添加事件绑定和实现

Ref_Audio_Selector/common/common.py

@@ -1,4 +1,7 @@
 from tools import my_utils
+from config import python_exec, is_half
+import subprocess
+import sys
 import os
 
 
@@ -118,6 +121,34 @@ def check_path_existence_and_return(path):
         return ""
 
 
+def open_file(filepath):
+    if sys.platform.startswith('darwin'):
+        subprocess.run(['open', filepath])  # macOS
+    elif os.name == 'nt':  # For Windows
+        os.startfile(filepath)
+    elif os.name == 'posix':  # For Linux, Unix, etc.
+        subprocess.run(['xdg-open', filepath])
+
+
+def start_new_service(script_path):
+    # 对于Windows系统
+    if sys.platform.startswith('win'):
+        cmd = f'start cmd /k {python_exec} {script_path}'
+    # 对于Mac或者Linux系统
+    else:
+        cmd = f'xterm -e {python_exec} {script_path}'
+
+    proc = subprocess.Popen(cmd, shell=True)
+
+    # 关闭之前启动的子进程
+    # proc.terminate()
+
+    # 或者如果需要强制关闭可以使用
+    # proc.kill()
+
+    return proc
+
+
 if __name__ == '__main__':
     dir = r'C:\Users\Administrator\Desktop/test'
     dir2 = r'"C:\Users\Administrator\Desktop\test2"'

Ref_Audio_Selector/ref_audio_selector_webui.py

@@ -6,10 +6,12 @@ import gradio as gr
 
 from Ref_Audio_Selector.config_param.log_config import logger
 
+import Ref_Audio_Selector.tool.model_manager as model_manager
 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.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
@@ -148,7 +150,7 @@ def sample(text_work_space_dir, text_role, text_sample_dir, text_base_voice_path
 
 
 # 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,
+def model_inference(text_work_space_dir, text_role, slider_request_concurrency_num, 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 \
@@ -175,7 +177,7 @@ def model_inference(text_work_space_dir, text_role, text_model_inference_voice_d
         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 = audio_inference.TTSURLComposer(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:
@@ -187,7 +189,8 @@ def model_inference(text_work_space_dir, text_role, text_model_inference_voice_d
         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)
+                                                                                                  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}"
 
@@ -267,7 +270,7 @@ def open_asr(asr_inp_dir, asr_opt_dir, asr_model, asr_model_size, asr_lang):
 
 
 # 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,
+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])
@@ -281,7 +284,7 @@ def text_similarity_analysis(text_work_space_dir, text_role,
         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)
+                                                                                  similarity_dir, slider_text_similarity_amplification_boundary)
 
         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}"
 
@@ -357,10 +360,9 @@ def sync_ref_audio(text_work_space_dir, text_role, text_sync_ref_audio_dir,
         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,
+            = time_util.time_monitor(audio_check.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:
@@ -402,7 +404,7 @@ def create_config(text_work_space_dir, text_role, text_template, text_sync_ref_a
 
 # 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)
+    url_composer = audio_inference.TTSURLComposer(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:
@@ -410,6 +412,111 @@ def whole_url(text_url, text_text, text_ref_path, text_ref_text, text_emotion):
     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_sync_inference_audio_dir, slider_audio_text_similarity_boundary):
+    text_delete_ref_audio_below_boundary_info = None
+    ref_audio_path, text_text_similarity_result_path, text_sync_inference_audio_dir = common.batch_clean_paths([ref_audio_path, text_text_similarity_result_path, text_sync_inference_audio_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_sync_inference_audio_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 save_generate_audio_url(generate_audio_url):
     rw_param.write(rw_param.generate_audio_url, generate_audio_url)
 
