From 6c88f1ea3299afe81fac6ea1c1fc74e19437ed59 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=80=B8=E6=B8=B8=E4=BB=99=E4=BA=BA?= <3439248121@qq.com> Date: Sun, 22 Jun 2025 03:35:34 +0800 Subject: [PATCH 1/2] cc --- GPT_SoVITS/inference_webui.py | 637 ++++++++++++++++++++++++++++++++++ 1 file changed, 637 insertions(+) diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index 5c7d0103..a4406d84 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -14,11 +14,14 @@ import re import sys import traceback import warnings +import soundfile # 新增导入 import torch import torchaudio from text.LangSegmenter import LangSegmenter + + logging.getLogger("markdown_it").setLevel(logging.ERROR) logging.getLogger("urllib3").setLevel(logging.ERROR) logging.getLogger("httpcore").setLevel(logging.ERROR) @@ -989,6 +992,255 @@ def get_tts_wav( yield opt_sr, (audio_opt * 32767).astype(np.int16) +import uuid +import shutil +from pydub import AudioSegment + +TEMP_FOLDER = "TEMP" # 临时文件夹路径 +os.makedirs(TEMP_FOLDER, exist_ok=True) + +def clean_temp_folder_on_startup(): + """启动时清理临时文件夹""" + try: + if os.path.exists(TEMP_FOLDER): + shutil.rmtree(TEMP_FOLDER) + os.makedirs(TEMP_FOLDER, exist_ok=True) + print("启动时已清理临时文件夹") + else: + os.makedirs(TEMP_FOLDER, exist_ok=True) + print("临时文件夹已创建") + except Exception as e: + print(f"启动时清理临时文件夹失败: {str(e)}") + +def split_text_by_punctuation(text, period_pause=0.3, comma_pause=0.15): + """改进的文本分割函数""" + if not text or not isinstance(text, str): + print("收到空或非字符串文本输入") + return [] + + segments = [] + current_segment = "" + punctuation_marks = ['，', ',', '。', '.'] + + for char in text: + current_segment += char + if char in punctuation_marks: + # 根据标点类型设置停顿时间 + pause = period_pause if char in ['。', '.'] else comma_pause + segments.append({ + "text": current_segment.strip(), + "pause": pause, + "punctuation": char + }) + current_segment = "" + + if current_segment: + segments.append({ + "text": current_segment.strip(), + "pause": comma_pause, # 默认使用非句号停顿时间 + "punctuation": "" + }) + + print(f"分割结果: {[seg['text'] for seg in segments]}") + return segments + +def generate_segment_audio(segment_data, ref_wav_path, prompt_text, prompt_language, text_language, top_k, top_p, temperature): + """增强的音频生成函数""" + try: + if not os.path.exists(ref_wav_path): + raise FileNotFoundError(f"参考音频不存在: {ref_wav_path}") + + # 生成音频 + sr, audio_data = next(get_tts_wav( + ref_wav_path=ref_wav_path, + prompt_text=prompt_text, + prompt_language=prompt_language, + text=segment_data["text"], + text_language=text_language, + how_to_cut=i18n("不切"), + top_k=top_k, + top_p=top_p, + temperature=temperature, + pause_second=0 # 不在内部添加停顿 + )) + + # 这里不再生成ID，由调用者提供 + temp_path = os.path.join(TEMP_FOLDER, "temp_generate.wav") + soundfile.write(temp_path, audio_data, sr) + + # 添加停顿 + audio = AudioSegment.from_wav(temp_path) + pause = AudioSegment.silent(duration=int(segment_data["pause"]*1000)) + final_audio = audio + pause + final_audio.export(temp_path, format="wav") + + return { + "success": True, + "audio_path": temp_path, + "text": segment_data["text"], + "pause": segment_data["pause"], + "message": "生成成功" + } + except Exception as e: + print(f"生成片段失败: {str(e)}", exc_info=True) + return { + "success": False, + "audio_path": None, + "text": segment_data["text"], + "pause": segment_data["pause"], + "message": f"生成失败: {str(e)}" + } + +def