90 lines
2.5 KiB
Python
90 lines
2.5 KiB
Python
import os
|
|
import yaml
|
|
import torch
|
|
import numpy as np
|
|
from torch.nn import functional as F
|
|
|
|
|
|
def sequence_mask(lengths, maxlen=None, dtype=torch.float32, device=None):
|
|
if maxlen is None:
|
|
maxlen = lengths.max()
|
|
row_vector = torch.arange(0, maxlen, 1).to(lengths.device)
|
|
matrix = torch.unsqueeze(lengths, dim=-1)
|
|
mask = row_vector < matrix
|
|
mask = mask.detach()
|
|
|
|
return mask.type(dtype).to(device) if device is not None else mask.type(dtype)
|
|
|
|
|
|
def apply_cmvn(inputs, mvn):
|
|
device = inputs.device
|
|
dtype = inputs.dtype
|
|
frame, dim = inputs.shape
|
|
meams = np.tile(mvn[0:1, :dim], (frame, 1))
|
|
vars = np.tile(mvn[1:2, :dim], (frame, 1))
|
|
inputs -= torch.from_numpy(meams).type(dtype).to(device)
|
|
inputs *= torch.from_numpy(vars).type(dtype).to(device)
|
|
|
|
return inputs.type(torch.float32)
|
|
|
|
|
|
def drop_and_add(
|
|
inputs: torch.Tensor,
|
|
outputs: torch.Tensor,
|
|
training: bool,
|
|
dropout_rate: float = 0.1,
|
|
stoch_layer_coeff: float = 1.0,
|
|
):
|
|
|
|
outputs = F.dropout(outputs, p=dropout_rate, training=training, inplace=True)
|
|
outputs *= stoch_layer_coeff
|
|
|
|
input_dim = inputs.size(-1)
|
|
output_dim = outputs.size(-1)
|
|
|
|
if input_dim == output_dim:
|
|
outputs += inputs
|
|
return outputs
|
|
|
|
|
|
def proc_tf_vocab(vocab_path):
|
|
with open(vocab_path, encoding="utf-8") as f:
|
|
token_list = [line.rstrip() for line in f]
|
|
if "<unk>" not in token_list:
|
|
token_list.append("<unk>")
|
|
return token_list
|
|
|
|
|
|
def gen_config_for_tfmodel(config_path, vocab_path, output_dir):
|
|
token_list = proc_tf_vocab(vocab_path)
|
|
with open(config_path, encoding="utf-8") as f:
|
|
config = yaml.safe_load(f)
|
|
|
|
config["token_list"] = token_list
|
|
|
|
if not os.path.exists(output_dir):
|
|
os.makedirs(output_dir)
|
|
|
|
with open(os.path.join(output_dir, "config.yaml"), "w", encoding="utf-8") as f:
|
|
yaml_no_alias_safe_dump(config, f, indent=4, sort_keys=False)
|
|
|
|
|
|
class NoAliasSafeDumper(yaml.SafeDumper):
|
|
# Disable anchor/alias in yaml because looks ugly
|
|
def ignore_aliases(self, data):
|
|
return True
|
|
|
|
|
|
def yaml_no_alias_safe_dump(data, stream=None, **kwargs):
|
|
"""Safe-dump in yaml with no anchor/alias"""
|
|
return yaml.dump(data, stream, allow_unicode=True, Dumper=NoAliasSafeDumper, **kwargs)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import sys
|
|
|
|
config_path = sys.argv[1]
|
|
vocab_path = sys.argv[2]
|
|
output_dir = sys.argv[3]
|
|
gen_config_for_tfmodel(config_path, vocab_path, output_dir)
|