import numpy as np import librosa import torch import laion_clap # quantization def int16_to_float32(x): return (x / 32767.0).astype(np.float32) def float32_to_int16(x): x = np.clip(x, a_min=-1., a_max=1.) return (x * 32767.).astype(np.int16) model = laion_clap.CLA...
public_training_code="https://github.com/LAION-AI/CLAP", public_training_data="LAION-Audio-630K", framework=["PyTorch"], reference="https://huggingface.co/laion/clap_htsat_unfused", similarity_fn_name="cosine", use_instructions=False, training_datasets={"LAION-Audio-630K": ["https://...
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See details of pretrained CLAP checkpoints in LAION-AI/CLAP. How to Use Training Preprocess the dataset: For training, you must first convert the audio files into their respective CLAP embeddings and EnCodec sequences. Once you have the converted data, you must write CSV files mapping each ...
[/usr/local/lib/python3.10/dist-packages/laion_clap/hook.py](https://localhost:8080/#) in load_ckpt(self, ckpt, model_id) 112 print('Load Checkpoint...') 113 ckpt = load_state_dict(ckpt, skip_params=True) --> 114 self.model.load_state_dict(ckpt) 115 param_names = [n for n...