Speech-Recognition-Translation-model

This project implements a complete pipeline for speech recognition and translation using state-of-the-art machine learning models. The goal is to convert audio speech into text and subsequently translate it into another language.

Key Features:

Speech-to-Text Conversion: Leveraging advanced speech recognition models to accurately transcribe spoken language into text.
Text Translation: High-quality translation of transcribed text from the source language to the target language using a pretrained translation model.
Hugging Face Deployment: The entire pipeline is deployed on Hugging Face, providing an API and web interface for easy interaction and real-time inference.

Project Objectives:

Provide a robust solution for speech recognition and translation, facilitating multilingual applications.
Simplify access to state-of-the-art models through Hugging Face integration.
Enable seamless real-time interaction with the model via a web interface or API.

This repository contains a Jupyter Notebook that implements a speech recognition and translation pipeline. The project focuses on recognizing spoken language and translating it into another language using state-of-the-art machine learning models and techniques.

Overview

The notebook demonstrates the following key steps:

Speech Recognition: Converts spoken language into text.
Translation: Translates the recognized text into the target language.
Model Deployment: The trained model is deployed using Hugging Face for easy access and use.

Techniques Used

🎛️ Preprocessing

• Audio Processing:

  • The raw audio data is preprocessed, including:
    • Resampling audio signals to match the input requirements of the speech recognition model.
    • Converting audio files into the appropriate format using libraries such as Librosa.

• Text Normalization:

  • Text normalization techniques are applied to handle special characters, punctuation, and case sensitivity for better translation quality.

🗣️ Speech Recognition Model

• Pretrained Model:

  • A pretrained model from Hugging Face's transformers library is used for speech recognition. The model transforms the input audio into text.

• Fine-Tuning:

  • Additional fine-tuning of the speech recognition model can be done on a custom dataset to enhance performance (if applicable).

🌐 Translation Model

• Machine Translation:

  • A machine translation model is employed to convert the recognized text into the target language.

• Language Pair:

  • The translation is performed between a source language (e.g., English) and a target language (e.g., French, Spanish, etc.).

🚀 Model Deployment

• Hugging Face Hub:

  • The model is deployed using the Hugging Face Hub, providing an easy-to-use interface for inference. Hugging Face's API allows users to interact with the model directly in production environments.

• Hugging Face Spaces:

  • The deployment process leverages Hugging Face Spaces, enabling users to test the model via a web interface.

How to Use the Notebook

1. Clone this repository:

   git clone https://github.com/HazemAbuelanin/Speech-Recognition-Translation-model.git
   cd speech-recognition-translation

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the notebook using Jupyter:

   jupyter notebook speech-recognition-translation.ipynb

4. Run the notebook using Jupyter:

   jupyter notebook pretrained-speechrecognition.ipynb

Model Deployment on Hugging Face

The model is deployed on Hugging Face for easy use:

• Hugging Face Hub: The model is published on Hugging Face Hub, making it accessible for inference. Users can send requests to the model and get predictions.

• Inference API: The model is available via Hugging Face's Inference API, allowing integration into various applications.

• Link to Deployed Model: Speech-recognition-translation

• Link to Deployed pretrained Model: Speech-recognition-translation-pretrained

Dependencies

• Dependencies are listed in the requirements.txt file.