shravan33/audio-nlp-processing-pipeline

Audio NLP Processing Pipeline

An end-to-end audio processing system that performs speech transcription and abstractive summarization using transformer-based models. The application ingests audio input from local files or YouTube sources and produces structured textual summaries through a modular NLP pipeline.

Overview

This project implements a multi-stage processing workflow designed to simulate real-world machine learning inference pipelines. The system converts raw audio into meaningful summaries using speech recognition and natural language processing components.

Primary capabilities:

• —Audio ingestion from multiple sources
• —Automatic speech-to-text transcription
• —Scalable processing of long transcripts via chunking
• —Transformer-based abstractive summarization
• —Interactive interface built with Streamlit

Architecture

Input Layer ├── Local audio upload └── YouTube ingestion (yt-dlp)

Processing Layer ├── Speech transcription (Whisper) ├── Text segmentation (chunking) └── Transformer summarization (T5)

Presentation Layer └── Streamlit application

Core Components

Audio Ingestion

Handles multiple audio sources:

• —Direct file uploads
• —YouTube audio extraction

Ensures standardized input format for downstream processing.

Speech Recognition

Uses OpenAI Whisper for transcription:

• —Robust handling of long-form speech
• —Automatic conversion of audio signals into text data.

Text Processing

Implements chunk-based segmentation to:

• —Prevent context length overflow in transformer models
• —Enable processing of long transcripts
• —Maintain semantic consistency across segments.

Summarization

Uses HuggingFace Transformers with a T5 model:

• —Text-to-text generation paradigm
• —Deterministic summarization pipeline
• —Multi-stage summarization for large inputs.

Technology Stack

• —Python
• —Streamlit
• —OpenAI Whisper
• —HuggingFace Transformers
• —PyTorch
• —yt-dlp

Local Setup

Clone repository:

git clone https://github.com/shravan606756/audio-nlp-processing-pipeline.git cd audio-nlp-processing-pipeline

Install dependencies: pip install -r requirements.txt

Run application:

Deployment

Configured for deployment using HuggingFace Spaces with Docker runtime.

Future Improvements

• —Model caching and lazy loading
• —Streaming transcription pipeline
• —GPU optimization
• —API-based microservice architecture