by laion
Open source · 29k downloads · 45 likes
The *Larger CLAP Music* model is an enhanced version of CLAP, specifically trained on musical data, designed to understand and connect natural language with audio in a way similar to how CLIP associates text and images. It can predict relevant textual descriptions from an audio clip without requiring task-specific training, using a contrastive approach that aligns audio and text representations within a shared latent space. Its key capabilities include zero-shot audio classification (without prior training on specific categories) and the extraction of features (embeddings) for either audio or text, enabling applications such as music analysis, similarity search, or automatic metadata generation. This model stands out for its musical specialization, delivering greater accuracy for music-related tasks compared to generic versions of CLAP. It proves particularly valuable for developers or researchers working on projects requiring a nuanced understanding of musical audio content from textual descriptions.
CLAP is to audio what CLIP is to image. This is an improved CLAP checkpoint, specifically trained on music.
CLAP (Contrastive Language-Audio Pretraining) is a neural network trained on a variety of (audio, text) pairs. It can be instructed in to predict the most relevant text snippet, given an audio, without directly optimizing for the task. The CLAP model uses a SWINTransformer to get audio features from a log-Mel spectrogram input, and a RoBERTa model to get text features. Both the text and audio features are then projected to a latent space with identical dimension. The dot product between the projected audio and text features is then used as a similar score.
You can use this model for zero shot audio classification or extracting audio and/or textual features.
pipelinefrom datasets import load_dataset
from transformers import pipeline
dataset = load_dataset("ashraq/esc50")
audio = dataset["train"]["audio"][-1]["array"]
audio_classifier = pipeline(task="zero-shot-audio-classification", model="laion/larger_clap_music")
output = audio_classifier(audio, candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"])
print(output)
>>> [{"score": 0.999, "label": "Sound of a dog"}, {"score": 0.001, "label": "Sound of vaccum cleaner"}]
You can also get the audio and text embeddings using ClapModel
from datasets import load_dataset
from transformers import ClapModel, ClapProcessor
librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
audio_sample = librispeech_dummy[0]
model = ClapModel.from_pretrained("laion/larger_clap_music")
processor = ClapProcessor.from_pretrained("laion/larger_clap_music")
inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt")
audio_embed = model.get_audio_features(**inputs)
from datasets import load_dataset
from transformers import ClapModel, ClapProcessor
librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
audio_sample = librispeech_dummy[0]
model = ClapModel.from_pretrained("laion/larger_clap_music").to(0)
processor = ClapProcessor.from_pretrained("laion/larger_clap_music")
inputs = processor(audios=audio_sample["audio"]["array"], return_tensors="pt").to(0)
audio_embed = model.get_audio_features(**inputs)
If you are using this model for your work, please consider citing the original paper:
@misc{https://doi.org/10.48550/arxiv.2211.06687,
doi = {10.48550/ARXIV.2211.06687},
url = {https://arxiv.org/abs/2211.06687},
author = {Wu, Yusong and Chen, Ke and Zhang, Tianyu and Hui, Yuchen and Berg-Kirkpatrick, Taylor and Dubnov, Shlomo},
keywords = {Sound (cs.SD), Audio and Speech Processing (eess.AS), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering},
title = {Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}