sander-wood
/

clamp-small-1024

@@ -17,7 +17,7 @@ In [CLaMP: Contrastive Language-Music Pre-training for Cross-Modal Symbolic Musi
 Two variants of CLaMP are introduced: [CLaMP-S/512](https://huggingface.co/sander-wood/clamp-small-512) and [CLaMP-S/1024](https://huggingface.co/sander-wood/clamp-small-1024). Both models consist of a 6-layer music encoder and a 6-layer text encoder with a hidden size of 768. While CLaMP-S/512 accepts input music sequences of up to 512 tokens in length, CLaMP-S/1024 allows for up to 1024 tokens. The maximum input length for the text encoder in both models is 128 tokens. These models are part of [Muzic](https://github.com/microsoft/muzic), a research initiative on AI music that leverages deep learning and artificial intelligence to enhance music comprehension and generation.
-As part of our effort to make CLaMP more accessible to researchers and developers, we have created three Hugging Face spaces that showcase its abilities. The first space, [CLaMP - Semantic Music Search](https://huggingface.co/spaces/sander-wood/clamp_semantic_music_search), enables users to search for musical pieces using natural language queries, such as "a happy jazz song." The second space, [CLaMP - Zero-Shot Music Classification](https://huggingface.co/spaces/sander-wood/clamp_zero_shot_music_classification), allows users to classify musical pieces based on their textual descriptions, without the need for any fine-tuning. Finally, the third space, [CLaMP - Similar Music Recommendation](https://huggingface.co/spaces/sander-wood/clamp_similar_music_recommendation), allows users to input a musical piece in ABC notation and receive recommendations for similar pieces based on their textual descriptions.
 These spaces leverage the power of CLaMP's pre-trained models to provide users with state-of-the-art cross-modal symbolic music information retrieval capabilities. We hope that these spaces will inspire researchers and developers to explore the possibilities of CLaMP and contribute to the advancement of the field of AI music.

 Two variants of CLaMP are introduced: [CLaMP-S/512](https://huggingface.co/sander-wood/clamp-small-512) and [CLaMP-S/1024](https://huggingface.co/sander-wood/clamp-small-1024). Both models consist of a 6-layer music encoder and a 6-layer text encoder with a hidden size of 768. While CLaMP-S/512 accepts input music sequences of up to 512 tokens in length, CLaMP-S/1024 allows for up to 1024 tokens. The maximum input length for the text encoder in both models is 128 tokens. These models are part of [Muzic](https://github.com/microsoft/muzic), a research initiative on AI music that leverages deep learning and artificial intelligence to enhance music comprehension and generation.
+As part of our effort to make CLaMP more accessible to researchers and developers, we have created three Hugging Face spaces that showcase its abilities. The first space, [CLaMP - Semantic Music Search](https://huggingface.co/spaces/sander-wood/clamp_semantic_music_search), enables users to search for musical pieces using natural language queries, such as "a happy jazz song." The second space, [CLaMP - Zero-Shot Music Classification](https://huggingface.co/spaces/sander-wood/clamp_zero_shot_music_classification), allows users to classify musical pieces based on their textual descriptions, without the need for any fine-tuning. Finally, the third space, [CLaMP - Similar Music Recommendation](https://huggingface.co/spaces/sander-wood/clamp_similar_music_recommendation), allows users to input a musical piece in MusicXML (.mxl) and receive recommendations for similar pieces based on their textual descriptions.
 These spaces leverage the power of CLaMP's pre-trained models to provide users with state-of-the-art cross-modal symbolic music information retrieval capabilities. We hope that these spaces will inspire researchers and developers to explore the possibilities of CLaMP and contribute to the advancement of the field of AI music.