@@ -461,8 +568,8 @@ if __name__ == '__main__':
             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_work_space_dir.blur(save_work_dir, [text_work_space_dir, text_role], [text_role])
-            text_role.input(save_role, [text_role], [])
+            text_role.blur(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="")
@@ -493,47 +600,62 @@ if __name__ == '__main__':
                                                      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")):
+                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():
-                        gr.Button(i18n("启动api"), variant="primary")
+                        button_start_api = gr.Button(i18n("启动api"), variant="primary")
-                        gr.Text(label=i18n("api启动信息"), value="", interactive=False)
+                        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"))
-                    gr.Text(label=i18n("请输入api服务模型切换接口地址"), value="", interactive=True)
+                    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="", interactive=True)
                     with gr.Row():
-                        gr.Dropdown(label=i18n("GPT模型列表"), choices=[], value="", interactive=True)
+                        dropdown_api_gpt_models = gr.Dropdown(label=i18n("GPT模型列表"), choices=model_manager.get_gpt_model_names(), value="", interactive=True)
-                        gr.Dropdown(label=i18n("SoVITS模型列表"), choices=[], value="", interactive=True)
+                        dropdown_api_sovits_models = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=model_manager.get_sovits_model_names(), value="", interactive=True)
-                        gr.Button(i18n("刷新模型路径"), variant="primary")
+                        button_refresh_api_model = gr.Button(i18n("刷新模型路径"), variant="primary")
+                        button_refresh_api_model.click(refresh_api_model, [], [dropdown_api_gpt_models, dropdown_api_sovits_models])
                     with gr.Row():
-                        gr.Text(label=i18n("GPT模型参数名"), value="", interactive=True)
+                        text_api_gpt_param = 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)
+                        text_api_sovits_param = 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)
+                    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():
-                        gr.Button(i18n("发起模型设置请求"), variant="primary")
+                        button_api_start_set_model_request = 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)
+                        text_api_start_set_model_request_info = 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")):
+                        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)
+                    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="", interactive=True)
                     with gr.Row():
-                        gr.Text(label=i18n("GPT模型参数名"), value="", interactive=True)
+                        text_api_v2_gpt_model_param = 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)
+                        dropdown_api_v2_gpt_models = gr.Dropdown(label=i18n("GPT模型列表"), choices=model_manager.get_gpt_model_names(), value="", interactive=True)
-                        gr.Button(i18n("刷新模型路径"), variant="primary")
+                        button_api_v2_refresh_gpt = 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)
+                        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():
-                        gr.Button(i18n("发起GPT模型设置请求"), variant="primary")
+                        button_api_v2_start_set_gpt_model_request = 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)
+                        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"))
-                    gr.Text(label=i18n("请输入api服务SoVITS模型切换接口地址"), value="", interactive=True)
+                    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="", interactive=True)
                     with gr.Row():
-                        gr.Text(label=i18n("SoVITS模型参数名"), value="", interactive=True)
+                        text_api_v2_sovits_model_param = 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)
+                        dropdown_api_v2_sovits_models = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=model_manager.get_sovits_model_names(), value="", interactive=True)
-                        gr.Button(i18n("刷新模型路径"), variant="primary")
+                        button_api_v2_refresh_sovits = 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)
+                        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():
-                        gr.Button(i18n("发起SoVITS模型设置请求"), variant="primary")
+                        button_api_v2_start_set_sovits_model_request = 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)
+                        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，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，"
@@ -567,7 +689,7 @@ if __name__ == '__main__':
             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,
+            slider_request_concurrency_num = 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")
@@ -606,7 +728,7 @@ if __name__ == '__main__':
             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,
+                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"), 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],
@@ -615,18 +737,22 @@ if __name__ == '__main__':
                 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,
+                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])
             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)
+                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="", interactive=True)
-                gr.Button(i18n("打开文本相似度分析结果文件"), variant="primary")
+                button_open_text_similarity_result = 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,
+                button_open_text_similarity_result.click(open_file, [text_text_similarity_result_path], [])
+            slider_audio_text_similarity_boundary = 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)
+            text_sync_inference_audio_dir2 = gr.Text(label=i18n("被同步的推理音频路径"),
+                                                    value="", 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")
+                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")
-                gr.Text(label=i18n("删除结果"), value="", interactive=True)
+                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_model_inference_voice_dir, text_text_similarity_result_path, text_sync_inference_audio_dir2, 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():
@@ -676,7 +802,7 @@ if __name__ == '__main__':
                             [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_work_space_dir, text_role, slider_request_concurrency_num, 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])

