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568 lines
31 KiB
Python
568 lines
31 KiB
Python
import os.path
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import os
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import traceback
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import gradio as gr
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import Ref_Audio_Selector.tool.audio_similarity as audio_similarity
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import Ref_Audio_Selector.tool.audio_inference as audio_inference
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import Ref_Audio_Selector.tool.audio_config as audio_config
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import Ref_Audio_Selector.tool.delete_inference_with_ref as delete_inference_with_ref
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import Ref_Audio_Selector.common.common as common
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import Ref_Audio_Selector.config_param.config_params as params
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import Ref_Audio_Selector.common.time_util as time_util
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from Ref_Audio_Selector.config_param.log_config import logger
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from tools.i18n.i18n import I18nAuto
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from config import python_exec, is_half
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from tools import my_utils
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from tools.asr.config import asr_dict
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from subprocess import Popen
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i18n = I18nAuto()
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rw_param = params.config_manager.get_rw_param()
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p_similarity = None
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p_asr = None
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p_text_similarity = None
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# 校验基础信息
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def check_base_info(text_work_space_dir, text_role):
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if text_work_space_dir is None or text_work_space_dir == '':
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raise Exception("工作目录不能为空")
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if text_role is None or text_role == '':
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raise Exception("角色不能为空")
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base_role_dir = os.path.join(text_work_space_dir, text_role)
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# 判断目录是否存在
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if not os.path.exists(base_role_dir):
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# 如果不存在，则创建目录
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os.makedirs(base_role_dir, exist_ok=True)
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return base_role_dir
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# 从list文件，提取参考音频
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def convert_from_list(text_work_space_dir, text_role, text_list_input):
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text_work_space_dir, text_list_input = common.batch_clean_paths([text_work_space_dir, text_list_input])
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text_convert_from_list_info = None
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text_sample_dir = None
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try:
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base_role_dir = check_base_info(text_work_space_dir, text_role)
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if text_list_input is None or text_list_input == '':
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raise Exception("list文件路径不能为空")
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ref_audio_all = os.path.join(base_role_dir,
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params.list_to_convert_reference_audio_dir)
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time_consuming, _ = time_util.time_monitor(audio_similarity.convert_from_list)(text_list_input, ref_audio_all)
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text_convert_from_list_info = f"耗时：{time_consuming:0.1f}秒；转换成功：生成目录{ref_audio_all}"
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text_sample_dir = ref_audio_all
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# audio_similarity.convert_from_list(text_list_input, ref_audio_all)
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except Exception as e:
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traceback.print_exc()
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text_convert_from_list_info = f"发生异常：{e}"
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text_sample_dir = ''
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return i18n(text_convert_from_list_info), text_sample_dir
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def start_similarity_analysis(work_space_dir, sample_dir, base_voice_path, need_similarity_output):
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similarity_list = None
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similarity_file_dir = None
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similarity_dir = os.path.join(work_space_dir, params.audio_similarity_dir)
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os.makedirs(similarity_dir, exist_ok=True)
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base_voice_file_name = common.get_filename_without_extension(base_voice_path)
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similarity_file = os.path.join(similarity_dir, f'{base_voice_file_name}.txt')
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global p_similarity
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if p_similarity is None:
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cmd = f'"{python_exec}" Ref_Audio_Selector/tool/speaker_verification/voice_similarity.py '
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cmd += f' -r "{base_voice_path}"'
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cmd += f' -c "{sample_dir}"'
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cmd += f' -o {similarity_file}'
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print(cmd)
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p_similarity = Popen(cmd, shell=True)
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p_similarity.wait()
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if need_similarity_output:
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similarity_list = audio_similarity.parse_similarity_file(similarity_file)
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similarity_file_dir = os.path.join(similarity_dir, base_voice_file_name)
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audio_similarity.copy_and_move(similarity_file_dir, similarity_list)
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p_similarity = None
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return similarity_list, similarity_file, similarity_file_dir
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else:
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return similarity_list, None, None
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# 基于一个基准音频，从参考音频目录中进行分段抽样
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def sample(text_work_space_dir, text_role, text_sample_dir, text_base_voice_path,
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text_subsection_num, text_sample_num, checkbox_similarity_output):
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text_work_space_dir, text_sample_dir, text_base_voice_path \
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= common.batch_clean_paths([text_work_space_dir, text_sample_dir, text_base_voice_path])
