forked from mrq/ai-voice-cloning
made validation working (will document later)
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@ -28,8 +28,8 @@ datasets:
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load_aligned_codes: False
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val: # I really do not care about validation right now
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name: ${validation_name}
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n_workers: 1
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batch_size: 1
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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: ${validation_path}
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fetcher_mode: ['lj']
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@ -131,13 +131,8 @@ train:
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lr_gamma: 0.5
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eval:
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pure: True
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output_state: gen
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injectors:
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gen_inj_eval:
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type: generator
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generator: generator
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in: hq
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out: [gen, codebook_commitment_loss]
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logger:
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print_freq: ${print_rate}
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47
src/utils.py
47
src/utils.py
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@ -684,7 +684,7 @@ class TrainingState():
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except Exception as e:
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use_tensorboard = False
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keys = ['loss_text_ce', 'loss_mel_ce', 'loss_gpt_total']
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keys = ['loss_text_ce', 'loss_mel_ce', 'loss_gpt_total', 'val_loss_text_ce', 'val_loss_mel_ce']
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infos = {}
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highest_step = self.last_info_check_at
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@ -1220,6 +1220,44 @@ def prepare_dataset( files, outdir, language=None, skip_existings=False, progres
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return f"Processed dataset to: {outdir}\n{joined}"
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def prepare_validation_dataset( voice, text_length ):
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indir = f'./training/{voice}/'
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infile = f'{indir}/dataset.txt'
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if not os.path.exists(infile):
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infile = f'{indir}/train.txt'
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with open(f'{indir}/train.txt', 'r', encoding="utf-8") as src:
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with open(f'{indir}/dataset.txt', 'w', encoding="utf-8") as dst:
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dst.write(src.read())
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if not os.path.exists(infile):
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raise Exception(f"Missing dataset: {infile}")
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with open(infile, 'r', encoding="utf-8") as f:
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lines = f.readlines()
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validation = []
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training = []
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for line in lines:
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split = line.split("|")
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filename = split[0]
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text = split[1]
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if len(text) < text_length:
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validation.append(line.strip())
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else:
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training.append(line.strip())
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with open(f'{indir}/train.txt', 'w', encoding="utf-8") as f:
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f.write("\n".join(training))
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with open(f'{indir}/validation.txt', 'w', encoding="utf-8") as f:
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f.write("\n".join(validation))
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msg = f"Culled {len(validation)} lines"
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print(msg)
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return msg
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def calc_iterations( epochs, lines, batch_size ):
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iterations = int(epochs * lines / float(batch_size))
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return iterations
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@ -1227,7 +1265,7 @@ def calc_iterations( epochs, lines, batch_size ):
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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, workers, 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, validation_rate, resume_path, half_p, bnb, workers, source_model, voice ):
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name = f"{voice}-finetune"
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dataset_path = f"./training/{voice}/train.txt"
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@ -1271,6 +1309,10 @@ def optimize_training_settings( epochs, learning_rate, text_ce_lr_weight, learni
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save_rate = epochs
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messages.append(f"Save rate is too small for the given iteration step, clamping save rate to: {save_rate}")
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if epochs < validation_rate:
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validation_rate = epochs
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messages.append(f"Validation rate is too small for the given iteration step, clamping validation rate to: {validation_rate}")
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if resume_path and not os.path.exists(resume_path):
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resume_path = None
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messages.append("Resume path specified, but does not exist. Disabling...")
