183 lines
19 KiB
Python
183 lines
19 KiB
Python
import argparse
|
|
import os
|
|
|
|
import torch
|
|
import torchaudio
|
|
|
|
from data.audio.unsupervised_audio_dataset import load_audio
|
|
from scripts.audio.gen.speech_synthesis_utils import do_spectrogram_diffusion, \
|
|
load_discrete_vocoder_diffuser, wav_to_mel, convert_mel_to_codes
|
|
from utils.audio import plot_spectrogram
|
|
from utils.util import load_model_from_config
|
|
|
|
|
|
def ceil_multiple(base, multiple):
|
|
res = base % multiple
|
|
if res == 0:
|
|
return base
|
|
return base + (multiple - res)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
conditioning_clips = {
|
|
# Male
|
|
'simmons': 'Y:\\clips\\books1\\754_Dan Simmons - The Rise Of Endymion 356 of 450\\00026.wav',
|
|
'carlin': 'Y:\\clips\\books1\\12_dchha13 Bubonic Nukes\\00097.wav',
|
|
'entangled': 'Y:\\clips\\books1\\3857_25_The_Entangled_Bank__000000000\\00123.wav',
|
|
'snowden': 'Y:\\clips\\books1\\7658_Edward_Snowden_-_Permanent_Record__000000004\\00027.wav',
|
|
# Female
|
|
'the_doctor': 'Y:\\clips\\books2\\37062___The_Doctor__000000003\\00206.wav',
|
|
'puppy': 'Y:\\clips\\books2\\17830___3_Puppy_Kisses__000000002\\00046.wav',
|
|
'adrift': 'Y:\\clips\\books2\\5608_Gear__W_Michael_-_Donovan_1-5_(2018-2021)_(book_4_Gear__W_Michael_-_Donovan_5_-_Adrift_(2021)_Gear__W_Michael_-_Adrift_(Donovan_5)_—_82__000000000\\00019.wav',
|
|
}
|
|
|
|
provided_codes = [
|
|
# but facts within easy reach of any one who cares to know them go to say that the greater abstenence of women is in some part
|
|
# due to an imperative conventionality and this conventionality is in a general way strongest were the patriarchal tradition
|
|
# the tradition that the woman is a chattel has retained its hold in greatest vigor
|
|
# 3570/5694/3570_5694_000008_000001.wav
|
|
[0, 0, 24, 0, 16, 0, 6, 0, 4, 0, 0, 0, 0, 0, 20, 0, 7, 0, 0, 19, 19, 0, 0, 6, 0, 0, 12, 12, 0, 4, 4, 0, 18, 18,
|
|
0, 10, 0, 6, 11, 11, 10, 10, 9, 9, 4, 4, 4, 5, 5, 0, 7, 0, 0, 0, 0, 12, 0, 22, 22, 0, 4, 4, 0, 13, 13, 5, 0, 7,
|
|
7, 0, 0, 19, 11, 0, 4, 4, 8, 20, 4, 4, 4, 7, 0, 9, 9, 0, 22, 4, 4, 0, 8, 0, 9, 5, 4, 4, 18, 11, 11, 8, 4, 4, 0,
|
|
0, 0, 19, 19, 7, 0, 0, 13, 5, 5, 0, 12, 12, 4, 4, 6, 6, 8, 8, 4, 4, 0, 26, 9, 9, 8, 0, 18, 0, 0, 4, 4, 6, 6,
