init

experiment_speaker_verification.py

@@ -17,7 +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
+from model_clap import ClapSE, ClapGeneralSE
 
 
 def get_embedding(model_class, model_name: str, dataset_name: str, data_split: str):
@@ -119,26 +119,31 @@ if __name__ == '__main__':
     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(ClapGeneralSE, "clap_general_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")
+    get_embedding(ClapGeneralSE, "clap_general_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("clap_general_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("clap_general_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")
+    cluster_embedding("clap_general_se", "ylacombe/expresso", "style")
 
 
 

model_clap.py

@@ -11,12 +11,12 @@ from transformers import ClapModel, ClapProcessor
 
 
 class ClapSE:
-    def __init__(self):
-        self.model = ClapModel.from_pretrained("laion/larger_clap_music_and_speech")
+    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("laion/larger_clap_music_and_speech")
+        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:
@@ -27,3 +27,9 @@ class ClapSE:
         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 ClapGeneralSE(ClapSE):
+
+    def __init__(self):
+        super().__init__(ckpt="laion/larger_clap_general")