import os.path import os import traceback import gradio as gr from Ref_Audio_Selector.config_param.log_config import logger import Ref_Audio_Selector.common.model_manager as model_manager import Ref_Audio_Selector.tool.audio_sample as audio_sample import Ref_Audio_Selector.tool.audio_inference as audio_inference import Ref_Audio_Selector.tool.audio_config as audio_config import Ref_Audio_Selector.tool.audio_check as audio_check import Ref_Audio_Selector.tool.text_check as text_check import Ref_Audio_Selector.common.common as common import Ref_Audio_Selector.config_param.config_params as params import Ref_Audio_Selector.common.time_util as time_util import Ref_Audio_Selector.ui_init.init_ui_param as init from tools.i18n.i18n import I18nAuto from config import python_exec, is_half from tools import my_utils from tools.asr.config import asr_dict from subprocess import Popen i18n = I18nAuto() rw_param = params.config_manager.get_rw_param() p_similarity = None p_asr = None p_text_similarity = None # 校验基础信息 def check_base_info(text_work_space_dir, text_role): if text_work_space_dir is None or text_work_space_dir == '': raise Exception("工作目录不能为空") if text_role is None or text_role == '': raise Exception("角色不能为空") base_role_dir = os.path.join(text_work_space_dir, text_role) # 判断目录是否存在 if not os.path.exists(base_role_dir): # 如果不存在，则创建目录 os.makedirs(base_role_dir, exist_ok=True) return base_role_dir # 从list文件，提取参考音频 def convert_from_list(text_work_space_dir, text_role, text_list_input): text_work_space_dir, text_list_input = common.batch_clean_paths([text_work_space_dir, text_list_input]) text_convert_from_list_info = None text_sample_dir = None try: base_role_dir = check_base_info(text_work_space_dir, text_role) if text_list_input is None or text_list_input == '': raise Exception("list文件路径不能为空") ref_audio_all = os.path.join(base_role_dir, params.list_to_convert_reference_audio_dir) time_consuming, _ = time_util.time_monitor(audio_sample.convert_from_list)(text_list_input, ref_audio_all) text_convert_from_list_info = f"耗时：{time_consuming:0.1f}秒；转换成功：生成目录{ref_audio_all}" text_sample_dir = ref_audio_all except Exception as e: logger.error("发生异常: \n%s", traceback.format_exc()) text_convert_from_list_info = f"发生异常：{e}" text_sample_dir = '' return i18n(text_convert_from_list_info), text_sample_dir def start_similarity_analysis(work_space_dir, sample_dir, speaker_verification, base_voice_path, need_similarity_output): similarity_list = None similarity_file_dir = None similarity_dir = os.path.join(work_space_dir, params.audio_similarity_dir) os.makedirs(similarity_dir, exist_ok=True) base_voice_file_name = common.get_filename_without_extension(base_voice_path) similarity_file = os.path.join(similarity_dir, f'{base_voice_file_name}.txt') global p_similarity if p_similarity is None: cmd = f'"{python_exec}" Ref_Audio_Selector/tool/speaker_verification/voice_similarity.py ' cmd += f' -r "{base_voice_path}"' cmd += f' -c "{sample_dir}"' cmd += f' -o "{similarity_file}"' cmd += f' -m {speaker_verification}' logger.info(cmd) p_similarity = Popen(cmd, shell=True) p_similarity.wait() similarity_list = audio_sample.parse_similarity_file(similarity_file) if need_similarity_output: similarity_file_dir = os.path.join(similarity_dir, base_voice_file_name) audio_sample.copy_and_move(similarity_file_dir, similarity_list) p_similarity = None return similarity_list, similarity_file, similarity_file_dir else: return similarity_list, None, None # 基于一个基准音频，从参考音频目录中进行分段抽样 def sample(text_work_space_dir, text_role, text_sample_dir, dropdown_speaker_verification, text_base_voice_path, slider_subsection_num, slider_sample_num, checkbox_similarity_output): text_work_space_dir, text_sample_dir, text_base_voice_path \ = common.batch_clean_paths([text_work_space_dir, text_sample_dir, text_base_voice_path]) ref_audio_dir = None text_sample_info = None try: base_role_dir = check_base_info(text_work_space_dir, text_role) if text_sample_dir is None or text_sample_dir == '': raise Exception("参考音频抽样目录不能为空，请先完成上一步操作") if text_base_voice_path is None or text_base_voice_path == '': raise Exception("基准音频路径不能为空") if slider_subsection_num is None or slider_subsection_num == '': raise Exception("分段数不能为空") if slider_sample_num is None or slider_sample_num == '': raise Exception("每段随机抽样个数不能为空") if dropdown_speaker_verification is None or dropdown_speaker_verification == '': raise Exception("说话人确认算法不能为空") ref_audio_dir = os.path.join(base_role_dir, params.reference_audio_dir) time_consuming, (similarity_list, _, _) \ = time_util.time_monitor(start_similarity_analysis)(base_role_dir, text_sample_dir, dropdown_speaker_verification, text_base_voice_path, checkbox_similarity_output) text_sample_info = f"耗时：{time_consuming:0.1f}秒；抽样成功：生成目录{ref_audio_dir}" if similarity_list is None: raise Exception("相似度分析失败") audio_sample.sample(ref_audio_dir, similarity_list, slider_subsection_num, slider_sample_num) except Exception as e: logger.error("发生异常: \n%s", traceback.format_exc()) text_sample_info = f"发生异常：{e}" ref_audio_dir = '' text_refer_audio_file_dir = ref_audio_dir return i18n(text_sample_info), text_refer_audio_file_dir # 根据参考音频和测试文本，执行批量推理 def model_inference(text_work_space_dir, text_role, slider_request_concurrency_num, text_refer_audio_file_dir, text_url, dropdown_refer_type_param, text_text, text_ref_path, text_ref_text, text_emotion, text_test_content_dir): text_work_space_dir, text_refer_audio_file_dir, text_test_content_dir \ = common.batch_clean_paths([text_work_space_dir, text_refer_audio_file_dir, text_test_content_dir]) inference_dir = None text_asr_audio_dir = None text_model_inference_info = None try: base_role_dir = check_base_info(text_work_space_dir, text_role) if text_refer_audio_file_dir is None or text_refer_audio_file_dir == '': raise Exception("待推理的参考音频所在目录不能为空，请先完成上一步操作") if text_url is None or text_url == '': raise Exception("推理服务请求地址不能为空") if text_text is None or text_text == '': raise Exception("文本参数名不能为空") if text_test_content_dir is None or text_test_content_dir == '': raise Exception("待推理文本路径不能为空") if (text_ref_path is None or text_ref_path == '') and (text_ref_text is None or text_ref_text == '') and ( text_emotion is None or text_emotion == ''): raise Exception("参考音频路径/文本和角色情绪二选一填写，不能全部为空") inference_dir = os.path.join(base_role_dir, params.inference_audio_dir) text_asr_audio_dir = os.path.join(inference_dir, params.inference_audio_text_aggregation_dir) url_composer = audio_inference.TTSURLComposer(text_url, dropdown_refer_type_param, text_emotion, text_text, text_ref_path, text_ref_text) url_composer.is_valid() text_list = common.read_text_file_to_list(text_test_content_dir) if text_list is None or len(text_list) == 0: raise Exception("待推理文本内容不能为空") ref_audio_manager = common.RefAudioListManager(text_refer_audio_file_dir) if len(ref_audio_manager.get_audio_list()) == 0: raise Exception("待推理的参考音频不能为空") time_consuming, _ = time_util.time_monitor(audio_inference.generate_audio_files_parallel)(url_composer, text_list, ref_audio_manager.get_ref_audio_list(), inference_dir, slider_request_concurrency_num) text_model_inference_info = f"耗时：{time_consuming:0.1f}秒；推理成功：生成目录{inference_dir}" except Exception as e: logger.error("发生异常: \n%s", traceback.format_exc()) text_model_inference_info = f"发生异常：{e}" text_asr_audio_dir = '' return i18n(text_model_inference_info), text_asr_audio_dir, inference_dir # 对推理生成音频执行asr def asr(text_work_space_dir, text_role, text_asr_audio_dir, dropdown_asr_model, dropdown_asr_size, dropdown_asr_lang): text_work_space_dir, text_asr_audio_dir \ = common.batch_clean_paths([text_work_space_dir, text_asr_audio_dir]) asr_file = None text_text_similarity_analysis_path = None text_asr_info = None try: base_role_dir = check_base_info(text_work_space_dir, text_role) if text_asr_audio_dir is None or text_asr_audio_dir == '': raise Exception("待asr的音频所在目录不能为空，请先完成上一步操作") if dropdown_asr_model is None or dropdown_asr_model == '': raise Exception("asr模型不能为空") if dropdown_asr_size is None or dropdown_asr_size == '': raise Exception("asr模型大小不能为空") if dropdown_asr_lang is None or dropdown_asr_lang == '': raise Exception("asr语言不能为空") time_consuming, asr_file = time_util.time_monitor(open_asr)(text_asr_audio_dir, base_role_dir, dropdown_asr_model, dropdown_asr_size, dropdown_asr_lang) text_text_similarity_analysis_path = asr_file text_asr_info = f"耗时：{time_consuming:0.1f}秒；asr成功：生成文件{asr_file}" except Exception as e: logger.error("发生异常: \n%s", traceback.format_exc()) text_asr_info = f"发生异常：{e}" text_text_similarity_analysis_path = '' return i18n(text_asr_info), text_text_similarity_analysis_path def open_asr(asr_inp_dir, asr_opt_dir, asr_model, asr_model_size, asr_lang): global p_asr if p_asr is None: asr_inp_dir = my_utils.clean_path(asr_inp_dir) asr_py_path = asr_dict[asr_model]["path"] if asr_py_path == 'funasr_asr.py': asr_py_path = 'funasr_asr_multi_level_dir.py' if asr_py_path == 'fasterwhisper_asr.py': asr_py_path = 'fasterwhisper_asr_multi_level_dir.py' cmd = f'"{python_exec}" Ref_Audio_Selector/tool/asr/{asr_py_path} ' cmd += f' -i "{asr_inp_dir}"' cmd += f' -o "{asr_opt_dir}"' cmd += f' -s {asr_model_size}' cmd += f' -l {asr_lang}' cmd += " -p %s" % ("float16" if is_half == True else "float32") logger.info(cmd) p_asr = Popen(cmd, shell=True) p_asr.wait() p_asr = None output_dir_abs = os.path.abspath(asr_opt_dir) output_file_name = os.path.basename(asr_inp_dir) # 构造输出文件路径 output_file_path = os.path.join(output_dir_abs, f'{params.asr_filename}.list') return output_file_path else: return None # 对asr生成的文件，与原本的文本内容，进行相似度分析 def text_similarity_analysis(text_work_space_dir, text_role, slider_text_similarity_amplification_boundary, text_text_similarity_analysis_path): text_work_space_dir, text_text_similarity_analysis_path \ = common.batch_clean_paths([text_work_space_dir, text_text_similarity_analysis_path]) similarity_dir = None text_text_similarity_analysis_info = None try: base_role_dir = check_base_info(text_work_space_dir, text_role) if text_text_similarity_analysis_path is None or text_text_similarity_analysis_path == '': raise Exception("asr生成的文件路径不能为空，请先完成上一步操作") similarity_dir = os.path.join(base_role_dir, params.text_similarity_output_dir) time_consuming, _ = time_util.time_monitor(open_text_similarity_analysis)(text_text_similarity_analysis_path, similarity_dir, slider_text_similarity_amplification_boundary) average_similarity_file = os.path.join(similarity_dir, f'{params.text_emotion_average_similarity_report_filename}.txt') text_text_similarity_analysis_info = f"耗时：{time_consuming:0.1f}秒；相似度分析成功：生成目录{similarity_dir}" except Exception as e: logger.error("发生异常: \n%s", traceback.format_exc()) text_text_similarity_analysis_info = f"发生异常：{e}" return i18n(text_text_similarity_analysis_info), average_similarity_file def open_text_similarity_analysis(asr_file_path, output_dir, similarity_enlarge_boundary=0.9): global p_text_similarity if p_text_similarity is None: cmd = f'"{python_exec}" Ref_Audio_Selector/tool/text_comparison/asr_text_process.py ' cmd += f' -a "{asr_file_path}"' cmd += f' -o "{output_dir}"' cmd += f' -b {similarity_enlarge_boundary}' logger.info(cmd) p_text_similarity = Popen(cmd, shell=True) p_text_similarity.wait() p_text_similarity = None return output_dir else: return None hide_voice_similarity_dir = '' # 根据一个参考音频，对指定目录下的音频进行相似度分析，并输出到另一个目录 def similarity_audio_output(text_work_space_dir, text_role, text_base_audio_path, text_compare_audio_dir, dropdown_speaker_verification): global hide_voice_similarity_dir text_work_space_dir, text_base_audio_path, text_compare_audio_dir \ = common.batch_clean_paths([text_work_space_dir, text_base_audio_path, text_compare_audio_dir]) text_similarity_audio_output_info = None try: base_role_dir = check_base_info(text_work_space_dir, text_role) if text_base_audio_path is None or text_base_audio_path == '': raise Exception("基准音频路径不能为空") if text_compare_audio_dir is None or text_compare_audio_dir == '': raise Exception("待分析的音频所在目录不能为空") if dropdown_speaker_verification is None or dropdown_speaker_verification == '': raise Exception("说话人验证模型不能为空") time_consuming, (similarity_list, similarity_file, similarity_file_dir) \ = time_util.time_monitor(start_similarity_analysis)(base_role_dir, text_compare_audio_dir, dropdown_speaker_verification, text_base_audio_path, True) if similarity_list is None: raise Exception("相似度分析失败") text_similarity_audio_output_info = f'耗时：{time_consuming:0.1f}秒；相似度分析成功：生成目录{similarity_file_dir}，文件{similarity_file}' hide_voice_similarity_dir = os.path.join(base_role_dir, params.audio_similarity_dir) except Exception as e: logger.error("发生异常: \n%s", traceback.format_exc()) text_similarity_audio_output_info = f"发生异常：{e}" return i18n(text_similarity_audio_output_info) # 根据参考音频目录的删除情况，将其同步到推理生成的音频目录中，即参考音频目录下，删除了几个参考音频，就在推理目录下，将这些参考音频生成的音频文件移除 def sync_ref_audio(text_work_space_dir, text_role, text_refer_audio_file_dir, text_inference_audio_file_dir): text_work_space_dir, text_refer_audio_file_dir, text_inference_audio_file_dir \ = common.batch_clean_paths([text_work_space_dir, text_refer_audio_file_dir, text_inference_audio_file_dir]) text_sync_ref_audio_info = None try: check_base_info(text_work_space_dir, text_role) if text_refer_audio_file_dir is None or text_refer_audio_file_dir == '': raise Exception("参考音频目录不能为空") if text_inference_audio_file_dir is None or text_inference_audio_file_dir == '': raise Exception("推理生成的音频目录不能为空") time_consuming, (delete_text_wav_num, delete_emotion_dir_num) \ = time_util.time_monitor(audio_check.sync_ref_audio)(text_refer_audio_file_dir, text_inference_audio_file_dir) text_sync_ref_audio_info = (f"耗时：{time_consuming:0.1f}秒；推理音频目录{text_inference_audio_file_dir}下，" f"text目录删除了{delete_text_wav_num}个推理音频，emotion目录下，删除了{delete_emotion_dir_num}个目录") except Exception as e: logger.error("发生异常: \n%s", traceback.format_exc()) text_sync_ref_audio_info = f"发生异常：{e}" return i18n(text_sync_ref_audio_info) hide_config_file = '' # 根据模板和参考音频目录，生成参考音频配置内容 def create_config(text_work_space_dir, text_role, text_template, text_refer_audio_file_dir): text_work_space_dir, text_refer_audio_file_dir \ = common.batch_clean_paths([text_work_space_dir, text_refer_audio_file_dir]) global hide_config_file config_file = None text_create_config_info = None try: base_role_dir = check_base_info(text_work_space_dir, text_role) if text_template is None or text_template == '': raise Exception("参考音频抽样目录不能为空") if text_refer_audio_file_dir is None or text_refer_audio_file_dir == '': raise Exception("参考音频目录不能为空") config_file = os.path.join(base_role_dir, f'{params.reference_audio_config_filename}.json') ref_audio_manager = common.RefAudioListManager(text_refer_audio_file_dir) time_consuming, _ = time_util.time_monitor(audio_config.generate_audio_config)(base_role_dir, text_template, ref_audio_manager.get_ref_audio_list(), config_file) text_create_config_info = f"耗时：{time_consuming:0.1f}秒；配置生成成功：生成文件{config_file}" hide_config_file = config_file except Exception as e: logger.error("发生异常: \n%s", traceback.format_exc()) text_create_config_info = f"发生异常：{e}" return i18n(text_create_config_info) # 基于请求路径和参数，合成完整的请求路径 def whole_url(text_url, dropdown_refer_type_param, text_text, text_ref_path, text_ref_text, text_emotion): url_composer = audio_inference.TTSURLComposer(text_url, dropdown_refer_type_param, text_emotion, text_text, text_ref_path, text_ref_text) if url_composer.is_emotion(): text_whole_url = url_composer.build_url_with_emotion('测试内容', '情绪类型', False) else: text_whole_url = url_composer.build_url_with_ref('测试内容', '参考路径', '参考文本', False) return text_whole_url def start_api(): text_start_api_info = None try: proc = common.start_new_service('api.py') text_start_api_info = "启动完成" except Exception as e: logger.error("发生异常: \n%s", traceback.format_exc()) text_start_api_info = f"发生异常：{e}" return text_start_api_info def refresh_api_model(): return ({"choices": model_manager.get_gpt_model_names(), "__type__": "update"}, {"choices": model_manager.get_sovits_model_names(), "__type__": "update"}) def api_set_model_whole_url(text_api_set_model_base_url, dropdown_api_gpt_models, dropdown_api_sovits_models, text_api_gpt_param, text_api_sovits_param): url = audio_inference.SetModelURLComposer("all", text_api_set_model_base_url, text_api_gpt_param, text_api_sovits_param) return url.build_get_url([dropdown_api_gpt_models, dropdown_api_sovits_models], False) def start_api_set_model(text_api_set_model_base_url, dropdown_api_gpt_models, dropdown_api_sovits_models, text_api_gpt_param, text_api_sovits_param): text_api_start_set_model_request_info = None try: if dropdown_api_gpt_models is None or dropdown_api_gpt_models == '': raise Exception("GPT模型不能为空") if dropdown_api_sovits_models is None or dropdown_api_sovits_models == '': raise Exception("Sovits模型不能为空") url = audio_inference.SetModelURLComposer("all", text_api_set_model_base_url, text_api_gpt_param, text_api_sovits_param) url.is_valid() time_consuming, result = time_util.time_monitor(audio_inference.start_api_set_model)(url, dropdown_api_gpt_models, dropdown_api_sovits_models) text_api_start_set_model_request_info = f"耗时：{time_consuming:0.1f}秒；请求结果：{result}" except Exception as e: logger.error("发生异常: \n%s", traceback.format_exc()) text_api_start_set_model_request_info = f"发生异常：{e}" return text_api_start_set_model_request_info def refresh_api_v2_gpt_model(): return {"choices": model_manager.get_gpt_model_names(), "__type__": "update"} def api_v2_set_gpt_whole_url(text_api_v2_set_gpt_model_base_url, text_api_v2_gpt_model_param, dropdown_api_v2_gpt_models): url = audio_inference.SetModelURLComposer("gpt", text_api_v2_set_gpt_model_base_url, text_api_v2_gpt_model_param, None) return url.build_get_url([dropdown_api_v2_gpt_models], False) def start_api_v2_set_gpt_model(text_api_v2_set_gpt_model_base_url, text_api_v2_gpt_model_param, dropdown_api_v2_gpt_models): text_api_v2_start_set_gpt_model_request_info = None try: if dropdown_api_v2_gpt_models is None or dropdown_api_v2_gpt_models == '': raise Exception("GPT模型不能为空") url = audio_inference.SetModelURLComposer("gpt", text_api_v2_set_gpt_model_base_url, text_api_v2_gpt_model_param, None) url.is_valid() time_consuming, result = time_util.time_monitor(audio_inference.start_api_v2_set_gpt_model)(url, dropdown_api_v2_gpt_models) text_api_v2_start_set_gpt_model_request_info = f"耗时：{time_consuming:0.1f}秒；请求结果：{result}" except Exception as e: logger.error("发生异常: \n%s", traceback.format_exc()) text_api_v2_start_set_gpt_model_request_info = f"发生异常：{e}" return text_api_v2_start_set_gpt_model_request_info def refresh_api_v2_sovits_model(): return {"choices": model_manager.get_sovits_model_names(), "__type__": "update"} def api_v2_set_sovits_whole_url(text_api_v2_set_sovits_model_base_url, text_api_v2_sovits_model_param, dropdown_api_v2_sovits_models): url = audio_inference.SetModelURLComposer("sovits", text_api_v2_set_sovits_model_base_url, None, text_api_v2_sovits_model_param) return url.build_get_url([dropdown_api_v2_sovits_models], False) def start_api_v2_set_sovits_model(text_api_v2_set_sovits_model_base_url, text_api_v2_sovits_model_param, dropdown_api_v2_sovits_models): text_api_v2_start_set_sovits_model_request_info = None try: if dropdown_api_v2_sovits_models is None or dropdown_api_v2_sovits_models == '': raise Exception("Sovits模型不能为空") url = audio_inference.SetModelURLComposer("sovits", text_api_v2_set_sovits_model_base_url, None, text_api_v2_sovits_model_param) url.is_valid() time_consuming, result = time_util.time_monitor(audio_inference.start_api_v2_set_sovits_model)(url, dropdown_api_v2_sovits_models) text_api_v2_start_set_sovits_model_request_info = f"耗时：{time_consuming:0.1f}秒；请求结果：{result}" except Exception as e: logger.error("发生异常: \n%s", traceback.format_exc()) text_api_v2_start_set_sovits_model_request_info = f"发生异常：{e}" return text_api_v2_start_set_sovits_model_request_info def open_file(file_path): common.open_file(my_utils.clean_path(file_path)) def delete_ref_audio_below_boundary(ref_audio_path, text_text_similarity_result_path, text_inference_audio_file_dir, slider_audio_text_similarity_boundary): text_delete_ref_audio_below_boundary_info = None ref_audio_path, text_text_similarity_result_path, text_inference_audio_file_dir = common.batch_clean_paths( [ref_audio_path, text_text_similarity_result_path, text_inference_audio_file_dir]) try: if ref_audio_path is None or ref_audio_path == '': raise Exception("参考音频路径不能为空") if text_text_similarity_result_path is None or text_text_similarity_result_path == '': raise Exception("文本相似度结果路径不能为空") time_consuming, count = time_util.time_monitor(text_check.delete_ref_audio_below_boundary)(ref_audio_path, text_text_similarity_result_path, text_inference_audio_file_dir, slider_audio_text_similarity_boundary) text_delete_ref_audio_below_boundary_info = f"耗时：{time_consuming:0.1f}秒；删除参考音频数量：{count}" except Exception as e: logger.error("发生异常: \n%s", traceback.format_exc()) text_delete_ref_audio_below_boundary_info = f"发生异常：{e}" return text_delete_ref_audio_below_boundary_info def change_lang_choices(key): # 根据选择的模型修改可选的语言 # return gr.Dropdown(choices=asr_dict[key]['lang']) return {"__type__": "update", "choices": asr_dict[key]['lang'], "value": asr_dict[key]['lang'][0]} def change_size_choices(key): # 根据选择的模型修改可选的模型尺寸 # return gr.Dropdown(choices=asr_dict[key]['size']) return {"__type__": "update", "choices": asr_dict[key]['size']} def save_work_dir(text_work_space_dir, text_role): text_work_space_dir = my_utils.clean_path(text_work_space_dir) rw_param.write(rw_param.work_dir, text_work_space_dir) if text_role is not None and text_role != '': return text_role else: role_dir = '' for i in range(1, 101): role_dir = f"role_{i}" dir_name = os.path.join(text_work_space_dir, role_dir) if not os.path.isdir(dir_name): break rw_param.write(rw_param.role, role_dir) return role_dir def chang_refer_type_param(selected_value): rw_param.write(rw_param.refer_type_param, selected_value) if selected_value == "参考音频": return {"visible": True, "__type__": "update"}, {"visible": True, "__type__": "update"}, {"visible": False, "__type__": "update"} else: return {"visible": False, "__type__": "update"}, {"visible": False, "__type__": "update"}, {"visible": True, "__type__": "update"} def switch_role_and_refresh(): global hide_voice_similarity_dir, hide_config_file init.init_all() reset_list = [] # 基础 reset_list.extend([ init.text_refer_audio_file_dir_default, init.text_inference_audio_file_dir_default ]) # 第一步 reset_list.extend([ init.text_sample_dir_default, '', # text_list_input '', # text_base_voice_path ]) # 第二步 # 第三步 reset_list.extend([ init.text_asr_audio_dir_default, init.text_text_similarity_analysis_path_default ]) # 第四步 reset_list.extend([ '', # text_base_audio_path '', # text_compare_audio_dir ]) hide_voice_similarity_dir = '' hide_config_file = '' # 第五步 return reset_list def init_ui(): init.init_all() with gr.Blocks() as app: gr.Markdown(value=i18n("基本介绍：这是一个从训练素材中，批量提取参考音频，并进行效果评估与配置生成的工具")) with gr.Accordion(label=i18n("基本信息")): with gr.Row(): text_work_space_dir = gr.Text(label=i18n("工作目录，后续操作所生成文件都会保存在此目录下"), value=init.text_work_space_dir_default, scale=4) text_role = gr.Text(label=i18n("角色名称"), value=init.text_role_default, scale=4) button_switch_role_and_refresh = gr.Button(i18n("切换并刷新"), variant="primary", scale=1) text_work_space_dir.blur(save_work_dir, [text_work_space_dir, text_role], [text_role]) text_role.blur(lambda value: rw_param.write(rw_param.role, value), [text_role], []) gr.Markdown(value=i18n("下方为公共参数，会随着进度自动填充，无需填写")) with gr.Row(): text_refer_audio_file_dir = gr.Text(label=i18n("参考音频所在目录"), value=init.text_refer_audio_file_dir_default) text_inference_audio_file_dir = gr.Text(label=i18n("推理音频所在目录"), value=init.text_inference_audio_file_dir_default) with gr.Tab(label=i18n("第一步：基于训练素材，生成待选参考音频列表")): gr.Markdown(value=i18n("1.1：选择list文件，并提取3-10秒的素材作为参考候选")) text_list_input = gr.Text(label=i18n("请输入list文件路径"), value="") with gr.Row(): button_convert_from_list = gr.Button(i18n("开始生成待参考列表"), variant="primary", scale=4) text_convert_from_list_info = gr.Text(label=i18n("参考列表生成结果"), value="", interactive=False, scale=4) button_convert_from_list_result_dir = gr.Button(i18n("打开目录"), variant="primary", scale=1) gr.Markdown(value=i18n("1.2：选择基准音频，执行相似度匹配，并分段随机抽样")) with gr.Row(): text_sample_dir = gr.Text(label=i18n("参考音频抽样目录"), value=init.text_sample_dir_default, interactive=True) dropdown_speaker_verification_1 = gr.Dropdown(label=i18n("说话人确认算法"), choices=list( model_manager.speaker_verification_models.keys()), value='speech_campplus_sv_zh-cn_16k-common', interactive=True) button_convert_from_list_result_dir.click(open_file, [text_sample_dir], []) button_convert_from_list.click(convert_from_list, [text_work_space_dir, text_role, text_list_input], [text_convert_from_list_info, text_sample_dir]) with gr.Row(): text_base_voice_path = gr.Text(label=i18n("请输入基准音频路径"), value="") slider_subsection_num = gr.Slider(minimum=1, maximum=10, step=1, label=i18n("请输入分段数"), value=init.slider_subsection_num_default, interactive=True) slider_sample_num = gr.Slider(minimum=1, maximum=10, step=1, label=i18n("请输入每段随机抽样个数"), value=init.slider_sample_num_default, interactive=True) checkbox_similarity_output = gr.Checkbox(label=i18n("是否将相似度匹配结果输出到临时目录？"), show_label=True) slider_subsection_num.change(lambda value: rw_param.write(rw_param.subsection_num, value), [slider_subsection_num], []) slider_sample_num.change(lambda value: rw_param.write(rw_param.sample_num, value), [slider_sample_num], []) with gr.Row(): button_sample = gr.Button(i18n("开始分段随机抽样"), variant="primary", scale=4) text_sample_info = gr.Text(label=i18n("分段随机抽样结果"), value="", interactive=False, scale=4) button_sample_result_open = gr.Button(i18n("打开目录"), variant="primary", scale=1) with gr.Tab(label=i18n("第二步：基于参考音频和测试文本，执行批量推理")): gr.Markdown(value=i18n("2.1：启动推理服务，并配置模型参数")) with gr.Accordion(label=i18n("详情")): with gr.Tab(label=i18n("主项目下api.py服务")): gr.Markdown(value=i18n("2.1.1：启动服务")) with gr.Row(): button_start_api = gr.Button(i18n("启动api"), variant="primary") text_start_api_info = gr.Text(label=i18n("api启动信息"), value="", interactive=False) button_start_api.click(start_api, [], [text_start_api_info]) gr.Markdown(value=i18n("2.1.2：设置模型参数")) text_api_set_model_base_url = gr.Text(label=i18n("请输入api服务模型切换接口地址"), value=init.text_api_set_model_base_url_default, interactive=True) text_api_set_model_base_url.blur( lambda value: rw_param.write(rw_param.api_set_model_base_url, value), [text_api_set_model_base_url], []) with gr.Row(): dropdown_api_gpt_models = gr.Dropdown(label=i18n("GPT模型列表"), choices=model_manager.get_gpt_model_names(), value="", interactive=True, scale=4) dropdown_api_sovits_models = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=model_manager.get_sovits_model_names(), value="", interactive=True, scale=4) button_refresh_api_model = gr.Button(i18n("刷新模型路径"), variant="primary", scale=1) button_refresh_api_model.click(refresh_api_model, [], [dropdown_api_gpt_models, dropdown_api_sovits_models]) with gr.Row(): text_api_gpt_param = gr.Text(label=i18n("GPT模型参数名"), value=init.text_api_gpt_param_default, interactive=True) text_api_sovits_param = gr.Text(label=i18n("SoVITS模型参数名"), value=init.text_api_sovits_param_default, interactive=True) text_api_gpt_param.blur(lambda value: rw_param.write(rw_param.api_gpt_param, value), [text_api_gpt_param], []) text_api_sovits_param.blur(lambda value: rw_param.write(rw_param.api_sovits_param, value), [text_api_sovits_param], []) gr.Markdown(value=i18n("2.1.3：发起设置请求")) text_api_set_model_whole_url = gr.Text(label=i18n("完整的模型参数设置请求地址"), value="", interactive=False) dropdown_api_gpt_models.change(api_set_model_whole_url, [text_api_set_model_base_url, dropdown_api_gpt_models, dropdown_api_sovits_models, text_api_gpt_param, text_api_sovits_param], [text_api_set_model_whole_url]) dropdown_api_sovits_models.change(api_set_model_whole_url, [text_api_set_model_base_url, dropdown_api_gpt_models, dropdown_api_sovits_models, text_api_gpt_param, text_api_sovits_param], [text_api_set_model_whole_url]) text_api_gpt_param.input(api_set_model_whole_url, [text_api_set_model_base_url, dropdown_api_gpt_models, dropdown_api_sovits_models, text_api_gpt_param, text_api_sovits_param], [text_api_set_model_whole_url]) text_api_sovits_param.input(api_set_model_whole_url, [text_api_set_model_base_url, dropdown_api_gpt_models, dropdown_api_sovits_models, text_api_gpt_param, text_api_sovits_param], [text_api_set_model_whole_url]) with gr.Row(): button_api_start_set_model_request = gr.Button(i18n("发起模型设置请求"), variant="primary") text_api_start_set_model_request_info = gr.Text(label=i18n("设置请求结果"), value="", interactive=False) button_api_start_set_model_request.click(start_api_set_model, [text_api_set_model_base_url, dropdown_api_gpt_models, dropdown_api_sovits_models, text_api_gpt_param, text_api_sovits_param], [text_api_start_set_model_request_info]) with gr.Tab(label=i18n("fast项目下api_v2.py服务")): gr.Markdown(value=i18n("2.1.1：请将训练完毕得模型，复制到你的项目文件下，启动服务")) gr.Markdown(value=i18n("2.1.2：设置GPT模型参数")) text_api_v2_set_gpt_model_base_url = gr.Text(label=i18n("请输入api服务GPT模型切换接口地址"), value=init.text_api_v2_set_gpt_model_base_url_default, interactive=True) text_api_v2_set_gpt_model_base_url.blur( lambda value: rw_param.write(rw_param.api_v2_set_gpt_model_base_url, value), [text_api_v2_set_gpt_model_base_url], []) with gr.Row(): text_api_v2_gpt_model_param = gr.Text(label=i18n("GPT模型参数名"), value=init.text_api_v2_gpt_model_param_default, interactive=True, scale=4) dropdown_api_v2_gpt_models = gr.Dropdown(label=i18n("GPT模型列表"), choices=model_manager.get_gpt_model_names(), value="", interactive=True, scale=4) text_api_v2_gpt_model_param.blur( lambda value: rw_param.write(rw_param.api_v2_gpt_model_param, value), [text_api_v2_gpt_model_param], []) button_api_v2_refresh_gpt = gr.Button(i18n("刷新模型路径"), variant="primary", scale=1) button_api_v2_refresh_gpt.click(refresh_api_v2_gpt_model, [], [dropdown_api_v2_gpt_models]) text_api_v2_set_gpt_model_whole_url = gr.Text(label=i18n("完整的GPT模型参数设置请求地址"), value="", interactive=False) text_api_v2_gpt_model_param.input(api_v2_set_gpt_whole_url, [text_api_v2_set_gpt_model_base_url, text_api_v2_gpt_model_param, dropdown_api_v2_gpt_models], [text_api_v2_set_gpt_model_whole_url]) dropdown_api_v2_gpt_models.change(api_v2_set_gpt_whole_url, [text_api_v2_set_gpt_model_base_url, text_api_v2_gpt_model_param, dropdown_api_v2_gpt_models], [text_api_v2_set_gpt_model_whole_url]) with gr.Row(): button_api_v2_start_set_gpt_model_request = gr.Button(i18n("发起GPT模型设置请求"), variant="primary") text_api_v2_start_set_gpt_model_request_info = gr.Text(label=i18n("设置请求结果"), value="", interactive=False) button_api_v2_start_set_gpt_model_request.click(start_api_v2_set_gpt_model, [text_api_v2_set_gpt_model_base_url, text_api_v2_gpt_model_param, dropdown_api_v2_gpt_models], [text_api_v2_start_set_gpt_model_request_info]) gr.Markdown(value=i18n("2.1.3：设置SoVITS模型参数")) text_api_v2_set_sovits_model_base_url = gr.Text(label=i18n("请输入api服务SoVITS模型切换接口地址"), value=init.text_api_v2_set_sovits_model_base_url_default, interactive=True) text_api_v2_set_sovits_model_base_url.blur( lambda value: rw_param.write(rw_param.api_v2_set_sovits_model_base_url, value), [text_api_v2_set_sovits_model_base_url], []) with gr.Row(): text_api_v2_sovits_model_param = gr.Text(label=i18n("SoVITS模型参数名"), value=init.text_api_v2_sovits_model_param_default, interactive=True, scale=4) dropdown_api_v2_sovits_models = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=model_manager.get_sovits_model_names(), value="", interactive=True, scale=4) button_api_v2_refresh_sovits = gr.Button(i18n("刷新模型路径"), variant="primary", scale=1) text_api_v2_sovits_model_param.blur( lambda value: rw_param.write(rw_param.api_v2_sovits_model_param, value), [text_api_v2_sovits_model_param], []) button_api_v2_refresh_sovits.click(refresh_api_v2_sovits_model, [], [dropdown_api_v2_sovits_models]) text_api_v2_set_sovits_model_whole_url = gr.Text(label=i18n("完整的SoVITS模型参数设置请求地址"), value="", interactive=False) text_api_v2_sovits_model_param.input(api_v2_set_sovits_whole_url, [text_api_v2_set_sovits_model_base_url, text_api_v2_sovits_model_param, dropdown_api_v2_sovits_models], [text_api_v2_set_sovits_model_whole_url]) dropdown_api_v2_sovits_models.change(api_v2_set_sovits_whole_url, [text_api_v2_set_sovits_model_base_url, text_api_v2_sovits_model_param, dropdown_api_v2_sovits_models], [text_api_v2_set_sovits_model_whole_url]) with gr.Row(): button_api_v2_start_set_sovits_model_request = gr.Button(i18n("发起SoVITS模型设置请求"), variant="primary") text_api_v2_start_set_sovits_model_request_info = gr.Text(label=i18n("设置请求结果"), value="", interactive=False) button_api_v2_start_set_sovits_model_request.click(start_api_v2_set_sovits_model, [text_api_v2_set_sovits_model_base_url, text_api_v2_sovits_model_param, dropdown_api_v2_sovits_models], [ text_api_v2_start_set_sovits_model_request_info]) with gr.Tab(label=i18n("第三方推理服务")): gr.Markdown(value=i18n("启动第三方推理服务，并完成参考音频打包，模型参数设置等操作")) gr.Markdown(value=i18n("2.2：配置推理服务参数信息，除api服务外，其他需要修改参数内容，参考音和角色情绪二选一，如果是角色情绪（第三方推理包），需要先执行第五步，" "将参考音频打包配置到推理服务下，在推理前，请确认完整请求地址是否与正常使用时的一致，包括角色名称，尤其是文本分隔符是否正确")) text_url = gr.Text(label=i18n("请输入推理服务请求地址与参数"), value=init.text_url_default) with gr.Row(): text_text = gr.Text(label=i18n("请输入文本参数名"), value=init.text_text_default) dropdown_refer_type_param = gr.Dropdown(label=i18n("类型"), choices=["参考音频", "角色情绪"], value=init.dropdown_refer_type_param_default, interactive=True) text_ref_path = gr.Text(label=i18n("请输入参考音频路径参数名"), value=init.text_ref_path_default, visible=True) text_ref_text = gr.Text(label=i18n("请输入参考音频文本参数名"), value=init.text_ref_text_default, visible=True) text_emotion = gr.Text(label=i18n("请输入角色情绪参数名"), value=init.text_emotion_default, visible=False) dropdown_refer_type_param.change(chang_refer_type_param, [dropdown_refer_type_param], [text_ref_path, text_ref_text, text_emotion]) text_whole_url = gr.Text(label=i18n("完整地址"), value=init.text_whole_url_default, interactive=False) text_text.blur(lambda value: rw_param.write(rw_param.text_param, value), [text_text], []) text_ref_path.blur(lambda value: rw_param.write(rw_param.ref_path_param, value), [text_ref_path], []) text_ref_text.blur(lambda value: rw_param.write(rw_param.ref_text_param, value), [text_ref_text], []) text_emotion.blur(lambda value: rw_param.write(rw_param.emotion_param, value), [text_emotion], []) text_url.input(whole_url, [text_url, dropdown_refer_type_param, text_text, text_ref_path, text_ref_text, text_emotion], [text_whole_url]) text_url.blur(lambda value: rw_param.write(rw_param.text_url, value), [text_url], []) text_text.input(whole_url, [text_url, dropdown_refer_type_param, text_text, text_ref_path, text_ref_text, text_emotion], [text_whole_url]) text_text.blur(lambda value: rw_param.write(rw_param.text_param, value), [text_text], []) dropdown_refer_type_param.change(whole_url, [text_url, dropdown_refer_type_param, text_text, text_ref_path, text_ref_text, text_emotion], [text_whole_url]) text_ref_path.input(whole_url, [text_url, dropdown_refer_type_param, text_text, text_ref_path, text_ref_text, text_emotion], [text_whole_url]) text_ref_path.blur(lambda value: rw_param.write(rw_param.ref_path_param, value), [text_ref_path], []) text_ref_text.input(whole_url, [text_url, dropdown_refer_type_param, text_text, text_ref_path, text_ref_text, text_emotion], [text_whole_url]) text_ref_text.blur(lambda value: rw_param.write(rw_param.ref_text_param, value), [text_ref_text], []) text_emotion.input(whole_url, [text_url, dropdown_refer_type_param, text_text, text_ref_path, text_ref_text, text_emotion], [text_whole_url]) text_emotion.blur(lambda value: rw_param.write(rw_param.emotion_param, value), [text_emotion], []) gr.Markdown(value=i18n("2.3：配置待推理文本，一句一行，尽量保证文本多样性，不同情绪、不同类型的都来一点")) with gr.Row(): text_test_content = gr.Text(label=i18n("请输入待推理文本路径"), value=init.text_test_content_default, scale=8) button_open_test_content_file = gr.Button(i18n("打开推理文本"), variant="primary", scale=1) button_open_test_content_file.click(open_file, [text_test_content], []) text_test_content.blur(lambda value: rw_param.write(rw_param.test_content_path, value), [text_test_content], []) gr.Markdown(value=i18n("2.4：开始批量推理，这个过程比较耗时，可以去干点别的")) slider_request_concurrency_num = gr.Slider(minimum=1, maximum=init.slider_request_concurrency_max_num, step=1, label=i18n( "请输入请求并发数，会根据此数创建对应数量的子进程并行发起推理请求"), value=init.slider_request_concurrency_num_default, interactive=True) slider_request_concurrency_num.change(lambda value: rw_param.write(rw_param.request_concurrency_num, value), [slider_request_concurrency_num], []) with gr.Row(): button_model_inference = gr.Button(i18n("开启批量推理"), variant="primary", scale=4) text_model_inference_info = gr.Text(label=i18n("批量推理结果"), value="", interactive=False, scale=4) button_model_inference_result_open = gr.Button(i18n("打开目录"), variant="primary", scale=1) with gr.Tab(label=i18n("第三步：进行参考音频推理效果准确度校验")): gr.Markdown(value=i18n("3.1：启动asr，获取推理音频文本")) text_asr_audio_dir = gr.Text(label=i18n("待asr的音频所在目录"), value=init.text_asr_audio_dir_default, interactive=True) with gr.Row(): dropdown_asr_model = gr.Dropdown( label=i18n("ASR 模型"), choices=list(asr_dict.keys()), interactive=True, value="达摩 ASR (中文)" ) dropdown_asr_size = gr.Dropdown( label=i18n("ASR 模型尺寸"), choices=["large"], interactive=True, value="large" ) dropdown_asr_lang = gr.Dropdown( label=i18n("ASR 语言设置"), choices=["zh"], interactive=True, value="zh" ) dropdown_asr_model.change(change_lang_choices, [dropdown_asr_model], [dropdown_asr_lang]) dropdown_asr_model.change(change_size_choices, [dropdown_asr_model], [dropdown_asr_size]) with gr.Row(): button_asr = gr.Button(i18n("启动asr"), variant="primary", scale=4) text_asr_info = gr.Text(label=i18n("asr结果"), value="", interactive=False, scale=4) button_asr_result_open = gr.Button(i18n("打开文件"), variant="primary", scale=1) gr.Markdown(value=i18n("3.2：启动文本相似度分析")) with gr.Row(): text_text_similarity_analysis_path = gr.Text(label=i18n("待分析的文件路径"), value=init.text_text_similarity_analysis_path_default, interactive=True) slider_text_similarity_amplification_boundary = gr.Slider(minimum=0, maximum=1, step=0.01, label=i18n( "文本相似度放大边界，因为原始模型输出的相似度差异太小，所以进行了一次放大，放大逻辑为，边界值以下归0，边界值到1的区间重新映射到0-1"), value=init.slider_text_similarity_amplification_boundary_default, interactive=True) slider_text_similarity_amplification_boundary.change( lambda value: rw_param.write(rw_param.text_similarity_amplification_boundary, value), [slider_text_similarity_amplification_boundary], []) button_asr.click(asr, [text_work_space_dir, text_role, text_asr_audio_dir, dropdown_asr_model, dropdown_asr_size, dropdown_asr_lang], [text_asr_info, text_text_similarity_analysis_path]) button_asr_result_open.click(open_file, [text_text_similarity_analysis_path], []) with