| import torch | |
| from datetime import datetime | |
| def prettify_date(date_str): | |
| if date_str is None: | |
| return "None" | |
| try: | |
| date_time_obj = datetime.strptime(date_str, "%Y-%m-%dT%H:%M:%S.%f") | |
| return date_time_obj.strftime("%Y-%m-%d %H:%M:%S") | |
| except ValueError: | |
| return "Invalid date format" | |
| def model_information(path): | |
| model_data = torch.load(path, map_location="cpu", weights_only=True) | |
| print(f"Loaded model from {path}") | |
| model_name = model_data.get("model_name", "None") | |
| epochs = model_data.get("epoch", "None") | |
| steps = model_data.get("step", "None") | |
| sr = model_data.get("sr", "None") | |
| f0 = model_data.get("f0", "None") | |
| dataset_length = model_data.get("dataset_length", "None") | |
| vocoder = model_data.get("vocoder", "None") | |
| creation_date = model_data.get("creation_date", "None") | |
| model_hash = model_data.get("model_hash", None) | |
| overtrain_info = model_data.get("overtrain_info", "None") | |
| model_author = model_data.get("author", "None") | |
| embedder_model = model_data.get("embedder_model", "None") | |
| speakers_id = model_data.get("speakers_id", 0) | |
| creation_date_str = prettify_date(creation_date) if creation_date else "None" | |
| return ( | |
| f"Model Name: {model_name}\n" | |
| f"Model Creator: {model_author}\n" | |
| f"Epochs: {epochs}\n" | |
| f"Steps: {steps}\n" | |
| f"Vocoder: {vocoder}\n" | |
| f"Sampling Rate: {sr}\n" | |
| f"Dataset Length: {dataset_length}\n" | |
| f"Creation Date: {creation_date_str}\n" | |
| f"Overtrain Info: {overtrain_info}\n" | |
| f"Embedder Model: {embedder_model}\n" | |
| f"Max Speakers ID: {speakers_id}" | |
| f"Hash: {model_hash}\n" | |
| ) | |