init

experiment_speaker_verification.py

@@ -1,3 +1,4 @@
+import argparse
 import json
 import os
 from os.path import join as p_join
@@ -105,82 +106,39 @@ def cluster_embedding(model_name, dataset_name, label_name: str):
                ncol=3 if len(label2id) > 12 else 1)
     plt.savefig(figure_path, bbox_inches='tight', dpi=600)
 
-def analyze_embedding(model_name: str, dataset_name: str, n_shot: int = 5, n_cross_validation: int = 5):
-    file_path = p_join("experiment_cache", "embeddings", f"{model_name}.{os.path.basename(dataset_name)}.json")
-    assert os.path.exists(file_path)
-    with open(file_path) as f:
-        embeddings = [json.loads(i) for i in f.readlines()]
-    df = pd.DataFrame(embeddings)
-    process_time = df["process_time"].mean()
-    df.groupby("speaker_ido")
-    sorted(df["speaker_id"].unique())
+
+def main(dataset_name, data_split, label_name):
+    get_embedding(MetaVoiceEmbedding, "meta_voice_se", dataset_name, data_split)
+    cluster_embedding("meta_voice_se", dataset_name, label_name)
+    get_embedding(PyannoteEmbedding, "pyannote_se", dataset_name, data_split)
+    cluster_embedding("pyannote_se", dataset_name, label_name)
+    get_embedding(CLAPEmbedding, "clap_se", dataset_name, data_split)
+    cluster_embedding("clap_se", dataset_name, label_name)
+    get_embedding(CLAPGeneralEmbedding, "clap_general_se", dataset_name, data_split)
+    cluster_embedding("clap_general_se", dataset_name, label_name)
+    get_embedding(HuBERTBaseEmbedding, "hubert_base_se", dataset_name, data_split)
+    cluster_embedding("hubert_base_se", dataset_name, label_name)
+    get_embedding(HuBERTXLEmbedding, "hubert_xl_se", dataset_name, data_split)
+    cluster_embedding("hubert_xl_se", dataset_name, label_name)
+    get_embedding(HuBERTLargeEmbedding, "hubert_large_se", dataset_name, data_split)
+    cluster_embedding("hubert_large_se", dataset_name, label_name)
+    get_embedding(Wav2VecEmbedding, "wav2vec_se", dataset_name, data_split)
+    cluster_embedding("wav2vec_se", dataset_name, label_name)
+    get_embedding(W2VBERTEmbedding, "w2v_bert_se", dataset_name, data_split)
+    cluster_embedding("w2v_bert_se", dataset_name, label_name)
+    get_embedding(XLSR300MEmbedding, "xlsr_300m_se", dataset_name, data_split)
+    cluster_embedding("xlsr_300m_se", dataset_name, label_name)
+    get_embedding(XLSR1BEmbedding, "xlsr_1b_se", dataset_name, data_split)
+    cluster_embedding("xlsr_1b_se", dataset_name, label_name)
+    get_embedding(XLSR2BEmbedding, "xlsr_2b_se", dataset_name, data_split)
+    cluster_embedding("xlsr_2b_se", dataset_name, label_name)
 
 
 if __name__ == '__main__':
-    # get_embedding(MetaVoiceEmbedding, "meta_voice_se", "asahi417/voxceleb1-test-split", "test")
-    # get_embedding(PyannoteEmbedding, "pyannote_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(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(W2VBERTEmbedding, "w2v_bert_se", "asahi417/voxceleb1-test-split", "test")
-    # get_embedding(Wav2VecEmbedding, "wav2vec_se", "asahi417/voxceleb1-test-split", "test")
-    # get_embedding(XLSR300MEmbedding, "xlsr_300m_se", "asahi417/voxceleb1-test-split", "test")
-    # get_embedding(XLSR1BEmbedding, "xlsr_1b_se", "asahi417/voxceleb1-test-split", "test")
-    # get_embedding(XLSR2BEmbedding, "xlsr_2b_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(CLAPEmbedding, "clap_se", "ylacombe/expresso", "train")
-    # get_embedding(CLAPGeneralEmbedding, "clap_general_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")
-    get_embedding(W2VBERTEmbedding, "w2v_bert_se", "ylacombe/expresso", "train")
-    # get_embedding(Wav2VecEmbedding, "wav2vec_se", "ylacombe/expresso", "train")
-    # get_embedding(XLSR300MEmbedding, "xlsr_300m_se", "ylacombe/expresso", "train")
-    # get_embedding(XLSR1BEmbedding, "xlsr_1b_se", "ylacombe/expresso", "train")
-    # get_embedding(XLSR2BEmbedding, "xlsr_2b_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("clap_se", "asahi417/voxceleb1-test-split", "speaker_id")
-    # cluster_embedding("clap_general_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("w2v_bert_se", "asahi417/voxceleb1-test-split", "speaker_id")
-    # cluster_embedding("wav2vec_se", "asahi417/voxceleb1-test-split", "speaker_id")
-    # cluster_embedding("xlsr_300m_se", "asahi417/voxceleb1-test-split", "speaker_id")
-    # cluster_embedding("xlsr_1b_se", "asahi417/voxceleb1-test-split", "speaker_id")
-    # cluster_embedding("xlsr_2b_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("clap_se", "ylacombe/expresso", "speaker_id")
-    # cluster_embedding("clap_general_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("w2v_bert_se", "ylacombe/expresso", "speaker_id")
-    # cluster_embedding("wav2vec_se", "ylacombe/expresso", "speaker_id")
-    # cluster_embedding("xlsr_300m_se", "ylacombe/expresso", "speaker_id")
-    # cluster_embedding("xlsr_1b_se", "ylacombe/expresso", "speaker_id")
-    # cluster_embedding("xlsr_2b_se", "ylacombe/expresso", "speaker_id")
-
-    # cluster_embedding("meta_voice_se", "ylacombe/expresso", "style")
-    # cluster_embedding("pyannote_se", "ylacombe/expresso", "style")
-    # cluster_embedding("clap_se", "ylacombe/expresso", "style")
-    # cluster_embedding("clap_general_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")
-    cluster_embedding("w2v_bert_se", "ylacombe/expresso", "style")
-    # cluster_embedding("wav2vec_se", "ylacombe/expresso", "style")
-    # cluster_embedding("xlsr_300m_se", "ylacombe/expresso", "style")
-    # cluster_embedding("xlsr_1b_se", "ylacombe/expresso", "style")
-    # cluster_embedding("xlsr_2b_se", "ylacombe/expresso", "style")
+    # main("asahi417/voxceleb1-test-split", "test", "speaker_id")
+    # main("ylacombe/expresso", "train", "speaker_id")
+    # main("ylacombe/expresso", "train", "style")
+    main("asahi417/j-tube-speech", "test", "speaker_id")