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@@ -220,12 +220,14 @@ def add_item(folder: str, method: str, label_lang: str):
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elif folder:
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err = folder
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return gr.Checkboxgroup(choices=items), build_html_error_message(
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- f"添加文件夹路径无效: {err}"
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+ i18n("Invalid path: {}").format(err)
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)
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formatted_data = json.dumps(dict_items, ensure_ascii=False, indent=4)
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logger.info(formatted_data)
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- return gr.Checkboxgroup(choices=items), build_html_ok_message("添加文件(夹)路径成功!")
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+ return gr.Checkboxgroup(choices=items), build_html_ok_message(
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+ i18n("Added path successfully!")
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+ )
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def remove_items(selected_items):
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@@ -237,13 +239,17 @@ def remove_items(selected_items):
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formatted_data = json.dumps(dict_items, ensure_ascii=False, indent=4)
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logger.info(formatted_data)
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return gr.Checkboxgroup(choices=items, value=[]), build_html_ok_message(
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- "删除文件(夹)路径成功!"
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+ i18n("Removed path successfully!")
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)
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def show_selected(options):
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selected_options = ", ".join(options)
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- return f"你选中了: {selected_options}" if options else "你没有选中任何选项"
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+
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+ if options:
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+ return i18n("Selected: {}").format(selected_options)
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+ else:
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+ return i18n("No selected options")
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def list_copy(list_file_path, method):
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@@ -260,7 +266,7 @@ def list_copy(list_file_path, method):
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if target_wav_path.is_file():
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continue
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target_wav_path.parent.mkdir(parents=True, exist_ok=True)
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- if method == "复制一份":
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+ if method == i18n("Copy"):
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shutil.copy(original_wav_path, target_wav_path)
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else:
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shutil.move(original_wav_path, target_wav_path.parent)
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@@ -273,22 +279,20 @@ def list_copy(list_file_path, method):
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)
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if target_lab_path.is_file():
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continue
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- if method == "复制一份":
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+ if method == i18n("Copy"):
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shutil.copy(original_lab_path, target_lab_path)
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else:
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shutil.move(original_lab_path, target_lab_path.parent)
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- if method == "直接移动":
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+ if method == i18n("Move"):
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with list_file_path.open("w", encoding="utf-8") as file:
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file.writelines("\n".join(lst))
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del lst
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- return build_html_ok_message("使用filelist")
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+ return build_html_ok_message(i18n("Use filelist"))
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def check_files(data_path: str, max_depth: int, label_model: str, label_device: str):
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- dict_to_language = {"中文": "ZH", "英文": "EN", "日文": "JP", "不打标": "WTF"}
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-
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global dict_items
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data_path = Path(data_path)
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for item, content in dict_items.items():
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@@ -296,8 +300,8 @@ def check_files(data_path: str, max_depth: int, label_model: str, label_device:
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tar_path = data_path / item_path.name
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if content["type"] == "folder" and item_path.is_dir():
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|
- cur_lang = dict_to_language[content["label_lang"]]
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- if cur_lang != "WTF":
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+ cur_lang = content["label_lang"]
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+ if cur_lang != "IGNORE":
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try:
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subprocess.run(
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[
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@@ -319,7 +323,7 @@ def check_files(data_path: str, max_depth: int, label_model: str, label_device:
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except Exception:
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print("Transcription error occurred")
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- if content["method"] == "复制一份":
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+ if content["method"] == i18n("Copy"):
