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draft various spk

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mimic3_make_harvard_sentences.py CHANGED
@@ -75,6 +75,7 @@ list_voices = [
75
  ] # special - for human we load specific style file - no Mimic3 is run
76
 
77
 
 
78
  # ================================================== INTERFACE MODELS
79
  LABELS = [
80
  'arousal', 'dominance', 'valence',
@@ -296,7 +297,7 @@ for _id, _voice in enumerate(list_voices):
296
  total_audio_mimic3 = []
297
  total_audio_styletts2 = []
298
  ix = 0
299
- for list_of_10 in harvard_individual_sentences[:4]: # 77
300
 
301
  text = ' '.join(list_of_10['sentences'])
302
 
@@ -367,8 +368,12 @@ for _id, _voice in enumerate(list_voices):
367
 
368
  # MIMIC3 = = = = = = = = = = = = = = END
369
 
370
-
371
-
 
 
 
 
372
 
373
  style_vec = msinference.compute_style(style_path) # use mimic-3 as prompt
374
 
 
75
  ] # special - for human we load specific style file - no Mimic3 is run
76
 
77
 
78
+
79
  # ================================================== INTERFACE MODELS
80
  LABELS = [
81
  'arousal', 'dominance', 'valence',
 
297
  total_audio_mimic3 = []
298
  total_audio_styletts2 = []
299
  ix = 0
300
+ for list_of_10 in harvard_individual_sentences[:1000]: # 77
301
 
302
  text = ' '.join(list_of_10['sentences'])
303
 
 
368
 
369
  # MIMIC3 = = = = = = = = = = = = = = END
370
 
371
+ if 'en_US' in _str:
372
+ style_path = 'mimic3_english_4x/' + _str + '.wav'
373
+ elif ('de_DE' in _str) or ('fr_FR' in _str):
374
+ style_path = 'mimic3_foreign_4x/' + _str + '.wav'
375
+ else:
376
+ print(f'use human / generated style for {_str}')
377
 
378
  style_vec = msinference.compute_style(style_path) # use mimic-3 as prompt
379
 
tts_harvard.py CHANGED
@@ -1,42 +1,49 @@
1
- # Synthesize all Harvard Lists - 767 sentences as single .wav
2
  #
3
- # 1.
 
 
 
 
 
4
  #
5
- # './prompt_mimic3_english/'
6
- #
7
- # 2.
8
- #
9
- # './prompt_mimic3_english_4x/'
10
- #
11
- # 3.
12
- #
13
- # './prompt_human/'
14
- #
15
- # 4.
16
- #
17
- # './prompt_mimic3_foreign/'
18
- #
19
- # 5.
20
- #
21
- # './prompt_mimic3_foreign_4x/
22
- #
23
- #
24
- # ----> THE .wavs will be used for visualisation
25
 
26
  import soundfile
27
  import json
28
  import numpy as np
29
  import audb
30
  from pathlib import Path
31
- import os
32
  LABELS = ['arousal', 'dominance', 'valence']
33
 
34
 
35
- def load_human_speech(split=None):
 
 
36
  DB = [
37
  # [dataset, version, table, has_timdeltas_or_is_full_wavfile]
38
  # ['crema-d', '1.1.1', 'emotion.voice.test', False],
39
- ['emodb', '1.2.0', 'emotion.categories.train.gold_standard', False],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
40
  ]
41
 
42
  output_list = []
@@ -58,38 +65,65 @@ def load_human_speech(split=None):
58
  output_list += [f for f in a.index] # use file (no timedeltas)
59
  return output_list
60
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
61
 
 
 
 
 
 
 
 
62
 
 
 
 
 
63
 
64
 
65
  # SYNTHESIZE mimic mimicx4 crema-d
66
  import msinference
67
- prompt_paths = {}
68
 
69
  with open('harvard.json', 'r') as f:
70
  harvard_individual_sentences = json.load(f)['sentences']
71
 
72
- for audio_prompt in [#'mimic3_english',
73
- #'mimic3_english_4x',
74
- 'human',
75
- 'mimic3_foreign',
76
- 'mimic3_foreign_4x']:
77
-
78
- if audio_prompt == 'human':
79
- prompt_paths = load_human_speech() # better emodb ?
80
- else:
81
- prompt_dir = '/data/dkounadis/artificial-styletts2/' + audio_prompt + '/'
82
- prompt_paths = [prompt_dir + f for f in os.listdir(prompt_dir)]
83
- prompt_paths = prompt_paths[:10]
84
- print(prompt_paths,'\n\n__________')
85
 
