Pyannote Speaker Diarization Endpoint

Table of Contents

Model Overview

The Speaker Diarization Model is a powerful tool for identifying and separating speakers in audio recordings. It uses advanced machine learning algorithms to analyze audio files and determine who is speaking and when.

Tasks and rank

Speech Recognition 21st

Sound Event Detection 13th

Audio Tagging 10th

Audio Signal Processing 22nd

Audio Classification 20th

Learn More About the Ranking System

Capabilities

What can it do?

• Identify speakers in an audio file
• Tell you when each speaker starts and stops talking
• Work with multiple speakers at the same time
• Handle overlapping speech (when two or more people talk at the same time)

It’s like having a superpower that lets you know who said what, and when. This model is incredibly fast and efficient when it comes to speaker diarization tasks. But what does that mean exactly?

Speed

Imagine you have a one-hour conversation that you want to analyze. Current Model can process it in just about 3 minutes! That’s because it uses a powerful Nvidia Tesla V100 SXM2 GPU for neural inference and an Intel Cascade Lake 6248 CPU for clustering. This means it can handle large audio files quickly and efficiently.

Accuracy

But speed isn’t everything. Current Model is also very accurate. It’s been tested on a variety of datasets and has shown impressive results. For example, on the AISHELL-4 dataset, it achieved a diarization error rate (DER) of 14.61%. That’s a fancy way of saying it correctly identified the speakers in the conversation 85.39% of the time.

Alternatives

Segmentation 480 Speaker segmentation

Segmentation 3.0 256 Speaker diarization

What makes it special?

• Fast and efficient: It can process a one-hour conversation in just a few minutes
• Accurate: It’s been tested on a variety of datasets and has shown high accuracy in identifying speakers
• Easy to use: You can use it with just a few lines of code, and it’s compatible with popular audio formats

Benchmark Results

Here are some results from testing the model on different datasets:

Dataset	DER%	FA%	Miss%	Conf%
AISHELL-4	14.61	3.31	4.35	6.95
AMI Mix-Headset	18.21	3.28	11.07	3.87
AMI Array1-01	29.00	2.71	21.61	4.68
CALLHOME Part2	30.24	3.71	16.86	9.66
DIHARD 3 Full	20.99	4.25	10.74	6.00
REPERE Phase 2	12.62	1.55	3.30	7.76
VoxConverse v0.0.2	12.76	3.45	3.85	5.46

Limitations

Current Model is a powerful tool for speaker diarization, but it’s not perfect. Let’s take a closer look at some of its limitations.

Processing Time

• Current Model takes around 3 minutes to process a one hour conversation. That’s a real-time factor of around 5%. This might not be ideal for applications that require fast processing times.
• What if you need to process a large number of audio files quickly? Current Model might not be the best choice.

Accuracy

• Current Model has a diarization error rate (DER) of around 14.61% on the AISHELL-4 dataset. That’s not bad, but it’s not perfect either.
• What if you need more accurate results? You might need to consider other options, like ==Other Models== that have been fine-tuned for specific datasets.

Examples

Analyze the audio file audio.wav and determine the number of speakers. {'audio.wav': [{'start': 0.0, 'end': 10.0, 'speaker': 'SPEAKER_01'}, {'start': 10.0, 'end': 20.0, 'speaker': 'SPEAKER_02'}]}

Process a one-hour conversation with an unknown number of speakers and provide the diarization output in RTTM format. Real-time factor: 5%. Processing time: approximately 3 minutes. Output: audio.rttm

Apply the pipeline to an audio file with a known number of speakers (2) and provide the diarization output. {'audio.wav': [{'start': 0.0, 'end': 5.0, 'speaker': 'SPEAKER_01'}, {'start': 5.0, 'end': 10.0, 'speaker': 'SPEAKER_02'}]}

Format

Speaker Diarization Model uses a pyannote.audio 2.0 pipeline to identify speakers in audio files. This model relies on a neural network architecture to detect voice activity and cluster speakers.

Supported Data Formats

This model supports audio files in .wav format. The output is provided in RTTM (Rich Transcription Time Marked) format, which is a standard format for annotating speech data.

Input Requirements

To use this model, you need to provide an audio file in .wav format. You can also specify the number of speakers in the audio file using the num_speakers option. For example:

diarization = pipeline("audio.wav", num_speakers=2)

Alternatively, you can provide lower and upper bounds on the number of speakers using the min_speakers and max_speakers options:

diarization = pipeline("audio.wav", min_speakers=2, max_speakers=5)

Output

The model outputs a diarization file in RTTM format, which contains information about the speakers, their start and end times, and the speech segments.

Example Code

Here’s an example of how to use the model:

from pyannote.audio import Pipeline

# Load the pipeline from Hugginface Hub
pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization@2022.07")

# Apply the pipeline to an audio file
diarization = pipeline("audio.wav")

# Dump the diarization output to disk using RTTM format
with open("audio.rttm", "w") as rttm:
    diarization.write_rttm(rttm)

Advanced Usage

You can also fine-tune the model’s hyperparameters for more accurate results. For example, you can increase the segmentation_onset threshold for more aggressive voice activity detection:

hparams = pipeline.parameters(instantiated=True)
hparams["segmentation_onset"] += 0.1
pipeline.instantiate(hparams)

Note that this may require more computational resources and may not always lead to better results.