-
-
Notifications
You must be signed in to change notification settings - Fork 241
/
cli.py
99 lines (77 loc) · 3.99 KB
/
cli.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import logging
import os
import click
from deep_speaker.audio import Audio
from deep_speaker.batcher import KerasFormatConverter
from deep_speaker.constants import SAMPLE_RATE, NUM_FRAMES
from deep_speaker.test import test
from deep_speaker.train import start_training
from deep_speaker.utils import ClickType as Ct, ensures_dir
from deep_speaker.utils import init_pandas
logger = logging.getLogger(__name__)
VERSION = '3.0a'
@click.group()
def cli():
logging.basicConfig(format='%(asctime)12s - %(levelname)s - %(message)s', level=logging.INFO)
init_pandas()
@cli.command('version', short_help='Prints the version.')
def version():
print(f'Version is {VERSION}.')
@cli.command('build-mfcc-cache', short_help='Build audio cache.')
@click.option('--working_dir', required=True, type=Ct.output_dir())
@click.option('--audio_dir', default=None)
@click.option('--sample_rate', default=SAMPLE_RATE, show_default=True, type=int)
def build_audio_cache(working_dir, audio_dir, sample_rate):
ensures_dir(working_dir)
if audio_dir is None:
audio_dir = os.path.join(working_dir, 'LibriSpeech')
Audio(cache_dir=working_dir, audio_dir=audio_dir, sample_rate=sample_rate)
@cli.command('build-keras-inputs', short_help='Build inputs to Keras.')
@click.option('--working_dir', required=True, type=Ct.input_dir())
@click.option('--counts_per_speaker', default='600,100', show_default=True, type=str) # train,test
def build_keras_inputs(working_dir, counts_per_speaker):
# counts_per_speaker: If you specify --counts_per_speaker 600,100, that means for each speaker,
# you're going to generate 600 samples for training and 100 for testing. One sample is 160 frames
# by default (~roughly 1.6 seconds).
counts_per_speaker = [int(b) for b in counts_per_speaker.split(',')]
kc = KerasFormatConverter(working_dir)
kc.generate(max_length=NUM_FRAMES, counts_per_speaker=counts_per_speaker)
kc.persist_to_disk()
@cli.command('test-model', short_help='Test a Keras model.')
@click.option('--working_dir', required=True, type=Ct.input_dir())
@click.option('--checkpoint_file', required=True, type=Ct.input_file())
def test_model(working_dir, checkpoint_file=None):
# export CUDA_VISIBLE_DEVICES=0; python cli.py test-model
# --working_dir /home/philippe/ds-test/triplet-training/
# --checkpoint_file ../ds-test/checkpoints-softmax/ResCNN_checkpoint_102.h5
# f-measure = 0.789, true positive rate = 0.733, accuracy = 0.996, equal error rate = 0.043
# export CUDA_VISIBLE_DEVICES=0; python cli.py test-model
# --working_dir /home/philippe/ds-test/triplet-training/
# --checkpoint_file ../ds-test/checkpoints-triplets/ResCNN_checkpoint_175.h5
# f-measure = 0.849, true positive rate = 0.798, accuracy = 0.997, equal error rate = 0.025
test(working_dir, checkpoint_file)
@cli.command('train-model', short_help='Train a Keras model.')
@click.option('--working_dir', required=True, type=Ct.input_dir())
@click.option('--pre_training_phase/--no_pre_training_phase', default=False, show_default=True)
def train_model(working_dir, pre_training_phase):
# PRE TRAINING
# commit a5030dd7a1b53cd11d5ab7832fa2d43f2093a464
# Merge: a11d13e b30e64e
# Author: Philippe Remy <[email protected]>
# Date: Fri Apr 10 10:37:59 2020 +0900
# LibriSpeech train-clean-data360 (600, 100). 0.985 on test set (enough for pre-training).
# TRIPLET TRAINING
# [...]
# Epoch 175/1000
# 2000/2000 [==============================] - 919s 459ms/step - loss: 0.0077 - val_loss: 0.0058
# Epoch 176/1000
# 2000/2000 [==============================] - 917s 458ms/step - loss: 0.0075 - val_loss: 0.0059
# Epoch 177/1000
# 2000/2000 [==============================] - 927s 464ms/step - loss: 0.0075 - val_loss: 0.0059
# Epoch 178/1000
# 2000/2000 [==============================] - 948s 474ms/step - loss: 0.0073 - val_loss: 0.0058
start_training(working_dir, pre_training_phase)
if __name__ == '__main__':
cli()