import os import sys import traceback import tqdm os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0" device = sys.argv[1] n_part = int(sys.argv[2]) i_part = int(sys.argv[3]) if len(sys.argv) == 7: exp_dir = sys.argv[4] version = sys.argv[5] is_half = sys.argv[6] else: i_gpu = sys.argv[4] exp_dir = sys.argv[5] os.environ["CUDA_VISIBLE_DEVICES"] = str(i_gpu) version = sys.argv[6] is_half = sys.argv[7] import fairseq import numpy as np import soundfile as sf import torch import torch.nn.functional as F if "privateuseone" not in device: device = "cpu" if torch.cuda.is_available(): device = "cuda" elif torch.backends.mps.is_available(): device = "mps" else: import torch_directml device = torch_directml.device(torch_directml.default_device()) def forward_dml(ctx, x, scale): ctx.scale = scale res = x.clone().detach() return res fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml f = open("%s/extract_f0_feature.log" % exp_dir, "a+") def printt(strr): print(strr) f.write("%s\n" % strr) f.flush() printt(sys.argv) model_path = "assets/hubert/hubert_base.pt" printt(exp_dir) wavPath = "%s/1_16k_wavs" % exp_dir outPath = ( "%s/3_feature256" % exp_dir if version == "v1" else "%s/3_feature768" % exp_dir ) os.makedirs(outPath, exist_ok=True) # wave must be 16k, hop_size=320 def readwave(wav_path, normalize=False): wav, sr = sf.read(wav_path) assert sr == 16000 #feats = torch.from_numpy(wav).float() feats = torch.from_numpy(wav) if is_half: feats = feats.half() else: feats = feats.float() if feats.dim() == 2: # double channels feats = feats.mean(-1) assert feats.dim() == 1, feats.dim() if normalize: with torch.no_grad(): feats = F.layer_norm(feats, feats.shape) feats = feats.view(1, -1) return feats # HuBERT model os.system('cls' if os.name == 'nt' else 'clear') print("Starting feature extraction...\n") printt("Loaded model {}".format(model_path)) # if hubert model is exist if os.access(model_path, os.F_OK) == False: printt( "Error: Extracting is shut down because %s does not exist, you may download it from https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main" % model_path ) exit(0) models, saved_cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task( [model_path], suffix="", ) model = models[0] model = model.to(device) printt("Using %s" % device) #if device not in ["mps", "cpu"]: # model = model.half() if is_half: model = model.half() else: model = model.float() model.eval() todo = sorted(list(os.listdir(wavPath)))[i_part::n_part] n = max(1, len(todo) // 10) # 最多打印十条 if len(todo) == 0: os.system('cls' if os.name == 'nt' else 'clear') printt("An error occurred in the feature extraction, make sure you have provided the audios correctly.") else: printt("- %s" % len(todo)) with tqdm.tqdm(total=len(todo)) as pbar: for idx, file in enumerate(todo): try: if file.endswith(".wav"): wav_path = "%s/%s" % (wavPath, file) out_path = "%s/%s" % (outPath, file.replace("wav", "npy")) if os.path.exists(out_path): continue feats = readwave(wav_path, normalize=saved_cfg.task.normalize) padding_mask = torch.BoolTensor(feats.shape).fill_(False) inputs = { "source": feats.to(device), "padding_mask": padding_mask.to(device), "output_layer": 9 if version == "v1" else 12, # layer 9 } with torch.no_grad(): logits = model.extract_features(**inputs) feats = ( model.final_proj(logits[0]) if version == "v1" else logits[0] ) feats = feats.squeeze(0).float().cpu().numpy() if np.isnan(feats).sum() == 0: np.save(out_path, feats, allow_pickle=False) else: printt("%s-contains nan" % file) # if idx % n == 0: # printt("now-%s,all-%s,%s,%s" % (idx, len(todo), file, feats.shape)) pbar.set_description(f"Processing: %s - Shape: %s" % (file, feats.shape)) except: printt(traceback.format_exc()) pbar.update(1) printt("\nFeature extraction completed successfully!")