请把下列shell语句转换成python语句：if [ "${token_type}" = bpe ]; then dict=data/lang_char/${train_set}_${bpemode}${nbpe}_units.txt bpemodel=data/lang_char/${train_set}_${bpemode}${nbpe} elif [ "${token_type}" = char ]; then dict=data/lang_char/lang_char.txt bpe_model= else echo "Error: not supported token_type" exit 0 fi dict=data/dict/lang_char_ori.txt train_set=train train_dev=dev recog_set=eval_$recog

以下是将给定的shell语句转换为Python语句的一种方法：

# 根据token_type的值设置dict和bpemodel变量
if token_type == "bpe":
    dict = f"data/lang_char/{train_set}_{bpemode}{nbpe}_units.txt"
    bpemodel = f"data/lang_char/{train_set}_{bpemode}{nbpe}"
elif token_type == "char":
    dict = "data/lang_char/lang_char.txt"
    bpemodel = ""
else:
    print("Error: not supported token_type")
    exit(0)

# 设置dict、train_set、train_dev和recog_set变量
dict = "data/dict/lang_char_ori.txt"
train_set = "train"
train_dev = "dev"
recog_set = f"eval_{recog}"

需要注意的是，这里使用了Python的f-string来格式化字符串，通过将变量值插入到字符串中。同时，这里使用了Python的if-elif-else语句来根据不同的条件执行不同的代码块。

相关问题

请把下列shell语句转换成python语句：#langid: 101 Cantonese , 302 Kazakh , 401 mongolian langs="101" recog="101" token_type=char # bpemode (unigram or bpe) nbpe=4500 bpemode=unigram

以下是将给定的shell语句转换为Python语句的一种方法：

# 设置langs、recog、token_type、nbpe和bpemode变量
langs = "101 Cantonese , 302 Kazakh , 401 mongolian"
recog = "101"
token_type = "char"
nbpe = 4500
bpemode = "unigram"

需要注意的是，这里直接使用Python的变量赋值语句来设置变量即可。

请将下列shell语句转换为python语句：if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then # Make train dict echo "Make a dictionary" mkdir -p $(dirname $dict) echo "<blank> 0" > ${dict} # 0 will be used for "blank" in CTC echo "<unk> 1" >> ${dict} # <unk> must be 1 if [ "${token_type}" = bpe ]; then # we borrowed these code and scripts which are related bpe from ESPnet. cut -f 2- -d" " data/${train_set}/text | sort > data/lang_char/input.txt tools/spm_train --input=data/lang_char/input.txt --vocab_size=${nbpe} --model_type=${bpemode} --model_prefix=${bpemodel} --input_sentence_size=100000000 tools/spm_encode --model=${bpemodel}.model --output_format=piece < data/lang_char/input.txt | tr ' ' '\n' | sort | uniq | awk '{print $0 " " NR+1}' >> ${dict} elif [ "${token_type}" = char ]; then tools/text2token.py -s 1 -n 1 data/${train_set}/text | cut -f 2- -d" " | tr " " "\n" \ | sort | uniq | grep -a -v -e '^\s*$' | awk '{print $0 " " NR+1}' >> ${dict} fi num_token=$(cat $dict | wc -l) echo "<sos/eos> $num_token" >> $dict # <eos> fi

以下是将上述shell语句转换为Python语句的代码：

if stage <= 2 and stop_stage >= 2:
    # Make train dict
    print("Make a dictionary")
    os.makedirs(os.path.dirname(dict), exist_ok=True)
    with open(dict, "w") as f:
        f.write("<blank> 0\n") # 0 will be used for "blank" in CTC
        f.write("<unk> 1\n") # <unk> must be 1

        if token_type == "bpe":
            # we borrowed these code and scripts which are related bpe from ESPnet.
            with open(f"data/{train_set}/text") as text_file:
                texts = [line.strip().split()[1:] for line in text_file]
            with open("data/lang_char/input.txt", "w") as input_file:
                for text in texts:
                    input_file.write(" ".join(text) + "\n")
            subprocess.run(f"tools/spm_train --input=data/lang_char/input.txt --vocab_size={nbpe} --model_type={bpemode} --model_prefix={bpemodel} --input_sentence_size=100000000", shell=True)
            with open(f"{bpemodel}.vocab") as vocab_file:
                vocab = [line.strip().split()[0] for line in vocab_file]
            with open(dict, "a") as dict_file:
                for i, vocab_item in enumerate(vocab):
                    dict_file.write(f"{vocab_item} {i+2}\n")
        elif token_type == "char":
            with open(f"data/{train_set}/text") as text_file:
                texts = [line.strip().split()[1] for line in text_file]
            texts = " ".join(texts)
            tokens = subprocess.check_output(f"echo '{texts}' | tools/spm_encode --model={bpemodel}.model --output_format=piece", shell=True)
            tokens = tokens.decode().split("\n")
            tokens = [token.strip() for token in tokens if token.strip() != ""]
            tokens = sorted(set(tokens))
            with open(dict, "a") as dict_file:
                for i, token in enumerate(tokens):
                    dict_file.write(f"{token} {i+2}\n")

        num_token = sum(1 for line in open(dict))
        with open(dict, "a") as dict_file:
            dict_file.write(f"<sos/eos> {num_token}\n") # <eos>

需要注意的是，在Python中也需要使用相应的模块和工具来实现shell语句中的一些操作，例如在此代码中使用了`os`、`subprocess`模块和`tools/spm_train`、`tools/spm_encode`脚本来进行文件操作和调用外部命令。另外，由于Python中没有直接对应的`$`符号，需要使用`f-string`或者`str.format()`方法来进行字符串格式化。