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Wav2vec2 Base BirdSet XCL
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Wav2vec2 Base BirdSet XCL

Developed by DBD-research-group
wav2vec 2.0 is a self-supervised learning framework for speech representation learning, capable of learning speech features from unlabeled audio data.
Audio Classification
Transformers
#Bird Sound Recognition#Self-supervised Learning#Audio Feature Extraction
Downloads 177
Release Time : 6/4/2024

Model Overview

wav2vec 2.0 is a Transformer-based speech recognition model that learns speech representations from unlabeled audio data through self-supervised learning, suitable for various speech processing tasks.

Model Features

Self-supervised Learning
Capable of learning speech representations from unlabeled audio data, reducing reliance on annotated data.
Efficient Speech Representation
Learns efficient speech feature representations through the Transformer architecture, suitable for various downstream tasks.
Multi-task Support
Supports multiple speech processing tasks such as speech recognition and speech classification.

Model Capabilities

Speech Recognition
Speech Representation Learning
Speech Classification

Use Cases

Speech Recognition
Automatic Speech Transcription
Converts speech to text, suitable for scenarios like meeting minutes and subtitle generation.
High-accuracy speech transcription results.
Speech Classification
Bird Sound Classification
Classifies bird sounds using the BirdSet dataset, applicable to ecological research.
Accurately identifies calls of different bird species.
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