Hugging Face's logo

ayoubkirouane
/
whisper-small-ar

Automatic Speech Recognition
Transformers
PyTorch
Arabic
whisper
Inference Endpoints
Model card Files Community
1
whisper-small-ar / README.md
ayoubkirouane's picture
ayoubkirouane
Update README.md
5ade76e
preview code
|
raw
history blame
2.66 kB
metadata
language:
  - ar
license: apache-2.0
base_model: openai/whisper-small
datasets:
  - mozilla-foundation/common_voice_11_0
model-index:
  - name: Whisper-small-ar
    results: []
library_name: transformers
pipeline_tag: automatic-speech-recognition

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 

# 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