File size: 5,200 Bytes
9b9c715 |
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 |
# Synthesize all Harvard Lists 77x lists of 10x sentences to single .wav ----- NEEDS TO BE RUN from https://github.com/audeering/shift/
#
# 1. using mimic3 style
# Folder: 'prompt_mimic3/'
# 2. using mimic3 4x accelerated style
# Folder: 'prompt_mimic3speed/'
# 3. using crema-d style
# Folder: 'prompt_human/'
#
# WAVS used from tts_paper_plot.py
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],
# ['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
# Generate 77 wavs
with open('voices.json', 'r') as f:
df = json.load(f)['voices']
voice_names = [v['voice'] for k,v in df.items()]
synthetic_wav_paths = []
synthetic_wav_paths_AFFECT = []
for voice in voice_names:
synthetic_wav_paths.append(
'assets/wavs/style_vector/' + voice.replace('/', '_').replace('#', '_').replace(
'cmu-arctic', 'cmu_arctic').replace('_low', '') + '.wav')
synthetic_wav_paths_AFFECT.append(
'assets/wavs/style_vector_v2/' + voice.replace('/', '_').replace('#', '_').replace(
'cmu-arctic', 'cmu_arctic').replace('_low', '') + '.wav')
print(len(synthetic_wav_paths))
natural_wav_paths = load_speech()
# SYNTHESIZE mimic mimicx4 crema-d
import msinference
with open('harvard.json', 'r') as f:
harvard_individual_sentences = json.load(f)['sentences']
for audio_prompt in ['mimic3', 'mimic3_speed', 'human']:
total_audio = []
ix = 0
for list_of_10 in harvard_individual_sentences:
# long_sentence = ' '.join(list_of_10['sentences'])
# harvard.append(long_sentence.replace('.', ' '))
for text in list_of_10['sentences']:
if audio_prompt == 'mimic3':
style_vec = msinference.compute_style(
synthetic_wav_paths[ix % 134])
elif audio_prompt == 'mimic3_speed':
style_vec = msinference.compute_style(
synthetic_wav_paths_AFFECT[ix % 134])
elif audio_prompt == 'human':
style_vec = msinference.compute_style(
natural_wav_paths[ix % len(natural_wav_paths)])
else:
print('unknonw list of style vecto')
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)
# concat before write
# -- for 10x sentenctes
print('_____________________')
# -- for 77x lists
total_audio = np.concatenate(total_audio)
soundfile.write(f'{audio_prompt}_770.wav', total_audio, 24000)
print(f'{audio_prompt}_full_770.wav')
|