OpenAI Whisper tutorial: Whisper - Transcription and diarization (speaker identification)

by Fabian Stehle on OCT 13, 2022

What is Whisper?

Whisper is an State-of-the-Art speech recognition system from OpenAI that has been trained on 680,000 hours of multilingual and multitask supervised data collected from the web. This large and diverse dataset leads to improved robustness to accents, background noise and technical language. In addition, it enables transcription in multiple languages, as well as translation from those languages into English. OpenAI released the models and code to serve as a foundation for building useful applications that leverage speech recognition.

One big downside of Whisper is though, that it can not tell you who is speaking in a conversation. That's a problem when analyzing conversations. This is where diarization comes in. Diarization is the process of identifying who is speaking in a conversation.

In this tutorial you will learn how to identify the speakers, and then match them with the transcriptions of Whisper. We will use pyannote-audio to accomplish this. Let's get started!

Preparing the audio

First, we need to prepare the audio file. We will use the first 20 minutes of Lex Fridmans podcast with Yann LeCun. To download the video and extract the audio, we will use yt-dlp package.

!pip install -U yt-dlp

We will also need ffmpeg installed

!wget -O - -q | xz -qdc| tar -x

Now we can do the actual download and audio extraction via the command line.

!yt-dlp -xv --ffmpeg-location ffmpeg-master-latest-linux64-gpl/bin --audio-format wav  -o download.wav --

Now we have the download.wav file in our working directory. Let's cut the first 20 minutes of the audio. We can use the pydub package for this with just a few lines of code.

!pip install pydub
from pydub import AudioSegment

t1 = 0 * 1000 # works in milliseconds
t2 = 20 * 60 * 1000

newAudio = AudioSegment.from_wav("download.wav")
a = newAudio[t1:t2]
a.export("audio.wav", format="wav") 

audio.wav is now the first 20 minutes of the audio file.

Pyannote's Diarization is an open-source toolkit written in Python for speaker diarization. Based on PyTorch machine learning framework, it provides a set of trainable end-to-end neural building blocks that can be combined and jointly optimized to build speaker diarization pipelines. also comes with pretrained models and pipelines covering a wide range of domains for voice activity detection, speaker segmentation, overlapped speech detection, speaker embedding reaching state-of-the-art performance for most of them.

Installing Pyannote and running it on the video audio to generate the diarizations.

!pip install
from import Pipeline

pipeline = Pipeline.from_pretrained('pyannote/speaker-diarization')
DEMO_FILE = {'uri': 'blabal', 'audio': 'audio.wav'}
dz = pipeline(DEMO_FILE)  

with open("diarization.txt", "w") as text_file:

Lets print this out to see what it looks like.

print(*list(dz.itertracks(yield_label = True))[:10], sep="\n")

The output:

(<Segment(2.03344, 36.8128)>, 0, 'SPEAKER_00')
(<Segment(38.1122, 51.3759)>, 0, 'SPEAKER_00')
(<Segment(51.8653, 90.2053)>, 1, 'SPEAKER_01')
(<Segment(91.2853, 92.9391)>, 1, 'SPEAKER_01')
(<Segment(94.8628, 116.497)>, 0, 'SPEAKER_00')
(<Segment(116.497, 124.124)>, 1, 'SPEAKER_01')
(<Segment(124.192, 151.597)>, 1, 'SPEAKER_01')
(<Segment(152.018, 179.12)>, 1, 'SPEAKER_01')
(<Segment(180.318, 194.037)>, 1, 'SPEAKER_01')
(<Segment(195.016, 207.385)>, 0, 'SPEAKER_00')

This looks pretty good already, but let's clean the data a little bit:

def millisec(timeStr):
  spl = timeStr.split(":")
  s = (int)((int(spl[0]) * 60 * 60 + int(spl[1]) * 60 + float(spl[2]) )* 1000)
  return s

import re
dz = open('diarization.txt').read().splitlines()
dzList = []
for l in dz:
  start, end =  tuple(re.findall('[0-9]+:[0-9]+:[0-9]+\.[0-9]+', string=l))
  start = millisec(start) - spacermilli
  end = millisec(end)  - spacermilli
  lex = not re.findall('SPEAKER_01', string=l)
  dzList.append([start, end, lex])

print(*dzList[:10], sep='\n')
[33, 34812, True]
[36112, 49375, True]
[49865, 88205, False]
[89285, 90939, False]
[92862, 114496, True]
[114496, 122124, False]
[122191, 149596, False]
[150018, 177119, False]
[178317, 192037, False]
[193015, 205385, True]

Now we have the diarization data in a list. The first two numbers are the start and end time of the speaker segment in milliseconds. The third number is a boolean that tells us if the speaker is Lex or not.

Preparing audio file from the diarization

Next, we will attach the audio segements according to the diarization, with a spacer as the delimiter.

from pydub import AudioSegment
import re 

sounds = spacer
segments = []

dz = open('diarization.txt').read().splitlines()
for l in dz:
  start, end =  tuple(re.findall('[0-9]+:[0-9]+:[0-9]+\.[0-9]+', string=l))
  start = int(millisec(start)) #milliseconds
  end = int(millisec(end))  #milliseconds
  sounds = sounds.append(audio[start:end], crossfade=0)
  sounds = sounds.append(spacer, crossfade=0)

sounds.export("dz.wav", format="wav") #Exports to a wav file in the current path.
[2000, 38779, 54042, 94382, 98036, 121670, 131297, 160702]

Transcription with Whisper

Next, we will use Whisper to transcribe the different segments of the audio file. Important: There is a version conflict with resulting in an error. Our workaround is to first run Pyannote and then whisper. You can safely ignore the error.

