File size: 7,352 Bytes
8a2aca3 08238e0 e154110 9b9c715 8a2aca3 9b9c715 8a2aca3 9b9c715 ccb2aa8 8a2aca3 9b9c715 8a2aca3 9b9c715 8a2aca3 9b9c715 08238e0 8a2aca3 9b9c715 08238e0 06d4bd1 9b9c715 06d4bd1 e1acca5 08238e0 e1acca5 06d4bd1 e154110 08238e0 e154110 5d9a91a 06d4bd1 08238e0 06d4bd1 08238e0 5d9a91a 08238e0 e154110 ccb2aa8 06d4bd1 e154110 ccb2aa8 06d4bd1 08238e0 06d4bd1 08238e0 ccb2aa8 06d4bd1 e154110 ccb2aa8 06d4bd1 08238e0 06d4bd1 08238e0 e1acca5 08238e0 06d4bd1 08238e0 ccb2aa8 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 |
# Synthesize all Harvard Lists 77x lists of 10x sentences to single .wav
# 1. using mimic3 english 1x/4x non-english 1x/4x
# Call visualize_tts_plesantness.py for 4figs [eng 1x/4x vs human, non-eng 1x/4x vs human-libri]
import soundfile
import json
import numpy as np
import audb
from pathlib import Path
LABELS = ['arousal', 'dominance', 'valence']
def load_speech(split=None):
DB = [
# [dataset, version, table, has_timdeltas_or_is_full_wavfile]
# ['crema-d', '1.1.1', 'emotion.voice.test', False],
#['librispeech', '3.1.0', 'test-clean', False],
['emodb', '1.2.0', 'emotion.categories.train.gold_standard', False],
# ['entertain-playtestcloud', '1.1.0', 'emotion.categories.train.gold_standard', True],
# ['erik', '2.2.0', 'emotion.categories.train.gold_standard', True],
# ['meld', '1.3.1', 'emotion.categories.train.gold_standard', False],
# ['msppodcast', '5.0.0', 'emotion.categories.train.gold_standard', False], # tandalone bucket because it has gt labels?
# ['myai', '1.0.1', 'emotion.categories.train.gold_standard', False],
# ['casia', None, 'emotion.categories.gold_standard', False],
# ['switchboard-1', None, 'sentiment', True],
# ['swiss-parliament', None, 'segments', True],
# ['argentinian-parliament', None, 'segments', True],
# ['austrian-parliament', None, 'segments', True],
# #'german', --> bundestag
# ['brazilian-parliament', None, 'segments', True],
# ['mexican-parliament', None, 'segments', True],
# ['portuguese-parliament', None, 'segments', True],
# ['spanish-parliament', None, 'segments', True],
# ['chinese-vocal-emotions-liu-pell', None, 'emotion.categories.desired', False],
# peoples-speech slow
# ['peoples-speech', None, 'train-initial', False]
]
output_list = []
for database_name, ver, table, has_timedeltas in DB:
a = audb.load(database_name,
sampling_rate=16000,
format='wav',
mixdown=True,
version=ver,
cache_root='/cache/audb/')
a = a[table].get()
if has_timedeltas:
print(f'{has_timedeltas=}')
# a = a.reset_index()[['file', 'start', 'end']]
# output_list += [[*t] for t
# in zip(a.file.values, a.start.dt.total_seconds().values, a.end.dt.total_seconds().values)]
else:
output_list += [f for f in a.index] # use file (no timedeltas)
return output_list
natural_wav_paths = load_speech()
# SYNTHESIZE mimic mimicx4 crema-d
import msinference
import os
from random import shuffle
import audiofile
with open('harvard.json', 'r') as f:
harvard_individual_sentences = json.load(f)['sentences']
synthetic_wav_paths = ['./enslow/' + i for i in
os.listdir('./enslow/')]
synthetic_wav_paths_4x = ['./style_vector_v2/' + i for i in
os.listdir('./style_vector_v2/')]
synthetic_wav_paths_foreign = ['./mimic3_foreign/' + i for i in os.listdir('./mimic3_foreign/') if 'en_U' not in i]
synthetic_wav_paths_foreign_4x = ['./mimic3_foreign_4x/' + i for i in os.listdir('./mimic3_foreign_4x/') if 'en_U' not in i] # very short segments
# filter very short styles
synthetic_wav_paths_foreign = [i for i in synthetic_wav_paths_foreign if audiofile.duration(i) > 2]
synthetic_wav_paths_foreign_4x = [i for i in synthetic_wav_paths_foreign_4x if audiofile.duration(i) > 2]
synthetic_wav_paths = [i for i in synthetic_wav_paths if audiofile.duration(i) > 2]
synthetic_wav_pathsn_4x = [i for i in synthetic_wav_paths_4x if audiofile.duration(i) > 2]
shuffle(synthetic_wav_paths_foreign_4x)
shuffle(synthetic_wav_paths_foreign)
shuffle(synthetic_wav_paths)
shuffle(synthetic_wav_paths_4x)
print(len(synthetic_wav_paths_foreign_4x), len(synthetic_wav_paths_foreign),
len(synthetic_wav_paths), len(synthetic_wav_paths_4x)) # 134 204 134 204
for audio_prompt in ['english',
'english_4x',
'human',
'foreign',
'foreign_4x']:
OUT_FILE = f'{audio_prompt}_hfullh.wav'
if not os.path.isfile(OUT_FILE):
total_audio = []
total_style = []
ix = 0
for list_of_10 in harvard_individual_sentences[:1000]:
# long_sentence = ' '.join(list_of_10['sentences'])
# harvard.append(long_sentence.replace('.', ' '))
for text in list_of_10['sentences']:
if audio_prompt == 'english':
_p = synthetic_wav_paths[ix % len(synthetic_wav_paths)] #134]
style_vec = msinference.compute_style(_p)
elif audio_prompt == 'english_4x':
_p = synthetic_wav_paths_4x[ix % len(synthetic_wav_paths_4x)] # 134]
style_vec = msinference.compute_style(_p)
elif audio_prompt == 'human':
_p = natural_wav_paths[ix % len(natural_wav_paths)]
style_vec = msinference.compute_style(_p)
elif audio_prompt == 'foreign':
_p = synthetic_wav_paths_foreign[ix % len(synthetic_wav_paths_foreign)] #179] # 204 some short styles are discarded
style_vec = msinference.compute_style(_p)
elif audio_prompt == 'foreign_4x':
_p = synthetic_wav_paths_foreign_4x[ix % len(synthetic_wav_paths_foreign_4x)] #179] # 204
style_vec = msinference.compute_style(_p)
else:
print('unknonw list of style vector')
print(ix, text)
ix += 1
x = msinference.inference(text,
style_vec,
alpha=0.3,
beta=0.7,
diffusion_steps=7,
embedding_scale=1)
total_audio.append(x)
_st, fsr = audiofile.read(_p)
total_style.append(_st[:len(x)])
# concat before write
# -- for 10x sentenctes
print('_____________________')
# -- for 77x lists
total_audio = np.concatenate(total_audio)
soundfile.write(OUT_FILE, total_audio, 24000)
total_style = np.concatenate(total_style)
soundfile.write('_st_' + OUT_FILE, total_style, fsr) # take this fs from the loading
else:
print('\nALREADY EXISTS\n')
|