|
|
11, 5, 0, 17, 17, 0, 0, 4, 4, 4, 4, 0, 0, 0, 21, 0, 8, 0, 0, 0, 0, 4, 4, 6, 6, 8, 0, 4, 4, 0, 0, 12, 0, 7, 7,
|
|
0, 0, 22, 0, 4, 4, 6, 11, 11, 7, 6, 6, 4, 4, 6, 11, 5, 4, 4, 4, 0, 21, 0, 13, 5, 5, 7, 7, 0, 0, 6, 6, 5, 0, 13,
|
|
0, 4, 4, 0, 7, 0, 0, 0, 24, 0, 0, 12, 12, 0, 0, 6, 0, 5, 0, 0, 9, 9, 0, 5, 0, 9, 0, 0, 19, 5, 5, 4, 4, 8, 20,
|
|
20, 4, 4, 4, 4, 0, 18, 18, 8, 0, 0, 0, 17, 0, 5, 0, 9, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 10, 0, 0, 12, 12, 4, 4, 0,
|
|
10, 0, 9, 0, 4, 4, 0, 0, 12, 0, 0, 8, 0, 17, 5, 5, 4, 4, 0, 0, 0, 23, 23, 0, 7, 0, 13, 0, 0, 0, 6, 0, 4, 0, 0,
|
|
0, 0, 14, 0, 16, 16, 0, 0, 5, 0, 4, 4, 0, 6, 8, 0, 4, 4, 7, 9, 4, 4, 4, 0, 10, 10, 17, 0, 0, 0, 23, 0, 5, 0, 0,
|
|
13, 13, 0, 7, 0, 0, 6, 6, 0, 10, 0, 25, 5, 5, 4, 4, 0, 0, 0, 19, 19, 8, 8, 9, 0, 0, 0, 0, 0, 25, 0, 5, 0, 9, 0,
|
|
0, 0, 6, 6, 10, 8, 8, 0, 9, 0, 0, 0, 7, 0, 0, 15, 0, 10, 0, 0, 0, 0, 6, 6, 0, 0, 22, 0, 0, 0, 4, 4, 4, 4, 0, 0,
|
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
|
7, 0, 9, 14, 0, 4, 0, 0, 6, 11, 10, 0, 0, 0, 12, 0, 4, 4, 0, 19, 19, 8, 9, 9, 0, 0, 25, 0, 5, 0, 9, 0, 0, 6, 6,
|
|
10, 8, 8, 9, 9, 0, 0, 7, 0, 0, 15, 0, 10, 0, 0, 0, 0, 6, 0, 22, 22, 0, 4, 4, 0, 0, 10, 0, 0, 0, 0, 12, 12, 0,
|
|
0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 10, 0, 9, 4, 4, 4, 7, 4, 4, 4, 0, 21, 0, 5, 0, 9, 0, 5, 5, 13, 13, 7, 0, 15,
|
|
15, 0, 0, 4, 4, 0, 18, 18, 0, 7, 0, 0, 22, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
|
0, 0, 0, 0, 0, 0, 0, 12, 12, 0, 0, 0, 6, 6, 13, 13, 8, 0, 0, 9, 9, 0, 21, 0, 0, 5, 5, 0, 0, 0, 12, 12, 0, 0, 6,
|
|
0, 0, 0, 4, 4, 0, 0, 0, 18, 0, 5, 0, 13, 0, 5, 4, 4, 6, 11, 5, 0, 4, 4, 23, 23, 7, 7, 0, 0, 0, 6, 0, 13, 13,
|
|
10, 10, 0, 0, 0, 0, 7, 13, 13, 0, 19, 11, 11, 0, 0, 7, 15, 15, 0, 0, 4, 4, 0, 6, 13, 13, 7, 7, 0, 0, 0, 14, 10,
|
|
10, 0, 0, 0, 0, 0, 6, 10, 10, 8, 8, 9, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 11, 5, 0, 4, 4, 0, 6, 13, 13, 7, 7, 0, 0, 0, 14, 10, 10, 0, 0, 0, 6, 10, 10,
|
|
8, 9, 9, 0, 0, 4, 4, 0, 6, 11, 7, 0, 6, 4, 4, 6, 11, 5, 4, 4, 4, 18, 18, 8, 0, 0, 17, 7, 0, 9, 0, 4, 10, 0, 0,
|
|
12, 12, 4, 4, 4, 7, 4, 4, 0, 0, 0, 19, 11, 0, 7, 0, 6, 0, 0, 0, 6, 0, 5, 0, 15, 15, 0, 0, 0, 4, 4, 4, 0, 0, 0,
|
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 0, 7, 0, 0, 0, 12, 0, 0, 4, 4, 0, 13, 5, 5, 0, 0, 0, 0, 6, 6, 0, 0,
