mrq/tortoise-tts
4
6
Fork 15
Code Issues 9 Pull Requests 1 Packages Projects Releases Wiki Activity

Update read.py to support multiple candidates

Browse Source
This commit is contained in:
James Betker 2022-05-22 05:26:01 -06:00
parent 8feb18b03f
commit e882484c4a
1 changed files with 11 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

14
tortoise/read.py
View File

@ -18,6 +18,7 @@ if __name__ == '__main__':
parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/longform/')
parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='standard')
parser.add_argument('--regenerate', type=str, help='Comma-separated list of clip numbers to re-generate, or nothing.', default=None)
parser.add_argument('--candidates', type=int, help='How many output candidates to produce per-voice. Only the first candidate is actually used in the final product, the others can be used manually.', default=1)
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_DIR)
parser.add_argument('--seed', type=int, help='Random seed which can be used to reproduce results.', default=None)
@ -59,9 +60,16 @@ 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, use_deterministic_seed=seed)
gen = gen.squeeze(0).cpu()
torchaudio.save(os.path.join(voice_outpath, f'{j}.wav'), gen, 24000)
preset=args.preset, k=args.candidates, use_deterministic_seed=seed)
if args.candidates == 1:
gen = gen.squeeze(0).cpu()
torchaudio.save(os.path.join(voice_outpath, f'{j}.wav'), gen, 24000)
else:
candidate_dir = os.path.join(voice_outpath, str(j))
os.makedirs(candidate_dir, exist_ok=True)
for k, g in enumerate(gen):
torchaudio.save(os.path.join(candidate_dir, f'{k}.wav'), g.squeeze(0).cpu(), 24000)
gen = gen[0].squeeze(0).cpu()
all_parts.append(gen)
full_audio = torch.cat(all_parts, dim=-1)