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Add a way to get deterministic behavior from tortoise and add debug states for reporting

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This commit is contained in:
James Betker 2022-05-17 12:11:18 -06:00
parent 93d0ce60d3
commit a1ae84c49d
4 changed files with 52 additions and 8 deletions
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3
.gitignore vendored
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@ -131,4 +131,5 @@ dmypy.json
.idea/*
.models/*
.custom/*
results/*
results/*
debug_states/*

31
tortoise/api.py
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@ -1,6 +1,7 @@
import os
import random
import uuid
from time import time
from urllib import request
import torch
@ -304,7 +305,8 @@ class TextToSpeech:
kwargs.update(presets[preset])
return self.tts(text, **kwargs)
def tts(self, text, voice_samples=None, conditioning_latents=None, k=1, verbose=True,
def tts(self, text, voice_samples=None, conditioning_latents=None, k=1, verbose=True, use_deterministic_seed=None,
return_deterministic_state=False,
# autoregressive generation parameters follow
num_autoregressive_samples=512, temperature=.8, length_penalty=1, repetition_penalty=2.0, top_p=.8, max_mel_tokens=500,
# CLVP & CVVP parameters
@ -359,6 +361,8 @@ class TextToSpeech:
:return: Generated audio clip(s) as a torch tensor. Shape 1,S if k=1 else, (k,1,S) where S is the sample length.
Sample rate is 24kHz.
"""
deterministic_seed = self.deterministic_state(seed=use_deterministic_seed)
text_tokens = torch.IntTensor(self.tokenizer.encode(text)).unsqueeze(0).cuda()
text_tokens = F.pad(text_tokens, (0, 1)) # This may not be necessary.
assert text_tokens.shape[-1] < 400, 'Too much text provided. Break the text up into separate segments and re-try inference.'
@ -465,7 +469,26 @@ class TextToSpeech:
return self.aligner.redact(clip.squeeze(1), text).unsqueeze(1)
return clip
wav_candidates = [potentially_redact(wav_candidate, text) for wav_candidate in wav_candidates]
if len(wav_candidates) > 1:
return wav_candidates
return wav_candidates[0]
if len(wav_candidates) > 1:
res = wav_candidates
else:
res = wav_candidates[0]
if return_deterministic_state:
return res, (deterministic_seed, text, voice_samples, conditioning_latents)
else:
return res
def deterministic_state(self, seed=None):
"""
Sets the random seeds that tortoise uses to the current time() and returns that seed so results can be
reproduced.
"""
seed = int(time()) if seed is None else seed
torch.manual_seed(seed)
random.seed(seed)
# Can't currently set this because of CUBLAS. TODO: potentially enable it if necessary.
# torch.use_deterministic_algorithms(True)
return seed

12
tortoise/do_tts.py
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@ -1,6 +1,7 @@
import argparse
import os
import torch
import torchaudio
from api import TextToSpeech
@ -19,6 +20,8 @@ if __name__ == '__main__':
parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this'
'should only be specified if you have custom checkpoints.', default='.models')
parser.add_argument('--candidates', type=int, help='How many output candidates to produce per-voice.', default=3)
parser.add_argument('--seed', type=int, help='Random seed which can be used to reproduce results.', default=None)
parser.add_argument('--produce_debug_state', type=bool, help='Whether or not to produce debug_state.pth, which can aid in reproducing problems. Defaults to true.', default=True)
args = parser.parse_args()
os.makedirs(args.output_path, exist_ok=True)
@ -27,11 +30,16 @@ if __name__ == '__main__':
selected_voices = args.voice.split(',')
for k, voice in enumerate(selected_voices):
voice_samples, conditioning_latents = load_voice(voice)
gen = tts.tts_with_preset(args.text, k=args.candidates, voice_samples=voice_samples, conditioning_latents=conditioning_latents,
preset=args.preset, clvp_cvvp_slider=args.voice_diversity_intelligibility_slider)
gen, dbg_state = tts.tts_with_preset(args.text, k=args.candidates, voice_samples=voice_samples, conditioning_latents=conditioning_latents,
preset=args.preset, clvp_cvvp_slider=args.voice_diversity_intelligibility_slider,
use_deterministic_seed=args.seed, return_deterministic_state=True)
if isinstance(gen, list):
for j, g in enumerate(gen):
torchaudio.save(os.path.join(args.output_path, f'{voice}_{k}_{j}.wav'), g.squeeze(0).cpu(), 24000)
else:
torchaudio.save(os.path.join(args.output_path, f'{voice}_{k}.wav'), gen.squeeze(0).cpu(), 24000)
if args.produce_debug_state:
os.makedirs('debug_states', exist_ok=True)
torch.save(dbg_state, f'debug_states/do_tts_debug_{voice}.pth')

14
tortoise/read.py
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@ -1,5 +1,6 @@
import argparse
import os
from time import time
import torch
import torchaudio
@ -22,6 +23,9 @@ if __name__ == '__main__':
default=.5)
parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this'
'should only be specified if you have custom checkpoints.', default='.models')
parser.add_argument('--seed', type=int, help='Random seed which can be used to reproduce results.', default=None)
parser.add_argument('--produce_debug_state', type=bool, help='Whether or not to produce debug_state.pth, which can aid in reproducing problems. Defaults to true.', default=True)
args = parser.parse_args()
tts = TextToSpeech(models_dir=args.model_dir)
@ -41,6 +45,7 @@ if __name__ == '__main__':
else:
texts = split_and_recombine_text(text)
seed = int(time()) if args.seed is None else args.seed
for selected_voice in selected_voices:
voice_outpath = os.path.join(outpath, selected_voice)
os.makedirs(voice_outpath, exist_ok=True)
@ -57,10 +62,17 @@ if __name__ == '__main__':
all_parts.append(load_audio(os.path.join(voice_outpath, f'{j}.wav'), 24000))
continue
gen = tts.tts_with_preset(text, voice_samples=voice_samples, conditioning_latents=conditioning_latents,
preset=args.preset, clvp_cvvp_slider=args.voice_diversity_intelligibility_slider)
preset=args.preset, clvp_cvvp_slider=args.voice_diversity_intelligibility_slider,
use_deterministic_seed=seed)
gen = gen.squeeze(0).cpu()
torchaudio.save(os.path.join(voice_outpath, f'{j}.wav'), gen, 24000)
all_parts.append(gen)
full_audio = torch.cat(all_parts, dim=-1)
torchaudio.save(os.path.join(voice_outpath, 'combined.wav'), full_audio, 24000)
if args.produce_debug_state:
os.makedirs('debug_states', exist_ok=True)
dbg_state = (seed, texts, voice_samples, conditioning_latents)
torch.save(dbg_state, f'debug_states/read_debug_{selected_voice}.pth')