| | 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")
|
| |
|
