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# 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 % 134]
style_vec = msinference.compute_style(_p)
elif audio_prompt == 'english_4x':
_p = synthetic_wav_paths_4x[ix % 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 % 204]
style_vec = msinference.compute_style(_p)
elif audio_prompt == 'foreign_4x':
_p = synthetic_wav_paths_foreign_4x[ix % 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') |