Update README.md

README.md

@@ -1,3 +1,60 @@
 ---
 license: cc-by-nc-sa-4.0
+pipeline_tag: audio-classification
+tags:
+- music
+- audio
 ---
+
+# Model Card: Pre-trained Audio Representation Models on AudioSet
+
+## Overview
+
+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.
+
+## Models
+
+### 1. [ALM/hubert-base-audioset](https://huggingface.co/ALM/hubert-base-audioset)
+
+- **Architecture**: HuBERT (Hubert-Base) transformer-based model
+- **Description**: This model is based on the HuBERT architecture, pre-trained on the full AudioSet dataset.
+
+### 2. [ALM/hubert-large-audioset](https://huggingface.co/ALM/hubert-large-audioset)
+
+- **Architecture**: HuBERT (Hubert-Large) transformer-based model
+- **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.
+
+### 3. [ALM/wav2vec2-base-audioset](https://huggingface.co/ALM/wav2vec2-base-audioset)
+
+- **Architecture**: Wav2Vec 2.0 (Wav2Vec2-Base) transformer-based model
+- **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.
+
+### 4. [ALM/wav2vec2-large-audioset](https://huggingface.co/ALM/wav2vec2-large-audioset)
+
+- **Architecture**: Wav2Vec 2.0 (Wav2Vec2-Large) transformer-based model
+- **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.
+
+## Intended Use
+
+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.
+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. 
+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.
+
+## Limitations and Considerations
+
+- The models are pre-trained on the full AudioSet dataset, which may not cover all possible audio domains comprehensively.
+- 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:
+
+```bib
+@article{ARCH,
+  title={Benchmarking Representations for Speech, Music, and Acoustic Events},
+  author={La Quatra, Moreno and Koudounas, Alkis and Vaiani, Lorenzo and Baralis, Elena and Garza, Paolo and Cagliero, Luca, and Siniscalchi, Sabato Marco},
+  year={2024},
+  booktitle={2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)}, 
+}
+```