| import os
|
| import sys
|
| import traceback
|
|
|
| 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].lower() == "true"
|
| 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].lower() == "true"
|
| 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(" ".join(sys.argv))
|
| model_path = "assets/hubert/hubert_base.pt"
|
|
|
| printt("exp_dir: " + 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)
|
|
|
|
|
|
|
| def readwave(wav_path, normalize=False):
|
| wav, sr = sf.read(wav_path)
|
| assert sr == 16000
|
| feats = torch.from_numpy(wav).float()
|
| if feats.dim() == 2:
|
| 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
|
|
|
|
|
|
|
| printt("load model(s) from {}".format(model_path))
|
|
|
| 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("move model to %s" % device)
|
| if is_half:
|
| if device not in ["mps", "cpu"]:
|
| model = model.half()
|
| model.eval()
|
|
|
| todo = sorted(list(os.listdir(wavPath)))[i_part::n_part]
|
| n = max(1, len(todo) // 10)
|
| if len(todo) == 0:
|
| printt("no-feature-todo")
|
| else:
|
| printt("all-feature-%s" % len(todo))
|
| 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.half().to(device)
|
| if is_half and device not in ["mps", "cpu"]
|
| else feats.to(device)
|
| ),
|
| "padding_mask": padding_mask.to(device),
|
| "output_layer": 9 if version == "v1" else 12,
|
| }
|
| 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" % (len(todo), idx, file, feats.shape))
|
| except:
|
| printt(traceback.format_exc())
|
| printt("all-feature-done")
|
|
|
