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import json |
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import os |
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from os.path import join as p_join |
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from tqdm import tqdm |
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from time import time |
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from datasets import load_dataset |
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from model_meta_voice import MetaVoiceSE |
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from model_pyannote_embedding import PyannoteSE |
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from model_w2v_bert import W2VBertSE |
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cache_dir = p_join("experiment_cache", "voxceleb1") |
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def get_embedding(model_class, model_name: str, dataset_name: str): |
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dataset = load_dataset(dataset_name, split="test") |
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file_path = p_join("experiment_cache", "embeddings", f"{model_name}.{dataset_name}.json") |
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os.makedirs(os.path.dirname(file_path), exist_ok=True) |
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if os.path.exists(file_path): |
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return |
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model = model_class() |
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embeddings = [] |
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for i in tqdm(dataset, total=len(dataset)): |
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start = time() |
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v = model.get_speaker_embedding(i["audio"]["array"], i["audio"]["sampling_rate"]) |
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embeddings.append({ |
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"model": model_name, |
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"embedding": v.tolist(), |
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"sampling_rate": i["audio"]["sampling_rate"], |
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"id": i["id"], |
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"speaker_id": i["speaker_id"], |
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"process_time": time() - start, |
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"dataset_name": os.path.basename(dataset_name) |
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}) |
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with open(file_path, "w") as f: |
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f.write("\n".join([json.dumps(i) for i in embeddings])) |
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if __name__ == '__main__': |
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get_embedding(MetaVoiceSE, "meta_voice_se", "asahi417/voxceleb1-test-split") |
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get_embedding(PyannoteSE, "pyannote_se", "asahi417/voxceleb1-test-split") |
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get_embedding(W2VBertSE, "w2v_bert_se", "asahi417/voxceleb1-test-split") |
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