mirror of
https://github.com/SWivid/F5-TTS.git
synced 2026-01-16 06:53:17 -08:00
add settings
This commit is contained in:
unknown
@@ -89,7 +89,11 @@ def main():
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if args.finetune:
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if not os.path.isdir(checkpoint_path):
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os.makedirs(checkpoint_path, exist_ok=True)
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shutil.copy2(ckpt_path, os.path.join(checkpoint_path, os.path.basename(ckpt_path)))
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file_checkpoint = os.path.join(checkpoint_path, os.path.basename(ckpt_path))
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if os.path.isfile(file_checkpoint) == False:
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shutil.copy2(ckpt_path, file_checkpoint)
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print("copy checkpoint for finetune")
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# Use the tokenizer and tokenizer_path provided in the command line arguments
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tokenizer = args.tokenizer
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@@ -46,6 +46,119 @@ device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is
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pipe = None
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# Save settings from a JSON file
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def save_settings(
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project_name,
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exp_name,
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learning_rate,
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batch_size_per_gpu,
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batch_size_type,
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max_samples,
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grad_accumulation_steps,
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max_grad_norm,
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epochs,
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num_warmup_updates,
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save_per_updates,
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last_per_steps,
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finetune,
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file_checkpoint_train,
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tokenizer_type,
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tokenizer_file,
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mixed_precision,
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):
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path_project = os.path.join(path_project_ckpts, project_name)
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os.makedirs(path_project, exist_ok=True)
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file_setting = os.path.join(path_project, "setting.json")
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settings = {
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"exp_name": exp_name,
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"learning_rate": learning_rate,
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"batch_size_per_gpu": batch_size_per_gpu,
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"batch_size_type": batch_size_type,
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"max_samples": max_samples,
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"grad_accumulation_steps": grad_accumulation_steps,
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"max_grad_norm": max_grad_norm,
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"epochs": epochs,
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"num_warmup_updates": num_warmup_updates,
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"save_per_updates": save_per_updates,
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"last_per_steps": last_per_steps,
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"finetune": finetune,
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"file_checkpoint_train": file_checkpoint_train,
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"tokenizer_type": tokenizer_type,
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"tokenizer_file": tokenizer_file,
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"mixed_precision": mixed_precision,
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}
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with open(file_setting, "w") as f:
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json.dump(settings, f, indent=4)
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return "Settings saved!"
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# Load settings from a JSON file
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def load_settings(project_name):
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project_name = project_name.replace("_pinyin", "").replace("_char", "")
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path_project = os.path.join(path_project_ckpts, project_name)
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file_setting = os.path.join(path_project, "setting.json")
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if os.path.isfile(file_setting) == False:
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settings = {
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"exp_name": "F5TTS_Base",
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"learning_rate": 1e-05,
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"batch_size_per_gpu": 1000,
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"batch_size_type": "frame",
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"max_samples": 64,
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"grad_accumulation_steps": 1,
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"max_grad_norm": 1,
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"epochs": 100,
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"num_warmup_updates": 2,
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"save_per_updates": 300,
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"last_per_steps": 200,
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"finetune": True,
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"file_checkpoint_train": "",
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"tokenizer_type": "pinyin",
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"tokenizer_file": "",
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"mixed_precision": "none",
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}
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return (
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settings["exp_name"],
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settings["learning_rate"],
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settings["batch_size_per_gpu"],
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settings["batch_size_type"],
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settings["max_samples"],
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settings["grad_accumulation_steps"],
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settings["max_grad_norm"],
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settings["epochs"],
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settings["num_warmup_updates"],
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settings["save_per_updates"],
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settings["last_per_steps"],
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settings["finetune"],
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settings["file_checkpoint_train"],
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settings["tokenizer_type"],
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settings["tokenizer_file"],
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settings["mixed_precision"],
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)
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with open(file_setting, "r") as f:
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settings = json.load(f)
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return (
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settings["exp_name"],
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settings["learning_rate"],
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settings["batch_size_per_gpu"],
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settings["batch_size_type"],
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settings["max_samples"],
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settings["grad_accumulation_steps"],
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settings["max_grad_norm"],
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settings["epochs"],
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settings["num_warmup_updates"],
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settings["save_per_updates"],
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settings["last_per_steps"],
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settings["finetune"],
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settings["file_checkpoint_train"],
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settings["tokenizer_type"],
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settings["tokenizer_file"],
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settings["mixed_precision"],
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)
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# Load metadata
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def get_audio_duration(audio_path):
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"""Calculate the duration of an audio file."""
