Whisper-transcription_and_diarization-speaker-identification-

Whisper Transcription and Diarization: Speaker Identification Project Introduction

This project focuses on utilizing OpenAI's Whisper technology for transcribing and diarizing audio files, with a particular emphasis on speaker identification. It is a comprehensive guide to understanding and applying the latest advancements in speech recognition and diarization using Python and various open-source tools.

What is Whisper?

Whisper is an advanced speech recognition system developed by OpenAI. Trained on 680,000 hours of multilingual and multitask supervised data sourced from the web, Whisper is exceptionally robust. It effectively handles different accents, background noises, and technical language. Notably, Whisper can transcribe audio in multiple languages and also translate these into English. OpenAI has made its models and source code publicly available to facilitate the development of practical applications harnessing state-of-the-art speech recognition technology.

The Challenge of Speaker Identification

A significant limitation of Whisper is its inability to discern different speakers in a conversation, a crucial requirement for analyzing dialogues. This is where diarization, or the identification of distinct speakers during an audio session, becomes essential. This project guide aims to demonstrate how to overcome Whisper’s limitation by integrating speaker identification, thereby enhancing its overall utility.

Step-by-Step Guide: Preparing the Audio

1. First Steps: Begin by preparing an audio file for analysis. The example used is the first 20 minutes of a podcast episode featuring Yann LeCun with Lex Fridman. The video is downloaded, and the audio is extracted using the yt-dlp package.

2. Software Requirements: Install ffmpeg to handle audio processing and the pydub package to trim the audio to the required length.

3. Audio Extraction: Using simple bash commands and Python scripts provided, download and process the podcast's audio, trimming it to a 20-minute segment stored as audio.wav.

Speaker Diarization with Pyannote

The project employs pyannote.audio, a Python toolkit designed for speaker diarization. With its foundation in PyTorch, this toolkit offers pretrained models that identify speaker segments – discerning who speaks and when – with high accuracy.

1. Installation: Install pyannote.audio to access its powerful diarization capabilities.

2. Implementation: Use the package's pipeline to generate diarized data from the prepared audio, resulting in a text file (diarization.txt) that outlines the start and end times of each speaker's segment within the audio file.

Processing and Cleaning Diarization Data

An important step is refining the diarization data for further usage. The guide provides a script to convert this data into a structured list, detailing each speaker's segment's timing down to milliseconds. This detailed information lays the groundwork for accurately matching dialogue transcriptions with the identified speakers.

Creating Audio Segments

The identified diarization segments are arranged with an audio delimiter between them. Using pydub, each segment is processed sequentially, and the complete audio sequence is exported into a new file, dz.wav.

Transcription with Whisper

Next, the Whisper system transcribes the audio segments from dz.wav. Although a known version conflict with pyannote.audio might cause an error, this is addressed by ensuring Whisper runs after Pyannote. The transcription result is stored in a WebVTT (.vtt) file format.

Integration of Transcriptions and Diarizations

The grand finale of this project is matching Whisper's transcriptions with the Pyannote diarizations. The project provides a script to create an HTML file where each section of the conversation is matched with its corresponding speaker label. This visually intuitive format allows users to navigate through the transcript with a clickable interface linked directly to the audio's timestamps.

Conclusion

Overall, this project leverages the cutting-edge capabilities of Whisper for transcription, coupled with the diarization skills of pyannote.audio, to create an automated and efficient solution for speaker identification. This guide empowers users to understand, implement, and expand upon these technologies for advanced speech processing tasks.