draft various spk
Browse files- mimic3_make_harvard_sentences.py +8 -3
- tts_harvard.py +82 -44
- visualize_tts_plesantness.py +7 -7
- voices.json +1612 -0
mimic3_make_harvard_sentences.py
CHANGED
@@ -75,6 +75,7 @@ list_voices = [
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] # special - for human we load specific style file - no Mimic3 is run
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# ================================================== INTERFACE MODELS
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LABELS = [
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'arousal', 'dominance', 'valence',
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@@ -296,7 +297,7 @@ for _id, _voice in enumerate(list_voices):
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total_audio_mimic3 = []
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total_audio_styletts2 = []
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ix = 0
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-
for list_of_10 in harvard_individual_sentences[:
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text = ' '.join(list_of_10['sentences'])
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@@ -367,8 +368,12 @@ for _id, _voice in enumerate(list_voices):
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# MIMIC3 = = = = = = = = = = = = = = END
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-
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-
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style_vec = msinference.compute_style(style_path) # use mimic-3 as prompt
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] # special - for human we load specific style file - no Mimic3 is run
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+
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# ================================================== INTERFACE MODELS
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LABELS = [
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'arousal', 'dominance', 'valence',
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total_audio_mimic3 = []
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total_audio_styletts2 = []
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ix = 0
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+
for list_of_10 in harvard_individual_sentences[:1000]: # 77
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text = ' '.join(list_of_10['sentences'])
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# MIMIC3 = = = = = = = = = = = = = = END
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if 'en_US' in _str:
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style_path = 'mimic3_english_4x/' + _str + '.wav'
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+
elif ('de_DE' in _str) or ('fr_FR' in _str):
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style_path = 'mimic3_foreign_4x/' + _str + '.wav'
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else:
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print(f'use human / generated style for {_str}')
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style_vec = msinference.compute_style(style_path) # use mimic-3 as prompt
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tts_harvard.py
CHANGED
@@ -1,42 +1,49 @@
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-
# Synthesize all Harvard Lists
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#
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# 1.
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#
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#
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#
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# 2.
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#
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# './prompt_mimic3_english_4x/'
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#
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# 3.
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#
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# './prompt_human/'
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#
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# 4.
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#
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# './prompt_mimic3_foreign/'
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#
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# 5.
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#
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# './prompt_mimic3_foreign_4x/
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#
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#
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# ----> THE .wavs will be used for visualisation
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import soundfile
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import json
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import numpy as np
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import audb
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from pathlib import Path
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-
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LABELS = ['arousal', 'dominance', 'valence']
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-
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DB = [
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# [dataset, version, table, has_timdeltas_or_is_full_wavfile]
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# ['crema-d', '1.1.1', 'emotion.voice.test', False],
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-
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]
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output_list = []
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@@ -58,38 +65,65 @@ def load_human_speech(split=None):
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output_list += [f for f in a.index] # use file (no timedeltas)
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return output_list
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# SYNTHESIZE mimic mimicx4 crema-d
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import msinference
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-
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with open('harvard.json', 'r') as f:
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harvard_individual_sentences = json.load(f)['sentences']
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for audio_prompt in [#'mimic3_english',
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#'mimic3_english_4x',
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'human',
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'mimic3_foreign',
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-
'mimic3_foreign_4x']:
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-
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if audio_prompt == 'human':
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prompt_paths = load_human_speech() # better emodb ?
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-
else:
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prompt_dir = '/data/dkounadis/artificial-styletts2/' + audio_prompt + '/'
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prompt_paths = [prompt_dir + f for f in os.listdir(prompt_dir)]
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prompt_paths = prompt_paths[:10]
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print(prompt_paths,'\n\n__________')
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total_audio = []
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ix = 0
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-
for list_of_10 in harvard_individual_sentences
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# long_sentence = ' '.join(list_of_10['sentences'])
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# harvard.append(long_sentence.replace('.', ' '))
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for text in list_of_10['sentences']:
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-
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print(ix, text)
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ix += 1
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x = msinference.inference(text,
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@@ -100,6 +134,10 @@ for audio_prompt in [#'mimic3_english',
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embedding_scale=1)
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total_audio.append(x)
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-
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-
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+
# Synthesize all Harvard Lists 77x lists of 10x sentences to single .wav
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#
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+
# 1. using mimic3 style
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+
# Folder: 'prompt_mimic3/'
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+
# 2. using mimic3 4x accelerated style
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+
# Folder: 'prompt_mimic3speed/'
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+
# 3. using crema-d style
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# Folder: 'prompt_human/'
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#
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+
# WAVS used from tts_paper_plot.py
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import soundfile
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import json
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import numpy as np
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import audb
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from pathlib import Path
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+
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LABELS = ['arousal', 'dominance', 'valence']
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+
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+
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+
def load_speech(split=None):
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DB = [
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# [dataset, version, table, has_timdeltas_or_is_full_wavfile]
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# ['crema-d', '1.1.1', 'emotion.voice.test', False],
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+
['librispeech', '3.1.0', 'train-clean-360', False],
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# ['emodb', '1.2.0', 'emotion.categories.train.gold_standard', False],
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# ['entertain-playtestcloud', '1.1.0', 'emotion.categories.train.gold_standard', True],
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# ['erik', '2.2.0', 'emotion.categories.train.gold_standard', True],
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# ['meld', '1.3.1', 'emotion.categories.train.gold_standard', False],
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# ['msppodcast', '5.0.0', 'emotion.categories.train.gold_standard', False], # tandalone bucket because it has gt labels?