|
|
7, 10, 10, 0, 9, 0, 5, 0, 14, 4, 4, 4, 0, 10, 0, 0, 0, 6, 0, 0, 0, 0, 0, 12, 0, 4, 4, 0, 0, 0, 11, 0, 0, 8, 0,
|
|
0, 0, 15, 0, 0, 14, 0, 4, 4, 4, 0, 10, 0, 9, 4, 4, 4, 4, 4, 0, 21, 0, 13, 5, 5, 7, 7, 0, 0, 6, 0, 5, 0, 0, 12,
|
|
0, 6, 0, 4, 0, 0, 25, 10, 0, 0, 0, 21, 0, 8, 0, 0, 13, 13, 0, 0, 4, 4, 4, 4, 0, 0, 0],
|
|
# the competitor with whom the entertainer wishes to institute a comparison is by this method made to serve as a means to the end
|
|
# 3570/5694/3570_5694_000011_000005.wav
|
|
[0, 0, 6, 11, 5, 0, 4, 0, 19, 19, 8, 17, 0, 0, 0, 0, 23, 0, 5, 5, 0, 0, 6, 6, 10, 10, 0, 0, 6, 6, 0, 8, 0, 13,
|
|
13, 0, 4, 4, 18, 18, 10, 0, 6, 11, 11, 4, 4, 4, 0, 0, 18, 18, 11, 0, 8, 0, 0, 0, 0, 17, 0, 0, 4, 0, 6, 11, 5,
|
|
0, 4, 4, 0, 5, 9, 9, 0, 6, 5, 5, 13, 13, 0, 0, 6, 6, 0, 7, 0, 10, 0, 9, 0, 0, 5, 0, 13, 4, 4, 0, 18, 10, 10, 0,
|
|
0, 12, 11, 11, 0, 5, 0, 0, 0, 12, 0, 0, 4, 4, 0, 0, 6, 6, 8, 0, 0, 4, 4, 4, 0, 10, 9, 9, 0, 0, 0, 0, 12, 0, 0,
|
|
6, 0, 10, 0, 0, 0, 6, 0, 16, 16, 0, 6, 5, 0, 4, 4, 7, 4, 4, 19, 19, 8, 0, 17, 0, 0, 0, 0, 0, 23, 0, 0, 7, 0, 0,
|
|
0, 13, 0, 10, 0, 0, 0, 0, 0, 12, 0, 0, 8, 0, 9, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
|
0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 0, 12, 0, 0, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 24, 0, 22, 0, 4, 4,
|
|
0, 6, 11, 10, 0, 0, 0, 12, 0, 0, 4, 4, 0, 0, 17, 5, 5, 0, 0, 0, 6, 11, 11, 8, 0, 0, 14, 14, 0, 0, 4, 4, 4, 4,
|
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 17, 17, 7, 0, 0, 0, 0, 14, 5, 0, 4, 4, 6, 8, 4,
|
|
4, 0, 0, 0, 12, 12, 0, 5, 5, 0, 13, 13, 0, 25, 5, 4, 4, 7, 0, 12, 4, 4, 4, 7, 4, 4, 0, 17, 5, 0, 0, 7, 0, 0, 9,
|
|
0, 0, 0, 0, 12, 0, 4, 4, 0, 6, 0, 8, 0, 4, 4, 6, 11, 5, 4, 4, 4, 0, 0, 5, 0, 9, 9, 0, 0, 0, 0, 14, 0, 0, 4, 4,
|
|
4, 4, 4, 0, 0],
|
|
# the livery becomes obnoxious to nearly all who are required to wear it
|
|
# 3570/5694/3570_5694_000014_000021.wav
|
|
[0, 0, 6, 11, 5, 0, 0, 4, 4, 0, 15, 10, 10, 0, 0, 25, 5, 0, 13, 13, 0, 22, 0, 0, 4, 0, 24, 24, 5, 0, 0, 0, 19,
|
|
19, 0, 8, 0, 17, 5, 5, 0, 12, 0, 4, 4, 4, 0, 8, 0, 0, 24, 0, 0, 0, 9, 9, 0, 8, 0, 0, 0, 0, 0, 28, 0, 0, 0, 10,
|
|
0, 8, 16, 0, 12, 12, 12, 0, 4, 0, 6, 6, 8, 0, 4, 4, 0, 9, 5, 0, 7, 7, 13, 0, 0, 15, 22, 22, 4, 4, 0, 0, 0, 0,