 
 
 
 
86
  total_audio = []
87
  ix = 0
88
- for list_of_10 in harvard_individual_sentences[:1]:
89
  # long_sentence = ' '.join(list_of_10['sentences'])
90
  # harvard.append(long_sentence.replace('.', ' '))
91
  for text in list_of_10['sentences']:
92
- style_vec = msinference.compute_style(prompt_paths[ix % len(prompt_paths)])
 
 
 
 
 
 
 
 
 
 
93
  print(ix, text)
94
  ix += 1
95
  x = msinference.inference(text,
@@ -100,6 +134,10 @@ for audio_prompt in [#'mimic3_english',
100
  embedding_scale=1)
101
 
102
  total_audio.append(x)
103
- total_audio = np.concatenate(total_audio) # -- concat 77x lists
104
- soundfile.write(f'{audio_prompt}_767_5.wav', total_audio, 24000)
105
- print(f'{audio_prompt}_767_5.wav')
 
 
 
 
 
1
+ # Synthesize all Harvard Lists 77x lists of 10x sentences to single .wav
2
  #
3
+ # 1. using mimic3 style
4
+ # Folder: 'prompt_mimic3/'
5
+ # 2. using mimic3 4x accelerated style
6
+ # Folder: 'prompt_mimic3speed/'
7
+ # 3. using crema-d style
8
+ # Folder: 'prompt_human/'
9
  #
10
+ # WAVS used from tts_paper_plot.py
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
11
 
12
  import soundfile
13
  import json
14
  import numpy as np
15
  import audb
16
  from pathlib import Path
17
+
18
  LABELS = ['arousal', 'dominance', 'valence']
19
 
20
 
21
+
22
+
23
+ def load_speech(split=None):
24
  DB = [
25
  # [dataset, version, table, has_timdeltas_or_is_full_wavfile]
26
  # ['crema-d', '1.1.1', 'emotion.voice.test', False],
27
+ ['librispeech', '3.1.0', 'train-clean-360', False],
28
+ # ['emodb', '1.2.0', 'emotion.categories.train.gold_standard', False],
29
+ # ['entertain-playtestcloud', '1.1.0', 'emotion.categories.train.gold_standard', True],
30
+ # ['erik', '2.2.0', 'emotion.categories.train.gold_standard', True],
31
+ # ['meld', '1.3.1', 'emotion.categories.train.gold_standard', False],
32
+ # ['msppodcast', '5.0.0', 'emotion.categories.train.gold_standard', False], # tandalone bucket because it has gt labels?
33
+ # ['myai', '1.0.1', 'emotion.categories.train.gold_standard', False],
34
+ # ['casia', None, 'emotion.categories.gold_standard', False],
35
+ # ['switchboard-1', None, 'sentiment', True],
36
+ # ['swiss-parliament', None, 'segments', True],
37
+ # ['argentinian-parliament', None, 'segments', True],
38
+ # ['austrian-parliament', None, 'segments', True],
39
+ # #'german', --> bundestag
40
+ # ['brazilian-parliament', None, 'segments', True],
41
+ # ['mexican-parliament', None, 'segments', True],
42
+ # ['portuguese-parliament', None, 'segments', True],
43
+ # ['spanish-parliament', None, 'segments', True],
44
+ # ['chinese-vocal-emotions-liu-pell', None, 'emotion.categories.desired', False],
45
+ # peoples-speech slow
46
+ # ['peoples-speech', None, 'train-initial', False]
47
  ]
48
 
49
  output_list = []
 
65
  output_list += [f for f in a.index] # use file (no timedeltas)
66
  return output_list
67
 
68
+
69
+
70
+
71
+
72
+ # Generate 77 wavs
73
+
74
+
75
+
76
+
77
+ with open('voices.json', 'r') as f:
78
+ df = json.load(f)['voices']
79
+ voice_names = [v['voice'] for k,v in df.items()]
80
+ synthetic_wav_paths = []
81
+ synthetic_wav_paths_AFFECT = []
82
+ for voice in voice_names:
83
 
