init

experiment_speaker_verification.py

@@ -19,8 +19,7 @@ from model_pyannote_embedding import PyannoteEmbedding
 from model_w2v_bert import W2VBERTEmbedding
 from model_clap import CLAPEmbedding, CLAPGeneralEmbedding
 from model_xls import XLSREmbedding
-from model_hubert import HuBERTXLEmbedding, HuBERTLargeEmbedding
-
+from model_hubert import HuBERTBaseEmbedding, HuBERTLargeEmbedding, HuBERTXLEmbedding
 
 
 def get_embedding(model_class, model_name: str, dataset_name: str, data_split: str):
@@ -68,7 +67,6 @@ def cluster_embedding(model_name, dataset_name, label_name: str):
         ]
         cluster_df = pd.DataFrame(cluster_info)
         cluster_df.to_csv(file_path_cluster, index=False)
-    # cluster_df = pd.read_csv(file_path_cluster)
 
     file_path_tsne = p_join("experiment_cache", "tsne", f"{model_name}.{os.path.basename(dataset_name)}.{label_name}.npy")
     if not os.path.exists(file_path_tsne):
@@ -123,45 +121,50 @@ if __name__ == '__main__':
     # get_embedding(W2VBERTEmbedding, "w2v_bert_se", "asahi417/voxceleb1-test-split", "test")
     # get_embedding(CLAPEmbedding, "clap_se", "asahi417/voxceleb1-test-split", "test")
     # get_embedding(CLAPGeneralEmbedding, "clap_general_se", "asahi417/voxceleb1-test-split", "test")
-    get_embedding(XLSREmbedding, "xlsr_se", "asahi417/voxceleb1-test-split", "test")
-    get_embedding(HuBERTLargeEmbedding, "hubert_large_se", "asahi417/voxceleb1-test-split", "test")
-    get_embedding(HuBERTXLEmbedding, "hubert_xl_se", "asahi417/voxceleb1-test-split", "test")
+    # get_embedding(XLSREmbedding, "xlsr_se", "asahi417/voxceleb1-test-split", "test")
+    get_embedding(HuBERTBaseEmbedding, "hubert_base_se", "asahi417/voxceleb1-test-split", "test")
+    # get_embedding(HuBERTLargeEmbedding, "hubert_large_se", "asahi417/voxceleb1-test-split", "test")
+    # get_embedding(HuBERTXLEmbedding, "hubert_xl_se", "asahi417/voxceleb1-test-split", "test")
 
     # get_embedding(MetaVoiceEmbedding, "meta_voice_se", "ylacombe/expresso", "train")
     # get_embedding(PyannoteEmbedding, "pyannote_se", "ylacombe/expresso", "train")
     # get_embedding(W2VBERTEmbedding, "w2v_bert_se", "ylacombe/expresso", "train")
     # get_embedding(CLAPEmbedding, "clap_se", "ylacombe/expresso", "train")
     # get_embedding(CLAPGeneralEmbedding, "clap_general_se", "ylacombe/expresso", "train")
-    get_embedding(XLSREmbedding, "xlsr_se", "ylacombe/expresso", "train")
-    get_embedding(HuBERTLargeEmbedding, "hubert_large_se", "ylacombe/expresso", "train")
-    get_embedding(HuBERTXLEmbedding, "hubert_xl_se", "ylacombe/expresso", "train")
+    # get_embedding(XLSREmbedding, "xlsr_se", "ylacombe/expresso", "train")
+    get_embedding(HuBERTBaseEmbedding, "hubert_base_se", "ylacombe/expresso", "train")
+    # get_embedding(HuBERTLargeEmbedding, "hubert_large_se", "ylacombe/expresso", "train")
+    # get_embedding(HuBERTXLEmbedding, "hubert_xl_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("xlsr_se", "asahi417/voxceleb1-test-split", "speaker_id")
-    cluster_embedding("hubert_large_se", "asahi417/voxceleb1-test-split", "speaker_id")
-    cluster_embedding("hubert_xl_se", "asahi417/voxceleb1-test-split", "speaker_id")
+    # cluster_embedding("xlsr_se", "asahi417/voxceleb1-test-split", "speaker_id")
+    cluster_embedding("hubert_base_se", "asahi417/voxceleb1-test-split", "speaker_id")
+    # cluster_embedding("hubert_large_se", "asahi417/voxceleb1-test-split", "speaker_id")
+    # cluster_embedding("hubert_xl_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("xlsr_se", "ylacombe/expresso", "speaker_id")
-    cluster_embedding("hubert_large_se", "ylacombe/expresso", "speaker_id")
-    cluster_embedding("hubert_xl_se", "ylacombe/expresso", "speaker_id")
+    # cluster_embedding("xlsr_se", "ylacombe/expresso", "speaker_id")
+    cluster_embedding("hubert_base_se", "ylacombe/expresso", "speaker_id")
+    # cluster_embedding("hubert_large_se", "ylacombe/expresso", "speaker_id")
+    # cluster_embedding("hubert_xl_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")
-    cluster_embedding("xlsr_se", "ylacombe/expresso", "style")
-    cluster_embedding("hubert_large_se", "ylacombe/expresso", "style")
-    cluster_embedding("hubert_xl_se", "ylacombe/expresso", "style")
+    # cluster_embedding("xlsr_se", "ylacombe/expresso", "style")
+    cluster_embedding("hubert_base_se", "ylacombe/expresso", "style")
+    # cluster_embedding("hubert_large_se", "ylacombe/expresso", "style")
+    # cluster_embedding("hubert_xl_se", "ylacombe/expresso", "style")
 
 
 

model_hubert.py

@@ -26,9 +26,16 @@ class HuBERTXLEmbedding:
         inputs = self.processor(wav, sampling_rate=self.processor.sampling_rate, return_tensors="pt")
         with torch.no_grad():
             outputs = self.model(**{k: v.to(self.device) for k, v in inputs.items()})
-        return outputs.last_hidden_state.mean(1).cpu().numpy()[0]
+        return outputs
+        # return outputs.last_hidden_state.mean(1).cpu().numpy()[0]
 
 
 class HuBERTLargeEmbedding(HuBERTXLEmbedding):
     def __init__(self):
         super().__init__("facebook/hubert-large-ll60k")
+
+
+class HuBERTBaseEmbedding(HuBERTXLEmbedding):
+    def __init__(self):
+        super().__init__("facebook/hubert-base-ls960")
+