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# -*- coding: utf-8 -*- #
"""*********************************************************************************************"""
# FileName [ observe_speaker.py ]
# Synopsis [ Analyze the speaker training set for LibriSpeech ]
# Author [ Andy T. Liu (Andi611) ]
# Copyright [ Copyleft(c), Speech Lab, NTU, Taiwan ]
"""*********************************************************************************************"""
###############
# IMPORTATION #
###############
import os
import torch
import numpy as np
import pandas as pd
from torch.utils.data.dataset import Dataset
############
# SETTINGS #
############
SPEAKER_THRESHOLD = 120
root = '../data/libri_mel160'
drop = True
max_timestep = 3000
max_label_len = 400
def get_speaker_from_path(x):
return x.split('/')[-1].split('.')[0].split('-')[0]
def get_all_speakers(X):
speaker_set = {}
for x in X:
speaker = get_speaker_from_path(x)
if speaker not in speaker_set:
speaker_set[speaker] = 0
else:
speaker_set[speaker] += 1
return speaker_set
def compute_speaker2idx(speakers):
idx = 0
speaker2idx = {}
for speaker in sorted(speakers):
if speaker not in speaker2idx and speakers[speaker] > SPEAKER_THRESHOLD: # eliminate the speakers with too few utterance
speaker2idx[speaker] = idx
idx += 1
return speaker2idx
########
# MAIN #
########
def main():
# Load the train-clean-100 set
tables = pd.read_csv(os.path.join(root, 'train-clean-100' + '.csv'))
# Compute speaker dictionary
print('[Dataset] - Computing speaker class...')
O = tables['file_path'].tolist()
speakers = get_all_speakers(O)
speaker2idx = compute_speaker2idx(speakers)
class_num = len(speaker2idx)
print('[Dataset] - Possible speaker classes: ', class_num)
train = tables.sample(frac=0.9, random_state=20190929) # random state is a seed value
test = tables.drop(train.index)
table = train.sort_values(by=['length'], ascending=False)
X = table['file_path'].tolist()
X_lens = table['length'].tolist()
# Crop seqs that are too long
if drop and max_timestep > 0:
table = table[table.length < max_timestep]
if drop and max_label_len > 0:
table = table[table.label.str.count('_')+1 < max_label_len]
# computer utterance per speaker
num_utt = []
for speaker in speakers:
if speaker in speaker2idx:
num_utt.append(speakers[speaker])
print('Average utterance per speaker: ', np.mean(num_utt))
# TODO: furthur analysis
if __name__ == '__main__':
main() |