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from pathlib import Path
import librosa
import numpy as np
import torch
def load_model(vec_path):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("load model(s) from {}".format(vec_path))
from fairseq import checkpoint_utils
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
[vec_path],
suffix="",
)
model = models[0]
model = model.to(device)
model.eval()
return model
def get_vec_units(con_model, audio_path, dev):
audio, sampling_rate = librosa.load(audio_path)
if len(audio.shape) > 1:
audio = librosa.to_mono(audio.transpose(1, 0))
if sampling_rate != 16000:
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
feats = torch.from_numpy(audio).float()
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
feats = feats.view(1, -1)
padding_mask = torch.BoolTensor(feats.shape).fill_(False)
inputs = {
"source": feats.to(dev),
"padding_mask": padding_mask.to(dev),
"output_layer": 9, # layer 9
}
with torch.no_grad():
logits = con_model.extract_features(**inputs)
feats = con_model.final_proj(logits[0])
return feats
if __name__ == '__main__':
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_path = "../../checkpoints/checkpoint_best_legacy_500.pt" # checkpoint_best_legacy_500.pt
vec_model = load_model(model_path)
# 这个不用改，自动在根目录下所有wav的同文件夹生成其对应的npy
file_lists = list(Path("../../data/vecfox").rglob('*.wav'))
nums = len(file_lists)
count = 0
for wav_path in file_lists:
npy_path = wav_path.with_suffix(".npy")
npy_content = get_vec_units(vec_model, str(wav_path), device).cpu().numpy()[0]
np.save(str(npy_path), npy_content)
count += 1
print(f"hubert process：{round(count * 100 / nums, 2)}%")
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