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speaker-diarization-3.1

Maintainer: pyannote

Total Score

198

Last updated 4/29/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The speaker-diarization-3.1 model is a pipeline developed by the pyannote team that performs speaker diarization on audio data. It is an updated version of the speaker-diarization-3.0 model, removing the problematic use of onnxruntime and running the speaker segmentation and embedding entirely in PyTorch. This should ease deployment and potentially speed up inference.

The model takes in mono audio sampled at 16kHz and outputs speaker diarization as an Annotation instance. It can handle stereo or multi-channel audio by automatically downmixing to mono, and it can resample audio files to 16kHz upon loading.

Compared to the previous speaker-diarization-3.0 model, this updated version should provide a smoother and more efficient experience for users integrating the model into their applications.

Model inputs and outputs

Inputs

  • Mono audio sampled at 16kHz: The pipeline accepts a single-channel audio file sampled at 16kHz. It can automatically handle stereo or multi-channel audio by downmixing to mono.

Outputs

  • Speaker diarization: The pipeline outputs a pyannote.core.Annotation instance containing the speaker diarization for the input audio.

Capabilities

The speaker-diarization-3.1 model is capable of accurately segmenting and labeling different speakers within an audio recording. It can handle challenging scenarios like overlapping speech and varying numbers of speakers. The model has been benchmarked on a wide range of datasets, including AISHELL-4, AliMeeting, AMI, AVA-AVD, DIHARD 3, MSDWild, REPERE, and VoxConverse, demonstrating robust performance across diverse audio scenarios.

What can I use it for?

The speaker-diarization-3.1 model can be valuable for a variety of audio-based applications that require identifying and separating different speakers. Some potential use cases include:

  • Meeting transcription and analysis: Automatically segmenting and labeling speakers in audio recordings of meetings, conferences, or interviews to facilitate post-processing and analysis.
  • Audio forensics and investigation: Separating and identifying speakers in audio evidence to aid in investigations and legal proceedings.
  • Podcast and audio content production: Streamlining the editing and post-production process for podcasts, audio books, and other multimedia content by automating speaker segmentation.
  • Conversational AI and voice assistants: Improving the ability of voice-based systems to track and respond to multiple speakers in real-time conversations.

Things to try

One interesting aspect of the speaker-diarization-3.1 model is its ability to control the number of speakers expected in the audio. By using the num_speakers, min_speakers, and max_speakers options, you can fine-tune the model's behavior to better suit your specific use case. For example, if you know the audio you're processing will have a fixed number of speakers, you can set num_speakers to that value to potentially improve the model's accuracy.

Additionally, the model provides hooks for monitoring the progress of the pipeline, which can be useful for long-running or batch processing tasks. By using the ProgressHook, you can gain visibility into the model's performance and troubleshoot any issues that may arise.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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