audioanalyser 0.0.1

A Python application for Azure AI Speech to Text service

Audio Analyser: Speech-to-Text & Analysis 🎙️

• Website • Report Bug • Request Feature • Contributing Guidelines

Overview

Discover Hidden Insights in Minutes: AI-Powered Audio Analysis for Your Call Recordings

Streamline call recording and audio file transcription, uncover actionable insights in seconds with advanced text analysis, powered by Microsoft Azure AI services

• Go beyond simple transcription: Discover sentiment, key information, and gain a multi-faceted understanding of your conversations through in-depth analysis and comprehensive reports.
• Audio Analyser leverages the power of Azure's advanced AI services to transform your audio data into valuable insight reports in no time.

Key Features

• Speech to Text: Convert spoken language into text using Azure's speech-to-text service.
• Text Analysis: Analyze text for various features using Azure's text analytics service.
• Instant Transcription:
  • Instantly transcribe audio files and recordings into text.
• Support for outputting results in different formats, including JSON, TXT and SQLite.
• Actionable Insights:
  • Analyze text for various features, including Overall Sentiment, Positive/Negative Sentiment Analysis, Identify Key Topics and Entities, Language, Personally Identifiable Information (PII).
  • Uncover sentiment and key information within conversations.
• Data-Driven Reports:
  • Generate detailed reports for easy sharing and analysis.
• Web Server: A CherryPy-based web server to handle incoming requests and process them.

Built on a Robust Foundation

• Azure-powered technology and a secure CherryPy web server ensure accurate analysis and reliable data management.
• Scalable architecture: Adapt seamlessly to your needs, handling large datasets with ease.

Experience the power of Audio Analyser today!

Dependencies

• CherryPy
• Azure Cognitive Services Speech SDK
• Azure AI Text Analytics
• Python standard libraries: asyncio, threading, logging, sqlite3, json
• Dotenv for environment variable management

Installation

Create a Virtual Environment

We recommend creating a virtual environment to install the Audio Analyser. This will ensure that the package is installed in an isolated environment and will not affect other projects.

python3 -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Installation and Setup

1. Install required Python packages:

   pip install cherrypy azure-ai-textanalytics azure-cognitiveservices-speech

2. Set up Azure services and obtain necessary API keys.

3. Configure environment variables for Azure services in a .env file.

Getting Started

Install audioanalyser with just one command:

pip install audioanalyser

Usage Instructions

To run the Audio Analyser CLI

1. Start the CLI using audioanalyser:

python -m audioanalyser

2. Follow the instructions to utilize speech-to-text and text analysis features.

3. Access the generated transcript and report files in the resources directory in the root folder.

To run the Audio Analyser server

1. Start the server using audioanalyser:

python -m audioanalyser -s

2. Access the server at the specified host and port to utilize speech-to-text and text analysis features.

Usage

To run the application, use the following command:

python server.py

This will start the CherryPy web server, and you can interact with the application through the defined endpoints.

Requirements

The minimum supported Python version is 3.6.

• Azure Cognitive Services for speech and text processing.
• CherryPy for the web server.
• Python's standard libraries including asyncio, sqlite3, and threading.

Configuration

Ensure that your Azure credentials and other configurations are correctly set in a .env file in the root directory. Please refer to the env.example file for the required environment variables.

License

The project is licensed under the terms of both the MIT license and the Apache License (Version 2.0).

• Apache License, Version 2.0
• MIT license

Contribution

We welcome contributions to audioanalyser. Please see the contributing instructions for more information.

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.

Acknowledgements

We would like to extend a big thank you to all the awesome contributors of audioanalyser for their help and support.