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@ -1297,6 +1339,7 @@ def optimize_training_settings( epochs, learning_rate, text_ce_lr_weight, learni
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gradient_accumulation_size,
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print_rate,
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save_rate,
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validation_rate,
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resume_path,
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messages
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)
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37
src/webui.py
37
src/webui.py
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@ -205,7 +205,8 @@ def optimize_training_settings_proxy( *args, **kwargs ):
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gr.update(value=tup[5]),
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gr.update(value=tup[6]),
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gr.update(value=tup[7]),
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"\n".join(tup[8])
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gr.update(value=tup[8]),
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"\n".join(tup[9])
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)
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def import_training_settings_proxy( voice ):
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@ -247,11 +248,15 @@ def import_training_settings_proxy( voice ):
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print_rate = int(config['logger']['print_freq'] / steps_per_iteration)
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save_rate = int(config['logger']['save_checkpoint_freq'] / steps_per_iteration)
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validation_rate = int(config['train']['val_freq'] / steps_per_iteration)
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statedir = f'{outdir}/training_state/' # NOOO STOP MIXING YOUR CASES
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half_p = config['fp16']
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bnb = True
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statedir = f'{outdir}/training_state/'
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resumes = []
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resume_path = None
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source_model = None
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source_model = get_halfp_model_path() if half_p else get_model_path('autoregressive.pth')
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if "pretrain_model_gpt" in config['path']:
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source_model = config['path']['pretrain_model_gpt']
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@ -267,8 +272,6 @@ def import_training_settings_proxy( voice ):
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messages.append(f"Latest resume found: {resume_path}")
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half_p = config['fp16']
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bnb = True
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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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@ -286,6 +289,7 @@ def import_training_settings_proxy( voice ):
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gradient_accumulation_size,
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print_rate,
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save_rate,
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validation_rate,
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resume_path,
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half_p,
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bnb,
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@ -295,7 +299,7 @@ def import_training_settings_proxy( voice ):
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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, workers, 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, validation_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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@ -312,7 +316,7 @@ def save_training_settings_proxy( epochs, learning_rate, text_ce_lr_weight, lear
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print_rate = int(print_rate * iterations / epochs)
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save_rate = int(save_rate * iterations / epochs)
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validation_rate = save_rate
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validation_rate = int(validation_rate * iterations / epochs)
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if iterations % save_rate != 0:
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adjustment = int(iterations / save_rate) * save_rate
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@ -497,7 +501,9 @@ def setup_gradio():
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gr.Textbox(label="Language", value="en"),
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gr.Checkbox(label="Skip Already Transcribed", value=False)
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]
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prepare_dataset_button = gr.Button(value="Prepare")
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transcribe_button = gr.Button(value="Transcribe")
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validation_text_cull_size = gr.Number(label="Validation Text Length Cull Size", value=12, precision=0)
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prepare_validation_button = gr.Button(value="Prepare Validation")
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with gr.Column():
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prepare_dataset_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
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with gr.Tab("Generate Configuration"):
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@ -524,6 +530,7 @@ def setup_gradio():
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training_settings = training_settings + [
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gr.Number(label="Print Frequency (in epochs)", value=5, precision=0),
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gr.Number(label="Save Frequency (in epochs)", value=5, precision=0),
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gr.Number(label="Validation Frequency (in epochs)", value=5, precision=0),
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]
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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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@ -823,11 +830,19 @@ def setup_gradio():
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],
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outputs=training_output #console_output
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)
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prepare_dataset_button.click(
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transcribe_button.click(
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prepare_dataset_proxy,
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inputs=dataset_settings,
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outputs=prepare_dataset_output #console_output
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)
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prepare_validation_button.click(
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prepare_validation_dataset,
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inputs=[
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dataset_settings[0],
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validation_text_cull_size,
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],
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outputs=prepare_dataset_output #console_output
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)
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refresh_dataset_list.click(
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lambda: gr.update(choices=get_dataset_list()),
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inputs=None,
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@ -835,11 +850,11 @@ def setup_gradio():
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)
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optimize_yaml_button.click(optimize_training_settings_proxy,
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inputs=training_settings,
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outputs=training_settings[1:9] + [save_yaml_output] #console_output
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outputs=training_settings[1:10] + [save_yaml_output] #console_output
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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[:13] + [save_yaml_output] #console_output
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outputs=training_settings[:14] + [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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