forked from camenduru/ai-voice-cloning
I don't remember.
This commit is contained in:
parent
47abde224c
commit
6925ec731b
101
src/utils.py
101
src/utils.py
|
|
@ -34,8 +34,6 @@ from tortoise.utils.audio import load_audio, load_voice, load_voices, get_voice_
|
|||
from tortoise.utils.text import split_and_recombine_text
|
||||
from tortoise.utils.device import get_device_name, set_device_name
|
||||
|
||||
import whisper
|
||||
|
||||
MODELS['dvae.pth'] = "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/3704aea61678e7e468a06d8eea121dba368a798e/.models/dvae.pth"
|
||||
|
||||
args = None
|
||||
|
|
@ -46,7 +44,6 @@ voicefixer = None
|
|||
whisper_model = None
|
||||
training_state = None
|
||||
|
||||
|
||||
def generate(
|
||||
text,
|
||||
delimiter,
|
||||
|
|
@ -501,9 +498,12 @@ class TrainingState():
|
|||
match = re.findall(r'iter: ([\d,]+)', line)
|
||||
if match and len(match) > 0:
|
||||
self.it = int(match[0].replace(",", ""))
|
||||
|
||||
self.checkpoints = int((self.its - self.it) / self.config['logger']['save_checkpoint_freq'])
|
||||
else:
|
||||
lapsed = False
|
||||
|
||||
message = None
|
||||
if line.find('%|') > 0:
|
||||
match = re.findall(r'(\d+)%\|(.+?)\| (\d+|\?)\/(\d+|\?) \[(.+?)<(.+?), +(.+?)\]', line)
|
||||
if match and len(match) > 0:
|
||||
|
|
@ -516,8 +516,6 @@ class TrainingState():
|
|||
until = match[5]
|
||||
rate = match[6]
|
||||
|
||||
epoch_percent = self.it / float(self.its) # self.epoch / float(self.epochs)
|
||||
|
||||
last_step = self.last_step
|
||||
self.last_step = step
|
||||
if last_step < step:
|
||||
|
|
@ -530,10 +528,12 @@ class TrainingState():
|
|||
self.it_time_delta = self.it_time_end-self.it_time_start
|
||||
self.it_time_start = time.time()
|
||||
try:
|
||||
rate = f'[{"{:.3f}".format(self.it_time_delta)}s/it]' if self.it_time_delta >= 1 else f'[{"{:.3f}".format(1/self.it_time_delta)}it/s]'
|
||||
rate = f'{"{:.3f}".format(self.it_time_delta)}s/it' if self.it_time_delta >= 1 else f'{"{:.3f}".format(1/self.it_time_delta)}it/s'
|
||||
self.it_rate = rate
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
message = f'[{self.epoch}/{self.epochs}, {self.it}/{self.its}, {step}/{steps}] [ETA: {self.eta_hhmmss}] [{self.epoch_rate}, {self.it_rate}] {self.status}'
|
||||
|
||||
"""
|
||||
# I wanted frequently updated ETA, but I can't wrap my noggin around getting it to work on an empty belly
|
||||
|
|
@ -550,13 +550,6 @@ class TrainingState():
|
|||
pass
|
||||
"""
|
||||
|
||||
message = f'[{self.epoch}/{self.epochs}] [{self.it}/{self.its}] [ETA: {self.eta_hhmmss}] {self.epoch_rate} / {self.it_rate} {self.status}'
|
||||
if progress is not None:
|
||||
progress(epoch_percent, message)
|
||||
|
||||
# print(f'{"{:.3f}".format(percent*100)}% {message}')
|
||||
self.buffer.append(f'[{"{:.3f}".format(epoch_percent*100)}% / {"{:.3f}".format(percent*100)}%] {message}')
|
||||
|
||||
if lapsed:
|
||||
self.epoch = self.epoch + 1
|
||||
self.it = int(self.epoch * (self.dataset_size / self.batch_size))
|
||||
|
|
@ -564,7 +557,7 @@ class TrainingState():
|
|||
self.epoch_time_end = time.time()
|
||||
self.epoch_time_delta = self.epoch_time_end-self.epoch_time_start
