wav2vec2-large-audioset Open-source Audio Model - A Free Tool for General Audio Tasks

Wav2vec2 Large Audioset

Developed by ALM

Audio representation model based on HuBERT architecture, pretrained on the complete AudioSet dataset, suitable for general audio tasks

Audio Classification

Transformers

#General Audio Representation #Multi-domain Applicability #Self-supervised Learning

Downloads 43

Release Time : 8/27/2023

Model Overview

This model adopts the HuBERT architecture and is pretrained on the complete AudioSet dataset, capable of extracting high-quality audio feature representations

Model Features

Pretrained on Complete AudioSet Dataset

Pretrained using the complete AudioSet dataset, covering a wide range of audio categories

Advantages of HuBERT Architecture

Utilizes HuBERT's self-supervised learning method to effectively capture latent structures in audio

General Audio Representation

Learned representations are applicable to various audio tasks, including music, speech, and environmental sound analysis

Model Capabilities

Audio Feature Extraction

Music Classification

Acoustic Event Detection

Speech Representation Learning

Use Cases

Audio Analysis

Music Genre Classification

Classify music segments to identify their genres

Environmental Sound Recognition

Identify environmental sound events in recordings (e.g., bird songs, alarms)

Speech Processing

Speech Emotion Recognition

Extract features from speech for emotion analysis

May slightly underperform compared to specialized speech pretrained models

🚀 Model Card: Pre-trained Audio Representation Models on AudioSet

This model card provides information about pre-trained audio representation models released by ALM. These models, pre-trained on the full AudioSet dataset, are designed for general-purpose Audio Representation Learning (ARL) tasks.

🚀 Quick Start

This section will guide you through the basic understanding and potential usage of these pre - trained audio representation models.

✨ Features

Multiple Architectures: The models are based on different architectures, including HuBERT and Wav2Vec 2.0, providing diverse approaches to audio representation learning.
General - Purpose: They are pre - trained on the full AudioSet dataset, suitable for a wide range of ARL tasks.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

No code examples are provided in the original document, so this section is skipped.

📚 Documentation

🔍 Models

Property	Details
Model 1	[ALM/hubert - base - audioset](https://huggingface.co/ALM/hubert - base - audioset). Architecture: HuBERT (Hubert - Base) transformer - based model. It is pre - trained on the full AudioSet dataset.
Model 2	[ALM/hubert - large - audioset](https://huggingface.co/ALM/hubert - large - audioset). Architecture: HuBERT (Hubert - Large) transformer - based model. Larger in size compared to the base model, it has increased capacity for capturing audio representations from the full AudioSet dataset.
Model 3	[ALM/wav2vec2 - base - audioset](https://huggingface.co/ALM/wav2vec2 - base - audioset). Architecture: Wav2Vec 2.0 (Wav2Vec2 - Base) transformer - based model. Trained on the full AudioSet dataset using SSL with CPC, it offers a different approach to audio representation learning compared to the HuBERT models.
Model 4	[ALM/wav2vec2 - large - audioset](https://huggingface.co/ALM/wav2vec2 - large - audioset). Architecture: Wav2Vec 2.0 (Wav2Vec2 - Large) transformer - based model. Similar to the base model but larger in size, it has enhanced capacity for learning audio representations from the full AudioSet dataset.

🎯 Intended Use

These pre - trained models are suitable for a wide range of ARL tasks, such as speech recognition, music classification, and acoustic event detection. They can be used for feature extraction and fine - tuned on task - specific datasets for downstream applications.

⚠️ Important Note

While these models are versatile across various audio domains, their performance in speech - related tasks may be relatively lower compared to specialized models like the original Wav2Vec and HuBERT models. This is because the AudioSet dataset used for pre - training includes a wide range of audio sources beyond speech.

⚠️ Limitations and Considerations

The models are pre - trained on the full AudioSet dataset, which may not comprehensively cover all possible audio domains.
Fine - tuning on domain - specific data may be necessary to achieve optimal performance for certain tasks.
Computational resources may be required for deploying and fine - tuning these models, especially the larger variants.

📖 Citation

If you use these pre - trained models in your work, please cite the following:

@INPROCEEDINGS{ARCH,
  author={La Quatra, Moreno and Koudounas, Alkis and Vaiani, Lorenzo and Baralis, Elena and Cagliero, Luca and Garza, Paolo and Siniscalchi, Sabato Marco},
  booktitle={2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)}, 
  title={Benchmarking Representations for Speech, Music, and Acoustic Events}, 
  year={2024},
  pages={505-509},
  keywords={Representation learning; Systematics; Conferences; Benchmark testing; Signal processing; Acoustics; Data models; Audio Representation Learning; Benchmark; Pre-trained Models; Self-Supervised Learning},
  doi={10.1109/ICASSPW62465.2024.10625960}
}

arXiv version: arxiv.org/abs/2405.00934

📄 License

The models are licensed under cc - by - nc - sa - 4.0.

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