process_all_segments(text, ref_wav_path, prompt_text, prompt_language, text_language, + top_k, top_p, temperature, period_pause, comma_pause): + """完整处理流程""" + # 输入验证 + if not text or not isinstance(text, str): + error_msg = "输入文本无效" + print(error_msg) + return [[1, error_msg, "错误"]], None + + if not os.path.exists(ref_wav_path): + error_msg = f"参考音频不存在: {ref_wav_path}" + print(error_msg) + return [[1, error_msg, "错误"]], None + + # 处理分段 + segments = split_text_by_punctuation(text, period_pause, comma_pause) + if not segments: + error_msg = "无法分割文本" + print(error_msg) + return [[1, error_msg, "错误"]], None + + results = [] + audio_files = [] + + # 修改这里：使用enumerate从1开始编号，而不是基于文件夹内容 + for i, segment in enumerate(segments, 1): + result = generate_segment_audio( + segment, ref_wav_path, prompt_text, + prompt_language, text_language, top_k, top_p, temperature + ) + + # 更新结果中的segment_id + result["segment_id"] = f"{i}temp" + if result["success"] and result["audio_path"]: + # 重命名文件以匹配新的编号 + new_path = os.path.join(TEMP_FOLDER, f"{result['segment_id']}.wav") + os.rename(result["audio_path"], new_path) + result["audio_path"] = new_path + audio_files.append(new_path) + + results.append(result) + print(f"处理进度: {i}/{len(segments)} - {result['message']}") + + # 准备显示数据 + df_data = [] + for i, result in enumerate(results, 1): + df_data.append([ + f"{i}temp", + result["text"], + result["message"] + ]) + + first_audio = audio_files[0] if audio_files else None + return df_data, first_audio + +def regenerate_segment(segment_id, new_text, ref_wav_path, prompt_text, + prompt_language, text_language, top_k, top_p, temperature, + period_pause, comma_pause): + try: + if not segment_id or not new_text: + raise ValueError("缺少片段ID或新文本内容") + + # 从文件名解析原始停顿时间 + try: + pause = 0.25 if segment_id.endswith(("。", ".")) else 0.1 + except: + pause = 0.1 # 默认值 + + is_period = segment_id.endswith(("。", ".")) + pause = period_pause if is_period else comma_pause + + segment_data = { + "text": new_text, + "pause": pause, + "punctuation": "。" if is_period else "，" + } + result = generate_segment_audio( + segment_data, ref_wav_path, prompt_text, + prompt_language, text_language, top_k, top_p, temperature + ) + + # 更新文件 + if result["success"]: + old_path = os.path.join(TEMP_FOLDER, f"{segment_id}.wav") + if os.path.exists(old_path): + os.remove(old_path) + os.rename(result["audio_path"], old_path) + result["audio_path"] = old_path + + return ( + result["audio_path"], + segment_id, + result["message"] + ) + except Exception as e: + print(f"重新生成片段失败: {str(e)}", exc_info=True) + return None, segment_id, f"重新生成失败: {str(e)}" + +def merge_all_segments(): + try: + # 获取并按编号排序片段 + segments = sorted( + [f for f in os.listdir(TEMP_FOLDER) if f.endswith(".wav") and f != "final_output.wav"], + key=lambda x: int(x.split("temp")[0]) + ) + if not segments: + raise ValueError("没有找到可合并的音频片段") + + combined = AudioSegment.empty() + for seg in segments: + seg_path = os.path.join(TEMP_FOLDER, seg) + audio = AudioSegment.from_wav(seg_path) + combined += audio + + # 保存最终结果 + output_path = os.path.join(TEMP_FOLDER, "final_output.wav") + combined.export(output_path, format="wav") + + print(f"成功合并 {len(segments)} 个片段") + return output_path, "合并成功" + except Exception as e: + print(f"合并片段失败: {str(e)}", exc_info=True) + return None, f"合并失败: {str(e)}" + +def clean_temp_files(): + """清理临时文件函数""" + try: + for filename in os.listdir(TEMP_FOLDER): + file_path = os.path.join(TEMP_FOLDER, filename) + try: + if os.path.isfile(file_path): + os.unlink(file_path) + except Exception as e: + print(f"删除文件 {file_path} 失败: {e}") + return "临时文件已清理" + except Exception as e: + return f"清理失败: {str(e)}" + +def on_segment_select(df, evt: gr.SelectData): + """当选择分段列表中的项目时更新显示""" + if evt.index: + selected_row = df.iloc[evt.index[0]] + audio_path = os.path.join(TEMP_FOLDER, f"{selected_row['编号']}.wav") + return ( + selected_row["编号"], + selected_row["文本内容"], + audio_path if os.path.exists(audio_path) else None + ) + return "1temp", "", None + def split(todo_text): todo_text = todo_text.replace("……", "。").replace("——", "，") if todo_text[-1] not in splits: @@ -1006,6 +1258,236 @@ def split(todo_text): else: i_split_head += 1 return todo_texts +# ======================== 合并功能实现 ======================== +def merge_selected_segments(merge_range, segment_list_data, ref_wav_path, prompt_text, + prompt_language, text_language, top_k, top_p, temperature, + pause_period, pause_comma): + """合并选中的句子并立即生成新音频""" + try: + if not merge_range: + return segment_list_data, "请输入合并范围（例如：1-3）", None + + # 解析合并范围 + start, end = map(int, merge_range.split('-')) + if start <= 0 or end <= 0 or start > end: + return segment_list_data, "无效的合并范围", None + + # 检查范围是否有效 + if end > len(segment_list_data): + return segment_list_data, f"结束编号 {end} 超过总段数 {len(segment_list_data)}", None + + # === 第一步：收集要删除的文件并立即删除 === + files_to_delete = [] + for i in range(start-1, end): + file_id = segment_list_data.iloc[i, 0] + file_path = os.path.join(TEMP_FOLDER, f"{file_id}.wav") + if os.path.exists(file_path): + os.remove(file_path) + files_to_delete.append(file_id) + + # === 第二步：合并文本 === + merged_text = "" + for i in range(start-1, end): + merged_text += segment_list_data.iloc[i, 1] # 文本内容在第二列 + + # 确定合并后的停顿类型（取最后一个片段的标点） + last_punctuation = segment_list_data.iloc[end-1, 1][-1] if segment_list_data.iloc[end-1, 1] else "" + is_period = last_punctuation in ["。", "."] + pause = pause_period if is_period else pause_comma + + # === 第三步：立即生成合并后的音频 === + segment_data = { + "text": merged_text, + "pause": pause, + "punctuation": last_punctuation + } + # 生成音频 + result = generate_segment_audio( + segment_data, ref_wav_path, prompt_text, + prompt_language, text_language, top_k, top_p, temperature + ) + if not result["success"]: + return segment_list_data, result["message"], None + + # === 第四步：构建新的分段列表 === + # 创建新的合并条目 - 使用起始编号 + new_id = f"{start}temp" + merged_entry = [new_id, merged_text, "已生成"] + + # 移动生成的音频到正确位置 + new_path = os.path.join(TEMP_FOLDER, f"{new_id}.wav") + shutil.move(result["audio_path"], new_path) + + # 构建新的分段列表 + new_segment_list = [] + + # 添加合并前的部分 + if start > 1: + new_segment_list.extend(segment_list_data.iloc[:start-1].values.tolist()) + + # 添加合并条目 + new_segment_list.append(merged_entry) + + # 添加合并后的部分 + if end < len(segment_list_data): + new_segment_list.extend(segment_list_data.iloc[end:].values.tolist()) + + # === 第五步：重新编号 === + reindexed_list = [] + new_id_counter = 1 + for segment in new_segment_list: + old_id = segment[0] + # 为新列表生成连续编号 + new_id = f"{new_id_counter}temp" + + # 重命名文件（如果存在） + old_path = os.path.join(TEMP_FOLDER, f"{old_id}.wav") + new_path = os.path.join(TEMP_FOLDER, f"{new_id}.wav") + + if os.path.exists(old_path) and old_id != new_id: + os.rename(old_path, new_path) + + # 更新ID + segment[0] = new_id + reindexed_list.append(segment) + new_id_counter += 1 + + return reindexed_list, "合并成功", new_id + + except Exception as e: + traceback.print_exc() + return segment_list_data, f"合并失败: {str(e)}", None + +# ======================== 实现拆分逻辑函数 ======================== +def split_selected_segment(split_id, segment_list_data, ref_wav_path, prompt_text, + prompt_language, text_language, top_k, top_p, temperature, + pause_period, pause_comma): + """拆分选中的句子并重新生成 - 使用倒序重命名避免冲突""" + try: + if not split_id: + return segment_list_data, "请输入要拆分的句子编号", None + + # 从文件名解析原始停顿时间 + try: + pause = 0.25 if split_id.endswith(("。", ".")) else 0.1 + except: + pause = 0.1 # 默认值 + + # 查找要拆分的句子 + df = segment_list_data + target_row = None + target_idx = None + + # 查找匹配的句子 + for idx, row in enumerate(df.itertuples()): + if str(row[1]) == split_id: # 第一列是ID + target_row = row + target_idx = idx + break + + if target_row is None: + return segment_list_data, f"未找到编号为 {split_id} 的句子", None + + # 获取原始文本 + original_text = target_row[2] # 第二列是文本 + + # 根据标点符号拆分文本 + segments = [] + current_segment = "" + punctuation_marks = ['，', ',', '。', '.', '？', '?', '！', '!', '；', ';'] + + for char in original_text: + current_segment += char + if char in punctuation_marks: + # 根据标点类型设置停顿时间 + pause = pause_period if char in ['。', '.', '？', '?', '！', '!'] else pause_comma + segments.append({ + "text": current_segment.strip(), + "pause": pause, + "punctuation": char + }) + current_segment = "" + + if current_segment: + segments.append({ + "text": current_segment.strip(), + "pause": pause_comma, # 默认使用非句号停顿时间 + "punctuation": "" + }) + + if len(segments) <= 1: + return segment_list_data, "句子无法拆分（没有标点符号）", None + + # 计算拆分后新增的句子数量 + num_new_segments = len(segments) + offset = num_new_segments - 1 # 拆分后增加的句子数 + + # === 第一步：删除原始音频文件 === + original_path = os.path.join(TEMP_FOLDER, f"{split_id}.wav") + if os.path.exists(original_path): + os.remove(original_path) + + # === 第二步：倒序重命名后续句子避免冲突 === + new_segment_list = [] + + # 添加拆分前的句子 + for i in range(target_idx): + new_segment_list.append(df.iloc[i].tolist()) + + # 倒序重命名从最后一个句子开始，避免冲突 + total_segments = len(df) + + # 从最后一个句子开始倒序遍历 + for i in range(total_segments - 1, target_idx, -1): + old_id = df.iloc[i, 0] + old_num = int(old_id.replace("temp", "")) + new_id_num = old_num + offset + new_id = f"{new_id_num}temp" + + # 重命名音频文件 + old_path = os.path.join(TEMP_FOLDER, f"{old_id}.wav") + new_path = os.path.join(TEMP_FOLDER, f"{new_id}.wav") + if os.path.exists(old_path): + os.rename(old_path, new_path) + + # 更新列表条目 + new_segment_list.append([new_id, df.iloc[i, 1], df.iloc[i, 2]]) + + # === 第三步：添加拆分后的新句子 === + for i, segment in enumerate(segments): + new_id = f"{target_idx+1+i}temp" # 新ID从原始位置开始 + new_segment_list.append([new_id, segment["text"], "待生成"]) + + # === 第四步：重新排序列表 === + # 按照ID中的数字排序 + new_segment_list.sort(key=lambda x: int(x[0].replace("temp", ""))) + + # === 第五步：生成拆分后的新句子 === + for i, segment in enumerate(segments): + segment_id = f"{target_idx+1+i}temp" + result = generate_segment_audio( + segment, ref_wav_path, prompt_text, + prompt_language, text_language, top_k, top_p, temperature + ) + + if result["success"]: + # 更新文件 + new_path = os.path.join(TEMP_FOLDER, f"{segment_id}.wav") + if os.path.exists(result["audio_path"]): + os.rename(result["audio_path"], new_path) + + # 更新状态 + for j, item in enumerate(new_segment_list): + if item[0] == segment_id: + new_segment_list[j][2] = "已生成" + break + + return new_segment_list, f"成功拆分为 {num_new_segments} 个句子", f"{target_idx+1}temp" + + except Exception as e: + traceback.print_exc() + return segment_list_data, f"拆分失败: {str(e)}", None + def cut1(inp): @@ -1315,6 +1797,160 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css ) GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], []) + + + # ======================== 插入分段合成UI开始 ======================== + with gr.Tab(i18n("分段合成模式")): + with gr.Row(): + with gr.Column(): + segmented_text = gr.Textbox(label=i18n("需要分段的文本"), lines=10, value="") + segment_button = gr.Button(i18n("分割文本并生成所有片段"), variant="primary") + segmented_output = gr.Audio(label=i18n("当前选中片段"), interactive=False) + + with gr.Row(): + segment_index = gr.Textbox(label=i18n("片段编号"), interactive=False, visible=False) + new_segment_text = gr.Textbox(label=i18n("新文本内容"), lines=2, max_lines=4) + regenerate_button = gr.Button(i18n("重新生成当前片段"), variant="primary") + with gr.Row(): + pause_period = gr.Slider( + minimum=0.1, + maximum=1.0, + step=0.05, + label=i18n("句号停顿时间(秒)"), + value=0.3, + interactive=True + ) + pause_comma = gr.Slider( + minimum=0.1, + maximum=0.5, + step=0.05, + label=i18n("非句号停顿时间(秒)"), + value=0.15, + interactive=True + ) + with gr.Row(): + clean_button = gr.Button(i18n("清理临时文件"), variant="secondary", scale=1) + confirm_button = gr.Button(i18n("确认并合并所有片段"), variant="primary", scale=2) + + final_output = gr.Audio(label=i18n("最终合成结果"), interactive=False) + segment_status = gr.Textbox(label=i18n("状态"), interactive=False) + # 添加合并功能 + with gr.Row(): + merge_range = gr.Textbox(label=i18n("合并范围（例如：1-3）"), scale=3) + merge_button = gr.Button(i18n("合并句子"), variant="primary", scale=1) + # 添加拆分功能控件 + with gr.Row(): + split_index = gr.Textbox(label=i18n("要拆分的句子编号"), scale=1) + split_button = gr.Button(i18n("拆分句子"), variant="primary", scale=1) + with gr.Column(): + segment_list = gr.Dataframe( + headers=["编号", "文本内容", "状态"], + datatype=["str", "str", "str"], + interactive=False, + label=i18n("分段列表"), + value=[] + ) + # 在分段合成UI部分添加以下代码（大约在line 3200附近） + + # ======================== 插入分段合成UI结束 ======================== + + # ======================== 插入分段合成事件绑定开始 ======================== + # 分割文本并生成所有片段 + segment_button.click( + process_all_segments, + inputs=[ + segmented_text, + inp_ref, + prompt_text, + prompt_language, + text_language, + top_k, + top_p, + temperature, + pause_period, # 新增句号停顿参数 + pause_comma # 新增非句号停顿参数 + ], + outputs=[segment_list, segmented_output] + ) + # 重新生成当前片段 + regenerate_button.click( + regenerate_segment, + inputs=[ + segment_index, # 第一个参数应该是segment_id + new_segment_text, + inp_ref, + prompt_text, + prompt_language, + text_language, + top_k, + top_p, + temperature, + pause_period, + pause_comma + ], + outputs=[segmented_output, segment_index, segment_status] + ) + + # 合并所有片段 + confirm_button.click( + merge_all_segments, + inputs=[], + outputs=[final_output, segment_status] + ) + + # 清理临时文件 + clean_button.click( + clean_temp_files, + inputs=[], + outputs=[segment_status] + ) + + # 当选择分段列表中的项目时 + segment_list.select( + fn=on_segment_select, + inputs=[segment_list], + outputs=[segment_index, new_segment_text, segmented_output], + ) + merge_button.click( + fn=merge_selected_segments, + inputs=[ + merge_range, + segment_list, + inp_ref, + prompt_text, + prompt_language, + text_language, + top_k, + top_p, + temperature, + pause_period, + pause_comma + ], + outputs=[segment_list, segment_status, segment_index] + ) + # ======================== 添加拆分按钮的事件绑定 ======================== + split_button.click( + fn=split_selected_segment, + inputs=[ + split_index, + segment_list, + inp_ref, + prompt_text, + prompt_language, + text_language, + top_k, + top_p, + temperature, + pause_period, + pause_comma + ], + outputs=[segment_list, segment_status, segment_index] + ) + # ======================== 插入分段合成事件绑定结束 ======================== + + + + # gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。")) # with gr.Row(): # text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="") @@ -1339,3 +1975,4 @@ if __name__ == "__main__": server_port=infer_ttswebui, # quiet=True, ) + From 7604f36bb270d0a897df0b8d4dd9d35f860d06cb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=80=B8=E6=B8=B8=E4=BB=99=E4=BA=BA?= <3439248121@qq.com> Date: Sun, 22 Jun 2025 03:51:59 +0800 Subject: [PATCH 2/2] =?UTF-8?q?=E7=94=A8=E4=BA=8E=E7=89=B9=E6=AE=8A?= =?UTF-8?q?=E7=94=A8=E9=80=94=E7=9A=84=E5=A4=A7=E6=95=B0=E6=8D=AE=E9=87=8F?= =?UTF-8?q?=E5=AE=9E=E9=AA=8C=EF=BC=8C=E8=AF=B7=E5=8B=BF=E5=90=88=E5=B9=B6?= =?UTF-8?q?=EF=BC=81=EF=BC=81=EF=BC=81=EF=BC=81=EF=BC=81=EF=BC=81=EF=BC=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- webui.py | 23 +++++++++++++---------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/webui.py b/webui.py index 9981cfcc..c6060920 100644 --- a/webui.py +++ b/webui.py @@ -1,3 +1,6 @@ +import os +os.environ["CUDA_VISIBLE_DEVICES"] = "0" # 限制为单卡 + import os import sys @@ -127,13 +130,13 @@ def set_default(): if version not in v3v4set: default_sovits_epoch = 8 default_sovits_save_every_epoch = 4 - max_sovits_epoch = 25 # 40 - max_sovits_save_every_epoch = 25 # 10 + max_sovits_epoch = 255 # 40 + max_sovits_save_every_epoch = 255 # 10 else: default_sovits_epoch = 2 default_sovits_save_every_epoch = 1 - max_sovits_epoch = 16 # 40 # 3 #训太多=作死 - max_sovits_save_every_epoch = 10 # 10 # 3 + max_sovits_epoch = 255 # 40 # 3 #训太多=作死 + max_sovits_save_every_epoch = 255 # 10 # 3 default_batch_size = max(1, default_batch_size) default_batch_size_s1 = max(1, default_batch_size_s1) @@ -507,7 +510,7 @@ def open1Ba( ): global p_train_SoVITS if p_train_SoVITS == None: - exp_name = exp_name.rstrip(" ") + exp_name=exp_name.rstrip(" ") config_file = ( "GPT_SoVITS/configs/s2.json" if version not in {"v2Pro", "v2ProPlus"} @@ -604,7 +607,7 @@ def open1Bb( ): global p_train_GPT if p_train_GPT == None: - exp_name = exp_name.rstrip(" ") + exp_name=exp_name.rstrip(" ") with open( "GPT_SoVITS/configs/s1longer.yaml" if version == "v1" else "GPT_SoVITS/configs/s1longer-v2.yaml" ) as f: @@ -1728,8 +1731,8 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css ) with gr.Row(): text_low_lr_rate = gr.Slider( - minimum=0.2, - maximum=0.6, + minimum=0, + maximum=1, step=0.05, label=i18n("文本模块学习率权重"), value=0.4, @@ -1738,7 +1741,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css lora_rank = gr.Radio( label=i18n("LoRA秩"), value="32", - choices=["16", "32", "64", "128"], + choices=["16", "32", "64", "128", "256", "512", "1024","2048", "4096"], visible=True if version in v3v4set else False, ) # v1v2 not need save_every_epoch = gr.Slider( @@ -1800,7 +1803,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI", analytics_enabled=False, js=js, css=css ) total_epoch1Bb = gr.Slider( minimum=2, - maximum=50, + maximum=max_sovits_epoch, step=1, label=i18n("总训练轮数total_epoch"), value=15,