|
||||
self.epoch_time_start = time.time()
|
||||
self.epoch_rate = f'[{"{:.3f}".format(self.epoch_time_delta)}s/epoch]' if self.epoch_time_delta >= 1 else f'[{"{:.3f}".format(1/self.epoch_time_delta)}epoch/s]' # I doubt anyone will have it/s rates, but its here
|
||||
self.epoch_rate = f'{"{:.3f}".format(self.epoch_time_delta)}s/epoch' if self.epoch_time_delta >= 1 else f'{"{:.3f}".format(1/self.epoch_time_delta)}epoch/s' # I doubt anyone will have it/s rates, but its here
|
||||
|
||||
#self.eta = (self.epochs - self.epoch) * self.epoch_time_delta
|
||||
self.epoch_time_deltas = self.epoch_time_deltas + self.epoch_time_delta
|
||||
|
|
@ -576,14 +569,12 @@ class TrainingState():
|
|||
except Exception as e:
|
||||
pass
|
||||
|
||||
percent = self.epoch / float(self.epochs)
|
||||
message = f'[{self.epoch}/{self.epochs}] [{self.it}/{self.its}] [ETA: {self.eta_hhmmss}] {self.epoch_rate} / {self.it_rate} {self.status}'
|
||||
|
||||
if message:
|
||||
percent = self.it / float(self.its) # self.epoch / float(self.epochs)
|
||||
if progress is not None:
|
||||
progress(percent, message)
|
||||
|
||||
print(f'{"{:.3f}".format(percent*100)}% {message}')
|
||||
self.buffer.append(f'{"{:.3f}".format(percent*100)}% {message}')
|
||||
self.buffer.append(f'[{"{:.3f}".format(percent*100)}%] {message}')
|
||||
|
||||
if line.find('INFO: [epoch:') >= 0:
|
||||
# easily rip out our stats...
|
||||
|
|
@ -677,12 +668,36 @@ def convert_to_halfp():
|
|||
torch.save(model, outfile)
|
||||
print(f'Converted model to half precision: {outfile}')
|
||||
|
||||
def whisper_transcribe( file, language=None ):
|
||||
# shouldn't happen, but it's for safety
|
||||
if not whisper_model:
|
||||
load_whisper_model(language=language if language else b'en')
|
||||
|
||||
if not args.whisper_cpp:
|
||||
return whisper_model.transcribe(file, language=language if language else "English")
|
||||
|
||||
res = whisper_model.transcribe(file)
|
||||
segments = whisper_model.extract_text_and_timestamps( res )
|
||||
|
||||
result = {
|
||||
'segments': []
|
||||
}
|
||||
for segment in segments:
|
||||
reparsed = {
|
||||
'start': segment[0],
|
||||
'end': segment[1],
|
||||
'text': segment[2],
|
||||
}
|
||||
result['segments'].append(reparsed)
|
||||
return result
|
||||
|
||||
|
||||
def prepare_dataset( files, outdir, language=None, progress=None ):
|
||||
unload_tts()
|
||||
|
||||
global whisper_model
|
||||
if whisper_model is None:
|
||||
load_whisper_model()
|
||||
load_whisper_model(language=language)
|
||||
|
||||
os.makedirs(outdir, exist_ok=True)
|
||||
|
||||
|
|
@ -693,7 +708,7 @@ def prepare_dataset( files, outdir, language=None, progress=None ):
|
|||
for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress):
|
||||
print(f"Transcribing file: {file}")
|
||||
|
||||
result = whisper_model.transcribe(file, language=language if language else "English")
|
||||
result = whisper_transcribe(file, language=language) # whisper_model.transcribe(file, language=language if language else "English")
|
||||
results[os.path.basename(file)] = result
|
||||
|
||||
print(f"Transcribed file: {file}, {len(result['segments'])} found.")