gr.Row(): button_text_similarity_analysis = gr.Button(i18n("启动文本相似度分析"), variant="primary") text_text_similarity_analysis_info = gr.Text(label=i18n("文本相似度分析结果"), value="", interactive=False) gr.Markdown(value=i18n("3.3：根据相似度分析结果，重点检查最后几条是否存在复读等问题")) with gr.Row(): text_text_similarity_result_path = gr.Text(label=i18n("文本相似度分析结果文件所在路径"), value=init.text_text_similarity_result_path_default, interactive=True, scale=7) button_open_text_similarity_result = gr.Button(i18n("打开结果文件"), variant="primary", scale=1) button_open_inference_dir = gr.Button(i18n("打开推理目录"), variant="primary", scale=1) button_text_similarity_analysis.click(text_similarity_analysis, [text_work_space_dir, text_role, slider_text_similarity_amplification_boundary, text_text_similarity_analysis_path], [text_text_similarity_analysis_info, text_text_similarity_result_path]) button_open_text_similarity_result.click(open_file, [text_text_similarity_result_path], []) button_open_inference_dir.click(open_file, [text_inference_audio_file_dir], []) slider_audio_text_similarity_boundary = gr.Slider(minimum=0, maximum=1, step=0.001, label=i18n("音频文本相似度边界值"), value=0.800, interactive=True) with gr.Row(): button_delete_ref_audio_below_boundary = gr.Button(i18n("删除音频文本相似度边界值以下的参考音频"), variant="primary") text_delete_ref_audio_below_boundary_info = gr.Text(label=i18n("删除结果"), value="", interactive=True) button_delete_ref_audio_below_boundary.click(delete_ref_audio_below_boundary, [text_refer_audio_file_dir, text_text_similarity_result_path, text_inference_audio_file_dir, slider_audio_text_similarity_boundary], [text_delete_ref_audio_below_boundary_info]) with gr.Tab(label=i18n("第四步：校验参考音频音质")): gr.Markdown(value=i18n("4.1：对结果按音频相似度排序，或许有用吧，主要还是耳朵听")) with gr.Row(): text_base_audio_path = gr.Text(label=i18n("请输入基准音频"), value="") text_compare_audio_dir = gr.Text(label=i18n("请输入待比较的音频文件目录"), value="") dropdown_speaker_verification_2 = gr.Dropdown(label=i18n("说话人确认算法"), choices=list( model_manager.speaker_verification_models.keys()), value='speech_campplus_sv_zh-cn_16k-common', interactive=True) with gr.Row(): button_similarity_audio_output = gr.Button(i18n("输出相似度-参考音频到临时目录"), variant="primary", scale=4) text_similarity_audio_output_info = gr.Text(label=i18n("输出结果"), value="", interactive=False, scale=4) button_similarity_audio_output_result_open = gr.Button(i18n("打开目录"), variant="primary", scale=1) button_similarity_audio_output.click(similarity_audio_output, [text_work_space_dir, text_role, text_base_audio_path, text_compare_audio_dir, dropdown_speaker_verification_2], [text_similarity_audio_output_info]) button_similarity_audio_output_result_open.click(lambda: open_file(hide_voice_similarity_dir), [], []) gr.Markdown(value=i18n("4.2：如果发现存在低音质的推理音频，那么就去参考音频目录下，把原参考音频删了")) gr.Markdown(value=i18n("4.3：删除参考音频之后，按下面的操作，会将推理音频目录下对应的音频也删掉")) with gr.Row(): button_sync_ref_audio = gr.Button(i18n("将参考音频的删除情况，同步到推理音频目录"), variant="primary") text_sync_ref_info = gr.Text(label=i18n("同步结果"), value="", interactive=False) button_sync_ref_audio.click(sync_ref_audio, [text_work_space_dir, text_role, text_refer_audio_file_dir, text_inference_audio_file_dir], [text_sync_ref_info]) with gr.Tab("第五步：生成参考音频配置文本"): gr.Markdown(value=i18n( "5.1：编辑模板，占位符说明：\${emotion}表示相对路径加音频文件名；\${ref_path}表示音频相对角色目录的文件路径；\${ref_text}：表示音频文本")) text_template = gr.Text(label=i18n("模板内容"), value=init.text_template_default, lines=10) text_template.blur(lambda value: rw_param.write(rw_param.text_template, value), [text_template], []) gr.Markdown(value=i18n("5.2：生成配置")) with gr.Row(): button_create_config = gr.Button(i18n("生成配置"), variant="primary", scale=4) text_create_config_info = gr.Text(label=i18n("生成结果"), value="", interactive=False, scale=4) button_create_config_result_open = gr.Button(i18n("打开文件"), variant="primary", scale=1) button_create_config.click(create_config, [text_work_space_dir, text_role, text_template, text_refer_audio_file_dir], [text_create_config_info]) button_create_config_result_open.click(lambda: open_file(hide_config_file), [], []) button_sample.click(sample, [text_work_space_dir, text_role, text_sample_dir, dropdown_speaker_verification_1, text_base_voice_path, slider_subsection_num, slider_sample_num, checkbox_similarity_output], [text_sample_info, text_refer_audio_file_dir]) button_sample_result_open.click(open_file, [text_refer_audio_file_dir], []) button_model_inference.click(model_inference, [text_work_space_dir, text_role, slider_request_concurrency_num, text_refer_audio_file_dir, text_url, dropdown_refer_type_param, text_text, text_ref_path, text_ref_text, text_emotion, text_test_content], [text_model_inference_info, text_asr_audio_dir, text_inference_audio_file_dir]) button_model_inference_result_open.click(open_file, [text_inference_audio_file_dir], []) # 设置重置刷新事件 refresh_list = [] # 基础 refresh_list.extend([ text_refer_audio_file_dir, text_inference_audio_file_dir ]) # 第一步 refresh_list.extend([ text_sample_dir, text_list_input, text_base_voice_path ]) # 第二步 # 第三步 refresh_list.extend([ text_asr_audio_dir, text_text_similarity_analysis_path ]) # 第四步 refresh_list.extend([ text_base_audio_path, text_compare_audio_dir ]) # 第五步 button_switch_role_and_refresh.click(switch_role_and_refresh, [], refresh_list) app.launch( server_port=params.server_port, inbrowser=True, quiet=True, ) if __name__ == "__main__": init_ui()