Ref_Audio_Selector/tool/audio_inference.py

@@ -3,16 +3,55 @@ import os
 import requests
 import itertools
 import multiprocessing
-from multiprocessing import Pool
 from concurrent.futures import ProcessPoolExecutor
 import numpy as np
 import Ref_Audio_Selector.config_param.config_params as params
-from Ref_Audio_Selector.common.time_util import timeit_decorator
 from urllib.parse import urlparse, parse_qs, urlencode, urlunparse, quote
 from Ref_Audio_Selector.config_param.log_config import logger, p_logger
 
 
-class URLComposer:
+class SetModelURLComposer:
+    def __init__(self, type, base_url, gpt_param_name, sovits_param_name):
+        self.type = type
+        self.base_url = base_url
+        self.gpt_param_name = gpt_param_name
+        self.sovits_param_name = sovits_param_name
+
+    def is_valid(self):
+        if self.base_url is None or self.base_url == '':
+            raise Exception("请求地址不能为空")
+        if self.type in ['gpt', 'all']:
+            if self.gpt_param_name is None or self.gpt_param_name == '':
+                raise Exception("GPT参数名不能为空")
+        if self.type in ['sovits', 'all']:
+            if self.sovits_param_name is None or self.sovits_param_name == '':
+                raise Exception("Sovits参数名不能为空")
+
+    def build_get_url(self, value_array, need_url_encode=True):
+        params = {}
+        if self.type == 'gpt':
+            params[self.gpt_param_name] = value_array[0]
+        if self.type == 'sovits':
+            params[self.sovits_param_name] = value_array[0]
+        if self.type == 'all':
+            params[self.gpt_param_name] = value_array[0]
+            params[self.sovits_param_name] = value_array[1]
+        return append_params_to_url(self.base_url, params, need_url_encode)
+
+    def build_post_url(self, value_array, need_url_encode=True):
+        url = append_params_to_url(self.base_url, {}, need_url_encode)
+        params = {}
+        if self.type == 'gpt':
+            params[self.gpt_param_name] = value_array[0]
+        if self.type == 'sovits':
+            params[self.sovits_param_name] = value_array[0]
+        if self.type == 'all':
+            params[self.gpt_param_name] = value_array[0]
+            params[self.sovits_param_name] = value_array[1]
+        return url, params
+
+
+class TTSURLComposer:
     def __init__(self, base_url, emotion_param_name, text_param_name, ref_path_param_name, ref_text_param_name):
         self.base_url = base_url
         self.emotion_param_name = emotion_param_name
@@ -34,30 +73,26 @@ class URLComposer:
         return self.emotion_param_name is not None and self.emotion_param_name != ''
 
     def build_url_with_emotion(self, text_value, emotion_value, need_url_encode=True):
-        if not self.emotion_param_name:
-            raise ValueError("Emotion parameter name is not set.")
         params = {
             self.text_param_name: text_value,
             self.emotion_param_name: emotion_value,
         }
-        return self._append_params_to_url(params, need_url_encode)
+        return append_params_to_url(self.base_url, params, need_url_encode)
 
     def build_url_with_ref(self, text_value, ref_path_value, ref_text_value, need_url_encode=True):
-        if self.emotion_param_name:
-            raise ValueError("Cannot use reference parameters when emotion parameter is set.")
         params = {
             self.text_param_name: text_value,
             self.ref_path_param_name: ref_path_value,
             self.ref_text_param_name: ref_text_value,
         }
-        return self._append_params_to_url(params, need_url_encode)
+        return append_params_to_url(self.base_url, params, need_url_encode)
 
