138 lines
4.1 KiB
Python
138 lines
4.1 KiB
Python
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) == 6:
|
|
exp_dir = sys.argv[4]
|
|
version = sys.argv[5]
|
|
else:
|
|
i_gpu = sys.argv[4]
|
|
exp_dir = sys.argv[5]
|
|
os.environ["CUDA_VISIBLE_DEVICES"] = str(i_gpu)
|
|
version = sys.argv[6]
|
|
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()
|
|
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
|
|
printt("load model(s) from {}".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("move model to %s" % device)
|
|
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) # 最多打印十条
|
|
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 device not in ["mps", "cpu"]
|
|
else 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" % (len(todo), idx, file, feats.shape))
|
|
except:
|
|
printt(traceback.format_exc())
|
|
printt("all-feature-done")
|