|
|
0, 0, 0, 7, 0, 15, 0, 0, 15, 0, 4, 4, 4, 18, 11, 11, 8, 0, 4, 4, 0, 7, 0, 13, 5, 4, 4, 13, 13, 5, 0, 0, 0, 30,
|
|
30, 16, 0, 0, 10, 0, 0, 0, 13, 5, 0, 14, 4, 4, 6, 6, 8, 0, 4, 4, 18, 18, 5, 5, 7, 7, 13, 13, 0, 4, 4, 0, 10, 0,
|
|
0, 0, 0, 6, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0],
|
|
# in the nature of things luxuries and the comforts of life belong to the leisure class
|
|
# 3570/5694/3570_5694_000006_000007.wav
|
|
[0, 0, 0, 0, 0, 10, 9, 0, 4, 4, 6, 11, 5, 4, 4, 4, 9, 9, 7, 7, 0, 0, 0, 0, 0, 0, 6, 0, 16, 16, 13, 13, 5, 0, 4, 4, 8, 0, 20, 4, 4, 4, 0, 6, 0, 11, 10, 0, 9, 0, 21, 0, 0, 0, 12, 12, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 15, 0, 16, 16, 0, 0, 28, 0, 0, 0, 16, 16, 0, 13, 13, 0, 10, 0, 5, 0, 0, 0, 12, 0, 0, 4, 4, 4, 0, 0, 7, 0, 9, 0, 14, 4, 4, 6, 11, 5, 4, 4, 0, 0, 19, 0, 8, 17, 17, 0, 0, 0, 0, 0, 20, 0, 8, 0, 13, 0, 6, 0, 12, 4, 4, 8, 0, 20, 4, 4, 4, 0, 0, 15, 0, 10, 10, 0, 0, 0, 20, 5, 0, 4, 4, 0, 0, 24, 5, 0, 0, 0, 15, 8, 0, 9, 0, 21, 0, 0, 0, 4, 4, 6, 8, 4, 4, 4, 6, 11, 5, 4, 4, 15, 15, 5, 10, 0, 0, 12, 0, 16, 13, 5, 5, 4, 4, 0, 19, 0, 15, 15, 0, 0, 7, 0, 0, 12, 12, 0, 0, 0, 12, 12, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0],
|
|
# from arcaic times down through all the length of the patriarchal regime it has been the office of the women to
|
|
# prepare and administer these luxuries and it has been the perquisite of the men of gentle birth and breeding
|
|
# to consume them
|
|
# 3570/5694/3570_5694_000007_000003.wav
|
|
[0, 0, 0, 0, 0, 0, 20, 13, 8, 0, 17, 0, 4, 4, 0, 7, 0, 13, 0, 0, 0, 0, 0, 19, 0, 0, 0, 7, 0, 0, 0, 0, 10, 0, 19, 0, 0, 0, 4, 4, 0, 0, 0, 0, 6, 0, 0, 0, 10, 0, 0, 17, 5, 0, 0, 0, 12, 0, 4, 0, 0, 0, 0, 14, 0, 0, 8, 0, 18, 0, 0, 0, 9, 0, 0, 0, 0, 4, 4, 0, 0, 0, 6, 11, 13, 8, 0, 16, 21, 21, 11, 0, 4, 4, 7, 0, 15, 0, 15, 15, 4, 4, 6, 11, 5, 5, 4, 4, 0, 15, 0, 5, 0, 0, 9, 9, 0, 21, 0, 0, 6, 11, 0, 4, 4, 8, 8, 20, 4, 4, 4, 6, 11, 5, 4, 4, 0, 0, 0, 23, 0, 7, 7, 0, 0, 0, 0, 0, 6, 6, 13, 13, 13, 10, 0, 0, 0, 0, 0, 7, 13, 13, 0, 19, 11, 11, 11, 0, 0, 7, 15, 15, 0, 4, 4, 4, 13, 13, 5, 0, 0, 0, 0, 21, 21, 0, 0, 10, 0, 0, 0, 0, 17, 5, 0, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 0, 0, 6, 4, 4, 0, 0, 11, 7, 7, 0, 0, 12, 0, 4, 4, 0, 