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equal_error_rate.py
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equal_error_rate.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Visualize speaker embeddings."""
from argparse import ArgumentParser
from pathlib import Path
from warnings import filterwarnings
import numpy as np
import torch
import torch.nn.functional as F
import torchaudio
from scipy.interpolate import interp1d
from scipy.optimize import brentq
from sklearn.metrics import roc_curve
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
def equal_error_rate(test_dir, test_txt, wav2mel_path, checkpoint_path):
"""Compute equal error rate on test set."""
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
test_dir_path = Path(test_dir)
test_txt_path = Path(test_txt)
wav2mel = torch.jit.load(wav2mel_path)
dvector = torch.jit.load(checkpoint_path).eval().to(device)
pairs = []
with test_txt_path.open() as file:
for line in file:
label, audio_path1, audio_path2 = line.strip().split()
pairs.append((label, audio_path1, audio_path2))
class MyDataset(Dataset):
def __init__(self):
self.pairs = pairs
def __len__(self):
return len(self.pairs)
def __getitem__(self, index):
label, path1, path2 = self.pairs[index]
audio_path1 = test_dir_path / path1
audio_path2 = test_dir_path / path2
wav_tensor1, sample_rate = torchaudio.load(audio_path1)
wav_tensor2, sample_rate = torchaudio.load(audio_path2)
mel_tensor1 = wav2mel(wav_tensor1, sample_rate)
mel_tensor2 = wav2mel(wav_tensor2, sample_rate)
return int(label), mel_tensor1, mel_tensor2
dataloader = DataLoader(
MyDataset(),
batch_size=1,
shuffle=False,
drop_last=False,
num_workers=8,
prefetch_factor=4,
)
labels, scores = [], []
for label, mel1, mel2 in tqdm(dataloader, ncols=0, desc="Calculate Similarity"):
mel1, mel2 = mel1.to(device), mel2.to(device)
with torch.no_grad():
emb1 = dvector.embed_utterance(mel1)
emb2 = dvector.embed_utterance(mel2)
score = F.cosine_similarity(emb1.unsqueeze(0), emb2.unsqueeze(0))
labels.append(label[0])
scores.append(score.item())
labels = np.array(labels)
scores = np.array(scores)
fpr, tpr, thresholds = roc_curve(labels, scores)
eer = brentq(lambda x: 1.0 - x - interp1d(fpr, tpr)(x), 0.0, 1.0)
thresh = interp1d(fpr, thresholds)(eer)
print("eer =", eer)
print("thresh =", thresh)
def get_eer(test_dir, test_txt, wav2mel_path, checkpoint_path):
"""Compute equal error rate on test set."""
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
test_dir_path = Path(test_dir)
test_txt_path = Path(test_txt)
wav2mel = torch.jit.load(wav2mel_path)
dvector = torch.jit.load(checkpoint_path).eval().to(device)
pairs = []
with test_txt_path.open() as file:
for line in file:
label, audio_path1, audio_path2 = line.strip().split()
pairs.append((label, audio_path1, audio_path2))
class MyDataset(Dataset):
def __init__(self):
self.pairs = pairs
def __len__(self):
return len(self.pairs)
def __getitem__(self, index):
label, path1, path2 = self.pairs[index]
audio_path1 = test_dir_path / path1
audio_path2 = test_dir_path / path2
wav_tensor1, sample_rate = torchaudio.load(audio_path1)
wav_tensor2, sample_rate = torchaudio.load(audio_path2)
mel_tensor1 = wav2mel(wav_tensor1, sample_rate)
mel_tensor2 = wav2mel(wav_tensor2, sample_rate)
return int(label), mel_tensor1, mel_tensor2
dataloader = DataLoader(
MyDataset(),
batch_size=1,
shuffle=False,
drop_last=False,
num_workers=8,
prefetch_factor=4,
)
labels, scores = [], []
for label, mel1, mel2 in tqdm(dataloader, ncols=0, desc="Calculate Similarity"):
mel1, mel2 = mel1.to(device), mel2.to(device)
with torch.no_grad():
emb1 = dvector.embed_utterance(mel1)
emb2 = dvector.embed_utterance(mel2)
score = F.cosine_similarity(emb1.unsqueeze(0), emb2.unsqueeze(0))
labels.append(label[0])
scores.append(score.item())
labels = np.array(labels)
scores = np.array(scores)
fpr, tpr, thresholds = roc_curve(labels, scores)
eer = brentq(lambda x: 1.0 - x - interp1d(fpr, tpr)(x), 0.0, 1.0)
thresh = interp1d(fpr, thresholds)(eer)
return eer, thresh
if __name__ == "__main__":
filterwarnings("ignore")
PARSER = ArgumentParser()
PARSER.add_argument("--test_dir", required=True)
PARSER.add_argument("--test_txt", required=True)
PARSER.add_argument("-w", "--wav2mel_path", required=True)
PARSER.add_argument("-c", "--checkpoint_path", required=True)
equal_error_rate(**vars(PARSER.parse_args()))