enroll_response = requests.post("http://47.108.228.186:8899/wav/enroll", files=enroll_data)

这是一个使用Python requests库向某个URL发送POST请求的代码片段。其中，enroll_data是一个包含音频文件等信息的字典或元组，请求会将该数据发送到"http://47.108.228.186:8899/wav/enroll"这个URL。具体实现细节需要根据上下文和完整代码来判断。

相关问题

wav_list = glob.glob("C:\ResNet\TIMIT\TEST\*\*\*.wav") print(f'找到{len(wav_list)}个训练音频') speaker_name_list = [] all_wav_list = [] speaker_wav_dict = dict({}) enroll_list = [] for wav in wav_list: wav_split = wav.split('\\') d_r = wav_split[-3] speaker_name = wav_split[-2] wav_name = wav_split[-1] speaker_name_list.append(speaker_name) wav_path = f"{d_r}\{speaker_name}\{wav_name}" all_wav_list.append(wav_path) if speaker_name in speaker_wav_dict: speaker_wav_dict[speaker_name].append(wav_path) else: speaker_wav_dict[speaker_name] = [wav_path] enroll_list.append(wav_path) SpeakerNameList = set(speaker_name_list) f = open("TIMIT-testlist.txt", 'w') num_pairs = 4000 for i in range(num_pairs): if i % 2 == 0: # label==1 wav1 = random.sample(enroll_list, 1)[0] id1 = wav1.split('\\')[-2] wav2 = random.sample(speaker_wav_dict[id1], 1)[0] label = 1 else: # label==0 wav1 = random.sample(enroll_list, 1)[0] id1 = wav1.split('\\')[-2] wav2 = random.sample(all_wav_list, 1)[0] id2 = wav2.split('-')[0] if id1 == id2: continue label = 0 f.write("{} .\{} .\{}\n".format(label, wav1, wav2)) f.close()

这段代码是在使用TIMIT数据集创建一份测试集清单文件(TIMIT-testlist.txt)，其中包含4000对音频(wav1, wav2)以及它们的标签(label)。具体流程如下：

1. 使用glob模块匹配TIMIT数据集TEST文件夹中所有的.wav文件，并统计数量。
2. 遍历所有.wav文件，将它们的路径存入all_wav_list列表中，并将每个speaker的第一条音频加入enroll_list列表中。
3. 构建speaker_wav_dict字典，键为说话人ID，值为该说话人的所有音频路径列表。
4. 遍历num_pairs次，每次随机选择两个音频wav1和wav2，并给它们标上0或1的标签。
5. 如果标签为1，则从enroll_list列表中随机选择一个音频作为wav1，再从该说话人的所有音频中随机选择一个作为wav2。
6. 如果标签为0，则从enroll_list列表中随机选择一个音频作为wav1，再从all_wav_list列表中随机选择一个作为wav2。要求wav2所属的说话人与wav1不同。
7. 将每对wav1和wav2以及它们的标签写入TIMIT-testlist.txt文件中。

这份测试集清单文件可以用于测试说话人识别模型的准确率。

select s.sex as sex, if(s.sex = 0, '女', '男') as sexText, s.political as political, dd.dict_value as politicalText, s.certificate as certificate, dd1.dict_value as certificateText, s.household as household, dd2.dict_value as householdText, s.pay_type as payType, dd3.dict_value as payTypeText, s.enroll_mode as enrollMode, dd4.dict_value as enrollModeText, s.admission_batch as admissionBatch, dd5.dict_value as admissionBatchTypeText, s.cultivation_level as cultivationLevel, dd6.dict_value as cultivationLevelText, s.cultivation_mode as cultivationMode, dd7.dict_value as cultivationModeText, s.learning_type as learningType, dd8.dict_value as learningTypeText, s.subject as subject, dd9.dict_value as subjectText, dd.is_del as is_del, dd.status as status from student as s left join data_dictionary as dd on s.political = dd.id left join data_dictionary as dd1 on s.certificate = dd1.id left join data_dictionary as dd2 on s.household = dd2.id left join data_dictionary as dd3 on s.pay_type = dd3.id left join data_dictionary as dd4 on s.enroll_mode = dd4.id left join data_dictionary as dd5 on s.admission_batch = dd5.id left join data_dictionary as dd6 on s.cultivation_level = dd6.id left join data_dictionary as dd7 on s.cultivation_mode = dd7.id left join data_dictionary as dd8 on s.learning_type = dd8.id left join data_dictionary as dd9 on s.subject = dd9.id where 1 = 1 and dd9.is_del = 1 and dd9.status = 1 and dd8.is_del = 1 and dd8.status = 1 and dd7.is_del = 1 and dd7.status = 1 and dd6.is_del = 1 and dd6.status = 1 and dd5.is_del = 1 and dd5.status = 1 and dd4.is_del = 1 and dd4.status = 1 and dd3.is_del = 1 and dd3.status = 1 and dd2.is_del = 1 and dd2.status = 1 and dd1.is_del = 1 and dd1.status = 1 and dd.is_del = 1 and dd.status = 1

这是一个 SQL 查询语句，用于查询学生信息表（student）中的各个字段，并通过左连接查询各个字段对应的字典表（data_dictionary）中的对应值。其中，查询的字段包括性别、政治面貌、证件类型、户籍类型、缴费类型、入学方式、录取批次、培养层次、培养方式、学习类型、专业等，并将对应的字典值查询出来。同时，WHERE 子句中包括了一系列过滤条件，用于限定查询结果的范围。