84
+ synthetic_wav_paths.append(
85
+ '/data/dkounadis/shift/assets/wavs/style_vector/' + voice.replace('/', '_').replace('#', '_').replace(
86
+ 'cmu-arctic', 'cmu_arctic').replace('_low', '') + '.wav')
87
+ synthetic_wav_paths_AFFECT.append(
88
+ '/data/dkounadis/shift/assets/wavs/style_vector_v2/' + voice.replace('/', '_').replace('#', '_').replace(
89
+ 'cmu-arctic', 'cmu_arctic').replace('_low', '') + '.wav')
90
+
91
 
92
+ print(len(synthetic_wav_paths))
93
+
94
+
95
+ natural_wav_paths = load_speech()
96
 
97
 
98
  # SYNTHESIZE mimic mimicx4 crema-d
99
  import msinference
100
+
101
 
102
  with open('harvard.json', 'r') as f:
103
  harvard_individual_sentences = json.load(f)['sentences']
104
 
 
 
 
 
 
 
 
 
 
 
 
 
 
105
 
106
+
107
+
108
+
109
+ for audio_prompt in ['mimic3', 'mimic3_speed', 'human']:
110
  total_audio = []
111
  ix = 0
112
+ for list_of_10 in harvard_individual_sentences:
113
  # long_sentence = ' '.join(list_of_10['sentences'])
114
  # harvard.append(long_sentence.replace('.', ' '))
115
  for text in list_of_10['sentences']:
116
+ if audio_prompt == 'mimic3':
117
+ style_vec = msinference.compute_style(
118
+ synthetic_wav_paths[ix % 134])
119
+ elif audio_prompt == 'mimic3_speed':
120
+ style_vec = msinference.compute_style(
121
+ synthetic_wav_paths_AFFECT[ix % 134])
122
+ elif audio_prompt == 'human':
123
+ style_vec = msinference.compute_style(
124
+ natural_wav_paths[ix % len(natural_wav_paths)])
125
+ else:
126
+ print('unknonw list of style vecto')
127
  print(ix, text)
128
  ix += 1
129
  x = msinference.inference(text,
 
134
  embedding_scale=1)
135
 
136
  total_audio.append(x)
137
+ # concat before write
138
+ # -- for 10x sentenctes
139
+ print('_____________________')
140
+ # -- for 77x lists
141
+ total_audio = np.concatenate(total_audio)
142
+ soundfile.write(f'{audio_prompt}_770.wav', total_audio, 24000)
143
+ print(f'{audio_prompt}_full_770.wav')
visualize_tts_plesantness.py CHANGED
@@ -81,12 +81,12 @@ def _sigmoid(x):
81
 
82
  # for mimic3/mimic3speed/human - concat all 77 and run timeseries with 7s hop 3s
83
  for long_audio in [
84
- 'mimic3_english_767_5.wav',
85
- 'mimic3_english_4x_767_5.wav',
86
- 'human_767_5.wav',
87
- 'mimic3_foregin_767_5.wav',
88
- 'mimic3_foreign_4x_767_5.wav'
89
- ]:
90
  file_interface = f'timeseries_{long_audio.replace("/", "")}.pkl'
91
  if not os.path.exists(file_interface):
92
 
@@ -106,7 +106,7 @@ for long_audio in [
106
  # pool
107
  h = self.pool_model.sap_linear(x).tanh()
108
  w = torch.matmul(h, self.pool_model.attention)
109
- w = stylesoftmax(1)
110
  mu = (x * w).sum(1)
111
  x = torch.cat(
112
  [
 
81
 
82
  # for mimic3/mimic3speed/human - concat all 77 and run timeseries with 7s hop 3s
83
  for long_audio in [
84
+ # 'mimic3.wav',
85
+ # 'mimic3_speedup.wav',
86
+ 'human_770.wav', # 'mimic3_all_77.wav', #
87
+ 'mimic3_770.wav',
88
+ 'mimic3_speed_770.wav'
89
+ ]:
90
  file_interface = f'timeseries_{long_audio.replace("/", "")}.pkl'
91
  if not os.path.exists(file_interface):
92
 