-    def _append_params_to_url(self, params, need_url_encode):
+
-        url_with_params = self.base_url
+def append_params_to_url(url_with_params, params, need_url_encode):
-        if params:
+    if params:
-            query_params = '&'.join([f"{k}={v}" for k, v in params.items()])
+        query_params = '&'.join([f"{k}={v}" for k, v in params.items()])
-            url_with_params += '?' + query_params if '?' not in self.base_url else '&' + query_params
+        url_with_params += '?' + query_params if '?' not in url_with_params else '&' + query_params
-        return url_with_params if not need_url_encode else safe_encode_query_params(url_with_params)
+    return url_with_params if not need_url_encode else safe_encode_query_params(url_with_params)
 
 
 def safe_encode_query_params(original_url):
@@ -87,8 +122,9 @@ def generate_audio_files_parallel(url_composer, text_list, emotion_list, output_
     emotion_groups = np.array_split(emotion_list, num_processes)
 
     with ProcessPoolExecutor(max_workers=num_processes) as executor:
-        futures = [executor.submit(generate_audio_files_for_emotion_group, url_composer, text_list, group, output_dir_path)
+        futures = [
-                   for group in emotion_groups]
+            executor.submit(generate_audio_files_for_emotion_group, url_composer, text_list, group, output_dir_path)
+            for group in emotion_groups]
         for future in futures:
             future.result()  # 等待所有进程完成
 
@@ -162,3 +198,33 @@ def inference_audio_from_api(url):
         return response.content
     else:
         raise Exception(f"Failed to fetch audio from API. Server responded with status code {response.status_code}.")
+
+
+def start_api_set_model(set_model_url_composer, gpt_models, sovits_models):
+    url, post_body = set_model_url_composer.build_post_url(gpt_models, sovits_models)
+    response = requests.post(url, json=post_body)
+    if response.status_code == 200:
+        result = response.text
+        return result
+    else:
+        return f'请求失败，状态码：{response.status_code}'
+
+
+def start_api_v2_set_gpt_model(set_model_url_composer, gpt_models):
+    url = set_model_url_composer.build_get_url([gpt_models])
+    response = requests.get(url)
+    if response.status_code == 200:
+        result = response.text
+        return result
+    else:
+        return f'请求失败，状态码：{response.status_code}'
+
+
+def start_api_v2_set_sovits_model(set_model_url_composer, sovits_models):
+    url = set_model_url_composer.build_get_url([sovits_models])
+    response = requests.get(url)
+    if response.status_code == 200:
+        result = response.text
+        return result
+    else:
+        return f'请求失败，状态码：{response.status_code}'

Ref_Audio_Selector/tool/model_manager.py

@@ -0,0 +1,34 @@
+import os
+import re
+
+pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth"
+pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
+SoVITS_weight_root = "SoVITS_weights"
+GPT_weight_root = "GPT_weights"
+os.makedirs(SoVITS_weight_root, exist_ok=True)
+os.makedirs(GPT_weight_root, exist_ok=True)
+
+
+def custom_sort_key(s):
+    # 使用正则表达式提取字符串中的数字部分和非数字部分
+    parts = re.split('(\d+)', s)
+    # 将数字部分转换为整数，非数字部分保持不变
+    parts = [int(part) if part.isdigit() else part for part in parts]
+    return parts
+
+
+def get_gpt_model_names():
+    gpt_names = [pretrained_gpt_name]
+    for name in os.listdir(SoVITS_weight_root):
+        if name.endswith(".ckpt"): gpt_names.append("%s/%s" % (SoVITS_weight_root, name))
+    sorted(gpt_names, key=custom_sort_key)
+    return gpt_names
+
+
+def get_sovits_model_names():
+    sovits_names = [pretrained_sovits_name]
+    for name in os.listdir(GPT_weight_root):
+        if name.endswith(".pth"): sovits_names.append("%s/%s" % (GPT_weight_root, name))
+    sorted(sovits_names, key=custom_sort_key)
+    return sovits_names
+

