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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 MetaVoiceSE
from model_pyannote_embedding import PyannoteSE
from model_w2v_bert import W2VBertSE
from model_clap import ClapSE, ClapGeneralSE
from model_xls import XLSRSE
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)
# 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):
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(MetaVoiceSE, "meta_voice_se", "asahi417/voxceleb1-test-split", "test")
# get_embedding(PyannoteSE, "pyannote_se", "asahi417/voxceleb1-test-split", "test")
# get_embedding(W2VBertSE, "w2v_bert_se", "asahi417/voxceleb1-test-split", "test")
# get_embedding(ClapSE, "clap_se", "asahi417/voxceleb1-test-split", "test")
# get_embedding(ClapGeneralSE, "clap_general_se", "asahi417/voxceleb1-test-split", "test")
get_embedding(XLSRSE, "xlsr_se", "asahi417/voxceleb1-test-split", "test")
# get_embedding(MetaVoiceSE, "meta_voice_se", "ylacombe/expresso", "train")
# get_embedding(PyannoteSE, "pyannote_se", "ylacombe/expresso", "train")
# get_embedding(W2VBertSE, "w2v_bert_se", "ylacombe/expresso", "train")
# get_embedding(ClapSE, "clap_se", "ylacombe/expresso", "train")
# get_embedding(ClapGeneralSE, "clap_general_se", "ylacombe/expresso", "train")
get_embedding(XLSRSE, "xlsr_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("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("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")
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