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.tool.audio_similarity as audio_similarity 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.delete_inference_with_ref as delete_inference_with_ref 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 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_similarity.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 # audio_similarity.convert_from_list(text_list_input, 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, 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}' logger.info(cmd) p_similarity = Popen(cmd, shell=True) p_similarity.wait() similarity_list = audio_similarity.parse_similarity_file(similarity_file) if need_similarity_output: similarity_file_dir = os.path.join(similarity_dir, base_voice_file_name) audio_similarity.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, 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") 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, 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}" # similarity_list, _, _ = start_similarity_analysis(base_role_dir, text_sample_dir, # text_base_voice_path, checkbox_similarity_output) if similarity_list is None: raise Exception("相似度分析失败") audio_similarity.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_model_inference_voice_dir = ref_audio_dir text_sync_ref_audio_dir = ref_audio_dir text_sync_ref_audio_dir2 = ref_audio_dir return i18n(text_sample_info), text_model_inference_voice_dir, text_sync_ref_audio_dir, text_sync_ref_audio_dir2 # 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, text_model_inference_voice_dir, text_url, text_text, text_ref_path, text_ref_text, text_emotion, text_test_content_dir): text_work_space_dir, text_model_inference_voice_dir, text_test_content_dir \ = common.batch_clean_paths([text_work_space_dir, text_model_inference_voice_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_model_inference_voice_dir is None or text_model_inference_voice_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.URLComposer(text_url, 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_model_inference_voice_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, 3) text_model_inference_info = f"耗时：{time_consuming:0.1f}秒；推理成功：生成目录{inference_dir}" # audio_inference.generate_audio_files(url_composer, text_list, ref_audio_manager.get_ref_audio_list(), # 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) # asr_file = 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.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, 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) 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}" # open_text_similarity_analysis(text_text_similarity_analysis_path, 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) 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 # 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): 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") time_consuming, (similarity_list, similarity_file, similarity_file_dir) \ = time_util.time_monitor(start_similarity_analysis)(base_role_dir, text_compare_audio_dir, text_base_audio_path, True) # similarity_list, similarity_file, similarity_file_dir = start_similarity_analysis( # base_role_dir, text_compare_audio_dir, 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}' 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_sync_ref_audio_dir, text_sync_inference_audio_dir): text_work_space_dir, text_sync_ref_audio_dir, text_sync_inference_audio_dir \ = common.batch_clean_paths([text_work_space_dir, text_sync_ref_audio_dir, text_sync_inference_audio_dir]) text_sync_ref_audio_info = None try: check_base_info(text_work_space_dir, text_role) if text_sync_ref_audio_dir is None or text_sync_ref_audio_dir == '': raise Exception("参考音频目录不能为空") if text_sync_inference_audio_dir is None or text_sync_inference_audio_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(delete_inference_with_ref.sync_ref_audio)(text_sync_ref_audio_dir, text_sync_inference_audio_dir) # delete_text_wav_num, delete_emotion_dir_num = delete_inference_with_ref.sync_ref_audio( # text_sync_ref_audio_dir, text_sync_inference_audio_dir) text_sync_ref_audio_info = (f"耗时：{time_consuming:0.1f}秒；推理音频目录{text_sync_inference_audio_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) # 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_sync_ref_audio_dir2): text_work_space_dir, text_sync_ref_audio_dir2 \ = common.batch_clean_paths([text_work_space_dir, text_sync_ref_audio_dir2]) 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_sync_ref_audio_dir2 is None or text_sync_ref_audio_dir2 == '': 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_sync_ref_audio_dir2) 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) # 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}" 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, text_text, text_ref_path, text_ref_text, text_emotion): url_composer = audio_inference.URLComposer(text_url, 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 save_generate_audio_url(generate_audio_url): rw_param.write(rw_param.generate_audio_url, generate_audio_url) def save_text_param(text_text): rw_param.write(rw_param.text_param, text_text) def save_ref_path_param(text_ref_path): rw_param.write(rw_param.ref_path_param, text_ref_path) def save_ref_text_param(text_ref_text): rw_param.write(rw_param.ref_text_param, text_ref_text) def save_emotion_param(text_emotion): rw_param.write(rw_param.emotion_param, text_emotion) 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 save_role(text_role): rw_param.write(rw_param.role, text_role) if __name__ == '__main__': default_work_space_dir = rw_param.read(rw_param.work_dir) default_role = rw_param.read(rw_param.role) default_base_dir = os.path.join(default_work_space_dir, default_role) 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.