mrq/tortoise-tts
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updates to scripts

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This commit is contained in:
James Betker 2022-04-20 17:24:09 -06:00
parent d7f81617b3
commit eef09d4e8f
3 changed files with 16 additions and 14 deletions
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2
api.py
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@ -202,7 +202,7 @@ class TextToSpeech:
'ultra_fast': {'num_autoregressive_samples': 32, 'diffusion_iterations': 16, 'cond_free': False},
'fast': {'num_autoregressive_samples': 96, 'diffusion_iterations': 32},
'standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 128},
'high_quality': {'num_autoregressive_samples': 512, 'diffusion_iterations': 2048},
'high_quality': {'num_autoregressive_samples': 512, 'diffusion_iterations': 1024},
}
kwargs.update(presets[preset])
return self.tts(text, voice_samples, **kwargs)

6
do_tts.py
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@ -11,6 +11,10 @@ if __name__ == '__main__':
parser.add_argument('--text', type=str, help='Text to speak.', default="I am a language model that has learned to speak.")
parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) '
'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='patrick_stewart')
parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='standard')
parser.add_argument('--voice_diversity_intelligibility_slider', type=float,
help='How to balance vocal diversity with the quality/intelligibility of the spoken text. 0 means highly diverse voice (not recommended), 1 means maximize intellibility',
default=.5)
parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/')
args = parser.parse_args()
os.makedirs(args.output_path, exist_ok=True)
@ -25,6 +29,6 @@ if __name__ == '__main__':
for cond_path in cond_paths:
c = load_audio(cond_path, 22050)
conds.append(c)
gen = tts.tts_with_preset(args.text, conds, preset='standard')
gen = tts.tts_with_preset(args.text, conds, preset=args.preset, clvp_cvvp_slider=args.voice_diversity_intelligibility_slider)
torchaudio.save(os.path.join(args.output_path, f'{voice}.wav'), gen.squeeze(0).cpu(), 24000)

22
read.py
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@ -28,11 +28,14 @@ def split_and_recombine_text(texts, desired_length=200, max_len=300):
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--textfile', type=str, help='A file containing the text to read.', default="data/riding_hood2.txt")
parser.add_argument('--textfile', type=str, help='A file containing the text to read.', default="data/riding_hood.txt")
parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) '
'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='patrick_stewart')
parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/longform/')
parser.add_argument('--generation_preset', type=str, help='Preset to use for generation', default='standard')
parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='standard')
parser.add_argument('--voice_diversity_intelligibility_slider', type=float,
help='How to balance vocal diversity with the quality/intelligibility of the spoken text. 0 means highly diverse voice (not recommended), 1 means maximize intellibility',
default=.5)
args = parser.parse_args()
outpath = args.output_path
@ -60,16 +63,11 @@ if __name__ == '__main__':
if not cond_paths:
print('Error: no valid voices specified. Try again.')
priors = []
conds = []
for cond_path in cond_paths:
c = load_audio(cond_path, 22050)
conds.append(c)
for j, text in enumerate(texts):
conds = priors.copy()
for cond_path in cond_paths:
c = load_audio(cond_path, 22050)
conds.append(c)
gen = tts.tts_with_preset(text, conds, preset=args.generation_preset)
gen = tts.tts_with_preset(text, conds, preset=args.preset, clvp_cvvp_slider=args.voice_diversity_intelligibility_slider)
torchaudio.save(os.path.join(voice_outpath, f'{j}.wav'), gen.squeeze(0).cpu(), 24000)
priors.append(torchaudio.functional.resample(gen, 24000, 22050).squeeze(0))
while len(priors) > 2:
priors.pop(0)