forked from mrq/ai-voice-cloning
added option to set worker size in training config generator (because the default is overkill), for whisper transcriptions, load a specialized language model if it exists (for now, only english), output transcription to web UI when done transcribing
This commit is contained in:
parent
37cab14272
commit
3e220ed306
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@ -11,7 +11,7 @@ use_tb_logger: true
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datasets:
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train:
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name: ${dataset_name}
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n_workers: 8
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n_workers: ${workers}
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batch_size: ${batch_size}
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mode: paired_voice_audio
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path: ${dataset_path}
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94
src/utils.py
94
src/utils.py
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@ -37,6 +37,8 @@ from tortoise.utils.text import split_and_recombine_text
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from tortoise.utils.device import get_device_name, set_device_name
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MODELS['dvae.pth'] = "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/3704aea61678e7e468a06d8eea121dba368a798e/.models/dvae.pth"
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WHISPER_MODELS = ["tiny", "base", "small", "medium", "large"]
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WHISPER_SPECIALIZED_MODELS = ["tiny.en", "base.en", "small.en", "medium.en"]
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args = None
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tts = None
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@ -663,6 +665,7 @@ class TrainingState():
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# rip out iteration info
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if not self.training_started:
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if line.find('Start training from epoch') >= 0:
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self.it_time_start = time.time()
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self.epoch_time_start = time.time()
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self.training_started = True # could just leverage the above variable, but this is python, and there's no point in these aggressive microoptimizations
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should_return = True
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@ -703,6 +706,7 @@ class TrainingState():
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self.it_time_delta = self.it_time_end-self.it_time_start
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self.it_time_start = time.time()
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self.it_taken = self.it_taken + 1
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if self.it_time_delta:
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try:
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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'
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self.it_rate = rate
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@ -733,9 +737,23 @@ class TrainingState():
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metric_loss = []
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if len(self.losses) > 0:
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metric_loss.append(f'Loss: {"{:3f}".format(self.losses[-1]["value"])}')
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if len(self.losses) >= 2:
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delta_loss = self.losses[-2]["value"] - self.losses[-1]["value"]
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delta_step = self.losses[-2]["step"] - self.losses[-1]["step"]
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inst_deriv = delta_loss / delta_step
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est_loss = delta_loss + (self.its - self.it) * inst_deriv
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metric_loss.append(f'Est. Final Loss: {"{:3f}".format(est_loss)}')
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print(delta_loss, delta_step, inst_deriv, est_loss)
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metric_loss = ", ".join(metric_loss)
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message = f'[{metric_step}] [{metric_rate}] [{metric_loss}] [ETA: {eta_hhmmss}]'
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message = f'[{metric_step}] [{metric_rate}] [ETA: {eta_hhmmss}] [{metric_loss}]'
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if lapsed:
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self.epoch = self.epoch + 1
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@ -764,6 +782,13 @@ class TrainingState():
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self.buffer.append(f'[{"{:.3f}".format(percent*100)}%] {message}')
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if line.find('INFO: [epoch:') >= 0:
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# to-do, actually validate this works, and probably kill training when it's found, the model's dead by this point
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if ': nan' in line:
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should_return = True
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print("! NAN DETECTED !")
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self.buffer.append("! NAN DETECTED !")
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# easily rip out our stats...
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match = re.findall(r'\b([a-z_0-9]+?)\b: +?([0-9]\.[0-9]+?e[+-]\d+|[\d,]+)\b', line)
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if match and len(match) > 0:
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@ -824,14 +849,14 @@ def run_training(config_path, verbose=False, gpus=1, keep_x_past_datasets=0, pro
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if result:
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yield result
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if progress is not None and message:
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progress(percent, message)
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if training_state:
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training_state.process.stdout.close()
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return_code = training_state.process.wait()
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training_state = None
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#if return_code:
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# raise subprocess.CalledProcessError(return_code, cmd)
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def get_training_losses():
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global training_state
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if not training_state or not training_state.losses:
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@ -866,6 +891,9 @@ def reconnect_training(verbose=False, progress=gr.Progress(track_tqdm=True)):
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if result:
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yield result
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if progress is not None and message:
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progress(percent, message)
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def stop_training():
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global training_state
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if training_state is None:
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@ -910,10 +938,10 @@ def convert_to_halfp():
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def whisper_transcribe( file, language=None ):
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# shouldn't happen, but it's for safety
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if not whisper_model:
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load_whisper_model(language=language if language else b'en')
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load_whisper_model(language=language)
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if not args.whisper_cpp:
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return whisper_model.transcribe(file, language=language if language else "English")
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return whisper_model.transcribe(file, language=language)
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res = whisper_model.transcribe(file)
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segments = whisper_model.extract_text_and_timestamps( res )
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@ -945,11 +973,8 @@ def prepare_dataset( files, outdir, language=None, progress=None ):
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transcription = []
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for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress):
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print(f"Transcribing file: {file}")
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result = whisper_transcribe(file, language=language) # whisper_model.transcribe(file, language=language if language else "English")
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result = whisper_transcribe(file, language=language)
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results[os.path.basename(file)] = result
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print(f"Transcribed file: {file}, {len(result['segments'])} found.")