24, 5, 0, 0, 5, 5, 9, 0, 4, 6, 6, 11, 5, 4, 4, 0, 0, 8, 0, 20, 0, 0, 0, 20, 0, 10, 0, 0, 0, 19, 5, 0, 4, 4, 8, 0, 20, 4, 4, 6, 11, 5, 4, 4, 4, 18, 8, 0, 0, 0, 17, 5, 0, 9, 9, 0, 0, 4, 4, 0, 6, 6, 8, 0, 0, 4, 4, 0, 23, 23, 13, 5, 5, 0, 0, 0, 0, 23, 23, 0, 7, 0, 0, 0, 13, 5, 0, 0, 0, 4, 4, 0, 7, 0, 9, 14, 0, 4, 4, 0, 0, 7, 0, 14, 0, 0, 0, 17, 17, 10, 0, 9, 0, 10, 10, 0, 0, 12, 12, 0, 0, 0, 6, 0, 5, 13, 13, 0, 0, 0, 0, 4, 4, 4, 6, 11, 11, 5, 0, 0, 0, 12, 5, 5, 4, 4, 15, 15, 0, 16, 0, 0, 0, 28, 0, 0, 0, 16, 0, 0, 13, 13, 10, 0, 5, 5, 0, 0, 12, 12, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 9, 0, 14, 4, 4, 10, 0, 6, 4, 4, 0, 11, 11, 7, 0, 0, 0, 12, 0, 4, 4, 0, 0, 0, 0, 24, 5, 0, 0, 5, 5, 9, 9, 4, 4, 4, 6, 11, 5, 4, 4, 0, 0, 0, 23, 0, 5, 0, 13, 0, 0, 0, 0, 0, 30, 30, 16, 10, 10, 0, 0, 0, 12, 0, 10, 0, 0, 6, 5, 0, 4, 4, 8, 20, 0, 4, 4, 6, 11, 5, 4, 4, 0, 17, 5, 0, 0, 0, 9, 0, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 20, 4, 4, 4, 0, 0, 21, 0, 5, 5, 0, 9, 9, 0, 0, 0, 6, 0, 15, 0, 5, 0, 4, 0, 0, 0, 24, 0, 10, 0, 13, 0, 0, 0, 0, 6, 11, 0, 0, 4, 0, 0, 7, 0, 9, 14, 14, 4, 4, 4, 0, 0, 24, 13, 5, 0, 0, 0, 5, 0, 0, 14, 10, 0, 9, 21, 21, 0, 4, 4, 0, 6, 8, 0, 4, 4, 0, 19, 8, 0, 9, 0, 0, 0, 0, 0, 0, 0, 12, 0, 16, 0, 17, 5, 0, 0, 4, 4, 6, 11, 5, 0, 17, 0, 4, 4, 4, 4, 0, 0],
|
|
# yes it is perfection she declared
|
|
# 1284/1180/1284_1180_000036_000000.wav
|
|
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 22, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 12, 0, 0, 4, 4, 4, 4, 0, 0, 10, 0, 6, 0, 4, 4, 0, 0, 10, 0, 0, 0, 0, 0, 12, 0, 4, 4, 0, 0, 0, 23, 0, 5, 0, 13, 13, 0, 0, 0, 0, 0, 0, 0, 20, 0, 0, 5, 0, 0, 0, 19, 0, 0, 6, 6, 0, 10, 0, 8, 0, 9, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 12, 11, 11, 5, 0, 4, 4, 0, 14, 0, 5, 0, 0, 0, 0, 19, 15, 15, 0, 0, 7, 0, 0, 0, 13, 0, 5, 0, 14, 4, 4, 4, 4, 0, 0, 0],
|
|
# then it must be somewhere in the blue forest
|
|
# 1284/1180/1284_1180_000016_000002.wav
|
|
[0, 0, 0, 6, 11, 5, 0, 9, 0, 4, 4, 10, 6, 4, 4, 0, 17, 17, 16, 0, 0, 12, 0, 6, 4, 4, 0, 24, 5, 5, 0, 0, 4, 4, 0, 0, 12, 12, 0, 8, 0, 0, 17, 5, 5, 0, 0, 18, 18, 11, 5, 0, 13, 13, 5, 0, 4, 4, 10, 9, 4, 4, 6, 11, 5, 4, 4, 0, 24, 15, 15, 16, 16, 0, 5, 5, 0, 0, 4, 4, 0, 0, 0, 20, 8, 8, 8, 0, 0, 0, 13, 13, 0, 5, 5, 0, 0, 0, 0, 0, 12, 12, 0, 0, 6, 0, 0, 4, 4, 4, 4, 0, 0, 0, 0],
