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import os, sys, traceback
# device=sys.argv[1]
n_part = int(sys.argv[2])
i_part = int(sys.argv[3])
if len(sys.argv) == 5:
exp_dir = sys.argv[4]
else:
i_gpu = sys.argv[4]
exp_dir = sys.argv[5]
os.environ["CUDA_VISIBLE_DEVICES"] = str(i_gpu)
import torch
import torch.nn.functional as F
import soundfile as sf
import numpy as np
from fairseq import checkpoint_utils
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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 = "hubert_base.pt"
printt(exp_dir)
wavPath = "%s/1_16k_wavs" % exp_dir
outPath = "%s/3_feature256" % 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))
models, saved_cfg, task = 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 != "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 != "cpu"
else feats.to(device),
"padding_mask": padding_mask.to(device),
"output_layer": 9, # layer 9
}
with torch.no_grad():
logits = model.extract_features(**inputs)
feats = model.final_proj(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")
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