Jarod/ai-voice-cloning
1
1
Fork 0
forked from mrq/ai-voice-cloning
Code Pull Requests Activity

editted utils and webui for hifigan

Browse Source
This commit is contained in:
Jarod Mica 2023-11-26 18:45:56 -08:00
parent 94f88886b0
commit 7b8cf348c8
2 changed files with 41 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

50
src/utils.py
View File

@ -38,6 +38,7 @@ from datetime import datetime
from datetime import timedelta
from tortoise.api import TextToSpeech as TorToise_TTS, MODELS, get_model_path, pad_or_truncate
from tortoise.api_fast import TextToSpeech as Toroise_TTS_Hifi
from tortoise.utils.audio import load_audio, load_voice, load_voices, get_voice_dir, get_voices
from tortoise.utils.text import split_and_recombine_text
from tortoise.utils.device import get_device_name, set_device_name, get_device_count, get_device_vram, get_device_batch_size, do_gc
@ -1073,10 +1074,11 @@ def generate_tortoise(**kwargs):
settings['autoregressive_model'] = deduce_autoregressive_model(selected_voice)
tts.load_autoregressive_model(settings['autoregressive_model'])
if settings['diffusion_model'] is not None:
if settings['diffusion_model'] == "auto":
settings['diffusion_model'] = deduce_diffusion_model(selected_voice)
tts.load_diffusion_model(settings['diffusion_model'])
if not args.use_hifigan:
if settings['diffusion_model'] is not None:
if settings['diffusion_model'] == "auto":
settings['diffusion_model'] = deduce_diffusion_model(selected_voice)
tts.load_diffusion_model(settings['diffusion_model'])
if settings['tokenizer_json'] is not None:
tts.load_tokenizer_json(settings['tokenizer_json'])
@ -1180,7 +1182,9 @@ def generate_tortoise(**kwargs):
latents_path = f'{dir}/cond_latents_{model_hash}.pth'
if voice == "random" or voice == "microphone":
if latents and settings is not None and settings['conditioning_latents']:
# if latents and settings is not None and settings['conditioning_latents']:
if latents and settings is not None and torch.any(settings['conditioning_latents']):
os.makedirs(dir, exist_ok=True)
torch.save(conditioning_latents, latents_path)
@ -1220,9 +1224,16 @@ def generate_tortoise(**kwargs):
raise Exception("Prompt settings editing requested, but received invalid JSON")
settings = get_settings( override=override )
gen, additionals = tts.tts(cut_text, **settings )
parameters['seed'] = additionals[0]
print(settings)
try:
if args.use_hifigan:
gen = tts.tts(cut_text, **settings)
else:
gen, additionals = tts.tts(cut_text, **settings )
parameters['seed'] = additionals[0]
except Exception as e:
raise RuntimeError(f'Possible latent mismatch: click the "(Re)Compute Voice Latents" button and then try again. Error: {e}')
run_time = time.time()-start_time
print(f"Generating line took {run_time} seconds")
@ -3293,6 +3304,7 @@ def setup_args(cli=False):
'latents-lean-and-mean': True,
'voice-fixer': False, # getting tired of long initialization times in a Colab for downloading a large dataset for it
'use-deepspeed': False,
'use-hifigan': False,
'voice-fixer-use-cuda': True,
@ -3352,6 +3364,8 @@ def setup_args(cli=False):
parser.add_argument("--voice-fixer", action='store_true', default=default_arguments['voice-fixer'], help="Uses python module 'voicefixer' to improve audio quality, if available.")
parser.add_argument("--voice-fixer-use-cuda", action='store_true', default=default_arguments['voice-fixer-use-cuda'], help="Hints to voicefixer to use CUDA, if available.")
parser.add_argument("--use-deepspeed", action='store_true', default=default_arguments['use-deepspeed'], help="Use deepspeed for speed bump.")