|
|
# happy youth that is ready to pack its valus and start for cathay on an hour's notice
|
|
# 4970/29093/4970_29093_000044_000002.wav
|
|
[0, 0, 0, 0, 11, 0, 7, 23, 0, 0, 0, 0, 23, 0, 22, 22, 0, 0, 0, 4, 4, 0, 0, 22, 8, 8, 16, 16, 0, 0, 0, 6, 6, 11, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 11, 7, 6, 0, 4, 4, 10, 0, 0, 12, 0, 4, 0, 13, 13, 5, 0, 7, 0, 0, 14, 22, 0, 0, 0, 4, 0, 6, 0, 8, 4, 4, 0, 0, 0, 0, 0, 0, 23, 0, 7, 0, 0, 19, 0, 0, 26, 4, 4, 4, 10, 0, 6, 0, 12, 4, 4, 0, 0, 0, 25, 0, 7, 0, 0, 0, 15, 0, 0, 16, 0, 0, 0, 0, 12, 0, 0, 0, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 9, 0, 14, 4, 4, 0, 12, 12, 0, 6, 0, 7, 0, 13, 0, 0, 0, 6, 0, 0, 4, 4, 0, 0, 0, 0, 20, 8, 0, 13, 0, 4, 4, 4, 0, 0, 19, 0, 7, 7, 0, 0, 0, 0, 0, 6, 11, 0, 0, 7, 0, 0, 0, 22, 0, 0, 0, 0, 0, 4, 4, 0, 0, 8, 0, 9, 0, 4, 4, 7, 9, 4, 4, 4, 0, 0, 0, 11, 8, 8, 16, 0, 0, 13, 13, 0, 0, 0, 27, 0, 12, 0, 4, 4, 0, 9, 8, 8, 0, 0, 0, 0, 6, 10, 0, 0, 0, 0, 0, 19, 5, 5, 0, 0, 4, 4, 4, 4, 4, 0],
|
|
# well then i must make some suggestions to you
|
|
# 1580/141084/1580_141084_000057_000000.wav
|
|
[0, 0, 0, 0, 0, 0, 0, 18, 0, 5, 0, 15, 0, 0, 15, 15, 4, 4, 0, 0, 6, 11, 5, 0, 0, 0, 9, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 10, 0, 4, 4, 0, 17, 0, 16, 0, 0, 12, 0, 6, 0, 4, 4, 0, 17, 17, 7, 0, 26, 5, 5, 4, 4, 0, 12, 12, 8, 8, 17, 17, 5, 0, 4, 4, 4, 12, 12, 16, 0, 21, 0, 0, 0, 0, 21, 21, 0, 5, 0, 0, 0, 12, 0, 0, 0, 6, 6, 0, 10, 0, 8, 8, 9, 0, 0, 0, 0, 0, 0, 12, 0, 0, 4, 4, 0, 0, 6, 0, 8, 0, 4, 4, 4, 0, 0, 22, 22, 0, 8, 16, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0],
|
|
# some others too big cotton county
|
|
# 1995/1826/1995_1826_000010_000002.wav
|
|
[0, 0, 0, 0, 12, 0, 8, 0, 17, 5, 4, 4, 0, 8, 0, 0, 6, 11, 5, 0, 13, 13, 0, 0, 12, 0, 4, 4, 0, 0, 6, 0, 8, 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 0, 0, 10, 0, 0, 0, 0, 21, 0, 0, 4, 4, 4, 0, 0, 0, 19, 0, 8, 0, 6, 6, 0, 0, 0, 6, 8, 0, 9, 9, 0, 0, 4, 0, 0, 0, 0, 19, 8, 8, 16, 0, 9, 9, 0, 0, 6, 6, 0, 0, 22, 0, 0, 0, 0, 4, 4, 0, 0, 0],
|
|
]
|
|
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('-text', type=str, help='Text to speak.', default='This is the real secret of life: to be completely engaged in what you are doing in the here and now. To realize that instead of work, it is play.')