 
106
  # pool
107
  h = self.pool_model.sap_linear(x).tanh()
108
  w = torch.matmul(h, self.pool_model.attention)
109
+ w = w.softmax(1)
110
  mu = (x * w).sum(1)
111
  x = torch.cat(
112
  [
voices.json ADDED
@@ -0,0 +1,1612 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "voices": {
3
+ "0": {
4
+ "voice": "en_US/vctk_low#p239",
5
+ "emotion": [
6
+ 0.001872760709375143,
7
+ 0.18340148776769638,
8
+ 0.3238210380077362
9
+ ],
10
+ "tgt_wav": "assets/wavs/en_US_vctk_p239.wav",
11
+ "affect_wav": "assets/wavs/en_US_vctk_p239_affect.wav",
12
+ "fig_file": "assets/en_US_vctk_p239.png",
13
+ "str_voice": "en_US_vctk_p239"
14
+ },
15
+ "1": {
16
+ "voice": "en_US/vctk_low#p236",
17
+ "emotion": [
18
+ 0.017000655410811305,
19
+ 0.20464985817670822,
20
+ 0.3406614363193512
21
+ ],
22
+ "tgt_wav": "assets/wavs/en_US_vctk_p236.wav",
23
+ "affect_wav": "assets/wavs/en_US_vctk_p236_affect.wav",
24
+ "fig_file": "assets/en_US_vctk_p236.png",
25
+ "str_voice": "en_US_vctk_p236"
26
+ },
27
+ "2": {
28
+ "voice": "en_US/vctk_low#p264",
29
+ "emotion": [
30
+ 0.0,
31
+ 0.14136632531881332,
32
+ 0.3482646197080612
33
+ ],
34
+ "tgt_wav": "assets/wavs/en_US_vctk_p264.wav",
35
+ "affect_wav": "assets/wavs/en_US_vctk_p264_affect.wav",
36
+ "fig_file": "assets/en_US_vctk_p264.png",
37
+ "str_voice": "en_US_vctk_p264"
38
+ },
39
+ "3": {
40
+ "voice": "en_US/vctk_low#p250",
41
+ "emotion": [
42
+ 0.0,
43
+ 0.1662377566099167,
44
+ 0.31436721980571747
45
+ ],
46
+ "tgt_wav": "assets/wavs/en_US_vctk_p250.wav",
47
+ "affect_wav": "assets/wavs/en_US_vctk_p250_affect.wav",
48
+ "fig_file": "assets/en_US_vctk_p250.png",
49
+ "str_voice": "en_US_vctk_p250"
50
+ },
51
+ "4": {
52
+ "voice": "en_US/vctk_low#p259",
53
+ "emotion": [
54
+ 0.1525903344154358,
55
+ 0.32237668335437775,
56
+ 0.3625539541244507
57
+ ],
58
+ "tgt_wav": "assets/wavs/en_US_vctk_p259.wav",
59
+ "affect_wav": "assets/wavs/en_US_vctk_p259_affect.wav",
60
+ "fig_file": "assets/en_US_vctk_p259.png",
61
+ "str_voice": "en_US_vctk_p259"
62
+ },
63
+ "5": {
64
+ "voice": "en_US/vctk_low#p247",
65
+ "emotion": [
66
+ 0.18136803060770035,
67
+ 0.3469696491956711,
68
+ 0.3744945675134659
69
+ ],
70
+ "tgt_wav": "assets/wavs/en_US_vctk_p247.wav",
71
+ "affect_wav": "assets/wavs/en_US_vctk_p247_affect.wav",
72
+ "fig_file": "assets/en_US_vctk_p247.png",
73
+ "str_voice": "en_US_vctk_p247"
74
+ },
75
+ "6": {
76
+ "voice": "en_US/vctk_low#p261",
77
+ "emotion": [
78
+ 0.018009521067142487,
79
+ 0.20371025055646896,
80
+ 0.3044930547475815
81
+ ],
82
+ "tgt_wav": "assets/wavs/en_US_vctk_p261.wav",
83
+ "affect_wav": "assets/wavs/en_US_vctk_p261_affect.wav",
84
+ "fig_file": "assets/en_US_vctk_p261.png",
85
+ "str_voice": "en_US_vctk_p261"
86
+ },
87
+ "7": {
88
+ "voice": "en_US/vctk_low#p263",
89
+ "emotion": [
90
+ 0.19148042052984238,
91
+ 0.3607906401157379,
92
+ 0.4013771116733551
93
+ ],
94
+ "tgt_wav": "assets/wavs/en_US_vctk_p263.wav",
95
+ "affect_wav": "assets/wavs/en_US_vctk_p263_affect.wav",
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