Plot A Speaker Timeline with Matplotlib

In this guide, we’ll show you how to plot a speaker timeline with matplotlib, using results from the speaker diarization model.

Quickstart

1 import assemblyai as aai
2 import matplotlib.pyplot as plt
3 
4 aai.settings.api_key = "YOUR_API_KEY"
5 
6 config = aai.TranscriptionConfig(speaker_labels=True, speech_models=["universal-3-pro", "universal-2"])
7 transcriber = aai.Transcriber()
8 transcript = transcriber.transcribe("./my-audio.mp3", config)
9 utterances = transcript.utterances
10 
11 def plot_speaker_timeline(utterances):
12     fig, ax = plt.subplots(figsize=(12, 4))
13     colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
14     speaker_colors = {}
15 
16     for utterance in utterances:
17         start = utterance.start / 60000 # in minutes
18         end = utterance.end / 60000 # in minutes
19         speaker = utterance.speaker
20 
21         if speaker not in speaker_colors:
22             speaker_colors[speaker] = colors[len(speaker_colors) % len(colors)] # set a colour for each new speaker
23 
24         ax.barh(speaker, end - start, left=start, color=speaker_colors[speaker], height=0.4) # create horizontal bar plot
25 
26     ax.set_xlabel('Time (mins)')
27     ax.set_ylabel('Speakers')
28     ax.set_title('Speaker Timeline')
29     ax.grid(True, which='both', linestyle='--', linewidth=0.5)
30     plt.show()
31 
32 plot_speaker_timeline(utterances)

Get Started

Before we begin, make sure you have an AssemblyAI account and an API key. You can sign up for a free account and get your API key from your dashboard.

Step-by-Step Instructions

Install the SDK.

$ pip install -U assemblyai
$ !pip install -U matplotlib

Import the assemblyai package and set the API key.

1 import assemblyai as aai
2 
3 aai.settings.api_key = "YOUR_API_KEY"

Create a TranscriptionConfig object and set speaker labels to True.

1 config = aai.TranscriptionConfig(speaker_labels=True, speech_models=["universal-3-pro", "universal-2"])

Create a Transcriber object.

1 transcriber = aai.Transcriber()

Use the Transcriber object’s transcribe method and pass in the audio file’s path and config object as parameters. The transcribe method saves the results of the transcription to the Transcriber object’s transcript attribute.

1 transcript = transcriber.transcribe("./my-audio.mp3", config)

Alternatively, you can use an audio URL available on the internet.

Extract the utterances from the transcript and set this to utterances.

1 utterances = transcript.utterances

Import the matplotlib.pyplot library. Then use the following plot_speaker_timeline function which results in a plot image of the speaker timeline. This function extracts the start and end timestamps of each utterance per speaker and plots the data onto the horizontal bar chart. The X and Y axis are labelled accordingly.

1 import matplotlib.pyplot as plt
2 
3 def plot_speaker_timeline(utterances):
4     fig, ax = plt.subplots(figsize=(12, 4))
5     colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
6     speaker_colors = {}
7 
8     for utterance in utterances:
9         start = utterance.start / 60000 # in minutes
10         end = utterance.end / 60000 # in minutes
11         speaker = utterance.speaker
12 
13         if speaker not in speaker_colors:
14             speaker_colors[speaker] = colors[len(speaker_colors) % len(colors)] # set a colour for each new speaker
15 
16         ax.barh(speaker, end - start, left=start, color=speaker_colors[speaker], height=0.4) # create horizontal bar plot
17 
18     ax.set_xlabel('Time (mins)')
19     ax.set_ylabel('Speakers')
20     ax.set_title('Speaker Timeline')
21     ax.grid(True, which='both', linestyle='--', linewidth=0.5)
22     plt.show()

Finally, call the plot_speaker_timeline function passing utterances as a parameter to see the plot image result.

1 plot_speaker_timeline(utterances)

1	import assemblyai as aai
2	import matplotlib.pyplot as plt
3
4	aai.settings.api_key = "YOUR_API_KEY"
5
6	config = aai.TranscriptionConfig(speaker_labels=True, speech_models=["universal-3-pro", "universal-2"])
7	transcriber = aai.Transcriber()
8	transcript = transcriber.transcribe("./my-audio.mp3", config)
9	utterances = transcript.utterances
10
11	def plot_speaker_timeline(utterances):
12	fig, ax = plt.subplots(figsize=(12, 4))
13	colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
14	speaker_colors = {}
15
16	for utterance in utterances:
17	start = utterance.start / 60000 # in minutes
18	end = utterance.end / 60000 # in minutes
19	speaker = utterance.speaker
20
21	if speaker not in speaker_colors:
22	speaker_colors[speaker] = colors[len(speaker_colors) % len(colors)] # set a colour for each new speaker
23
24	ax.barh(speaker, end - start, left=start, color=speaker_colors[speaker], height=0.4) # create horizontal bar plot
25
26	ax.set_xlabel('Time (mins)')
27	ax.set_ylabel('Speakers')
28	ax.set_title('Speaker Timeline')
29	ax.grid(True, which='both', linestyle='--', linewidth=0.5)
30	plt.show()
31
32	plot_speaker_timeline(utterances)