|
|
parser.add_argument('-opt_code_gen', type=str, help='Path to options YAML file used to train the code_gen model', default='D:\\dlas\\options\\train_encoder_build_ctc_alignments.yml')
|
|
parser.add_argument('-code_gen_model_name', type=str, help='Name of the code_gen model in opt.', default='generator')
|
|
parser.add_argument('-code_gen_model_path', type=str, help='Path to saved code_gen model weights', default='D:\\dlas\\experiments\\train_encoder_build_ctc_alignments\\models\\31000_generator_ema.pth')
|
|
parser.add_argument('-opt', type=str, help='Path to options YAML file used to train the diffusion model', default='X:\\dlas\\experiments\\train_diffusion_tts5_medium.yml')
|
|
parser.add_argument('-diffusion_model_name', type=str, help='Name of the diffusion model in opt.', default='generator')
|
|
parser.add_argument('-diffusion_model_path', type=str, help='Path to saved model weights', default='X:\\dlas\\experiments\\train_diffusion_tts5_medium\\models\\73000_generator_ema.pth')
|
|
parser.add_argument('-sr_opt', type=str, help='Path to options YAML file used to train the SR diffusion model', default='X:\\dlas\\experiments\\train_diffusion_tts6_upsample.yml')
|
|
parser.add_argument('-sr_diffusion_model_name', type=str, help='Name of the SR diffusion model in opt.', default='generator')
|
|
parser.add_argument('-sr_diffusion_model_path', type=str, help='Path to saved model weights for the SR diffuser', default='X:\\dlas\\experiments\\train_diffusion_tts6_upsample\\models\\31000_generator_ema.pth')
|
|
parser.add_argument('-cond', type=str, help='Type of conditioning voice', default='simmons')
|
|
parser.add_argument('-diffusion_steps', type=int, help='Number of diffusion steps to perform to create the generate. Lower steps reduces quality, but >40 is generally pretty good.', default=100)
|
|
parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='../results/use_diffuse_tts')
|
|
parser.add_argument('-device', type=str, help='Device to run on', default='cuda')
|
|
args = parser.parse_args()
|
|
os.makedirs(args.output_path, exist_ok=True)
|
|
|
|
# Fixed parameters.
|
|
base_sample_rate = 5500
|
|
sr_sample_rate = 22050
|
|
|
|
print("Loading provided conditional audio..")