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waveform, sampling_rate = torchaudio.load(file)
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@ -988,7 +1013,7 @@ EPOCH_SCHEDULE = [ 9, 18, 25, 33 ]
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def schedule_learning_rate( iterations, schedule=EPOCH_SCHEDULE ):
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return [int(iterations * d) for d in schedule]
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def optimize_training_settings( epochs, learning_rate, text_ce_lr_weight, learning_rate_schedule, batch_size, gradient_accumulation_size, print_rate, save_rate, resume_path, half_p, bnb, source_model, voice ):
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def optimize_training_settings( epochs, learning_rate, text_ce_lr_weight, learning_rate_schedule, batch_size, gradient_accumulation_size, print_rate, save_rate, resume_path, half_p, bnb, workers, source_model, voice ):
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name = f"{voice}-finetune"
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dataset_name = f"{voice}-train"
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dataset_path = f"./training/{voice}/train.txt"
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@ -1065,7 +1090,7 @@ def optimize_training_settings( epochs, learning_rate, text_ce_lr_weight, learni
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messages
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)
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def save_training_settings( iterations=None, learning_rate=None, text_ce_lr_weight=None, learning_rate_schedule=None, batch_size=None, gradient_accumulation_size=None, print_rate=None, save_rate=None, name=None, dataset_name=None, dataset_path=None, validation_name=None, validation_path=None, output_name=None, resume_path=None, half_p=None, bnb=None, source_model=None ):
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def save_training_settings( iterations=None, learning_rate=None, text_ce_lr_weight=None, learning_rate_schedule=None, batch_size=None, gradient_accumulation_size=None, print_rate=None, save_rate=None, name=None, dataset_name=None, dataset_path=None, validation_name=None, validation_path=None, output_name=None, resume_path=None, half_p=None, bnb=None, workers=None, source_model=None ):
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if not source_model:
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source_model = f"./models/tortoise/autoregressive{'_half' if half_p else ''}.pth"
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@ -1090,6 +1115,8 @@ def save_training_settings( iterations=None, learning_rate=None, text_ce_lr_weig
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'float16': 'true' if half_p else 'false',
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'bitsandbytes': 'true' if bnb else 'false',
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'workers': workers if workers else 2,
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}
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if resume_path:
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@ -1581,9 +1608,9 @@ def unload_tts():
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global tts
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if tts:
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print("Unloading TTS")
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del tts
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tts = None
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print("Unloaded TTS")
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do_gc()
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def reload_tts( model=None ):
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@ -1656,55 +1683,44 @@ def unload_voicefixer():
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global voicefixer
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if voicefixer:
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print("Unloading Voicefixer")
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del voicefixer
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voicefixer = None
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print("Unloaded Voicefixer")
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do_gc()
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def load_whisper_model(name=None, progress=None, language=b'en'):
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def load_whisper_model(language=None, model_name=None, progress=None):
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global whisper_model
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if not name:
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name = args.whisper_model
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if not model_name:
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model_name = args.whisper_model
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else:
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args.whisper_model = name
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args.whisper_model = model_name
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save_args_settings()
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notify_progress(f"Loading Whisper model: {args.whisper_model}", progress)
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if language and f'{model_name}.{language}' in WHISPER_SPECIALIZED_MODELS:
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model_name = f'{model_name}.{language}'
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print(f"Loading specialized model for language: {language}")
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notify_progress(f"Loading Whisper model: {model_name}", progress)
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if args.whisper_cpp:
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from whispercpp import Whisper
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whisper_model = Whisper(name, models_dir='./models/', language=language.encode('ascii'))
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if not language:
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language = 'auto'
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whisper_model = Whisper(model_name, models_dir='./models/', language=language.encode('ascii'))
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else:
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import whisper
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whisper_model = whisper.load_model(args.whisper_model)
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whisper_model = whisper.load_model(model_name)
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print("Loaded Whisper model")
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def unload_whisper():
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global whisper_model
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if whisper_model:
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print("Unloading Whisper")
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del whisper_model
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whisper_model = None
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print("Unloaded Whisper")
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do_gc()
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"""
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def update_whisper_model(name, progress=None):
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if not name:
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return
