fh2207/ai-voice-cloning-de
1
0
Fork 0
forked from mrq/ai-voice-cloning
Code Pull Requests Activity

added dropdown to select autoregressive model for TTS, fixed a bug where the settings saveer constantly fires I hate gradio so much why are dropdown.change broken to contiuously fire and send an empty array

Browse Source
This commit is contained in:
mrq 2023-02-18 14:10:26 +00:00
parent a9bd17c353
commit 2615cafd75
3 changed files with 75 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

0
models/finetunes/.gitkeep Executable file
View File

57
src/utils.py
View File

@ -62,6 +62,7 @@ def setup_args():
'defer-tts-load': False,
'device-override': None,
'whisper-model': "base",
'autoregressive-model': None,
'concurrency-count': 2,
'output-sample-rate': 44100,
'output-volume': 1,
@ -87,6 +88,7 @@ def setup_args():
parser.add_argument("--defer-tts-load", default=default_arguments['defer-tts-load'], action='store_true', help="Defers loading TTS model")
parser.add_argument("--device-override", default=default_arguments['device-override'], help="A device string to override pass through Torch")
parser.add_argument("--whisper-model", default=default_arguments['whisper-model'], help="Specifies which whisper model to use for transcription.")
parser.add_argument("--autoregressive-model", default=default_arguments['autoregressive-model'], help="Specifies which autoregressive model to use for sampling.")
parser.add_argument("--sample-batch-size", default=default_arguments['sample-batch-size'], type=int, help="Sets how many batches to use during the autoregressive samples pass")
parser.add_argument("--concurrency-count", type=int, default=default_arguments['concurrency-count'], help="How many Gradio events to process at once")
parser.add_argument("--output-sample-rate", type=int, default=default_arguments['output-sample-rate'], help="Sample rate to resample the output to (from 24KHz)")
@ -151,10 +153,8 @@ def generate(
global args
global tts
try:
tts
except NameError:
raise Exception("TTS is still initializing...")
if not tts:
raise Exception("TTS is uninitialized or still initializing...")
if voice != "microphone":
voices = [voice]
@ -493,7 +493,7 @@ def setup_tortoise(restart=False):
tts = None
print("Initializating TorToiSe...")
tts = TextToSpeech(minor_optimizations=not args.low_vram)
tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=args.autoregressive_model)
get_model_path('dvae.pth')
print("TorToiSe initialized, ready for generation.")
return tts
@ -720,7 +720,47 @@ def get_voice_list(dir=get_voice_dir()):
os.makedirs(dir, exist_ok=True)
return sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 ]) + ["microphone", "random"]
def export_exec_settings( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, defer_tts_load, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume, whisper_model ):
def get_autoregressive_models(dir="./models/finetuned/"):
os.makedirs(dir, exist_ok=True)
return [get_model_path('autoregressive.pth')] + sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 ])
def update_autoregressive_model(path_name):
global tts
if not tts:
raise Exception("TTS is uninitialized or still initializing...")
print(f"Loading model: {path_name}")
if hasattr(tts, 'load_autoregressive_model') and tts.load_autoregressive_model(path_name):
args.autoregressive_model = path_name
save_args_settings()
# polyfill in case a user did NOT update the packages
else:
from tortoise.models.autoregressive import UnifiedVoice
previous_path = tts.autoregressive_model_path
tts.autoregressive_model_path = path_name if path_name and os.path.exists(path_name) else get_model_path('autoregressive.pth')
del tts.autoregressive
tts.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30,
model_dim=1024,
heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False,
train_solo_embeddings=False).cpu().eval()
tts.autoregressive.load_state_dict(torch.load(tts.autoregressive_model_path))
tts.autoregressive.post_init_gpt2_config(kv_cache=tts.use_kv_cache)
if tts.preloaded_tensors:
tts.autoregressive = tts.autoregressive.to(tts.device)
if previous_path != tts.autoregressive_model_path:
args.autoregressive_model = path_name
save_args_settings()
print(f"Loaded model: {tts.autoregressive_model_path}")
return path_name
def update_args( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, defer_tts_load, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume ):
global args
args.listen = listen
@ -731,7 +771,6 @@ def export_exec_settings( listen, share, check_for_updates, models_from_local_on
args.force_cpu_for_conditioning_latents = force_cpu_for_conditioning_latents
args.defer_tts_load = defer_tts_load
args.device_override = device_override
args.whisper_model = whisper_model
args.sample_batch_size = sample_batch_size
args.embed_output_metadata = embed_output_metadata
args.latents_lean_and_mean = latents_lean_and_mean
@ -741,6 +780,9 @@ def export_exec_settings( listen, share, check_for_updates, models_from_local_on
args.output_sample_rate = output_sample_rate
args.output_volume = output_volume
save_args_settings()
def save_args_settings():
settings = {
'listen': None if args.listen else args.listen,
'share': args.share,
@ -751,6 +793,7 @@ def export_exec_settings( listen, share, check_for_updates, models_from_local_on
'defer-tts-load': args.defer_tts_load,
'device-override': args.device_override,
'whisper-model': args.whisper_model,
'autoregressive-model': args.autoregressive_model,
'sample-batch-size': args.sample_batch_size,
'embed-output-metadata': args.embed_output_metadata,
'latents-lean-and-mean': args.latents_lean_and_mean,

32
src/webui.py
View File

@ -90,10 +90,8 @@ def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm
global tts
global args
try:
tts
except NameError:
raise gr.Error("TTS is still initializing...")
if not tts:
raise Exception("TTS is uninitialized or still initializing...")
voice_samples, conditioning_latents = load_voice(voice, load_latents=False)
@ -213,6 +211,14 @@ def update_voices():
def history_copy_settings( voice, file ):
return import_generate_settings( f"./results/{voice}/{file}" )
def update_model_settings( autoregressive_model, whisper_model ):
if args.autoregressive_model != autoregressive_model:
update_autoregressive_model(autoregressive_model)
args.whisper_model = whisper_model
save_args_settings()
def setup_gradio():
global args
global ui
@ -370,13 +376,17 @@ def setup_gradio():
gr.Number(label="Concurrency Count", precision=0, value=args.concurrency_count),
gr.Number(label="Ouptut Sample Rate", precision=0, value=args.output_sample_rate),
gr.Slider(label="Ouptut Volume", minimum=0, maximum=2, value=args.output_volume),
gr.Dropdown(label="Whisper Model", value=args.whisper_model, choices=["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large"]),
]
autoregressive_model_dropdown = gr.Dropdown(get_autoregressive_models(), label="Autoregressive Model", value=args.autoregressive_model)
whisper_model_dropdown = gr.Dropdown(["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large"], label="Whisper Model", value=args.whisper_model)
save_settings_button = gr.Button(value="Save Settings")
gr.Button(value="Check for Updates").click(check_for_updates)
gr.Button(value="Reload TTS").click(reload_tts)
gr.Button(value="(Re)Load TTS").click(reload_tts)
for i in exec_inputs:
i.change( fn=export_exec_settings, inputs=exec_inputs )
i.change( fn=update_args, inputs=exec_inputs )
# console_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
@ -533,6 +543,14 @@ def setup_gradio():
outputs=save_yaml_output #console_output
)
save_settings_button.click(update_model_settings,
inputs=[
autoregressive_model_dropdown,
whisper_model_dropdown,
],
outputs=None
)
if os.path.isfile('./config/generate.json'):
ui.load(import_generate_settings, inputs=None, outputs=input_settings)