par laion
Open source · 30k downloads · 38 likes
Le modèle *larger clap music and speech* est une version améliorée de CLAP, conçue pour comprendre et relier le langage à l'audio, à l'image de ce que CLIP fait pour les images. Spécialement entraîné sur des données de musique et de parole, il excelle dans des tâches comme la classification audio sans entraînement préalable ou l'extraction de caractéristiques audio et textuelles. Ses capacités principales incluent l'analyse de contenus audio variés et la génération de correspondances précises entre des descriptions textuelles et des extraits sonores. Ce modèle se distingue par sa polyvalence, permettant d'identifier des sons ou des genres musicaux à partir de simples instructions textuelles, sans nécessiter de données d'entraînement spécifiques. Il s'avère particulièrement utile pour des applications comme la recherche audio, la transcription automatique ou l'organisation de bibliothèques musicales.
CLAP is to audio what CLIP is to image. This is an improved CLAP checkpoint, specifically trained on music and speech.
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_and_speech")
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_and_speech")
processor = ClapProcessor.from_pretrained("laion/larger_clap_music_and_speech")
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_and_speech").to(0)
processor = ClapProcessor.from_pretrained("laion/larger_clap_music_and_speech")
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}
}