Transcribe & Diarize Videos

Generate transcriptions of videos with speaker labels.


I wanted to generate transcriptions of videos with speaker labels. Segmenting or labeling the speakers in audio like this is referred to as Diarization or Diarisation (wikipedia). Unfortunately, OpenAi’s Whisper doesn’t do diarization.


You need to install these tools per the instructions on their respective repos:

  1. yt-dlp: like youtube-dl minus some bugs. Use this to download the video.

  2. WhisperX: performs the Diarization

Additionally, I used a machine with an Nvidia 3090 GPU running Ubuntu 22.04.


I’m going to transcribe this video as an example.

1. Download the audio file with yt-dlp.

The -o "audio.%(ext)s" argument is used to name the output as audo.mp3. The %(ext)s is a placeholder for the file extension. The --extract-audio and --audio-format mp3 arguments are used to extract the audio from the video and convert it to mp3 format.

yt-dlp --extract-audio --audio-format mp3 \
    -o "audio.%(ext)s"

The above command will generate audio.mp3 in the current directory.

2. Generate the transcript with diarization.

This is done with WhisperX. Make sure you carefully follow the instructions in the WhisperX repo corresponding to Speaker Diarization: you have to click on three Hugging Face repos and accept their terms & conditions.

The video I’m working with has 2 speakers, so that’s why I’m setting --min_speakers and --max_speakers equal to 2. The --hf_token argument is the Hugging Face token you get from following the instructions in the WhisperX repo.

whisperx audio.mp3 --model large-v2 --diarize \
    --min_speakers 2 --max_speakers 2 --hf_token <your_hf_token>

This will produce files with the following extensions audio.{srt, vtt, txt, tsv, json} in the current directory. You can limit the formats with --output_format and write these files to a different directory with --output_dir. The .json file contains the most detailed information about the diarization, with world-level predictions, whereas the .vtt and .srt files will contain a more human-readable transcript with speaker labels. I suggest looking at these files to see which one suits your needs.

If looking at the .json file, I recommend using jq with a command like this to see the first row of the segments array in that file:

jq '.segments[0]' audio.json


Here’s a preview of the .vtt file:

> head -n40  audio.vtt


00:00.248 --> 00:13.531
[SPEAKER_01]: Hi, this is Jeremy Howard, and you're listening to Coffee Time Data Science, a podcast for data science enthusiasts, where I interview practitioners, researchers, and Kagglers about their journey, experience, and talk all things data science.

00:13.531 --> 00:17.151
[SPEAKER_01]: And before we begin, I apologize for the change to our schedule.

00:17.151 --> 00:22.593
[SPEAKER_01]: Of course, usually you would be seeing Chai Time Data Science on this channel with Sanyam Bhutani.

00:22.593 --> 00:24.373
[SPEAKER_01]: Unfortunately, he's not available today.

00:24.373 --> 00:29.514
[SPEAKER_01]: He had a prior appointment on another podcast, and he was not able to join Chai Time Data Science.

00:29.974 --> 00:34.338
[SPEAKER_01]: We hope you enjoy this special episode of Coffee Time Data Science.

00:34.338 --> 00:45.148
[SPEAKER_01]: And without further ado, I would like to invite our very special VIP guest, newly anointed Kaggle Grand Master, Sanyam Bhutani.

00:45.148 --> 00:47.190
[SPEAKER_01]: Sanyam, welcome to Coffee Time Data Science.

00:48.372 --> 00:49.073
[SPEAKER_00]: Thank you, Jeremy.

00:49.073 --> 00:53.537
[SPEAKER_00]: Usually, I'm very anti coffee, but I'll have to allow that.

00:53.537 --> 00:55.678
[SPEAKER_00]: I still can't believe you weren't kidding.

00:55.678 --> 00:59.421
[SPEAKER_00]: And I mentioned in our message also, like I, I think I don't deserve this.

00:59.421 --> 01:00.042
[SPEAKER_00]: But thank you.

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