帮我写一个timit训练集统计bigram，并使用arpa语言模型格式将其保存为TXT文件的代码

好的，以下是使用Python编写的示例代码：

import os
from nltk import bigrams
from nltk.tokenize import word_tokenize
from nltk.lm.preprocessing import pad_both_ends
from nltk.lm import Vocabulary
from nltk.lm.models import Laplace

# TIMIT数据集文件夹路径
timit_folder_path = "/path/to/timit/folder"
# 训练文本文件路径
train_file_path = os.path.join(timit_folder_path, "train.txt")
# bigram统计结果保存路径
bigram_file_path = os.path.join(timit_folder_path, "timit.bigram")
# arpa格式保存路径
arpa_file_path = os.path.join(timit_folder_path, "timit.arpa")

# 读取训练文本
with open(train_file_path, "r") as f:
    train_text = f.read()

# 分词
train_tokens = word_tokenize(train_text)

# 添加开始和结束标记
train_tokens = list(pad_both_ends(train_tokens, n=2))

# 构建词汇表
vocab = Vocabulary(train_tokens, unk_cutoff=1)

# 构建bigram模型
bigram = bigrams(train_tokens)
laplace = Laplace(2, vocab)

# 统计bigram
for bg in bigram:
    laplace.counts[bg]

# 保存bigram到txt文件
with open(bigram_file_path, "w") as f:
    for bg, count in laplace.counts.items():
        f.write("{}\t{}\n".format(" ".join(bg), count))

# 保存arpa格式语言模型到文件
with open(arpa_file_path, "w") as f:
    f.write(laplace.toarpa())

请注意，此代码需要使用NLTK库。您可以使用以下命令安装它：

pip install nltk

另外，请将代码中的`timit_folder_path`替换为您的TIMIT数据集文件夹路径。