Installing Open AI Whisper.

!pip install git+ 

Running Open AI whisper on the prepared audio file. It writes the transcription into a file. You can adjust the model size to your needs. You can find all models on the model card on Github.

!whisper dz.wav --language en --model base
[00:00.000 --> 00:04.720]  The following is a conversation with Yann LeCun,
[00:04.720 --> 00:06.560]  his second time on the podcast.
[00:06.560 --> 00:11.160]  He is the chief AI scientist at Meta, formerly Facebook,
[00:11.160 --> 00:15.040]  professor at NYU, touring award winner,
[00:15.040 --> 00:17.600]  one of the seminal figures in the history
[00:17.600 --> 00:20.460]  of machine learning and artificial intelligence,

In order to work with .vtt files, we need to install the webvtt-py library.

!pip install -U webvtt-py

Lets take a look at the data:

import webvtt

captions = [[(int)(millisec(caption.start)), (int)(millisec(caption.end)),  caption.text] for caption in'dz.wav.vtt')]
print(*captions[:8], sep='\n')
[0, 4720, 'The following is a conversation with Yann LeCun,']
[4720, 6560, 'his second time on the podcast.']
[6560, 11160, 'He is the chief AI scientist at Meta, formerly Facebook,']
[11160, 15040, 'professor at NYU, touring award winner,']
[15040, 17600, 'one of the seminal figures in the history']
[17600, 20460, 'of machine learning and artificial intelligence,']
[20460, 23940, 'and someone who is brilliant and opinionated']
[23940, 25400, 'in the best kind of way,']

Matching the Transcriptions and the Diarizations

Next, we will match each transcribtion line to some diarizations, and display everything by generating a HTML file. To get the correct timing, we should take care of the parts in original audio that were in no diarization segment. We append a new div for each segment in our audio.

# we need this fore our HTML file (basicly just some styling)
preS = '<!DOCTYPE html>\n<html lang="en">\n  <head>\n    <meta charset="UTF-8">\n    <meta name="viewport" content="width=device-width, initial-scale=1.0">\n    <meta http-equiv="X-UA-Compatible" content="ie=edge">\n    <title>Lexicap</title>\n    <style>\n        body {\n            font-family: sans-serif;\n            font-size: 18px;\n            color: #111;\n            padding: 0 0 1em 0;\n        }\n        .l {\n          color: #050;\n        }\n        .s {\n            display: inline-block;\n        }\n        .e {\n            display: inline-block;\n        }\n        .t {\n            display: inline-block;\n        }\n        #player {\n\t\tposition: sticky;\n\t\ttop: 20px;\n\t\tfloat: right;\n\t}\n    </style>\n  </head>\n  <body>\n    <h2>Yann LeCun: Dark Matter of Intelligence and Self-Supervised Learning | Lex Fridman Podcast #258</h2>\n  <div  id="player"></div>\n    <script>\n      var tag = document.createElement(\'script\');\n      tag.src = "";\n      var firstScriptTag = document.getElementsByTagName(\'script\')[0];\n      firstScriptTag.parentNode.insertBefore(tag, firstScriptTag);\n      var player;\n      function onYouTubeIframeAPIReady() {\n        player = new YT.Player(\'player\', {\n          height: \'210\',\n          width: \'340\',\n          videoId: \'SGzMElJ11Cc\',\n        });\n      }\n      function setCurrentTime(timepoint) {\n        player.seekTo(timepoint);\n   player.playVideo();\n   }\n    </script><br>\n'
postS = '\t</body>\n</html>'

from datetime import timedelta

html = list(preS)

for i in range(len(segments)):
  idx = 0
  for idx in range(len(captions)):
    if captions[idx][0] >= (segments[i] - spacermilli):
  while (idx < (len(captions))) and ((i == len(segments) - 1) or (captions[idx][1] < segments[i+1])):
    c = captions[idx]  
    start = dzList[i][0] + (c[0] -segments[i])

    if start < 0: 
      start = 0
    idx += 1

    start = start / 1000.0
    startStr = '{0:02d}:{1:02d}:{2:02.2f}'.format((int)(start // 3600), 
                                            (int)(start % 3600 // 60), 
                                            start % 60)
    html.append('\t\t\t<div class="c">\n')
    html.append(f'\t\t\t\t<a class="l" href="#{startStr}" id="{startStr}">link</a> |\n')
    html.append(f'\t\t\t\t<div class="s"><a href="javascript:void(0);" onclick=setCurrentTime({int(start)})>{startStr}</a></div>\n')
    html.append(f'\t\t\t\t<div class="t">{"[Lex]" if dzList[i][2] else "[Yann]"} {c[2]}</div>\n')

s = "".join(html)

with open("lexicap.html", "w") as text_file:

You can take a look at the results here or view the complete code as notebook

Thank you for reading. If you enjoyed this tutorial you can find more and continue reading on our tutorial page - Fabian Stehle, Junior Data Scientist at New Native


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Fabian Stehle

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