File size: 4,103 Bytes
528df8b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 |
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")
|