embedding
Maintainer: pyannote
74
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The embedding model from pyannote is a speaker embedding model that uses the canonical x-vector TDNN-based architecture, but with filter banks replaced by trainable SincNet features. This model reaches 2.8% equal error rate (EER) on the VoxCeleb 1 test set without any additional processing like voice activity detection (VAD) or probabilistic linear discriminant analysis (PLDA). Compared to similar models like the segmentation and speaker-diarization models from pyannote, the embedding model focuses specifically on extracting speaker embeddings from audio.
Model inputs and outputs
The embedding model takes in an audio file and outputs a numpy array representing the speaker embedding for the entire file. This embedding can then be used for tasks like speaker verification, where you can compare the embeddings of two speakers to determine how similar they are.
Inputs
- Audio file: The model accepts a single audio file as input, which can be in any format supported by the underlying audio library.
Outputs
- Speaker embedding: The model outputs a numpy array of shape
(1, D), whereDis the dimensionality of the speaker embedding. This embedding represents the speaker characteristics extracted from the entire input audio file.
Capabilities
The embedding model is capable of extracting robust speaker embeddings from audio data, which can be useful for a variety of applications like speaker verification, diarization, and identification. By using trainable SincNet features, the model is able to achieve strong performance on speaker verification tasks without the need for additional processing steps.
What can I use it for?
The embedding model can be used in a variety of applications that require speaker-level information, such as:
- Speaker verification: The model can be used to generate speaker embeddings that can be compared to determine if two audio samples are from the same speaker. This is useful for applications like access control or fraud detection.
- Speaker diarization: The model's embeddings can be used as input to a speaker diarization system to identify and segment different speakers within a longer audio recording.
- Speaker identification: The model's embeddings can be used to identify specific speakers within a dataset, which can be useful for applications like transcription or meeting analysis.
Things to try
One interesting thing to try with the embedding model is to use it in combination with other audio processing techniques, such as voice activity detection (VAD) or probabilistic linear discriminant analysis (PLDA). By combining the model's speaker embeddings with these additional processing steps, you may be able to achieve even better performance on speaker verification and diarization tasks.
Another interesting experiment would be to fine-tune the model on a specific dataset or domain of interest, which could potentially improve its performance on certain types of audio data. The maintainer's profile mentions that they offer consulting services to help users make the most of their open-source models, which could be a valuable resource for those looking to customize or optimize the embedding model for their specific needs.
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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