|
|
sr_cond = load_audio(conditioning_clips[args.cond], sr_sample_rate).to(args.device)
|
|
if sr_cond.shape[-1] > 88000:
|
|
sr_cond = sr_cond[:,:88000]
|
|
cond_mel = wav_to_mel(sr_cond)
|
|
cond = torchaudio.functional.resample(sr_cond, sr_sample_rate, base_sample_rate)
|
|
torchaudio.save(os.path.join(args.output_path, 'cond_base.wav'), cond.cpu(), base_sample_rate)
|
|
torchaudio.save(os.path.join(args.output_path, 'cond_sr.wav'), sr_cond.cpu(), sr_sample_rate)
|
|
|
|
print("Generating codes for text..")
|
|
codegen = load_model_from_config(args.opt_code_gen, args.code_gen_model_name, also_load_savepoint=False,
|
|
load_path=args.code_gen_model_path, device='cuda').eval()
|
|
codes = codegen.generate(cond_mel, [args.text])
|
|
del codegen
|
|
|
|
print("Loading Diffusion Models..")
|
|
diffusion = load_model_from_config(args.opt, args.diffusion_model_name, also_load_savepoint=False,
|
|
load_path=args.diffusion_model_path, device='cpu').eval()
|
|
diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=args.diffusion_steps, schedule='cosine')
|
|
aligned_codes_compression_factor = base_sample_rate * 221 // 11025
|
|
sr_diffusion = load_model_from_config(args.sr_opt, args.sr_diffusion_model_name, also_load_savepoint=False,
|
|
load_path=args.sr_diffusion_model_path, device='cpu').eval()
|
|
sr_diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=args.diffusion_steps, schedule='linear')
|
|
|
|
with torch.no_grad():
|
|
for p, code in enumerate([codes]):
|
|
print("Loading data..")
|
|
aligned_codes = code.to(args.device)
|
|
|
|
print("Performing initial diffusion..")
|
|
output_shape = (1, 1, ceil_multiple(aligned_codes.shape[-1]*aligned_codes_compression_factor, 2048))
|
|
diffusion = diffusion.cuda()
|
|
output_base = diffuser.p_sample_loop(diffusion, output_shape, noise=torch.zeros(output_shape, device=args.device),
|
|
model_kwargs={'tokens': aligned_codes,
|
|
'conditioning_input': cond.unsqueeze(0)})
|
|
diffusion = diffusion.cpu()
|
|
torchaudio.save(os.path.join(args.output_path, f'{p}_output_mean_base.wav'), output_base.cpu().squeeze(0), base_sample_rate)
|
|
|
|
print("Performing SR diffusion..")
|
|
output_shape = (1, 1, output_base.shape[-1] * (sr_sample_rate // base_sample_rate))
|
|
sr_diffusion = sr_diffusion.cuda()
|
|
output = sr_diffuser.p_sample_loop(sr_diffusion, output_shape, noise=torch.zeros(output_shape, device=args.device),
|
|
model_kwargs={'tokens': torch.zeros_like(aligned_codes),
|
|
'conditioning_input': sr_cond.unsqueeze(0),
|
|
'lr_input': output_base})
|
|
sr_diffusion = sr_diffusion.cpu()
|
|
torchaudio.save(os.path.join(args.output_path, f'{p}_output_mean_sr.wav'), output.cpu().squeeze(0), sr_sample_rate)
|