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ai-voice-cloning-fork
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I don't remember.

master
mrq 2023-02-27 19:20:06 +07:00
parent 47abde224c
commit 6925ec731b
2 changed files with 85 additions and 48 deletions
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101
src/utils.py
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@ -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.text import split_and_recombine_text
from tortoise.utils.device import get_device_name, set_device_name 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" MODELS['dvae.pth'] = "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/3704aea61678e7e468a06d8eea121dba368a798e/.models/dvae.pth"
args = None args = None
@ -46,7 +44,6 @@ voicefixer = None
whisper_model = None whisper_model = None
training_state = None training_state = None
def generate( def generate(
text, text,
delimiter, delimiter,
@ -501,9 +498,12 @@ class TrainingState():
match = re.findall(r'iter: ([\d,]+)', line) match = re.findall(r'iter: ([\d,]+)', line)
if match and len(match) > 0: if match and len(match) > 0:
self.it = int(match[0].replace(",", "")) self.it = int(match[0].replace(",", ""))
self.checkpoints = int((self.its - self.it) / self.config['logger']['save_checkpoint_freq'])
else: else:
lapsed = False lapsed = False
message = None
if line.find('%|') > 0: if line.find('%|') > 0:
match = re.findall(r'(\d+)%\|(.+?)\| (\d+|\?)\/(\d+|\?) \[(.+?)<(.+?), +(.+?)\]', line) match = re.findall(r'(\d+)%\|(.+?)\| (\d+|\?)\/(\d+|\?) \[(.+?)<(.+?), +(.+?)\]', line)
if match and len(match) > 0: if match and len(match) > 0:
@ -516,8 +516,6 @@ class TrainingState():
until = match[5] until = match[5]
rate = match[6] rate = match[6]
epoch_percent = self.it / float(self.its) # self.epoch / float(self.epochs)
last_step = self.last_step last_step = self.last_step
self.last_step = step self.last_step = step
if 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_delta = self.it_time_end-self.it_time_start
self.it_time_start = time.time() self.it_time_start = time.time()
try: 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 self.it_rate = rate
except Exception as e: except Exception as e:
pass 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 # 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 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: if lapsed:
self.epoch = self.epoch + 1 self.epoch = self.epoch + 1
self.it = int(self.epoch * (self.dataset_size / self.batch_size)) 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_end = time.time()
self.epoch_time_delta = self.epoch_time_end-self.epoch_time_start self.epoch_time_delta = self.epoch_time_end-self.epoch_time_start
self.epoch_time_start = time.time() 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.eta = (self.epochs - self.epoch) * self.epoch_time_delta
self.epoch_time_deltas = self.epoch_time_deltas + 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: except Exception as e:
pass pass
percent = self.epoch / float(self.epochs) if message:
message = f'[{self.epoch}/{self.epochs}] [{self.it}/{self.its}] [ETA: {self.eta_hhmmss}] {self.epoch_rate} / {self.it_rate} {self.status}' percent = self.it / float(self.its) # self.epoch / float(self.epochs)
if progress is not None: if progress is not None:
progress(percent, message) 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: if line.find('INFO: [epoch:') >= 0:
# easily rip out our stats... # easily rip out our stats...
@ -677,12 +668,36 @@ def convert_to_halfp():
torch.save(model, outfile) torch.save(model, outfile)
print(f'Converted model to half precision: {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 ): def prepare_dataset( files, outdir, language=None, progress=None ):
unload_tts() unload_tts()
global whisper_model global whisper_model
if whisper_model is None: if whisper_model is None:
load_whisper_model() load_whisper_model(language=language)
os.makedirs(outdir, exist_ok=True) 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): for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress):
print(f"Transcribing file: {file}") 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 results[os.path.basename(file)] = result
print(f"Transcribed file: {file}, {len(result['segments'])} found.") print(f"Transcribed file: {file}, {len(result['segments'])} found.")
@ -1037,11 +1052,13 @@ def setup_args():
'defer-tts-load': False, 'defer-tts-load': False,
'device-override': None, 'device-override': None,
'prune-nonfinal-outputs': True, 'prune-nonfinal-outputs': True,
'whisper-model': "base",
'autoregressive-model': None,
'concurrency-count': 2, 'concurrency-count': 2,
'output-sample-rate': 44100, 'output-sample-rate': 44100,
'output-volume': 1, 'output-volume': 1,
'autoregressive-model': None,
