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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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import hdbscan |
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import numpy as np |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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from sklearn.manifold import TSNE |
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import pandas as pd |
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from datasets import load_dataset |
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from model_meta_voice import MetaVoiceEmbedding |
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from model_pyannote_embedding import PyannoteEmbedding |
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from model_w2v_bert import W2VBERTEmbedding |
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from model_clap import CLAPEmbedding, CLAPGeneralEmbedding |
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from model_xls import XLSREmbedding |
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from model_hubert import HuBERTXLEmbedding, HuBERTLargeEmbedding |
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def get_embedding(model_class, model_name: str, dataset_name: str, data_split: str): |
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dataset = load_dataset(dataset_name, split=data_split) |
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file_path = p_join("experiment_cache", "embeddings", f"{model_name}.{os.path.basename(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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tmp = { |
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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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"process_time": time() - start, |
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"dataset_name": os.path.basename(dataset_name) |
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} |
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tmp.update({k: v for k, v in i.items() if k != "audio"}) |
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embeddings.append(tmp) |
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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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def cluster_embedding(model_name, dataset_name, label_name: str): |
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file_path_embedding = p_join("experiment_cache", "embeddings", f"{model_name}.{os.path.basename(dataset_name)}.json") |
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file_path_cluster = p_join("experiment_cache", "cluster", f"{model_name}.{os.path.basename(dataset_name)}.{label_name}.csv") |
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if not os.path.exists(file_path_cluster): |
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print('CLUSTERING') |
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os.makedirs(os.path.dirname(file_path_cluster), exist_ok=True) |
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assert os.path.exists(file_path_embedding) |
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with open(file_path_embedding) as f: |
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data = [json.loads(i) for i in f.readlines()] |
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clusterer = hdbscan.HDBSCAN() |
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embeddings = [i["embedding"] for i in data] |
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keys = [i["id"] for i in data] |
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clusterer.fit(np.stack(embeddings)) |
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print(f'{clusterer.labels_.max()} clusters found from {len(data)} data points') |
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print(f"generating report for {label_name}") |
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label = [i[label_name] for i in data] |
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cluster_info = [ |
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{"id": k, "cluster": c, f"label.{label_name}": l} for c, k, l in zip(clusterer.labels_, keys, label) if c != -1 |
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] |
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cluster_df = pd.DataFrame(cluster_info) |
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cluster_df.to_csv(file_path_cluster, index=False) |
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file_path_tsne = p_join("experiment_cache", "tsne", f"{model_name}.{os.path.basename(dataset_name)}.{label_name}.npy") |
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if not os.path.exists(file_path_tsne): |
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os.makedirs(os.path.dirname(file_path_tsne), exist_ok=True) |
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print('DIMENSION REDUCTION') |
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assert os.path.exists(file_path_embedding) |
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with open(file_path_embedding) as f: |
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data = np.stack([json.loads(i)['embedding'] for i in f.readlines()]) |
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print(f'Dimension reduction: {data.shape}') |
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embedding_2d = TSNE(n_components=2, random_state=0).fit_transform(data) |
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np.save(file_path_tsne, embedding_2d) |
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embedding_2d = np.load(file_path_tsne) |
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print('PLOT') |
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figure_path = p_join("experiment_cache", "figure", f"2d.latent_space.{model_name}.{os.path.basename(dataset_name)}.{label_name}.png") |
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os.makedirs(os.path.dirname(figure_path), exist_ok=True) |
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with open(file_path_embedding) as f: |
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label = np.stack([json.loads(i)[label_name] for i in f.readlines()]) |
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label_type = sorted(list(set(label))) |
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label2id = {v: n for n, v in enumerate(label_type)} |
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plt.figure() |
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scatter = plt.scatter( |
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embedding_2d[:, 0], |
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embedding_2d[:, 1], |
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s=8, |
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c=[label2id[i] for i in label], |
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cmap=sns.color_palette('Spectral', len(label_type), as_cmap=True) |
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) |
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plt.gca().set_aspect('equal', 'datalim') |
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plt.legend(handles=scatter.legend_elements(num=len(label_type))[0], |
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labels=label_type, |
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bbox_to_anchor=(1.04, 1), |
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borderaxespad=0, |
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loc='upper left', |
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ncol=3 if len(label2id) > 12 else 1) |
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plt.savefig(figure_path, bbox_inches='tight', dpi=600) |
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def analyze_embedding(model_name: str, dataset_name: str, n_shot: int = 5, n_cross_validation: int = 5): |
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file_path = p_join("experiment_cache", "embeddings", f"{model_name}.{os.path.basename(dataset_name)}.json") |
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assert os.path.exists(file_path) |
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with open(file_path) as f: |
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embeddings = [json.loads(i) for i in f.readlines()] |
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df = pd.DataFrame(embeddings) |
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process_time = df["process_time"].mean() |
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df.groupby("speaker_ido") |
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sorted(df["speaker_id"].unique()) |
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if __name__ == '__main__': |
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get_embedding(XLSREmbedding, "xlsr_se", "asahi417/voxceleb1-test-split", "test") |
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get_embedding(HuBERTLargeEmbedding, "hubert_large_se", "asahi417/voxceleb1-test-split", "test") |
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get_embedding(HuBERTXLEmbedding, "hubert_xl_se", "asahi417/voxceleb1-test-split", "test") |
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get_embedding(XLSREmbedding, "xlsr_se", "ylacombe/expresso", "train") |
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get_embedding(HuBERTLargeEmbedding, "hubert_large_se", "ylacombe/expresso", "train") |
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get_embedding(HuBERTXLEmbedding, "hubert_xl_se", "ylacombe/expresso", "train") |
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cluster_embedding("xlsr_se", "asahi417/voxceleb1-test-split", "speaker_id") |
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cluster_embedding("hubert_large_se", "asahi417/voxceleb1-test-split", "speaker_id") |
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cluster_embedding("hubert_xl_se", "asahi417/voxceleb1-test-split", "speaker_id") |
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cluster_embedding("xlsr_se", "ylacombe/expresso", "speaker_id") |
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cluster_embedding("hubert_large_se", "ylacombe/expresso", "speaker_id") |
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cluster_embedding("hubert_xl_se", "ylacombe/expresso", "speaker_id") |
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cluster_embedding("xlsr_se", "ylacombe/expresso", "style") |
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cluster_embedding("hubert_large_se", "ylacombe/expresso", "style") |
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cluster_embedding("hubert_xl_se", "ylacombe/expresso", "style") |
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