def main():
print_arguments(args)
# 创建保存模型的文件夹
if not os.path.exists(args.save_model_path):
os.makedirs(args.save_model_path)
# 加载数据字典
with open(args.vocab_path, 'r', encoding='utf-8') as f:
vocabulary = eval(f.read())
vocabulary = "".join(vocabulary)
# 获取模型
model = GatedConv(vocabulary)
# 加载预训练模型
if args.restore_model:
model = torch.load(args.restore_model)
model = model.cuda()
train(model=model,
train_manifest_path=args.train_manifest_path,
dev_manifest_path=args.dev_manifest_path,
vocab_path=args.vocab_path,
epochs=args.epochs,
batch_size=args.batch_size,
learning_rate=args.learning_rate)
decoder = GreedyDecoder(dataloader.dataset.labels_str)
cer = 0
print("decoding")
with torch.no_grad():
for i, (x, y, x_lens, y_lens) in tqdm(enumerate(dataloader)):
x = x.cuda()
outs, out_lens = model(x, x_lens)
outs = F.softmax(outs, 1)
outs = outs.transpose(1, 2)
ys = []
offset = 0
for y_len in y_lens:
ys.append(y[offset:offset + y_len])
offset += y_len
out_strings, out_offsets = decoder.decode(outs, out_lens)
y_strings = decoder.convert_to_strings(ys)
for pred, truth in zip(out_strings, y_strings):
trans, ref = pred[0], truth[0]
cer += decoder.cer(trans, ref) / float(len(ref))
cer /= len(dataloader.dataset)
model.train()
return cer
if __name__ == "__main__":
vocabulary = joblib.load(LABEL_PATH)
vocabulary = "".join(vocabulary)
model = GatedConv(vocabulary)
model.cuda()
train(model)