Inspiration
The inspiration for this project came from the desire to create a tool that could automatically transcribe and analyze audio content, providing valuable insights quickly and efficiently.
What it does
This Flask application takes an audio file as input, transcribes its content using Google Cloud Speech-to-Text API, and then performs sentiment analysis on the transcribed text using Google Cloud Natural Language API. The result is displayed to the user, showing both the transcription and the sentiment analysis (positive, negative, or neutral).
How I built it
I built this application using Python and Flask framework for the backend. The audio processing uses the pydub library, and Google Cloud APIs are utilized for speech-to-text and natural language processing. The application is structured to handle file uploads, chunk the audio for processing, and perform sentiment analysis on the transcribed text.
Challenges I ran into
One of the main challenges was efficiently handling large audio files and processing them in smaller, manageable chunks for transcription. Ensuring accurate transcription and sentiment analysis while maintaining performance was also a challenge. Additionally, managing API requests and responses required careful error handling and optimization.
Accomplishments that I'm proud of
I'm proud of successfully integrating multiple technologies to create a seamless audio transcription and sentiment analysis tool. Handling large audio files and processing them effectively demonstrates the robustness of the application. Additionally, implementing error handling and logging ensures a smooth user experience despite unexpected issues.
What I learned
Through building this project, I gained a deeper understanding of audio processing techniques, particularly in handling large audio files and working with audio chunks. I also enhanced my skills in integrating external APIs into web applications and handling asynchronous tasks for efficient processing.
What's next for Untitled
In the future, I plan to enhance the user interface and add more features such as speaker diarization, which identifies different speakers in the audio, and entity recognition to extract important entities from the transcribed text. Additionally, improving the accuracy of sentiment analysis and exploring real-time transcription capabilities could further enhance the application's functionality.