OpenAI Whisper Tutorial: Create a Speaker Identification App

Discovering OpenAI Whisper: A Comprehensive Overview

OpenAI Whisper is at the forefront of advanced speech recognition systems, revolutionizing the way we engage with audio data. Enhanced by a massive dataset of 680,000 hours of multilingual, multitask supervised inputs sourced from the web, Whisper excels in accurately transcribing speech across different languages. This extensive training allows the system to exhibit remarkable resilience to various factors, including accents, background noise, and specialized jargon. Furthermore, Whisper not only transcribes audio but is also capable of translating into English, making it an invaluable tool for global communication.

Limitations of Whisper: Speaker Identification Challenges

Despite its groundbreaking capabilities, Whisper faces challenges in speaker identification during conversations. Diarization, which is the process of recognizing and differentiating between speakers in a dialogue, is fundamental for effective conversation analysis. This article provides a tutorial on utilizing Whisper for speaker recognition and transcription using the pyannote-audio toolkit.

Audio Preparation

To begin our analysis, we first need to prepare the audio file. For demonstration purposes, we will utilize the first 20 minutes of Lex Fridman's podcast featuring Yann LeCun. The following steps outline the process:

• Download the podcast video using the yt-dlp package.
• Use ffmpeg to extract the audio.
• Trim the audio to 20 minutes using the pydub library.

Upon completion, we will have a file named audio.wav that contains the desired audio segment.

Implementing Pyannote for Speaker Diarization

pyannote.audio is an open-source toolkit housed in the Python ecosystem, specifically designed for speaker diarization. Leveraging the PyTorch machine learning framework, it encompasses various trainable neural blocks to construct comprehensive speaker diarization pipelines. Pyannote also supplies pretrained models and pipelines, ensuring state-of-the-art performance across numerous domains, including:

• Voice activity detection
• Speaker segmentation
• Overlapped speech detection
• Speaker embedding

Installation and Execution of Pyannote

To commence speaker diarization, you will first need to install the Pyannote library. After installation, you can execute the library on the extracted audio file to generate speaker diarization outputs:

python -m pyannote.audio diarize audio.wav

The output reflects the timing and identity of speakers, with essential data inputs it generates. Following this, we can refine and clean the data to prepare it for transcription.

Transcribing Audio with OpenAI Whisper

Before using Whisper, ensure you have the necessary libraries installed. There is a known version conflict with pyannote.audio that could lead to errors. Our practical solution is to first run the Pyannote process and then follow up with Whisper.

Run OpenAI Whisper on the prepared audio like so:

python -m whisper audio.wav --output_dir transcriptions

Whisper will generate transcription files that can be adjusted for model size as per your requirements. For managing .vtt format files, you will also have to install the webvtt-py library.

Matching Transcriptions with Diarizations

We now need to correlate each line of the transcription with its corresponding diarization data. This matching process ensures that the timing is accurate, particularly for the sections of audio where no diarization was recorded. Using the extracted data, we can generate a neat HTML file to display the results clearly.

Applications of Your New Skills

The knowledge you've gained through this tutorial opens doors to multiple opportunities:

• Develop an innovative Whisper-based application at an AI hackathon, collaborating with passionate individuals globally.
• Participate in New Native's Slingshot program to accelerate your project and take it to market, potentially solving pressing global challenges with AI.
• Or, alternatively, keep this newfound knowledge to yourself and allow others to make an impact, though this isn't the recommended approach!

Over 54,000 enthusiasts have come together during AI hackathons organized by lablab.ai, producing more than 900 prototypes. This is a vibrant community where you can make a significant difference!

For further insights and complete code examples, refer to the notebook that outlines the process.