'whisper-model': "base",
'whisper-cpp': False,
'training-default-halfp': False, 'training-default-halfp': False,
'training-default-bnb': True, '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("--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("--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("--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("--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("--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-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("--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-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") 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 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 global args
args.listen = listen 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.concurrency_count = concurrency_count
args.output_sample_rate = output_sample_rate args.output_sample_rate = output_sample_rate
args.output_volume = output_volume 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_halfp = training_default_halfp
args.training_default_bnb = training_default_bnb args.training_default_bnb = training_default_bnb
@ -1140,8 +1164,6 @@ def save_args_settings():
'defer-tts-load': args.defer_tts_load, 'defer-tts-load': args.defer_tts_load,
'prune-nonfinal-outputs': args.prune_nonfinal_outputs, 'prune-nonfinal-outputs': args.prune_nonfinal_outputs,
'device-override': args.device_override, 'device-override': args.device_override,
'whisper-model': args.whisper_model,
'autoregressive-model': args.autoregressive_model,
'sample-batch-size': args.sample_batch_size, 'sample-batch-size': args.sample_batch_size,
'embed-output-metadata': args.embed_output_metadata, 'embed-output-metadata': args.embed_output_metadata,
'latents-lean-and-mean': args.latents_lean_and_mean, 'latents-lean-and-mean': args.latents_lean_and_mean,
@ -1150,6 +1172,10 @@ def save_args_settings():
'concurrency-count': args.concurrency_count, 'concurrency-count': args.concurrency_count,
'output-sample-rate': args.output_sample_rate, 'output-sample-rate': args.output_sample_rate,
'output-volume': args.output_volume, '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-halfp': args.training_default_halfp,
'training-default-bnb': args.training_default_bnb, 'training-default-bnb': args.training_default_bnb,
@ -1292,9 +1318,7 @@ def update_autoregressive_model(autoregressive_model_path):
if not tts: if not tts:
if tts_loading: if tts_loading:
raise Exception("TTS is still initializing...") raise Exception("TTS is still initializing...")
return
load_tts( model=autoregressive_model_path )
return # redundant to proceed onward
print(f"Loading model: {autoregressive_model_path}") print(f"Loading model: {autoregressive_model_path}")
@ -1348,7 +1372,7 @@ def unload_voicefixer():
do_gc() do_gc()
def load_whisper_model(name=None, progress=None): def load_whisper_model(name=None, progress=None, language=b'en'):
global whisper_model global whisper_model
if not name: if not name:
@ -1358,7 +1382,12 @@ def load_whisper_model(name=None, progress=None):
save_args_settings() save_args_settings()
notify_progress(f"Loading Whisper model: {args.whisper_model}", progress) 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") print("Loaded Whisper model")
def unload_whisper(): def unload_whisper():
@ -1372,10 +1401,13 @@ def unload_whisper():
do_gc() do_gc()
"""
def update_whisper_model(name, progress=None): def update_whisper_model(name, progress=None):
if not name: if not name:
return return
args.whisper_model = name
save_args_settings()
global whisper_model global whisper_model
if whisper_model: if whisper_model:
@ -1383,4 +1415,5 @@ def update_whisper_model(name, progress=None):
load_whisper_model(name) load_whisper_model(name)
else: else:
args.whisper_model = name args.whisper_model = name
save_args_settings() save_args_settings()
"""

32
src/webui.py
Unescape Escape View File

@ -537,7 +537,12 @@ def setup_gradio():
autoregressive_models = get_autoregressive_models() 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]) 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) 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(): with gr.Row():
autoregressive_models_update_button = gr.Button(value="Refresh Model List") autoregressive_models_update_button = gr.Button(value="Refresh Model List")
gr.Button(value="Check for Updates").click(check_for_updates) gr.Button(value="Check for Updates").click(check_for_updates)
@ -559,22 +564,21 @@ def setup_gradio():
outputs=autoregressive_model_dropdown, 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: for i in exec_inputs:
i.change( fn=update_args, inputs=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) # console_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)