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ref_audio_dir = None
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text_sample_info = None
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try:
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base_role_dir = check_base_info(text_work_space_dir, text_role)
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if text_sample_dir is None or text_sample_dir == '':
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raise Exception("参考音频抽样目录不能为空，请先完成上一步操作")
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if text_base_voice_path is None or text_base_voice_path == '':
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raise Exception("基准音频路径不能为空")
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if text_subsection_num is None or text_subsection_num == '':
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raise Exception("分段数不能为空")
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if text_sample_num is None or text_sample_num == '':
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raise Exception("每段随机抽样个数不能为空")
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ref_audio_dir = os.path.join(base_role_dir, params.reference_audio_dir)
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time_consuming, similarity_list, _, _ = (
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time_util.time_monitor(start_similarity_analysis)(base_role_dir,
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text_sample_dir,
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text_base_voice_path,
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checkbox_similarity_output))
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text_sample_info = f"耗时：{time_consuming:0.1f}秒；抽样成功：生成目录{ref_audio_dir}"
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# similarity_list, _, _ = start_similarity_analysis(base_role_dir, text_sample_dir,
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# text_base_voice_path, checkbox_similarity_output)
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if similarity_list is None:
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raise Exception("相似度分析失败")
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audio_similarity.sample(ref_audio_dir, similarity_list, int(text_subsection_num), int(text_sample_num))
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except Exception as e:
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traceback.print_exc()
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text_sample_info = f"发生异常：{e}"
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ref_audio_dir = ''
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text_model_inference_voice_dir = ref_audio_dir
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text_sync_ref_audio_dir = ref_audio_dir
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text_sync_ref_audio_dir2 = ref_audio_dir
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return i18n(text_sample_info), text_model_inference_voice_dir, text_sync_ref_audio_dir, text_sync_ref_audio_dir2
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# 根据参考音频和测试文本，执行批量推理
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def model_inference(text_work_space_dir, text_role, text_model_inference_voice_dir, text_url,
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text_text, text_ref_path, text_ref_text, text_emotion,
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text_test_content_dir):
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text_work_space_dir, text_model_inference_voice_dir, text_test_content_dir \
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= common.batch_clean_paths([text_work_space_dir, text_model_inference_voice_dir, text_test_content_dir])
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inference_dir = None
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text_asr_audio_dir = None
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text_model_inference_info = None
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try:
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base_role_dir = check_base_info(text_work_space_dir, text_role)
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if text_model_inference_voice_dir is None or text_model_inference_voice_dir == '':
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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")
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if text_url is None or text_url == '':
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raise Exception("推理服务请求地址不能为空")
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if text_text is None or text_text == '':
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raise Exception("文本参数名不能为空")
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if text_test_content_dir is None or text_test_content_dir == '':
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raise Exception("待推理文本路径不能为空")
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if (text_ref_path is None or text_ref_path == '') and (text_ref_text is None or text_ref_text == '') and (
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text_emotion is None or text_emotion == ''):
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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")
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inference_dir = os.path.join(base_role_dir, params.inference_audio_dir)
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text_asr_audio_dir = os.path.join(inference_dir,
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params.inference_audio_text_aggregation_dir)
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url_composer = audio_inference.URLComposer(text_url, text_emotion, text_text, text_ref_path, text_ref_text)
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url_composer.is_valid()
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text_list = common.read_text_file_to_list(text_test_content_dir)
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if text_list is None or len(text_list) == 0:
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raise Exception("待推理文本内容不能为空")
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ref_audio_manager = common.RefAudioListManager(text_model_inference_voice_dir)
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if len(ref_audio_manager.get_audio_list()) == 0:
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raise Exception("待推理的参考音频不能为空")
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time_consuming, _ = time_util.time_monitor(audio_inference.generate_audio_files)(url_composer, text_list,
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ref_audio_manager.get_ref_audio_list(),
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inference_dir)
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text_model_inference_info = f"耗时：{time_consuming:0.1f}秒；推理成功：生成目录{inference_dir}"
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# audio_inference.generate_audio_files(url_composer, text_list, ref_audio_manager.get_ref_audio_list(),
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# inference_dir)
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except Exception as e:
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traceback.print_exc()
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text_model_inference_info = f"发生异常：{e}"
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text_asr_audio_dir = ''
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return i18n(text_model_inference_info), text_asr_audio_dir, text_asr_audio_dir
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# 对推理生成音频执行asr
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def asr(text_work_space_dir, text_role, text_asr_audio_dir, dropdown_asr_model,
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dropdown_asr_size, dropdown_asr_lang):
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text_work_space_dir, text_asr_audio_dir \