Ref_Audio_Selector/tool/text_check.py

@@ -0,0 +1,77 @@
+import os
+import Ref_Audio_Selector.common.common as common
+import Ref_Audio_Selector.tool.audio_check as audio_check
+from Ref_Audio_Selector.config_param.log_config import logger
+
+
+def parse_text_similarity_result_txt(file_path):
+    """
+    解析指定格式的txt文件，每行格式：f"{item['average_similarity_score']}|{item['count']}|{item['emotion']}"
+
+    :param file_path: txt文件的路径
+    :return: 包含解析后数据的字典列表
+    """
+    data_list = []
+    with open(file_path, 'r', encoding='utf-8') as file:
+        for line in file:
+            # 使用'|'作为分隔符分割每行数据
+            parts = line.strip().split('|')
+            if len(parts) == 3:
+                # 将分割后的字符串转换为浮点数、整数和字符串
+                try:
+                    item = {
+                        'average_similarity_score': float(parts[0]),
+                        'count': int(parts[1]),
+                        'emotion': parts[2]
+                    }
+                    data_list.append(item)
+                except ValueError as e:
+                    # 如果转换失败，打印错误信息并跳过该行
+                    logger.error(f"Error parsing line: {line.strip()} - {e}")
+
+    return data_list
+
+
+def remove_low_similarity_files(ref_audio_list, report_list, audio_text_similarity_boundary):
+    """
+    根据条件删除低相似度音频文件并返回删除数量。
+    
+    :param ref_audio_list: 包含音频路径和情感属性的列表
+    :param report_list: 包含相似度评分和情感属性的列表
+    :param audio_text_similarity_boundary: 相似度阈值
+    :return: 删除的文件数量
+    """
+    deleted_count = 0
+
+    # 筛选出平均相似度低于阈值的报告
+    low_similarity_reports = [report for report in report_list if
+                              report['average_similarity_score'] < audio_text_similarity_boundary]
+
+    # 遍历低相似度报告，查找并删除对应音频文件
+    for report in low_similarity_reports:
+        emotion = report['emotion']
+        # 查找ref_audio_list中相同情感的音频文件路径
+        matching_refs = [ref for ref in ref_audio_list if ref['emotion'] == emotion]
+        for match in matching_refs:
+            ref_path = match['ref_path']
+            # 检查文件是否存在，然后尝试删除
+            if os.path.exists(ref_path):
+                try:
+                    os.remove(ref_path)
+                    deleted_count += 1
+                    logger.info(f"Deleted file: {ref_path}")
+                except Exception as e:
+                    logger.error(f"Error deleting file {ref_path}: {e}")
+            else:
+                logger.error(f"File not found: {ref_path}")
+
+    return deleted_count
+
+
+def delete_ref_audio_below_boundary(ref_audio_path, text_similarity_result_path, sync_inference_audio_dir,
+                                    audio_text_similarity_boundary):
+    ref_audio_list = common.RefAudioListManager(ref_audio_path)
+    report_list = parse_text_similarity_result_txt(text_similarity_result_path)
+    count = remove_low_similarity_files(ref_audio_list, report_list, audio_text_similarity_boundary)
+    audio_check.sync_ref_audio(ref_audio_path, sync_inference_audio_dir)
+    return count

Ref_Audio_Selector/tool/text_comparison/asr_text_process.py

@@ -56,7 +56,7 @@ def calculate_average_similarity_by_emotion(data_list):
         similarity_score = item['similarity_score']
         result_dict[emotion].append(similarity_score)
 
-    average_scores = [{'emotion': emotion, 'average_similarity_score': sum(scores) / len(scores)}
+    average_scores = [{'emotion': emotion, 'average_similarity_score': sum(scores) / len(scores), 'count': len(scores)}
                       for emotion, scores in result_dict.items()]
 
     average_scores.sort(key=lambda x: x['average_similarity_score'], reverse=True)
@@ -123,7 +123,7 @@ def process(asr_file_path, output_dir, similarity_enlarge_boundary):
     average_similarity_file = os.path.join(output_dir,
                                            f'{params.text_emotion_average_similarity_report_filename}.txt')
     average_similarity_content = \
-        '\n'.join([f"{item['average_similarity_score']}|{item['emotion']}" for item in average_similarity_list])
+        '\n'.join([f"{item['average_similarity_score']}|{item['count']}|{item['emotion']}" for item in average_similarity_list])
     common.write_text_to_file(average_similarity_content, average_similarity_file)
 
     emotion_detail_list = group_and_sort_by_field(records, 'emotion')