|
||||
|
|
@ -1037,11 +1052,13 @@ def setup_args():
|
|||
'defer-tts-load': False,
|
||||
'device-override': None,
|
||||
'prune-nonfinal-outputs': True,
|
||||
'whisper-model': "base",
|
||||
'autoregressive-model': None,
|
||||
'concurrency-count': 2,
|
||||
'output-sample-rate': 44100,
|
||||
'output-volume': 1,
|
||||
|
||||
'autoregressive-model': None,
|
||||
'whisper-model': "base",
|
||||
'whisper-cpp': False,
|
||||
|
||||
'training-default-halfp': False,
|
||||
'training-default-bnb': True,
|
||||
|
|
@ -1067,13 +1084,15 @@ 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("--prune-nonfinal-outputs", default=default_arguments['prune-nonfinal-outputs'], action='store_true', help="Deletes non-final output files on completing a generation")
|
||||
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)")
|
||||
parser.add_argument("--output-volume", type=float, default=default_arguments['output-volume'], help="Adjusts volume of output")
|
||||
|
||||
parser.add_argument("--autoregressive-model", default=default_arguments['autoregressive-model'], help="Specifies which autoregressive model to use for sampling.")
|
||||
parser.add_argument("--whisper-model", default=default_arguments['whisper-model'], help="Specifies which whisper model to use for transcription.")
|
||||
parser.add_argument("--whisper-cpp", default=default_arguments['whisper-cpp'], action='store_true', help="Leverages lightmare/whispercpp for transcription")
|
||||
|
||||
parser.add_argument("--training-default-halfp", action='store_true', default=default_arguments['training-default-halfp'], help="Training default: halfp")
|
||||
parser.add_argument("--training-default-bnb", action='store_true', default=default_arguments['training-default-bnb'], help="Training default: bnb")
|
||||
|
||||
|
|
@ -1103,7 +1122,7 @@ def setup_args():
|
|||
|
||||
return args
|
||||
|
||||
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, prune_nonfinal_outputs, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume, training_default_halfp, training_default_bnb ):
|
||||
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, prune_nonfinal_outputs, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume, autoregressive_model, whisper_model, whisper_cpp, training_default_halfp, training_default_bnb ):
|
||||
global args
|
||||
|
||||
args.listen = listen
|
||||
|
|
@ -1123,6 +1142,11 @@ def update_args( listen, share, check_for_updates, models_from_local_only, low_v
|
|||
args.concurrency_count = concurrency_count
|
||||
args.output_sample_rate = output_sample_rate
|
||||
args.output_volume = output_volume
|
||||
|
||||
args.autoregressive_model = autoregressive_model
|
||||
args.whisper_model = whisper_model
|
||||
args.whisper_cpp = whisper_cpp
|
||||
|
||||
args.training_default_halfp = training_default_halfp
|
||||
args.training_default_bnb = training_default_bnb
|
||||
|
||||
|
|
@ -1140,8 +1164,6 @@ def save_args_settings():
|
|||
'defer-tts-load': args.defer_tts_load,
|
||||
'prune-nonfinal-outputs': args.prune_nonfinal_outputs,
|
||||
'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,
|
||||
|
|
@ -1150,6 +1172,10 @@ def save_args_settings():
|
|||
'concurrency-count': args.concurrency_count,
|
||||
'output-sample-rate': args.output_sample_rate,
|
||||
'output-volume': args.output_volume,
|
||||
|
||||
'autoregressive-model': args.autoregressive_model,
|
||||
'whisper-model': args.whisper_model,
|
||||
'whisper-cpp': args.whisper_cpp,
|
||||
|
||||
'training-default-halfp': args.training_default_halfp,
|
||||
'training-default-bnb': args.training_default_bnb,
|
||||
|
|
@ -1292,9 +1318,7 @@ def update_autoregressive_model(autoregressive_model_path):
|
|||
if not tts:
|
||||
if tts_loading:
|
||||
raise Exception("TTS is still initializing...")