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= common.batch_clean_paths([text_work_space_dir, text_asr_audio_dir])
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asr_file = None
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text_text_similarity_analysis_path = None
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text_asr_info = None
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try:
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base_role_dir = check_base_info(text_work_space_dir, text_role)
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if text_asr_audio_dir is None or text_asr_audio_dir == '':
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raise Exception("待asr的音频所在目录不能为空，请先完成上一步操作")
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if dropdown_asr_model is None or dropdown_asr_model == '':
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raise Exception("asr模型不能为空")
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if dropdown_asr_size is None or dropdown_asr_size == '':
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raise Exception("asr模型大小不能为空")
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if dropdown_asr_lang is None or dropdown_asr_lang == '':
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raise Exception("asr语言不能为空")
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time_consuming, asr_file = time_util.time_monitor(open_asr)(text_asr_audio_dir, base_role_dir,
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dropdown_asr_model, dropdown_asr_size,
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dropdown_asr_lang)
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# asr_file = open_asr(text_asr_audio_dir, base_role_dir, dropdown_asr_model, dropdown_asr_size,
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# dropdown_asr_lang)
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text_text_similarity_analysis_path = asr_file
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text_asr_info = f"耗时：{time_consuming:0.1f}秒；asr成功：生成文件{asr_file}"
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except Exception as e:
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traceback.print_exc()
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text_asr_info = f"发生异常：{e}"
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text_text_similarity_analysis_path = ''
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return i18n(text_asr_info), text_text_similarity_analysis_path
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def open_asr(asr_inp_dir, asr_opt_dir, asr_model, asr_model_size, asr_lang):
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global p_asr
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if p_asr is None:
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asr_inp_dir = my_utils.clean_path(asr_inp_dir)
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asr_py_path = asr_dict[asr_model]["path"]
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if asr_py_path == 'funasr_asr.py':
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asr_py_path = 'funasr_asr_multi_level_dir.py'
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if asr_py_path == 'fasterwhisper.py':
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asr_py_path = 'fasterwhisper_asr_multi_level_dir.py'
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cmd = f'"{python_exec}" Ref_Audio_Selector/tool/asr/{asr_py_path} '
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cmd += f' -i "{asr_inp_dir}"'
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cmd += f' -o "{asr_opt_dir}"'
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cmd += f' -s {asr_model_size}'
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cmd += f' -l {asr_lang}'
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cmd += " -p %s" % ("float16" if is_half == True else "float32")
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print(cmd)
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p_asr = Popen(cmd, shell=True)
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p_asr.wait()
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p_asr = None
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output_dir_abs = os.path.abspath(asr_opt_dir)
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output_file_name = os.path.basename(asr_inp_dir)
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# 构造输出文件路径
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output_file_path = os.path.join(output_dir_abs, f'{params.asr_filename}.list')
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return output_file_path
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else:
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return None
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# 对asr生成的文件，与原本的文本内容，进行相似度分析
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def text_similarity_analysis(text_work_space_dir, text_role,
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text_text_similarity_analysis_path):
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text_work_space_dir, text_text_similarity_analysis_path \
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= common.batch_clean_paths([text_work_space_dir, text_text_similarity_analysis_path])
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similarity_dir = None
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text_text_similarity_analysis_info = None
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try:
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base_role_dir = check_base_info(text_work_space_dir, text_role)
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if text_text_similarity_analysis_path is None or text_text_similarity_analysis_path == '':
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raise Exception("asr生成的文件路径不能为空，请先完成上一步操作")
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similarity_dir = os.path.join(base_role_dir, params.text_similarity_output_dir)
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time_consuming, _ = time_util.time_monitor(open_text_similarity_analysis)(text_text_similarity_analysis_path,
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similarity_dir)
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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}"
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# open_text_similarity_analysis(text_text_similarity_analysis_path, similarity_dir)
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except Exception as e:
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traceback.print_exc()
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text_text_similarity_analysis_info = f"发生异常：{e}"
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return i18n(text_text_similarity_analysis_info)
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def open_text_similarity_analysis(asr_file_path, output_dir, similarity_enlarge_boundary=0.8):
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global p_text_similarity
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if p_text_similarity is None:
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cmd = f'"{python_exec}" Ref_Audio_Selector/tool/text_comparison/asr_text_process.py '
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cmd += f' -a "{asr_file_path}"'
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cmd += f' -o "{output_dir}"'
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cmd += f' -b {similarity_enlarge_boundary}'
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print(cmd)
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p_text_similarity = Popen(cmd, shell=True)
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p_text_similarity.wait()
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p_text_similarity = None
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return output_dir
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else:
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return None
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# 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
