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license: cc-by-nc-sa-4.0 |
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--- |
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# Model Card: Pre-trained Audio Representation Models on AudioSet |
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## Overview |
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This model card presents information about pre-trained audio representation models released by ALM. These models are pre-trained on the full AudioSet dataset and are intended for general-purpose Audio Representation Learning (ARL) tasks. |
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## Models |
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### 1. [ALM/hubert-base-audioset](https://huggingface.co/ALM/hubert-base-audioset) |
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- **Architecture**: HuBERT (Hubert-Base) transformer-based model |
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- **Description**: This model is based on the HuBERT architecture, pre-trained on the full AudioSet dataset. |
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### 2. [ALM/hubert-large-audioset](https://huggingface.co/ALM/hubert-large-audioset) |
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- **Architecture**: HuBERT (Hubert-Large) transformer-based model |
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- **Description**: Similar to the hubert-base-audioset model, this variant is larger in size, providing increased capacity for capturing audio representations from the full AudioSet dataset. |
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### 3. [ALM/wav2vec2-base-audioset](https://huggingface.co/ALM/wav2vec2-base-audioset) |
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- **Architecture**: Wav2Vec 2.0 (Wav2Vec2-Base) transformer-based model |
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- **Description**: This model is based on the Wav2Vec 2.0 architecture, trained on the full AudioSet dataset using SSL with CPC. It offers a different approach to audio representation learning compared to the HuBERT models. |
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### 4. [ALM/wav2vec2-large-audioset](https://huggingface.co/ALM/wav2vec2-large-audioset) |
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- **Architecture**: Wav2Vec 2.0 (Wav2Vec2-Large) transformer-based model |
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- **Description**: Similar to the wav2vec2-base-audioset model, this variant is larger in size, providing enhanced capacity for learning audio representations from the full AudioSet dataset. |
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## Intended Use |
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These pre-trained models are intended for a wide range of ARL tasks, including but not limited to speech recognition, music classification, and acoustic event detection. They serve as powerful tools for feature extraction and can be fine-tuned on task-specific datasets for downstream applications. |
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It's important to note that while these models offer versatility across various audio domains, their performance in speech-related tasks may be relatively lower compared to specialized models such as the original Wav2Vec and HuBERT models. |
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This is due to the diverse nature of the AudioSet dataset used for pre-training, which includes a wide range of audio sources beyond speech. |
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## Limitations and Considerations |
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- The models are pre-trained on the full AudioSet dataset, which may not cover all possible audio domains comprehensively. |
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- Fine-tuning on domain-specific data may be necessary to achieve optimal performance for certain tasks. |
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- Computational resources may be required for deploying and fine-tuning these models, especially the larger variants. |
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## Citation |
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If you use these pre-trained models in your work, please cite the following: |
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```bib |
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@article{ARCH, |
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title={Benchmarking Representations for Speech, Music, and Acoustic Events}, |
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author={La Quatra, Moreno and Koudounas, Alkis and Vaiani, Lorenzo and Baralis, Elena and Garza, Paolo and Cagliero, Luca, and Siniscalchi, Sabato Marco}, |
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year={2024} |
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} |
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``` |
