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Update sweep & eval_multiple with new voices

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This commit is contained in:
James Betker 2022-04-13 17:03:36 -06:00
parent 3214ca0dfe
commit 56f8385b99
4 changed files with 50 additions and 47 deletions
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13
api.py
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@ -140,6 +140,13 @@ class TextToSpeech:
average_conditioning_embeddings=True).cpu().eval()
self.autoregressive.load_state_dict(torch.load('.models/autoregressive_diverse.pth'))
self.autoregressive_for_latents = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30,
model_dim=1024,
heads=16, number_text_tokens=256, start_text_token=255, checkpointing=False,
train_solo_embeddings=False,
average_conditioning_embeddings=True).cpu().eval()
self.autoregressive_for_latents.load_state_dict(torch.load('.models/autoregressive_diverse.pth'))
self.clip = VoiceCLIP(dim_text=512, dim_speech=512, dim_latent=512, num_text_tokens=256, text_enc_depth=12,
text_seq_len=350, text_heads=8,
num_speech_tokens=8192, speech_enc_depth=12, speech_heads=8, speech_seq_len=430,
@ -221,11 +228,11 @@ class TextToSpeech:
# The diffusion model actually wants the last hidden layer from the autoregressive model as conditioning
# inputs. Re-produce those for the top results. This could be made more efficient by storing all of these
# results, but will increase memory usage.
self.autoregressive = self.autoregressive.cuda()
best_latents = self.autoregressive(conds, text, torch.tensor([text.shape[-1]], device=conds.device), best_results,
self.autoregressive_for_latents = self.autoregressive_for_latents.cuda()
best_latents = self.autoregressive_for_latents(conds, text, torch.tensor([text.shape[-1]], device=conds.device), best_results,
torch.tensor([best_results.shape[-1]*self.autoregressive.mel_length_compression], device=conds.device),
return_latent=True, clip_inputs=False)
self.autoregressive = self.autoregressive.cpu()
self.autoregressive_for_latents = self.autoregressive_for_latents.cpu()
print("Performing vocoding..")
wav_candidates = []

43
eval_multiple.py
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@ -6,32 +6,35 @@ from api import TextToSpeech
from utils.audio import load_audio
if __name__ == '__main__':
fname = 'Y:\\libritts\\test-clean\\transcribed-brief-w2v.tsv'
outpath = 'D:\\tmp\\tortoise-tts-eval\\diverse_new_decoder_1'
fname = 'Y:\\clips\\books2\\subset512-oco.tsv'
stop_after = 128
outpath_base = 'D:\\tmp\\tortoise-tts-eval\\diverse'
outpath_real = 'D:\\tmp\\tortoise-tts-eval\\real'
os.makedirs(outpath, exist_ok=True)
os.makedirs(outpath_real, exist_ok=True)
with open(fname, 'r', encoding='utf-8') as f:
lines = [l.strip().split('\t') for l in f.readlines()]
recorder = open(os.path.join(outpath, 'transcript.tsv'), 'w', encoding='utf-8')
tts = TextToSpeech()
for e, line in enumerate(lines):
transcript = line[0]
if len(transcript) > 120:
continue # We need to support this, but cannot yet.
path = os.path.join(os.path.dirname(fname), line[1])
cond_audio = load_audio(path, 22050)
torchaudio.save(os.path.join(outpath_real, os.path.basename(line[1])), cond_audio, 22050)
sample = tts.tts(transcript, [cond_audio, cond_audio], num_autoregressive_samples=256, k=1,
repetition_penalty=2.0, length_penalty=2, temperature=.5, top_p=.5,
diffusion_temperature=.7, cond_free_k=2, diffusion_iterations=100)
for k in range(4):
outpath = f'{outpath_base}_{k}'
os.makedirs(outpath, exist_ok=True)
recorder = open(os.path.join(outpath, 'transcript.tsv'), 'w', encoding='utf-8')
for e, line in enumerate(lines):
if e >= stop_after:
break
transcript = line[0]
path = os.path.join(os.path.dirname(fname), line[1])
cond_audio = load_audio(path, 22050)
torchaudio.save(os.path.join(outpath_real, os.path.basename(line[1])), cond_audio, 22050)
sample = tts.tts(transcript, [cond_audio, cond_audio], num_autoregressive_samples=128, k=1,
repetition_penalty=2.0, length_penalty=2, temperature=.5, top_p=.5,
diffusion_temperature=.7, cond_free_k=2, diffusion_iterations=70)
down = torchaudio.functional.resample(sample, 24000, 22050)
fout_path = os.path.join(outpath, os.path.basename(line[1]))
torchaudio.save(fout_path, down.squeeze(0), 22050)
down = torchaudio.functional.resample(sample, 24000, 22050)
fout_path = os.path.join(outpath, os.path.basename(line[1]))
torchaudio.save(fout_path, down.squeeze(0), 22050)
recorder.write(f'{transcript}\t{fout_path}\n')
recorder.flush()
recorder.close()
recorder.write(f'{transcript}\t{fout_path}\n')
recorder.flush()
recorder.close()