|
||||
|
||||
load_tts( model=autoregressive_model_path )
|
||||
return # redundant to proceed onward
|
||||
return
|
||||
|
||||
print(f"Loading model: {autoregressive_model_path}")
|
||||
|
||||
|
|
@ -1348,7 +1372,7 @@ def unload_voicefixer():
|
|||
|
||||
do_gc()
|
||||
|
||||
def load_whisper_model(name=None, progress=None):
|
||||
def load_whisper_model(name=None, progress=None, language=b'en'):
|
||||
global whisper_model
|
||||
|
||||
if not name:
|
||||
|
|
@ -1358,7 +1382,12 @@ def load_whisper_model(name=None, progress=None):
|
|||
save_args_settings()
|
||||
|
||||
notify_progress(f"Loading Whisper model: {args.whisper_model}", progress)
|
||||
whisper_model = whisper.load_model(args.whisper_model)
|
||||
if args.whisper_cpp:
|
||||
from whispercpp import Whisper
|
||||
whisper_model = Whisper(name, models_dir='./models/', language=language)
|
||||
else:
|
||||
import whisper
|
||||
whisper_model = whisper.load_model(args.whisper_model)
|
||||
print("Loaded Whisper model")
|
||||
|
||||
def unload_whisper():
|
||||
|
|
@ -1372,10 +1401,13 @@ def unload_whisper():
|
|||
|
||||
do_gc()
|
||||
|
||||
"""
|
||||
def update_whisper_model(name, progress=None):
|
||||
if not name:
|
||||
return
|
||||
|
||||
args.whisper_model = name
|
||||
save_args_settings()
|
||||
|
||||
global whisper_model
|
||||
if whisper_model:
|
||||
|
|
@ -1383,4 +1415,5 @@ def update_whisper_model(name, progress=None):
|
|||
load_whisper_model(name)
|
||||
else:
|
||||
args.whisper_model = name
|
||||
save_args_settings()
|
||||
save_args_settings()
|
||||
"""
|
||||
32
src/webui.py
32
src/webui.py
|
|
@ -537,7 +537,12 @@ def setup_gradio():
|
|||
|
||||
autoregressive_models = get_autoregressive_models()
|
||||
autoregressive_model_dropdown = gr.Dropdown(choices=autoregressive_models, label="Autoregressive Model", value=args.autoregressive_model if args.autoregressive_model else autoregressive_models[0])
|
||||
|
||||
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)
|
||||
use_whisper_cpp = gr.Checkbox(label="Use Whisper.cpp", value=args.whisper_cpp)
|
||||
|
||||
exec_inputs = exec_inputs + [ autoregressive_model_dropdown, whisper_model_dropdown, use_whisper_cpp, training_halfp, training_bnb ]
|
||||
|
||||
with gr.Row():
|
||||
autoregressive_models_update_button = gr.Button(value="Refresh Model List")
|
||||
gr.Button(value="Check for Updates").click(check_for_updates)
|
||||
|
|
@ -559,22 +564,21 @@ def setup_gradio():
|
|||
outputs=autoregressive_model_dropdown,
|
||||
)
|
||||
|
||||
autoregressive_model_dropdown.change(
|
||||
fn=update_autoregressive_model,
|
||||
inputs=autoregressive_model_dropdown,
|
||||
outputs=None
|
||||
)
|
||||
whisper_model_dropdown.change(
|
||||
fn=update_whisper_model,
|
||||
inputs=whisper_model_dropdown,
|
||||
outputs=None
|
||||
)
|
||||
|
||||
exec_inputs = exec_inputs + [ training_halfp, training_bnb ]
|
||||
|
||||
|
||||
for i in exec_inputs:
|
||||
i.change( fn=update_args, inputs=exec_inputs )
|
||||
|
||||
autoregressive_model_dropdown.change(
|
||||
fn=update_autoregressive_model,
|
||||
inputs=autoregressive_model_dropdown,
|
||||
outputs=None
|
||||
)
|
||||
"""
|
||||
whisper_model_dropdown.change(
|
||||
fn=update_whisper_model,
|
||||
inputs=whisper_model_dropdown,
|
||||
outputs=None
|
||||
)
|
||||
"""
|
||||
|
||||
# console_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user