parser.add_argument("--use-hifigan", action='store_true', default=default_arguments['use-hifigan'], help="Use Hifigan instead of Diffusion")
parser.add_argument("--force-cpu-for-conditioning-latents", default=default_arguments['force-cpu-for-conditioning-latents'], action='store_true', help="Forces computing conditional latents to be done on the CPU (if you constantyl OOM on low chunk counts)")
parser.add_argument("--defer-tts-load", default=default_arguments['defer-tts-load'], action='store_true', help="Defers loading TTS model")
parser.add_argument("--prune-nonfinal-outputs", default=default_arguments['prune-nonfinal-outputs'], action='store_true', help="Deletes non-final output files on completing a generation")
@ -3437,6 +3451,7 @@ def get_default_settings( hypenated=True ):
'latents-lean-and-mean': args.latents_lean_and_mean,
'voice-fixer': args.voice_fixer,
'use-deepspeed': args.use_deepspeed,
'use-hifigan': args.use_hifigan,
'voice-fixer-use-cuda': args.voice_fixer_use_cuda,
'concurrency-count': args.concurrency_count,
'output-sample-rate': args.output_sample_rate,
@ -3491,6 +3506,7 @@ def update_args( **kwargs ):
args.voice_fixer = settings['voice_fixer']
args.voice_fixer_use_cuda = settings['voice_fixer_use_cuda']
args.use_deepspeed = settings['use_deepspeed']
args.use_hifigan = settings['use_hifigan']
args.concurrency_count = settings['concurrency_count']
args.output_sample_rate = 44000
args.autocalculate_voice_chunk_duration_size = settings['autocalculate_voice_chunk_duration_size']
@ -3662,8 +3678,22 @@ def load_tts( restart=False,
if get_device_name() == "cpu":
print("!!!! WARNING !!!! No GPU available in PyTorch. You may need to reinstall PyTorch.")
print(f"Loading TorToiSe... (AR: {autoregressive_model}, diffusion: {diffusion_model}, vocoder: {vocoder_model})")
tts = TorToise_TTS(minor_optimizations=not args.low_vram, autoregressive_model_path=autoregressive_model, diffusion_model_path=diffusion_model, vocoder_model=vocoder_model, tokenizer_json=tokenizer_json, unsqueeze_sample_batches=args.unsqueeze_sample_batches, use_deepspeed=args.use_deepspeed)
if args.use_hifigan:
print("Loading Tortoise with Hifigan")
tts = Toroise_TTS_Hifi(autoregressive_model_path=autoregressive_model,
tokenizer_json=tokenizer_json,
use_deepspeed=args.use_deepspeed)
else:
print(f"Loading TorToiSe... (AR: {autoregressive_model}, diffusion: {diffusion_model}, vocoder: {vocoder_model})")
tts = TorToise_TTS(minor_optimizations=not args.low_vram,
autoregressive_model_path=autoregressive_model,
diffusion_model_path=diffusion_model,
vocoder_model=vocoder_model,
tokenizer_json=tokenizer_json,
unsqueeze_sample_batches=args.unsqueeze_sample_batches,
use_deepspeed=args.use_deepspeed)
elif args.tts_backend == "vall-e":
if valle_model:
args.valle_model = valle_model

1
src/webui.py
View File

@ -644,6 +644,7 @@ def setup_gradio():
EXEC_SETTINGS['latents_lean_and_mean'] = gr.Checkbox(label="Slimmer Computed Latents", value=args.latents_lean_and_mean)
EXEC_SETTINGS['voice_fixer'] = gr.Checkbox(label="Use Voice Fixer on Generated Output", value=args.voice_fixer)
EXEC_SETTINGS['use_deepspeed'] = gr.Checkbox(label="Use DeepSpeed for Speed Bump.", value=args.use_deepspeed)
EXEC_SETTINGS['use_hifigan'] = gr.Checkbox(label="Use Hifigan instead of Diffusion.", value=args.use_hifigan)
EXEC_SETTINGS['voice_fixer_use_cuda'] = gr.Checkbox(label="Use CUDA for Voice Fixer", value=args.voice_fixer_use_cuda)
EXEC_SETTINGS['force_cpu_for_conditioning_latents'] = gr.Checkbox(label="Force CPU for Conditioning Latents", value=args.force_cpu_for_conditioning_latents)
EXEC_SETTINGS['defer_tts_load'] = gr.Checkbox(label="Do Not Load TTS On Startup", value=args.defer_tts_load)