dataset.
+## 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
 
-## Model description
+- **Arabic Language Support:** Whisper-small-ar is optimized for recognizing and transcribing the Arabic language accurately. It can handle various Arabic dialects and accents.
 
-More information needed
+- **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.
 
-## Intended uses & limitations
+- **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.
 
-More information needed
+- **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.
 
-## Training and evaluation data
+- **Compatible with Hugging Face Transformers:** You can easily integrate and utilize this model in your ASR projects using the Hugging Face Transformers library.
 
-More information needed
+### Use Cases
 
-## Training procedure
+Whisper-small-ar can be employed in a variety of ASR use cases, including:
 
-### Training hyperparameters
+- **Transcription Services:** Convert spoken Arabic content, such as audio recordings, podcasts, or videos, into written text for indexing, search, or translation purposes.
 
-The following hyperparameters were used during training:
-- learning_rate: 1e-05
-- train_batch_size: 16
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 250
-- training_steps: 500
+- **Voice Assistants:** Enhance voice-activated systems and virtual assistants with accurate Arabic speech recognition capabilities.
 
-### Training results
+- **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
 
-### Framework versions
+pipe = pipeline("automatic-speech-recognition", model="ayoubkirouane/whisper-small-ar")
 
-- Transformers 4.33.2
-- Pytorch 2.0.1+cu118
-- Datasets 2.14.5
-- Tokenizers 0.13.3
+def transcribe(audio):
+    text = pipe(audio)["text"]
+    return text
+```