I don't remember.
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6925ec731b
101
src/utils.py
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_
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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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import whisper
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MODELS['dvae.pth'] = "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/3704aea61678e7e468a06d8eea121dba368a798e/.models/dvae.pth"
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args = None
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@ -46,7 +44,6 @@ voicefixer = None
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whisper_model = None
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training_state = None
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def generate(
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text,
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delimiter,
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@ -501,9 +498,12 @@ class TrainingState():
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match = re.findall(r'iter: ([\d,]+)', line)
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if match and len(match) > 0:
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self.it = int(match[0].replace(",", ""))
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self.checkpoints = int((self.its - self.it) / self.config['logger']['save_checkpoint_freq'])
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else:
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lapsed = False
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message = None
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if line.find('%|') > 0:
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match = re.findall(r'(\d+)%\|(.+?)\| (\d+|\?)\/(\d+|\?) \[(.+?)<(.+?), +(.+?)\]', line)
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if match and len(match) > 0:
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@ -516,8 +516,6 @@ class TrainingState():
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until = match[5]
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rate = match[6]
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epoch_percent = self.it / float(self.its) # self.epoch / float(self.epochs)
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last_step = self.last_step
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self.last_step = step
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if last_step < step:
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@ -530,10 +528,12 @@ 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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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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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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except Exception as e:
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pass
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message = f'[{self.epoch}/{self.epochs}, {self.it}/{self.its}, {step}/{steps}] [ETA: {self.eta_hhmmss}] [{self.epoch_rate}, {self.it_rate}] {self.status}'
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"""
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# I wanted frequently updated ETA, but I can't wrap my noggin around getting it to work on an empty belly
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@ -550,13 +550,6 @@ class TrainingState():
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pass
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"""
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message = f'[{self.epoch}/{self.epochs}] [{self.it}/{self.its}] [ETA: {self.eta_hhmmss}] {self.epoch_rate} / {self.it_rate} {self.status}'
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if progress is not None:
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progress(epoch_percent, message)
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# print(f'{"{:.3f}".format(percent*100)}% {message}')
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self.buffer.append(f'[{"{:.3f}".format(epoch_percent*100)}% / {"{:.3f}".format(percent*100)}%] {message}')
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if lapsed:
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self.epoch = self.epoch + 1
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self.it = int(self.epoch * (self.dataset_size / self.batch_size))
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@ -564,7 +557,7 @@ class TrainingState():
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self.epoch_time_end = time.time()
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self.epoch_time_delta = self.epoch_time_end-self.epoch_time_start
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self.epoch_time_start = time.time()
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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
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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
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#self.eta = (self.epochs - self.epoch) * self.epoch_time_delta
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self.epoch_time_deltas = self.epoch_time_deltas + self.epoch_time_delta
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@ -576,14 +569,12 @@ class TrainingState():
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except Exception as e:
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pass
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percent = self.epoch / float(self.epochs)
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message = f'[{self.epoch}/{self.epochs}] [{self.it}/{self.its}] [ETA: {self.eta_hhmmss}] {self.epoch_rate} / {self.it_rate} {self.status}'
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if message:
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percent = self.it / float(self.its) # self.epoch / float(self.epochs)
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if progress is not None:
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progress(percent, message)
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print(f'{"{:.3f}".format(percent*100)}% {message}')
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self.buffer.append(f'{"{:.3f}".format(percent*100)}% {message}')
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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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# easily rip out our stats...
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@ -677,12 +668,36 @@ def convert_to_halfp():
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torch.save(model, outfile)
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print(f'Converted model to half precision: {outfile}')
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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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if not args.whisper_cpp:
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return whisper_model.transcribe(file, language=language if language else "English")
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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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result = {
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'segments': []
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}
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for segment in segments:
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reparsed = {
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'start': segment[0],
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'end': segment[1],
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'text': segment[2],
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}
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result['segments'].append(reparsed)
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return result
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def prepare_dataset( files, outdir, language=None, progress=None ):
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unload_tts()
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global whisper_model
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if whisper_model is None:
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load_whisper_model()
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load_whisper_model(language=language)
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os.makedirs(outdir, exist_ok=True)
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@ -693,7 +708,7 @@ def prepare_dataset( files, outdir, language=None, progress=None ):
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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_model.transcribe(file, language=language if language else "English")
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result = whisper_transcribe(file, language=language) # whisper_model.transcribe(file, language=language if language else "English")
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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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@ -1037,11 +1052,13 @@ def setup_args():
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'defer-tts-load': False,
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'device-override': None,
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'prune-nonfinal-outputs': True,
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'whisper-model': "base",
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'autoregressive-model': None,
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'concurrency-count': 2,
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'output-sample-rate': 44100,
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'output-volume': 1,
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'autoregressive-model': None,
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'whisper-model': "base",
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'whisper-cpp': False,
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'training-default-halfp': False,
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'training-default-bnb': True,
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@ -1067,13 +1084,15 @@ def setup_args():
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parser.add_argument("--defer-tts-load", default=default_arguments['defer-tts-load'], action='store_true', help="Defers loading TTS model")
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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")
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parser.add_argument("--device-override", default=default_arguments['device-override'], help="A device string to override pass through Torch")
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parser.add_argument("--whisper-model", default=default_arguments['whisper-model'], help="Specifies which whisper model to use for transcription.")