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def similarity_audio_output(text_work_space_dir, text_role, text_base_audio_path,
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text_compare_audio_dir):
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text_work_space_dir, text_base_audio_path, text_compare_audio_dir \
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= common.batch_clean_paths([text_work_space_dir, text_base_audio_path, text_compare_audio_dir])
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text_similarity_audio_output_info = None
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try:
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base_role_dir = check_base_info(text_work_space_dir, text_role)
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if text_base_audio_path is None or text_base_audio_path == '':
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raise Exception("基准音频路径不能为空")
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if text_compare_audio_dir is None or text_compare_audio_dir == '':
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raise Exception("待分析的音频所在目录不能为空")
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time_consuming, similarity_list, similarity_file, similarity_file_dir \
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= time_util.time_monitor(start_similarity_analysis)(base_role_dir,
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text_compare_audio_dir, text_base_audio_path, True)
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# similarity_list, similarity_file, similarity_file_dir = start_similarity_analysis(
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# base_role_dir, text_compare_audio_dir, text_base_audio_path, True)
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if similarity_list is None:
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raise Exception("相似度分析失败")
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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}'
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except Exception as e:
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traceback.print_exc()
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text_similarity_audio_output_info = f"发生异常：{e}"
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return i18n(text_similarity_audio_output_info)
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# 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
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def sync_ref_audio(text_work_space_dir, text_role, text_sync_ref_audio_dir,
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text_sync_inference_audio_dir):
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text_work_space_dir, text_sync_ref_audio_dir, text_sync_inference_audio_dir \
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= common.batch_clean_paths([text_work_space_dir, text_sync_ref_audio_dir, text_sync_inference_audio_dir])
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text_sync_ref_audio_info = None
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try:
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check_base_info(text_work_space_dir, text_role)
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if text_sync_ref_audio_dir is None or text_sync_ref_audio_dir == '':
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raise Exception("参考音频目录不能为空")
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if text_sync_inference_audio_dir is None or text_sync_inference_audio_dir == '':
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raise Exception("推理生成的音频目录不能为空")
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time_consuming, delete_text_wav_num, delete_emotion_dir_num \
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= time_util.time_monitor(delete_inference_with_ref.sync_ref_audio)(text_sync_ref_audio_dir,
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text_sync_inference_audio_dir)
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# delete_text_wav_num, delete_emotion_dir_num = delete_inference_with_ref.sync_ref_audio(
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# text_sync_ref_audio_dir, text_sync_inference_audio_dir)
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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，"
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f"text目录删除了{delete_text_wav_num}个参考音频，emotion目录下，删除了{delete_emotion_dir_num}个目录")
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except Exception as e:
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traceback.print_exc()
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text_sync_ref_audio_info = f"发生异常：{e}"
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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:
|
||
traceback.print_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_work_dir(text_work_space_dir, text_role):
|
||
text_work_space_dir = my_utils.clean_path(text_work_space_dir)
|
||
rw_param.write_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_role(role_dir)
|
||
return role_dir
|
||
|
||
|
||
def save_role(text_role):
|
||
rw_param.write_role(text_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=rw_param.read_work_dir())
|
||
text_role = gr.Text(label=i18n("角色名称"), value=rw_param.read_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.Accordion(label=i18n("第一步：基于训练素材，生成待选参考音频列表"), open=False):
|
||
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"))
|
||
text_sample_dir = gr.Text(label=i18n("参考音频抽样目录"), value="", 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="")
|
||
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")
|
||
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.Accordion(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"))
|
||
text_model_inference_voice_dir = gr.Text(label=i18n("待推理的参考音频所在目录"), value="", interactive=True)
|
||
text_url = gr.Text(label=i18n("请输入推理服务请求地址与参数"), value="")
|
||
with gr.Row():
|
||
text_text = gr.Text(label=i18n("请输入文本参数名"), value="text")
|
||
text_ref_path = gr.Text(label=i18n("请输入参考音频路径参数名"), value="")
|
||
text_ref_text = gr.Text(label=i18n("请输入参考音频文本参数名"), value="")
|
||
text_emotion = gr.Text(label=i18n("请输入角色情绪参数名"), value="emotion")
|
||
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_text.input(whole_url, [text_url, text_text, text_ref_path, text_ref_text, text_emotion],
|
||
[text_whole_url])
|
||
text_ref_path.input(whole_url, [text_url, text_text, text_ref_path, text_ref_text, text_emotion],
|
||
[text_whole_url])
|
||
text_ref_text.input(whole_url, [text_url, text_text, text_ref_path, text_ref_text, text_emotion],
|
||
[text_whole_url])
|
||
text_emotion.input(whole_url, [text_url, text_text, text_ref_path, text_ref_text, text_emotion],
|
||
[text_whole_url])
|
||
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"))
|
||
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"))
|
||
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.Accordion(label=i18n("第三步：进行参考音频效果校验与筛选"), open=False):
|
||
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="", 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"))
|
||
text_text_similarity_analysis_path = gr.Text(label=i18n("待分析的文件路径"), value="", 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"))
|
||
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():
|
||
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])
|
||
with gr.Row():
|
||
text_sync_ref_audio_dir = gr.Text(label=i18n("参考音频路径"), value="", interactive=True)
|
||
text_sync_inference_audio_dir = gr.Text(label=i18n("被同步的推理音频路径"), value="", 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.Accordion("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"))
|
||
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"))
|
||
text_sync_ref_audio_dir2 = gr.Text(label=i18n("参考音频路径"), value="", 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,
|
||
text_subsection_num, text_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,
|
||
)
|