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"""CLAP embedding.
- feature dimension: 512
- source: https://huggingface.co/laion/larger_clap_music_and_speech
"""
from typing import Optional
import torch
import librosa
import numpy as np
from transformers import ClapModel, ClapProcessor
class CLAPEmbedding:
def __init__(self, ckpt: str = "laion/larger_clap_music_and_speech"):
self.model = ClapModel.from_pretrained(ckpt)
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model.to(self.device)
self.model.eval()
self.processor = ClapProcessor.from_pretrained(ckpt)
def get_speaker_embedding(self, wav: np.ndarray, sampling_rate: Optional[int] = None) -> np.ndarray:
if sampling_rate != self.processor.feature_extractor.sampling_rate:
wav = librosa.resample(wav, orig_sr=sampling_rate, target_sr=self.processor.feature_extractor.sampling_rate)
inputs = self.processor(
audios=wav, sampling_rate=self.processor.feature_extractor.sampling_rate, return_tensors="pt"
)
with torch.no_grad():
outputs = self.model.get_audio_features(**{k: v.to(self.device) for k, v in inputs.items()})
return outputs.cpu().numpy()[0]
class CLAPGeneralEmbedding(CLAPEmbedding):
def __init__(self):
super().__init__(ckpt="laion/larger_clap_general")
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