import json import os from os.path import join as p_join from tqdm import tqdm from time import time import hdbscan import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.manifold import TSNE import pandas as pd from datasets import load_dataset from model_meta_voice import MetaVoiceEmbedding from model_pyannote_embedding import PyannoteEmbedding from model_clap import CLAPEmbedding, CLAPGeneralEmbedding from model_speaker_embedding import ( W2VBERTEmbedding, Wav2VecEmbedding, XLSR300MEmbedding, XLSR1BEmbedding, XLSR2BEmbedding, HuBERTBaseEmbedding, HuBERTLargeEmbedding, HuBERTXLEmbedding ) def get_embedding(model_class, model_name: str, dataset_name: str, data_split: str): dataset = load_dataset(dataset_name, split=data_split) file_path = p_join("experiment_cache", "embeddings", f"{model_name}.{os.path.basename(dataset_name)}.json") os.makedirs(os.path.dirname(file_path), exist_ok=True) if os.path.exists(file_path): return model = model_class() embeddings = [] for i in tqdm(dataset, total=len(dataset)): start = time() v = model.get_speaker_embedding(i["audio"]["array"], i["audio"]["sampling_rate"]) tmp = { "model": model_name, "embedding": v.tolist(), "sampling_rate": i["audio"]["sampling_rate"], "process_time": time() - start, "dataset_name": os.path.basename(dataset_name) } tmp.update({k: v for k, v in i.items() if k != "audio"}) embeddings.append(tmp) with open(file_path, "w") as f: f.write("\n".join([json.dumps(i) for i in embeddings])) def cluster_embedding(model_name, dataset_name, label_name: str): file_path_embedding = p_join("experiment_cache", "embeddings", f"{model_name}.{os.path.basename(dataset_name)}.json") file_path_cluster = p_join("experiment_cache", "cluster", f"{model_name}.{os.path.basename(dataset_name)}.{label_name}.csv") if not os.path.exists(file_path_cluster): print('CLUSTERING') os.makedirs(os.path.dirname(file_path_cluster), exist_ok=True) assert os.path.exists(file_path_embedding) with open(file_path_embedding) as f: data = [json.loads(i) for i in f.readlines()] clusterer = hdbscan.HDBSCAN() embeddings = [i["embedding"] for i in data] keys = [i["id"] for i in data] clusterer.fit(np.stack(embeddings)) # data x dimension print(f'{clusterer.labels_.max()} clusters found from {len(data)} data points') print(f"generating report for {label_name}") label = [i[label_name] for i in data] cluster_info = [ {"id": k, "cluster": c, f"label.{label_name}": l} for c, k, l in zip(clusterer.labels_, keys, label) if c != -1 ] cluster_df = pd.DataFrame(cluster_info) cluster_df.to_csv(file_path_cluster, index=False) 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): os.makedirs(os.path.dirname(file_path_tsne), exist_ok=True) print('DIMENSION REDUCTION') assert os.path.exists(file_path_embedding) with open(file_path_embedding) as f: data = np.stack([json.loads(i)['embedding'] for i in f.readlines()]) # data x dimension print(f'Dimension reduction: {data.shape}') embedding_2d = TSNE(n_components=2, random_state=0).fit_transform(data) np.save(file_path_tsne, embedding_2d) embedding_2d = np.load(file_path_tsne) print('PLOT') figure_path = p_join("experiment_cache", "figure", f"2d.latent_space.{model_name}.{os.path.basename(dataset_name)}.{label_name}.png") os.makedirs(os.path.dirname(figure_path), exist_ok=True) with open(file_path_embedding) as f: label = np.stack([json.loads(i)[label_name] for i in f.readlines()]) # data x dimension label_type = sorted(list(set(label))) label2id = {v: n for n, v in enumerate(label_type)} plt.figure() scatter = plt.scatter( embedding_2d[:, 0], embedding_2d[:, 1], s=8, c=[label2id[i] for i in label], cmap=sns.color_palette('Spectral', len(label_type), as_cmap=True) ) plt.gca().set_aspect('equal', 'datalim') plt.legend(handles=scatter.legend_elements(num=len(label_type))[0], labels=label_type, bbox_to_anchor=(1.04, 1), borderaxespad=0, loc='upper left', 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()) 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")