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args.whisper_model = name
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save_args_settings()
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global whisper_model
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if whisper_model:
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unload_whisper()
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load_whisper_model(name)
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else:
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args.whisper_model = name
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save_args_settings()
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"""
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19
src/webui.py
19
src/webui.py
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@ -268,6 +268,8 @@ def import_training_settings_proxy( voice ):
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if "ext" in config and "bitsandbytes" in config["ext"]:
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bnb = config["ext"]["bitsandbytes"]
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workers = config['datasets']['train']['n_workers']
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messages = "\n".join(messages)
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return (
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@ -282,12 +284,13 @@ def import_training_settings_proxy( voice ):
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resume_path,
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half_p,
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bnb,
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workers,
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source_model,
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messages
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)
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def save_training_settings_proxy( epochs, learning_rate, text_ce_lr_weight, learning_rate_schedule, batch_size, gradient_accumulation_size, print_rate, save_rate, resume_path, half_p, bnb, source_model, voice ):
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def save_training_settings_proxy( epochs, learning_rate, text_ce_lr_weight, learning_rate_schedule, batch_size, gradient_accumulation_size, print_rate, save_rate, resume_path, half_p, bnb, workers, source_model, voice ):
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name = f"{voice}-finetune"
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dataset_name = f"{voice}-train"
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dataset_path = f"./training/{voice}/train.txt"
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@ -330,6 +333,7 @@ def save_training_settings_proxy( epochs, learning_rate, text_ce_lr_weight, lear
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resume_path=resume_path,
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half_p=half_p,
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bnb=bnb,
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workers=workers,
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source_model=source_model,
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))
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return "\n".join(messages)
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@ -466,7 +470,7 @@ def setup_gradio():
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with gr.Column():
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dataset_settings = [
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gr.Dropdown( choices=voice_list, label="Dataset Source", type="value", value=voice_list[0] if len(voice_list) > 0 else "" ),
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gr.Textbox(label="Language", placeholder="English")
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gr.Textbox(label="Language", value="en")
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]
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prepare_dataset_button = gr.Button(value="Prepare")
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with gr.Column():
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@ -499,11 +503,16 @@ def setup_gradio():
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training_settings = training_settings + [
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gr.Textbox(label="Resume State Path", placeholder="./training/${voice}-finetune/training_state/${last_state}.state"),
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]
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with gr.Row():
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training_halfp = gr.Checkbox(label="Half Precision", value=args.training_default_halfp)
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training_bnb = gr.Checkbox(label="BitsAndBytes", value=args.training_default_bnb)
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training_workers = gr.Number(label="Worker Processes", value=2, precision=0)
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source_model = gr.Dropdown( choices=autoregressive_models, label="Source Model", type="value", value=autoregressive_models[0] )
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dataset_list_dropdown = gr.Dropdown( choices=dataset_list, label="Dataset", type="value", value=dataset_list[0] if len(dataset_list) else "" )
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training_settings = training_settings + [ training_halfp, training_bnb, source_model, dataset_list_dropdown ]
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training_settings = training_settings + [ training_halfp, training_bnb, training_workers, source_model, dataset_list_dropdown ]
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with gr.Row():
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refresh_dataset_list = gr.Button(value="Refresh Dataset List")
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@ -572,7 +581,7 @@ def setup_gradio():
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autoregressive_model_dropdown = gr.Dropdown(choices=autoregressive_models, label="Autoregressive Model", value=args.autoregressive_model if args.autoregressive_model else autoregressive_models[0])
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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)
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whisper_model_dropdown = gr.Dropdown(WHISPER_MODELS, label="Whisper Model", value=args.whisper_model)
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use_whisper_cpp = gr.Checkbox(label="Use Whisper.cpp", value=args.whisper_cpp)
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exec_inputs = exec_inputs + [ autoregressive_model_dropdown, whisper_model_dropdown, use_whisper_cpp, training_halfp, training_bnb ]
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@ -797,7 +806,7 @@ def setup_gradio():
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)
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import_dataset_button.click(import_training_settings_proxy,
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inputs=dataset_list_dropdown,
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outputs=training_settings[:11] + [save_yaml_output] #console_output
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outputs=training_settings[:13] + [save_yaml_output] #console_output
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)
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save_yaml_button.click(save_training_settings_proxy,
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inputs=training_settings,
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