cleanup
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7c71f7239c
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cb273b8428
47
src/utils.py
47
src/utils.py
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@ -1274,6 +1274,15 @@ def optimize_training_settings( **kwargs ):
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if vram > (k-1):
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return v
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return 1
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if settings['gpus'] > get_device_count():
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settings['gpus'] = get_device_count()
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messages.append(f"GPU count exceeds defacto GPU count, clamping to: {settings['gpus']}")
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if settings['gpus'] <= 1:
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settings['gpus'] = 1
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else:
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messages.append(f"! EXPERIMENTAL ! Multi-GPU training is extremely particular, expect issues.")
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# assuming you have equal GPUs
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vram = get_device_vram() * settings['gpus']
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@ -1303,14 +1312,14 @@ def optimize_training_settings( **kwargs ):
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messages.append("Resume path specified, but does not exist. Disabling...")
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if settings['bitsandbytes']:
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messages.append("BitsAndBytes requested. Please note this is ! EXPERIMENTAL !")
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messages.append("! EXPERIMENTAL ! BitsAndBytes requested.")
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if settings['half_p']:
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if settings['bitsandbytes']:
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settings['half_p'] = False
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messages.append("Half Precision requested, but BitsAndBytes is also requested. Due to redundancies, disabling half precision...")
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else:
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messages.append("Half Precision requested. Please note this is ! EXPERIMENTAL !")
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messages.append("! EXPERIMENTAL ! Half Precision requested.")
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if not os.path.exists(get_halfp_model_path()):
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convert_to_halfp()
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@ -1343,12 +1352,21 @@ def save_training_settings( **kwargs ):
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settings['iterations'] = calc_iterations(epochs=settings['epochs'], lines=lines, batch_size=settings['batch_size'])
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messages.append(f"For {settings['epochs']} epochs with {lines} lines, iterating for {settings['iterations']} steps")
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iterations_per_epoch = int(settings['iterations'] / settings['epochs'])
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iterations_per_epoch = settings['iterations'] / settings['epochs']
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settings['print_rate'] = int(settings['print_rate'] * iterations_per_epoch)
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settings['save_rate'] = int(settings['save_rate'] * iterations_per_epoch)
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settings['validation_rate'] = int(settings['validation_rate'] * iterations_per_epoch)
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iterations_per_epoch = int(iterations_per_epoch)
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if settings['print_rate'] < 1:
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settings['print_rate'] = 1
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if settings['save_rate'] < 1:
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settings['save_rate'] = 1
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if settings['validation_rate'] < 1:
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settings['validation_rate'] = 1
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settings['validation_batch_size'] = int(settings['batch_size'] / settings['gradient_accumulation_size'])
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settings['iterations'] = calc_iterations(epochs=settings['epochs'], lines=lines, batch_size=settings['batch_size'])
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@ -1809,9 +1827,7 @@ def save_args_settings():
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# super kludgy )`;
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def import_generate_settings(file="./config/generate.json"):
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global GENERATE_SETTINGS_ARGS
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defaults = {
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res = {
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'text': None,
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'delimiter': None,
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'emotion': None,
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@ -1836,19 +1852,9 @@ def import_generate_settings(file="./config/generate.json"):
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}
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settings, _ = read_generate_settings(file, read_latents=False)
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res = []
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if GENERATE_SETTINGS_ARGS is not None:
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for k in GENERATE_SETTINGS_ARGS:
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if k not in defaults:
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continue
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res.append(defaults[k] if not settings or k not in settings or not settings[k] is None else settings[k])
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else:
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for k in defaults:
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res.append(defaults[k] if not settings or k not in settings or not settings[k] is None else settings[k])
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return tuple(res)
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if settings is not None:
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res.update(settings)
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return res
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def reset_generation_settings():
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with open(f'./config/generate.json', 'w', encoding="utf-8") as f:
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@ -1978,7 +1984,8 @@ def deduce_autoregressive_model(voice=None):
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if os.path.isdir(dir):
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counts = sorted([ int(d[:-8]) for d in os.listdir(dir) if d[-8:] == "_gpt.pth" ])
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names = [ f'{dir}/{d}_gpt.pth' for d in counts ]
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return names[-1]
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if len(names) > 0:
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return names[-1]
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if args.autoregressive_model != "auto":
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return args.autoregressive_model
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23
src/webui.py
23
src/webui.py
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@ -27,6 +27,7 @@ GENERATE_SETTINGS = {}
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TRANSCRIBE_SETTINGS = {}
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EXEC_SETTINGS = {}
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TRAINING_SETTINGS = {}
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GENERATE_SETTINGS_ARGS = []
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PRESETS = {
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'Ultra Fast': {'num_autoregressive_samples': 16, 'diffusion_iterations': 30, 'cond_free': False},
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@ -144,6 +145,18 @@ def history_view_results( voice ):
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gr.Dropdown.update(choices=sorted(files))
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)
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def import_generate_settings_proxy( file=None ):
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global GENERATE_SETTINGS_ARGS
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settings = import_generate_settings( file )
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res = []
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for k in GENERATE_SETTINGS_ARGS:
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res.append(settings[k] if k in settings else None)
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print(GENERATE_SETTINGS_ARGS)
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print(settings)
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print(res)
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return tuple(res)
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def compute_latents_proxy(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
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compute_latents( voice=voice, voice_latents_chunks=voice_latents_chunks, progress=progress )
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return voice
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@ -221,7 +234,10 @@ def import_training_settings_proxy( voice ):
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if k not in settings:
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continue
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output[k] = settings[k]
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output = list(output.values())
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print(list(TRAINING_SETTINGS.keys()))
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print(output)
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messages.append(f"Imported training settings: {injson}")
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return output[:-1] + ["\n".join(messages)]
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@ -250,7 +266,7 @@ def history_copy_settings( voice, file ):
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def setup_gradio():
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global args
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global ui
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if not args.share:
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def noop(function, return_value=None):
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def wrapped(*args, **kwargs):
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@ -273,6 +289,7 @@ def setup_gradio():
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autoregressive_models = get_autoregressive_models()
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dataset_list = get_dataset_list()
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global GENERATE_SETTINGS_ARGS
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GENERATE_SETTINGS_ARGS = list(inspect.signature(generate_proxy).parameters.keys())[:-1]
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for i in range(len(GENERATE_SETTINGS_ARGS)):
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arg = GENERATE_SETTINGS_ARGS[i]
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@ -639,7 +656,7 @@ def setup_gradio():
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)
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copy_button.click(import_generate_settings,
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copy_button.click(import_generate_settings_proxy,
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inputs=audio_in, # JSON elements cannot be used as inputs
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outputs=generate_settings
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)
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@ -738,7 +755,7 @@ def setup_gradio():
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)
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if os.path.isfile('./config/generate.json'):
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ui.load(import_generate_settings, inputs=None, outputs=generate_settings)
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ui.load(import_generate_settings_proxy, inputs=None, outputs=generate_settings)
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if args.check_for_updates:
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ui.load(check_for_updates)
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