From 25b65cdfd00400ab9bd3477830cd66c88491e82d Mon Sep 17 00:00:00 2001 From: Downupanddownup Date: Sat, 27 Apr 2024 22:09:03 +0800 Subject: [PATCH] =?UTF-8?q?=E8=B0=83=E6=95=B4ui=E5=B8=83=E5=B1=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../ref_audio_selector_webui.py | 102 ++++++++++++++---- Ref_Audio_Selector/tool/audio_inference.py | 5 - 2 files changed, 82 insertions(+), 25 deletions(-) diff --git a/Ref_Audio_Selector/ref_audio_selector_webui.py b/Ref_Audio_Selector/ref_audio_selector_webui.py index 0589c75..0167033 100644 --- a/Ref_Audio_Selector/ref_audio_selector_webui.py +++ b/Ref_Audio_Selector/ref_audio_selector_webui.py @@ -104,7 +104,7 @@ def start_similarity_analysis(work_space_dir, sample_dir, base_voice_path, need_ # 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, - text_subsection_num, text_sample_num, checkbox_similarity_output): + 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]) @@ -116,9 +116,9 @@ def sample(text_work_space_dir, text_role, text_sample_dir, text_base_voice_path 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 text_subsection_num is None or text_subsection_num == '': + if slider_subsection_num is None or slider_subsection_num == '': raise Exception("分段数不能为空") - if text_sample_num is None or text_sample_num == '': + 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) @@ -135,7 +135,7 @@ def sample(text_work_space_dir, text_role, text_sample_dir, text_base_voice_path if similarity_list is None: raise Exception("相似度分析失败") - audio_similarity.sample(ref_audio_dir, similarity_list, int(text_subsection_num), int(text_sample_num)) + 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()) @@ -463,7 +463,7 @@ if __name__ == '__main__': 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"), open=False): + 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(): @@ -477,20 +477,67 @@ if __name__ == '__main__': [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="") - text_subsection_num = gr.Text(label=i18n("请输入分段数"), value="10") - text_sample_num = gr.Text(label=i18n("请输入每段随机抽样个数"), value="4") + 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"), open=False): - gr.Markdown(value=i18n("2.1：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")) + 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(): @@ -516,15 +563,16 @@ if __name__ == '__main__': 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("2.2：配置待推理文本，一句一行，不要太多，10条即可")) + 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("2.3：启动推理服务，如果还没启动的话")) 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"), open=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)) @@ -555,8 +603,11 @@ if __name__ == '__main__': 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')) - 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) + 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]) @@ -568,7 +619,16 @@ if __name__ == '__main__': 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")) - gr.Markdown(value=i18n("3.4：对结果按音频相似度排序，筛选低音质音频")) + 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="") @@ -578,6 +638,8 @@ if __name__ == '__main__': 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)) @@ -592,13 +654,13 @@ if __name__ == '__main__': 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("第四步：生成参考音频配置文本", open=False): - gr.Markdown(value=i18n("4.1：编辑模板")) + 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("4.2：生成配置")) + 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, @@ -610,7 +672,7 @@ if __name__ == '__main__': [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, - text_subsection_num, text_sample_num, checkbox_similarity_output], + 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, diff --git a/Ref_Audio_Selector/tool/audio_inference.py b/Ref_Audio_Selector/tool/audio_inference.py index b146e28..f6b4569 100644 --- a/Ref_Audio_Selector/tool/audio_inference.py +++ b/Ref_Audio_Selector/tool/audio_inference.py @@ -12,9 +12,6 @@ from urllib.parse import urlparse, parse_qs, urlencode, urlunparse, quote from Ref_Audio_Selector.config_param.log_config import logger, p_logger -# 假设手动指定端口范围为9400-9500 -available_ports = list(range(9400, 9500)) - class URLComposer: def __init__(self, base_url, emotion_param_name, text_param_name, ref_path_param_name, ref_text_param_name): self.base_url = base_url @@ -86,8 +83,6 @@ def generate_audio_files_parallel(url_composer, text_list, emotion_list, output_ if num_processes is None: num_processes = multiprocessing.cpu_count() - num_processes = min(num_processes, len(available_ports)) # 限制进程数不超过可用端口数 - # 将emotion_list均匀分成num_processes个子集 emotion_groups = np.array_split(emotion_list, num_processes)