Segmentation

Table of Contents

Model Overview

The Current Model is a powerful tool for speaker segmentation and overlap-aware resegmentation. This model uses deep learning to detect when a speaker is talking and when there are multiple speakers talking at the same time.

Tasks and rank

Speech Recognition 2nd

Sound Event Detection 2nd

Audio Tagging 2nd

Audio Signal Processing 2nd

Audio Classification 2nd

Learn More About the Ranking System

Capabilities

Primary Tasks

The Current Model can perform the following tasks:

• Voice Activity Detection: It can identify when someone is speaking in an audio recording.
• Overlapped Speech Detection: It can detect when multiple people are speaking at the same time.
• Resegmentation: It can resegment an audio recording to improve the accuracy of speaker diarization.
• Raw Scores: It can provide raw segmentation scores for further analysis.

Strengths

The Current Model has several strengths that make it stand out:

• High Accuracy: It has been trained on a large dataset and has achieved high accuracy in detecting voice activity and overlapped speech.
• Flexibility: It can be used for various applications, such as speaker diarization, speech recognition, and audio analysis.
• Open-Source: It’s an open-source model, which means it’s free to use and modify.

Alternatives

Segmentation 3.0 256 Speaker diarization

Performance

The Current Model showcases remarkable performance in various tasks, including voice activity detection, overlapped speech detection, and resegmentation. Let’s dive into the details.

Speed

The model’s speed is impressive, allowing for efficient processing of audio files. For instance, it can detect voice activity and overlapped speech in a matter of seconds. This is particularly useful in applications where real-time processing is crucial.

Accuracy

The model’s accuracy is high, with impressive results in various datasets, including AMI Mix-Headset, DIHARD3, and VoxConverse. For example, in voice activity detection, the model achieves an onset threshold of 0.684 and an offset threshold of 0.577 in the AMI Mix-Headset dataset.

Efficiency

The model’s efficiency is evident in its ability to process large-scale datasets with ease. It can handle multiple tasks simultaneously, such as voice activity detection and overlapped speech detection, without compromising on accuracy.

Examples

Detect voice activity in the provided audio file. Speech regions detected: [(0.5, 1.2), (2.1, 3.5), (4.8, 5.1)]

Identify overlapped speech in the given audio recording. Overlapped speech regions detected: [(1.8, 2.5), (3.2, 4.1)]

Resegment the audio file using the provided baseline annotation. Resegmented speech regions: [(0.2, 1.5), (2.8, 4.3), (5.5, 6.8)]

Limitations

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

Reliance on pyannote.audio

The model relies heavily on pyannote.audio 2.1.1, which means that any limitations or issues with pyannote.audio can impact the performance of the Current Model.

Hyper-parameter Tuning

The model requires careful tuning of hyper-parameters to achieve optimal results. This can be time-consuming and may require significant expertise.

Limited Contextual Understanding

While the Current Model can detect speaker segments and overlapped speech, it may not always understand the context of the conversation. This can lead to errors in segmentation or incorrect identification of speakers.

Data Quality Issues

The model is only as good as the data it’s trained on. If the training data is noisy, biased, or incomplete, the Current Model may not perform well.

Format

The Current Model is an open-source model that relies on the pyannote.audio library. It’s designed for speaker segmentation, voice activity detection, overlapped speech detection, and resegmentation.

Architecture

The model uses a neural network architecture to analyze audio inputs and detect speaker segments. It’s trained on various datasets, including AMI Mix-Headset, DIHARD3, and VoxConverse.

Data Formats

The model accepts audio files in WAV format as input. You can use the VoiceActivityDetection and OverlappedSpeechDetection pipelines to process audio files and obtain speech regions and overlapped speech regions, respectively.

Input Requirements

To use the model, you need to:

1. Install pyannote.audio 2.1.1
2. Create an access token on the Hugging Face website
3. Instantiate the pre-trained model using the Model.from_pretrained method

Here’s an example code snippet:

from pyannote.audio import Model
model = Model.from_pretrained("pyannote/segmentation", use_auth_token="ACCESS_TOKEN_GOES_HERE")

Output Formats

The model produces output in the form of pyannote.core.Annotation instances, which contain speech regions and overlapped speech regions.

For example, you can use the VoiceActivityDetection pipeline to obtain speech regions:

from pyannote.audio.pipelines import VoiceActivityDetection
pipeline = VoiceActivityDetection(segmentation=model)
vad = pipeline("audio.wav")

The vad variable will contain a pyannote.core.Annotation instance with speech regions.

Hyper-parameters

The model has several hyper-parameters that can be adjusted for optimal performance. These include:

• onset and offset thresholds for voice activity detection and overlapped speech detection
• min_duration_on and min_duration_off parameters for removing short speech regions and filling non-speech regions

You can instantiate the pipelines with custom hyper-parameters using the instantiate method:

HYPER_PARAMETERS = {
    "onset": 0.5,
    "offset": 0.5,
    "min_duration_on": 0.0,
    "min_duration_off": 0.0
}
pipeline.instantiate(HYPER_PARAMETERS)