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import argparse
from pathlib import Path
import librosa
import numpy as np
import torch
from laion_clap import CLAP_Module
from tqdm import tqdm
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--data_path",
"-d",
required=True,
type=str,
help="Path of the original wav files",
)
parser.add_argument(
"--save_path",
"-s",
required=True,
type=str,
help="Path to save the clap audio embedding '.npy' files",
)
parser.add_argument(
"--clap_ckpt",
"-c",
required=True,
type=str,
help="Path of the pretrained clap checkpoint",
)
parser.add_argument(
"--enable_fusion",
"-e",
default=True,
type=bool,
help="Whether to enable the feature fusion of the clap model. Depends on the clap checkpoint you are using",
)
parser.add_argument(
"--audio_encoder",
"-a",
default="HTSAT-tiny",
type=str,
help="Audio encoder of the clap model. Depends on the clap checkpoint you are using",
)
args = parser.parse_args()
model = CLAP_Module(enable_fusion=args.enable_fusion, aencoder=args.audio_encoder)
model.load_ckpt(args.clap_ckpt)
data_path = Path(args.data_path)
save_path = Path(args.save_path)
with torch.no_grad():
for wav_path in tqdm(
data_path.glob("**/*.wav"), dynamic_ncols=True, colour="yellow"
):
wav, _ = librosa.load(wav_path, sr=48000)
clap_embeding = model.get_audio_embedding_from_data(
x=wav[np.newaxis], use_tensor=False
)
clap_embeding = clap_embeding.squeeze(axis=0)
out_path = save_path / wav_path.with_suffix(".npy").relative_to(data_path)
out_path.parent.mkdir(exist_ok=True)
np.save(out_path, clap_embeding)
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