import torch import numpy as np import huggingface_hub import zipfile import os from collections import OrderedDict def model_info(model_path): model = torch.load(model_path, map_location=torch.device('cpu')) info = { 'config': model['config'], 'info': model['info'], 'epochs': model['info'].split('epoch')[0], 'sr': model['sr'], 'f0': model['f0'], 'size': model['size'] if 'size' in model['weight'] else 'fp32', } return info def merge(path1, path2, alpha1, sr, f0, info, name, version): try: def extract(ckpt): a = ckpt["model"] opt = OrderedDict() opt["weight"] = {} for key in a.keys(): if "enc_q" in key: continue opt["weight"][key] = a[key] return opt ckpt1 = torch.load(path1, map_location="cpu") ckpt2 = torch.load(path2, map_location="cpu") cfg = ckpt1["config"] if "model" in ckpt1: ckpt1 = extract(ckpt1) else: ckpt1 = ckpt1["weight"] if "model" in ckpt2: ckpt2 = extract(ckpt2) else: ckpt2 = ckpt2["weight"] if sorted(list(ckpt1.keys())) != sorted(list(ckpt2.keys())): return "Fail to merge the models. The model architectures are not the same." opt = OrderedDict() opt["weight"] = {} for key in ckpt1.keys(): # try: if key == "emb_g.weight" and ckpt1[key].shape != ckpt2[key].shape: min_shape0 = min(ckpt1[key].shape[0], ckpt2[key].shape[0]) opt["weight"][key] = ( alpha1 * (ckpt1[key][:min_shape0].float()) + (1 - alpha1) * (ckpt2[key][:min_shape0].float()) ).half() else: opt["weight"][key] = ( alpha1 * (ckpt1[key].float()) + (1 - alpha1) * (ckpt2[key].float()) ).half() # except: # pdb.set_trace() opt["config"] = cfg """ if(sr=="40k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 10, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 40000] elif(sr=="48k"):opt["config"] = [1025, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10,6,2,2,2], 512, [16, 16, 4, 4], 109, 256, 48000] elif(sr=="32k"):opt["config"] = [513, 32, 192, 192, 768, 2, 6, 3, 0, "1", [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 4, 2, 2, 2], 512, [16, 16, 4, 4,4], 109, 256, 32000] """ opt["sr"] = sr opt["f0"] = 1 if f0 == "Yes" else 0 opt["version"] = version opt["info"] = info torch.save(opt, "models/" + name + ".pth") return "models/" + name + ".pth" except: return "Fail to merge the models. The model architectures are not the same." # <- L if u see this u suck def model_quant(model_path, size): """ Quantize the model to a lower precision. - this is the floating point version Args: model_path: str, path to the model file size: str, one of ["fp2", "fp4", "fp8", "fp16"] Returns: str, message indicating the success of the operation """ size_options = ["fp2", "fp4", "fp8", "fp16"] if size not in size_options: raise ValueError(f"Size must be one of {size_options}") model_base = torch.load(model_path, map_location=torch.device('cpu')) model = model_base['weight'] #model = json.loads(json.dumps(model)) if size == "fp16": for key in model.keys(): model[key] = model[key].half() # 16-bit floating point elif size == "fp8": for key in model.keys(): model[key] = model[key].half().half() # 8-bit floating point <- this is the most common one elif size == "fp4": for key in model.keys(): model[key] = model[key].half().half().half() # 4-bit floating point <- ok maybe you're mentally ill if you choose this (very low precision) elif size == "fp2": for key in model.keys(): model[key] = model[key].half().half().half().half() # 2-bit floating point <- if you choose this you're a fucking dickhead coming print(model_path) output_path = model_path.split('.pth')[0] + f'_{size}.pth' output_style = { 'weight': model, 'config': model_base['config'], 'info': model_base['info'], 'sr': model_base['sr'], 'f0': model_base['f0'], 'credits': f"Quantized to {size} precision, using Ilaria RVC, (Mikus's script)", "size": size } torch.save(output_style, output_path) #AmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithraxAmerithrax # our data isnt safe anymore currently typing this and there is a 100% chance that it'll be stolen and used for training another fucking dogshit language model by a horrible company like openai # i say this as a person who communicates with microsoft and i will stop mentioning this as they're so closely tied together nowadays # as fred durst has said - "That's your best friend and your worst enemy - your own brain." - keep your shit local and never trust scumbag companies even if they make the models oss - they're stealing data # this is probably the only rant i'll have in this entire space and i put it in a notable spot return "Model quantized successfully" # <- enjoy this fucking hot shit that looks like a steaming turd paired with skibidi toilet and the unibomber def upload_model(repo, pth, index, token): """ Upload a model to the Hugging Face Hub Args: repo: str, the name of the repository pth: str, path to the model file index: str, the index of the model in the repository token: str, the API token Returns: str, message indicating the success of the operation """ readme = f""" # {repo} This is a model uploaded by Ilaria RVC, using Mikus's script. """ repo_name = repo.split('/')[1] with zipfile.ZipFile(f'{repo_name}.zip', 'w') as zipf: zipf.write(pth, os.path.basename(pth)) zipf.write(index, os.path.basename(index)) zipf.writestr('README.md', readme) huggingface_hub.HfApi().create_repo(token=token, name=repo, exist_ok=True) huggingface_hub.HfApi().upload_file(token=token, path=f'{repo.split("/")[1]}.zip', repo_id=repo) os.remove(f'{repo.split("/")[1]}.zip') return "Model uploaded successfully"