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# ['myai', '1.0.1', 'emotion.categories.train.gold_standard', False],
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# ['casia', None, 'emotion.categories.gold_standard', False],
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# ['switchboard-1', None, 'sentiment', True],
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# ['swiss-parliament', None, 'segments', True],
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# ['argentinian-parliament', None, 'segments', True],
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# ['austrian-parliament', None, 'segments', True],
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# #'german', --> bundestag
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# ['brazilian-parliament', None, 'segments', True],
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# ['mexican-parliament', None, 'segments', True],
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# ['portuguese-parliament', None, 'segments', True],
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# ['spanish-parliament', None, 'segments', True],
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# ['chinese-vocal-emotions-liu-pell', None, 'emotion.categories.desired', False],
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# peoples-speech slow
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# ['peoples-speech', None, 'train-initial', False]
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]
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output_list = []
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output_list += [f for f in a.index] # use file (no timedeltas)
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return output_list
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+
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# Generate 77 wavs
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with open('voices.json', 'r') as f:
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df = json.load(f)['voices']
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voice_names = [v['voice'] for k,v in df.items()]
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+
synthetic_wav_paths = []
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+
synthetic_wav_paths_AFFECT = []
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for voice in voice_names:
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synthetic_wav_paths.append(
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'/data/dkounadis/shift/assets/wavs/style_vector/' + voice.replace('/', '_').replace('#', '_').replace(
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'cmu-arctic', 'cmu_arctic').replace('_low', '') + '.wav')
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+
synthetic_wav_paths_AFFECT.append(
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'/data/dkounadis/shift/assets/wavs/style_vector_v2/' + voice.replace('/', '_').replace('#', '_').replace(
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'cmu-arctic', 'cmu_arctic').replace('_low', '') + '.wav')
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+
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print(len(synthetic_wav_paths))
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+
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+
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+
natural_wav_paths = load_speech()
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# SYNTHESIZE mimic mimicx4 crema-d
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import msinference
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+
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with open('harvard.json', 'r') as f:
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harvard_individual_sentences = json.load(f)['sentences']
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+
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for audio_prompt in ['mimic3', 'mimic3_speed', 'human']:
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total_audio = []
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ix = 0
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+
for list_of_10 in harvard_individual_sentences:
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# long_sentence = ' '.join(list_of_10['sentences'])
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# harvard.append(long_sentence.replace('.', ' '))
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for text in list_of_10['sentences']:
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+
if audio_prompt == 'mimic3':
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+
style_vec = msinference.compute_style(
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+
synthetic_wav_paths[ix % 134])
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+
elif audio_prompt == 'mimic3_speed':
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+
style_vec = msinference.compute_style(
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+
synthetic_wav_paths_AFFECT[ix % 134])
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+
elif audio_prompt == 'human':
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+
style_vec = msinference.compute_style(
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natural_wav_paths[ix % len(natural_wav_paths)])
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+
else:
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+
print('unknonw list of style vecto')
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print(ix, text)
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ix += 1
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x = msinference.inference(text,
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embedding_scale=1)
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total_audio.append(x)
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+
# concat before write
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+
# -- for 10x sentenctes
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+
print('_____________________')
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+
# -- for 77x lists
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+
total_audio = np.concatenate(total_audio)
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+
soundfile.write(f'{audio_prompt}_770.wav', total_audio, 24000)
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+
print(f'{audio_prompt}_full_770.wav')
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visualize_tts_plesantness.py
CHANGED
@@ -81,12 +81,12 @@ def _sigmoid(x):
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# for mimic3/mimic3speed/human - concat all 77 and run timeseries with 7s hop 3s
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for long_audio in [
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-
'
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'
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-
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-
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-
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-
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file_interface = f'timeseries_{long_audio.replace("/", "")}.pkl'
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if not os.path.exists(file_interface):
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@@ -106,7 +106,7 @@ for long_audio in [
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# pool
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h = self.pool_model.sap_linear(x).tanh()
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w = torch.matmul(h, self.pool_model.attention)
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-
w =
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mu = (x * w).sum(1)
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x = torch.cat(
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[
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# for mimic3/mimic3speed/human - concat all 77 and run timeseries with 7s hop 3s
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for long_audio in [
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+
# 'mimic3.wav',
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+
# 'mimic3_speedup.wav',
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+
'human_770.wav', # 'mimic3_all_77.wav', #
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+
'mimic3_770.wav',
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+
'mimic3_speed_770.wav'
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+
]:
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file_interface = f'timeseries_{long_audio.replace("/", "")}.pkl'
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if not os.path.exists(file_interface):
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# pool
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h = self.pool_model.sap_linear(x).tanh()
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w = torch.matmul(h, self.pool_model.attention)
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+
w = w.softmax(1)
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mu = (x * w).sum(1)
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x = torch.cat(
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[
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voices.json
ADDED
@@ -0,0 +1,1612 @@
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1 |
+
{
|
2 |
+
"voices": {
|
3 |
+
"0": {
|
4 |
+
"voice": "en_US/vctk_low#p239",
|
5 |
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"emotion": [
|
6 |
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|
7 |
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0.18340148776769638,
|
8 |
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0.3238210380077362
|
9 |
+
],
|
10 |
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