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=default_work_space_dir) text_role = gr.Text(label=i18n("角色名称"), value=default_role) text_work_space_dir.input(save_work_dir, [text_work_space_dir, text_role], [text_role]) text_role.input(save_role, [text_role], []) 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") text_convert_from_list_info = gr.Text(label=i18n("参考列表生成结果"), value="", interactive=False) 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")) default_sample_dir = common.check_path_existence_and_return( os.path.join(default_base_dir, params.list_to_convert_reference_audio_dir)) text_sample_dir = gr.Text(label=i18n("参考音频抽样目录"), value=default_sample_dir, interactive=True) 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=5, 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=4, 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) with gr.Row(): button_sample = gr.Button(i18n("开始分段随机抽样"), variant="primary") text_sample_info = gr.Text(label=i18n("分段随机抽样结果"), value="", interactive=False) 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")): default_model_inference_voice_dir = common.check_path_existence_and_return( os.path.join(default_base_dir, params.reference_audio_dir)) text_model_inference_voice_dir = gr.Text(label=i18n("待推理的参考音频所在目录"), value=default_model_inference_voice_dir, interactive=True) 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服务")): gr.Markdown(value=i18n("2.1.1：启动服务")) with gr.Row(): gr.Button(i18n("启动api"), variant="primary") gr.Text(label=i18n("api启动信息"), value="", interactive=False) gr.Markdown(value=i18n("2.1.2：设置模型参数")) gr.Text(label=i18n("请输入api服务模型切换接口地址"), value="", interactive=True) with gr.Row(): gr.Dropdown(label=i18n("GPT模型列表"), choices=[], value="", interactive=True) gr.Dropdown(label=i18n("SoVITS模型列表"), choices=[], value="", interactive=True) gr.Button(i18n("刷新模型路径"), variant="primary") with gr.Row(): gr.Text(label=i18n("GPT模型参数名"), value="", interactive=True) gr.Text(label=i18n("SoVITS模型参数名"), value="", interactive=True) gr.Markdown(value=i18n("2.1.3：发起设置请求")) gr.Text(label=i18n("完整的模型参数设置请求地址"), value="", interactive=False) with gr.Row(): gr.Button(i18n("发起模型设置请求"), variant="primary") gr.Text(label=i18n("设置请求结果"), value="", interactive=False) with gr.Tab(label=i18n("fast项目下api_v2服务")): gr.Markdown(value=i18n("2.1.1：请到你的项目下，启动服务")) gr.Markdown(value=i18n("2.1.2：设置GPT模型参数")) gr.Text(label=i18n("请输入api服务GPT模型切换接口地址"), value="", interactive=True) with gr.Row(): gr.Text(label=i18n("GPT模型参数名"), value="", interactive=True) gr.Dropdown(label=i18n("GPT模型列表"), choices=[], value="", interactive=True) gr.Button(i18n("刷新模型路径"), variant="primary") gr.Text(label=i18n("完整的GPT模型参数设置请求地址"), value="", interactive=False) with gr.Row(): gr.Button(i18n("发起GPT模型设置请求"), variant="primary") gr.Text(label=i18n("设置请求结果"), value="", interactive=False) gr.Markdown(value=i18n("2.1.3：设置SoVITS模型参数")) gr.Text(label=i18n("请输入api服务SoVITS模型切换接口地址"), value="", interactive=True) with gr.Row(): gr.Text(label=i18n("SoVITS模型参数名"), value="", interactive=True) gr.Dropdown(label=i18n("SoVITS模型列表"), choices=[], value="", interactive=True) gr.Button(i18n("刷新模型路径"), variant="primary") gr.Text(label=i18n("完整的SoVITS模型参数设置请求地址"), value="", interactive=False) with gr.Row(): gr.Button(i18n("发起SoVITS模型设置请求"), variant="primary") gr.Text(label=i18n("设置请求结果"), value="", interactive=False) 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，参考音频路径/文本和角色情绪二选一，如果是角色情绪，需要先执行第四步，" "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=rw_param.read(rw_param.generate_audio_url)) with gr.Row(): text_text = gr.Text(label=i18n("请输入文本参数名"), value=rw_param.read(rw_param.text_param)) text_ref_path = gr.Text(label=i18n("请输入参考音频路径参数名"), value=rw_param.read(rw_param.ref_path_param)) text_ref_text = gr.Text(label=i18n("请输入参考音频文本参数名"), value=rw_param.read(rw_param.ref_text_param)) text_emotion = gr.Text(label=i18n("请输入角色情绪参数名"), value=rw_param.read(rw_param.emotion_param)) text_whole_url = gr.Text(label=i18n("完整地址"), value="", interactive=False) text_url.input(whole_url, [text_url, text_text, text_ref_path, text_ref_text, text_emotion], [text_whole_url]) text_url.blur(save_generate_audio_url, [text_url], []) text_text.input(whole_url, [text_url, text_text, text_ref_path, text_ref_text, text_emotion], [text_whole_url]) text_text.blur(save_text_param, [text_text], []) text_ref_path.input(whole_url, [text_url, text_text, text_ref_path, text_ref_text, text_emotion], [text_whole_url]) text_ref_path.blur(save_ref_path_param, [text_ref_path], []) text_ref_text.input(whole_url, [text_url, text_text, text_ref_path, text_ref_text, text_emotion], [text_whole_url]) text_ref_text.blur(save_ref_text_param, [text_ref_text], []) text_emotion.input(whole_url, [text_url, text_text, text_ref_path, text_ref_text, text_emotion], [text_whole_url]) text_emotion.blur(save_emotion_param, [text_emotion], []) gr.Markdown(value=i18n("2.3：配置待推理文本，一句一行，不要太多，10条即可")) default_test_content_path = params.default_test_text_path text_test_content = gr.Text(label=i18n("请输入待推理文本路径"), value=default_test_content_path) 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")) gr.Slider(minimum=1, maximum=10, 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=3, interactive=True) with gr.Row(): button_model_inference = gr.Button(i18n("开启批量推理"), variant="primary") text_model_inference_info = gr.Text(label=i18n("批量推理结果"), value="", interactive=False) with gr.Tab(label=i18n("第三步：进行参考音频效果校验与筛选")): gr.Markdown(value=i18n("3.1：启动asr，获取推理音频文本")) default_asr_audio_dir = common.check_path_existence_and_return( os.path.join(default_base_dir, params.inference_audio_dir, params.inference_audio_text_aggregation_dir)) text_asr_audio_dir = gr.Text(label=i18n("待asr的音频所在目录"), value=default_asr_audio_dir, interactive=True) with gr.Row(): dropdown_asr_model = gr.Dropdown( label=i18n("ASR 模型"), choices=[], 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" ) with gr.Row(): button_asr = gr.Button(i18n("启动asr"), variant="primary") text_asr_info = gr.Text(label=i18n("asr结果"), value="", interactive=False) gr.Markdown(value=i18n("3.2：启动文本相似度分析")) default_text_similarity_analysis_path = common.check_path_existence_and_return( os.path.join(default_base_dir, params.asr_filename + '.list')) with gr.Row(): text_text_similarity_analysis_path = gr.Text(label=i18n("待分析的文件路径"), value=default_text_similarity_analysis_path, interactive=True) gr.Slider(minimum=0, maximum=1, step=0.01, label=i18n("文本相似度放大边界"), value=0.90, interactive=True) 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]) 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) button_text_similarity_analysis.click(text_similarity_analysis, [text_work_space_dir, text_role, text_text_similarity_analysis_path], [text_text_similarity_analysis_info]) 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(): gr.Text(label=i18n("文本相似度分析结果文件所在路径"), value="", interactive=True) gr.Button(i18n("打开文本相似度分析结果文件"), variant="primary") gr.Slider(minimum=0, maximum=1, step=0.01, label=i18n("音频文本相似度边界值"), value=0.80, interactive=True) with gr.Row(): gr.Button(i18n("删除音频文本相似度边界值以下的参考音频"), variant="primary") gr.Text(label=i18n("删除结果"), value="", interactive=True) 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="") 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") text_similarity_audio_output_info = gr.Text(label=i18n("输出结果"), value="", interactive=False) button_similarity_audio_output.click(similarity_audio_output, [text_work_space_dir, text_role, text_base_audio_path, text_compare_audio_dir], [text_similarity_audio_output_info]) 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(): default_sync_ref_audio_dir = common.check_path_existence_and_return( os.path.join(default_base_dir, params.reference_audio_dir)) text_sync_ref_audio_dir = gr.Text(label=i18n("参考音频路径"), value=default_sync_ref_audio_dir, interactive=True) default_sync_inference_audio_dir = common.check_path_existence_and_return( os.path.join(default_base_dir, params.inference_audio_dir)) text_sync_inference_audio_dir = gr.Text(label=i18n("被同步的推理音频路径"), value=default_sync_inference_audio_dir, interactive=True) 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_sync_ref_audio_dir, text_sync_inference_audio_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")) default_template_path = params.default_template_path default_template_content = common.read_file(default_template_path) text_template_path = gr.Text(label=i18n("模板文件路径"), value=default_template_path, interactive=True) text_template = gr.Text(label=i18n("模板内容"), value=default_template_content, lines=10) gr.Markdown(value=i18n("5.2：生成配置")) default_sync_ref_audio_dir2 = common.check_path_existence_and_return( os.path.join(default_base_dir, params.reference_audio_dir)) text_sync_ref_audio_dir2 = gr.Text(label=i18n("参考音频路径"), value=default_sync_ref_audio_dir2, interactive=True) with gr.Row(): button_create_config = gr.Button(i18n("生成配置"), variant="primary") text_create_config_info = gr.Text(label=i18n("生成结果"), value="", interactive=False) button_create_config.click(create_config, [text_work_space_dir, text_role, text_template, text_sync_ref_audio_dir2], [text_create_config_info]) button_sample.click(sample, [text_work_space_dir, text_role, text_sample_dir, text_base_voice_path, slider_subsection_num, slider_sample_num, checkbox_similarity_output], [text_sample_info, text_model_inference_voice_dir, text_sync_ref_audio_dir, text_sync_ref_audio_dir2]) button_model_inference.click(model_inference, [text_work_space_dir, text_role, text_model_inference_voice_dir, text_url, text_text, text_ref_path, text_ref_text, text_emotion, text_test_content], [text_model_inference_info, text_asr_audio_dir, text_sync_inference_audio_dir]) app.launch( server_port=9423, quiet=True, )