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parser.add_argument("--autoregressive-model", default=default_arguments['autoregressive-model'], help="Specifies which autoregressive model to use for sampling.")
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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")
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parser.add_argument("--concurrency-count", type=int, default=default_arguments['concurrency-count'], help="How many Gradio events to process at once")
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parser.add_argument("--output-sample-rate", type=int, default=default_arguments['output-sample-rate'], help="Sample rate to resample the output to (from 24KHz)")
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parser.add_argument("--output-volume", type=float, default=default_arguments['output-volume'], help="Adjusts volume of output")
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parser.add_argument("--autoregressive-model", default=default_arguments['autoregressive-model'], help="Specifies which autoregressive model to use for sampling.")
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parser.add_argument("--whisper-model", default=default_arguments['whisper-model'], help="Specifies which whisper model to use for transcription.")
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parser.add_argument("--whisper-cpp", default=default_arguments['whisper-cpp'], action='store_true', help="Leverages lightmare/whispercpp for transcription")
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parser.add_argument("--training-default-halfp", action='store_true', default=default_arguments['training-default-halfp'], help="Training default: halfp")
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parser.add_argument("--training-default-bnb", action='store_true', default=default_arguments['training-default-bnb'], help="Training default: bnb")
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@ -1103,7 +1122,7 @@ def setup_args():
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return args
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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 ):
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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 ):
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global args
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args.listen = listen
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@ -1123,6 +1142,11 @@ def update_args( listen, share, check_for_updates, models_from_local_only, low_v
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args.concurrency_count = concurrency_count
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args.output_sample_rate = output_sample_rate
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args.output_volume = output_volume
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args.autoregressive_model = autoregressive_model
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args.whisper_model = whisper_model
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args.whisper_cpp = whisper_cpp
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args.training_default_halfp = training_default_halfp
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args.training_default_bnb = training_default_bnb
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@ -1140,8 +1164,6 @@ def save_args_settings():
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'defer-tts-load': args.defer_tts_load,
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'prune-nonfinal-outputs': args.prune_nonfinal_outputs,
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'device-override': args.device_override,
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'whisper-model': args.whisper_model,
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'autoregressive-model': args.autoregressive_model,
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'sample-batch-size': args.sample_batch_size,
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'embed-output-metadata': args.embed_output_metadata,
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'latents-lean-and-mean': args.latents_lean_and_mean,
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@ -1150,6 +1172,10 @@ def save_args_settings():
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'concurrency-count': args.concurrency_count,
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'output-sample-rate': args.output_sample_rate,
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'output-volume': args.output_volume,
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'autoregressive-model': args.autoregressive_model,
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'whisper-model': args.whisper_model,
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'whisper-cpp': args.whisper_cpp,
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'training-default-halfp': args.training_default_halfp,
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'training-default-bnb': args.training_default_bnb,
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@ -1292,9 +1318,7 @@ def update_autoregressive_model(autoregressive_model_path):
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if not tts:
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if tts_loading:
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raise Exception("TTS is still initializing...")
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load_tts( model=autoregressive_model_path )
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return # redundant to proceed onward
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return
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print(f"Loading model: {autoregressive_model_path}")
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@ -1348,7 +1372,7 @@ def unload_voicefixer():
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do_gc()
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def load_whisper_model(name=None, progress=None):
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def load_whisper_model(name=None, progress=None, language=b'en'):
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global whisper_model
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if not name:
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@ -1358,7 +1382,12 @@ def load_whisper_model(name=None, progress=None):
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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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whisper_model = whisper.load_model(args.whisper_model)
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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)
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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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print("Loaded Whisper model")
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def unload_whisper():
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@ -1372,10 +1401,13 @@ def unload_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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@ -1383,4 +1415,5 @@ def update_whisper_model(name, progress=None):
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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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save_args_settings()
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"""
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32
src/webui.py
32
src/webui.py
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@ -537,7 +537,12 @@ def setup_gradio():
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autoregressive_models = get_autoregressive_models()
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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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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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with gr.Row():
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autoregressive_models_update_button = gr.Button(value="Refresh Model List")
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gr.Button(value="Check for Updates").click(check_for_updates)
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@ -559,22 +564,21 @@ def setup_gradio():
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outputs=autoregressive_model_dropdown,
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)
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autoregressive_model_dropdown.change(
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fn=update_autoregressive_model,
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inputs=autoregressive_model_dropdown,
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outputs=None
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)
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whisper_model_dropdown.change(
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fn=update_whisper_model,
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inputs=whisper_model_dropdown,
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outputs=None
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)
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exec_inputs = exec_inputs + [ training_halfp, training_bnb ]
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for i in exec_inputs:
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i.change( fn=update_args, inputs=exec_inputs )
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autoregressive_model_dropdown.change(
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fn=update_autoregressive_model,
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inputs=autoregressive_model_dropdown,
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outputs=None
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)
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"""
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whisper_model_dropdown.change(
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fn=update_whisper_model,
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inputs=whisper_model_dropdown,
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outputs=None
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)
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"""
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# console_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
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