17
read.py
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@ -30,24 +30,13 @@ if __name__ == '__main__':
# These are voices drawn randomly from the training set. You are free to substitute your own voices in, but testing
# has shown that the model does not generalize to new voices very well.
preselected_cond_voices = {
# Male voices
'dotrice': ['voices/dotrice/1.wav', 'voices/dotrice/2.wav'],
'harris': ['voices/harris/1.wav', 'voices/harris/2.wav'],
'lescault': ['voices/lescault/1.wav', 'voices/lescault/2.wav'],
'otto': ['voices/otto/1.wav', 'voices/otto/2.wav'],
'obama': ['voices/obama/1.wav', 'voices/obama/2.wav'],
'carlin': ['voices/carlin/1.wav', 'voices/carlin/2.wav'],
# Female voices
'atkins': ['voices/atkins/1.wav', 'voices/atkins/2.wav'],
'grace': ['voices/grace/1.wav', 'voices/grace/2.wav'],
'kennard': ['voices/kennard/1.wav', 'voices/kennard/2.wav'],
'mol': ['voices/mol/1.wav', 'voices/mol/2.wav'],
'lj': ['voices/lj/1.wav', 'voices/lj/2.wav'],
'emma_stone': ['voices/emma_stone/1.wav','voices/emma_stone/2.wav','voices/emma_stone/3.wav'],
'tom_hanks': ['voices/tom_hanks/1.wav','voices/tom_hanks/2.wav','voices/tom_hanks/3.wav'],
}
parser = argparse.ArgumentParser()
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='Use a preset conditioning voice (defined above). Overrides cond_path.', default='dotrice')
parser.add_argument('-voice', type=str, help='Use a preset conditioning voice (defined above). Overrides cond_path.', default='emma_stone')
parser.add_argument('-num_samples', type=int, help='How many total outputs the autoregressive transformer should produce.', default=512)
parser.add_argument('-batch_size', type=int, help='How many samples to process at once in the autoregressive model.', default=16)
parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='results/longform/')

24
sweep.py
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@ -24,19 +24,24 @@ def permutations(args):
if __name__ == '__main__':
fname = 'Y:\\libritts\\test-clean\\transcribed-brief-w2v.tsv'
outpath_base = 'D:\\tmp\\tortoise-tts-eval\\std_sweep3'
fname = 'Y:\\clips\\books2\\subset512-oco.tsv'
stop_after = 128
outpath_base = 'D:\\tmp\\tortoise-tts-eval\\sweep'
outpath_real = 'D:\\tmp\\tortoise-tts-eval\\real'
arg_ranges = {
'top_p': [.3,.4,.5,.6],
'temperature': [.5, .6],
'top_p': [.5, 1],
'temperature': [.5, 1],
'diffusion_temperature': [.6, 1],
'cond_free_k': [0, 1, 4],
'repetition_penalty': [1.0, 2.0]
}
cfgs = permutations(arg_ranges)
shuffle(cfgs)
for cfg in cfgs:
outpath = os.path.join(outpath_base, f'{cfg["top_p"]}_{cfg["temperature"]}')
cfg_desc = '_'.join([f'{k}-{v}' for k,v in cfg.items()])
outpath = os.path.join(outpath_base, f'{cfg_desc}')
os.makedirs(outpath, exist_ok=True)
os.makedirs(outpath_real, exist_ok=True)
with open(fname, 'r', encoding='utf-8') as f:
@ -45,15 +50,14 @@ if __name__ == '__main__':
recorder = open(os.path.join(outpath, 'transcript.tsv'), 'w', encoding='utf-8')
tts = TextToSpeech()
for e, line in enumerate(lines):
if e >= stop_after:
break
transcript = line[0]
if len(transcript) > 120:
continue # We need to support this, but cannot yet.
path = os.path.join(os.path.dirname(fname), line[1])
cond_audio = load_audio(path, 22050)
torchaudio.save(os.path.join(outpath_real, os.path.basename(line[1])), cond_audio, 22050)
sample = tts.tts(transcript, [cond_audio, cond_audio], num_autoregressive_samples=256, k=1, diffusion_iterations=200,
repetition_penalty=2.0, length_penalty=2, temperature=.5, top_p=.5,
diffusion_temperature=.7, cond_free_k=2, **cfg)
sample = tts.tts(transcript, [cond_audio, cond_audio], num_autoregressive_samples=256,
k=1, diffusion_iterations=70, length_penalty=1.0, **cfg)
down = torchaudio.functional.resample(sample, 24000, 22050)
fout_path = os.path.join(outpath, os.path.basename(line[1]))
torchaudio.save(fout_path, down.squeeze(0), 22050)