init

experiment_speaker_verification.py

@@ -17,6 +17,7 @@ from datasets import load_dataset
 from model_meta_voice import MetaVoiceSE
 from model_pyannote_embedding import PyannoteSE
 from model_w2v_bert import W2VBertSE
+from model_clap import ClapSE
 
 
 def get_embedding(model_class, model_name: str, dataset_name: str, data_split: str):
@@ -114,22 +115,30 @@ def analyze_embedding(model_name: str, dataset_name: str, n_shot: int = 5, n_cro
 
 
 if __name__ == '__main__':
-    # cache embedding
-    # get_embedding(MetaVoiceSE, "meta_voice_se", "asahi417/voxceleb1-test-split", "test")
-    # get_embedding(PyannoteSE, "pyannote_se", "asahi417/voxceleb1-test-split", "test")
-    # get_embedding(W2VBertSE, "w2v_bert_se", "asahi417/voxceleb1-test-split", "test")
-    # get_embedding(MetaVoiceSE, "meta_voice_se", "ylacombe/expresso", "train")
-    # get_embedding(PyannoteSE, "pyannote_se", "ylacombe/expresso", "train")
-    # get_embedding(W2VBertSE, "w2v_bert_se", "ylacombe/expresso", "train")
+    get_embedding(MetaVoiceSE, "meta_voice_se", "asahi417/voxceleb1-test-split", "test")
+    get_embedding(PyannoteSE, "pyannote_se", "asahi417/voxceleb1-test-split", "test")
+    get_embedding(W2VBertSE, "w2v_bert_se", "asahi417/voxceleb1-test-split", "test")
+    get_embedding(ClapSE, "clap_se", "asahi417/voxceleb1-test-split", "test")
+
+    get_embedding(MetaVoiceSE, "meta_voice_se", "ylacombe/expresso", "train")
+    get_embedding(PyannoteSE, "pyannote_se", "ylacombe/expresso", "train")
+    get_embedding(W2VBertSE, "w2v_bert_se", "ylacombe/expresso", "train")
+    get_embedding(ClapSE, "clap_se", "ylacombe/expresso", "train")
+
     cluster_embedding("meta_voice_se", "asahi417/voxceleb1-test-split", "speaker_id")
     cluster_embedding("pyannote_se", "asahi417/voxceleb1-test-split", "speaker_id")
     cluster_embedding("w2v_bert_se", "asahi417/voxceleb1-test-split", "speaker_id")
+    cluster_embedding("clap_se", "asahi417/voxceleb1-test-split", "speaker_id")
+
     cluster_embedding("meta_voice_se", "ylacombe/expresso", "speaker_id")
     cluster_embedding("pyannote_se", "ylacombe/expresso", "speaker_id")
     cluster_embedding("w2v_bert_se", "ylacombe/expresso", "speaker_id")
+    cluster_embedding("clap_se", "ylacombe/expresso", "speaker_id")
+
     cluster_embedding("meta_voice_se", "ylacombe/expresso", "style")
     cluster_embedding("pyannote_se", "ylacombe/expresso", "style")
     cluster_embedding("w2v_bert_se", "ylacombe/expresso", "style")
+    cluster_embedding("clap_se", "ylacombe/expresso", "style")
 
 
 

model_clap.py

@@ -0,0 +1,29 @@
+"""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 ClapSE:
+    def __init__(self):
+        self.model = ClapModel.from_pretrained("laion/larger_clap_music_and_speech")
+        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("laion/larger_clap_music_and_speech")
+
+    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]

test.py

@@ -1,4 +1,5 @@
 import librosa
+from model_clap import ClapSE
 from model_meta_voice import MetaVoiceSE
 from model_pyannote_embedding import PyannoteSE
 from model_w2v_bert import W2VBertSE
@@ -6,18 +7,22 @@ from model_w2v_bert import W2VBertSE
 
 def test():
     wav, sr = librosa.load("sample.wav")
-    print("MetaVoiceSE")
-    model = MetaVoiceSE()
-    v = model.get_speaker_embedding(wav, sr)
-    print(v.shape)
-    print("PyannoteSE")
-    model = PyannoteSE()
-    v = model.get_speaker_embedding(wav, sr)
-    print(v.shape)
-    print("W2VBertSE")
-    model = W2VBertSE()
+    print("CLAP")
+    model = ClapSE()
     v = model.get_speaker_embedding(wav, sr)
     print(v.shape)
+    # print("MetaVoiceSE")
+    # model = MetaVoiceSE()
+    # v = model.get_speaker_embedding(wav, sr)
+    # print(v.shape)
+    # print("PyannoteSE")
+    # model = PyannoteSE()
+    # v = model.get_speaker_embedding(wav, sr)
+    # print(v.shape)
+    # print("W2VBertSE")
+    # model = W2VBertSE()
+    # v = model.get_speaker_embedding(wav, sr)
+    # print(v.shape)
 
 
 if __name__ == '__main__':