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@@ -330,6 +443,26 @@ def start_training(
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print(cmd)
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save_settings(
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dataset_name,
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exp_name,
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learning_rate,
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batch_size_per_gpu,
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batch_size_type,
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max_samples,
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grad_accumulation_steps,
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max_grad_norm,
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epochs,
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num_warmup_updates,
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save_per_updates,
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last_per_steps,
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finetune,
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file_checkpoint_train,
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tokenizer_type,
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tokenizer_file,
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mixed_precision,
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)
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try:
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# Start the training process
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training_process = subprocess.Popen(cmd, shell=True)
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@@ -1225,6 +1358,42 @@ If you encounter a memory error, try reducing the batch size per GPU to a smalle
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start_button = gr.Button("Start Training")
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stop_button = gr.Button("Stop Training", interactive=False)
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if projects_selelect is not None:
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(
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exp_namev,
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learning_ratev,
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batch_size_per_gpuv,
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batch_size_typev,
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max_samplesv,
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grad_accumulation_stepsv,
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max_grad_normv,
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epochsv,
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num_warmupv_updatesv,
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save_per_updatesv,
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last_per_stepsv,
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finetunev,
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file_checkpoint_trainv,
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tokenizer_typev,
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tokenizer_filev,
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mixed_precisionv,
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) = load_settings(projects_selelect)
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exp_name.value = exp_namev
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learning_rate.value = learning_ratev
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batch_size_per_gpu.value = batch_size_per_gpuv
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batch_size_type.value = batch_size_typev
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max_samples.value = max_samplesv
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grad_accumulation_steps.value = grad_accumulation_stepsv
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max_grad_norm.value = max_grad_normv
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epochs.value = epochsv
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num_warmup_updates.value = num_warmupv_updatesv
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save_per_updates.value = save_per_updatesv
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last_per_steps.value = last_per_stepsv
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ch_finetune.value = finetunev
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file_checkpoint_train.value = file_checkpoint_train
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tokenizer_type.value = tokenizer_typev
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tokenizer_file.value = tokenizer_filev
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mixed_precision.value = mixed_precisionv
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txt_info_train = gr.Text(label="info", value="")
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start_button.click(
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fn=start_training,
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@@ -1279,6 +1448,29 @@ If you encounter a memory error, try reducing the batch size per GPU to a smalle
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check_finetune, inputs=[ch_finetune], outputs=[file_checkpoint_train, tokenizer_file, tokenizer_type]
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)
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cm_project.change(
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fn=load_settings,
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inputs=[cm_project],
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outputs=[
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exp_name,
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learning_rate,
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batch_size_per_gpu,
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batch_size_type,
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max_samples,
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grad_accumulation_steps,
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max_grad_norm,
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epochs,
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num_warmup_updates,
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save_per_updates,
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last_per_steps,
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ch_finetune,
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file_checkpoint_train,
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tokenizer_type,
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tokenizer_file,
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mixed_precision,
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],
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)
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with gr.TabItem("test model"):
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exp_name = gr.Radio(label="Model", choices=["F5-TTS", "E2-TTS"], value="F5-TTS")
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list_checkpoints, checkpoint_select = get_checkpoints_project(projects_selelect, False)
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