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os.makedirs(tar_path, exist_ok=True)
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shutil.copytree(
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src=str(item_path), dst=str(tar_path), dirs_exist_ok=True
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@@ -330,7 +334,9 @@ def check_files(data_path: str, max_depth: int, label_model: str, label_device:
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elif content["type"] == "file" and item_path.is_file():
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|
list_copy(item_path, content["method"])
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- return build_html_ok_message("文件移动完毕"), new_explorer(data_path, max_depth=max_depth)
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|
+ return build_html_ok_message(i18n("Move files successfully")), new_explorer(
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|
+ data_path, max_depth=max_depth
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|
+ )
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def train_process(
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@@ -457,7 +463,7 @@ def train_process(
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logger.info(train_cmd)
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subprocess.run(train_cmd)
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- return build_html_ok_message("训练终止")
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|
+ return build_html_ok_message(i18n("Training stopped"))
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|
def tensorboard_process(
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@@ -468,13 +474,17 @@ def tensorboard_process(
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):
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|
global p_tensorboard
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|
if if_tensorboard == True and p_tensorboard == None:
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|
|
- yield build_html_ok_message(f"Tensorboard界面已开启, 访问 http://{host}:{port}")
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|
+ url = f"http://{host}:{port}"
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|
+ yield build_html_ok_message(
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|
+ i18n("Tensorboard interface is launched at {}").format(url)
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|
+ )
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|
+ prefix = ["tensorboard"]
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|
+ if Path("fishenv").exists():
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|
+ prefix = ["fishenv/python.exe", "fishenv/Scripts/tensorboard.exe"]
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|
+
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|
p_tensorboard = subprocess.Popen(
|
|
|
- [
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|
- "fishenv/python.exe",
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|
- "fishenv/Scripts/tensorboard.exe"
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|
|
- if Path("fishenv").exists()
|
|
|
- else "tensorboard",
|
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|
+ prefix
|
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|
+ + [
|
|
|
"--logdir",
|
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|
tensorboard_dir,
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|
"--host",
|
|
|
@@ -488,7 +498,7 @@ def tensorboard_process(
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|
|
elif if_tensorboard == False and p_tensorboard != None:
|
|
|
kill_process(p_tensorboard.pid)
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|
|
p_tensorboard = None
|
|
|
- yield build_html_error_message("Tensorboard界面已关闭")
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|
|
+ yield build_html_error_message(i18n("Tensorboard interface is closed"))
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|
|
|
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|
|
def fresh_tb_dir():
|
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|
@@ -517,7 +527,11 @@ def llama_lora_merge(llama_weight, lora_weight, llama_lora_output):
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|
or not Path(lora_weight).exists()
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|
|
or not Path(llama_weight).exists()
|
|
|
):
|
|
|
- return build_html_error_message("路径错误，请检查模型文件是否存在于对应路径")
|
|
|
+ return build_html_error_message(
|
|
|
+ i18n(
|
|
|
+ "Path error, please check the model file exists in the corresponding path"
|
|
|
+ )
|
|
|
+ )
|
|
|
|
|
|
merge_cmd = [
|
|
|
PYTHON,
|
|
|
@@ -535,7 +549,7 @@ def llama_lora_merge(llama_weight, lora_weight, llama_lora_output):
|
|
|
]
|
|
|
logger.info(merge_cmd)
|
|
|
subprocess.run(merge_cmd)
|
|
|
- return build_html_ok_message("融合终止")
|
|
|
+ return build_html_ok_message(i18n("Merge successfully"))
|
|
|
|
|
|
|
|
|
init_vqgan_yml = load_yaml_data_in_fact(vqgan_yml_path)
|
|
|
@@ -546,70 +560,88 @@ with gr.Blocks(
|
|
|
js=js,
|
|
|
theme=seafoam,
|
|
|
analytics_enabled=False,
|
|
|
- title="Fish-Speech 鱼语",
|
|
|
+ title="Fish Speech",
|
|
|
) as demo:
|
|
|
with gr.Row():
|
|
|
with gr.Column():
|
|
|
- with gr.Tab("\U0001F4D6 数据集准备"):
|
|
|
+ with gr.Tab("\U0001F4D6 " + i18n("Data Preprocessing")):
|
|
|
with gr.Row():
|
|
|
textbox = gr.Textbox(
|
|
|
- label="\U0000270F 输入音频&转写源文件夹路径",
|
|
|
- info="音频装在一个以说话人命名的文件夹内作为区分",
|
|
|
+ label="\U0000270F "
|
|
|
+ + i18n("Input Audio & Source Path for Transcription"),
|
|
|
+ info=i18n("Speaker is identified by the folder name"),
|
|
|
interactive=True,
|
|
|
)
|
|
|
with gr.Row(equal_height=False):
|
|
|
with gr.Column():
|
|
|
output_radio = gr.Radio(
|
|
|
- label="\U0001F4C1 选择源文件(夹)处理方式",
|
|
|
- choices=["复制一份", "直接移动"],
|
|
|
- value="复制一份",
|
|
|
+ label="\U0001F4C1 "
|
|
|
+ + i18n("Select source file processing method"),
|
|
|
+ choices=[i18n("Copy"), i18n("Move")],
|
|
|
+ value=i18n("Copy"),
|
|
|
interactive=True,
|
|
|
)
|
|
|
with gr.Column():
|
|
|
- error = gr.HTML(label="错误信息")
|
|
|
+ error = gr.HTML(label=i18n("Error Message"))
|
|
|
if_label = gr.Checkbox(
|
|
|
- label="是否开启打标WebUI", scale=0, show_label=True
|
|
|
+ label=i18n("Open Labeler WebUI"), scale=0, show_label=True
|
|
|
)
|
|
|
with gr.Row():
|
|
|
- add_button = gr.Button("\U000027A1提交到处理区", variant="primary")
|
|
|
- remove_button = gr.Button("\U000026D4 取消所选内容")
|
|
|
+ add_button = gr.Button(
|
|
|
+ "\U000027A1 " + i18n("Add to Processing Area"),
|
|
|
+ variant="primary",
|
|
|
+ )
|
|
|
+ remove_button = gr.Button(
|
|
|
+ "\U000026D4 " + i18n("Remove Selected Data")
|
|
|
+ )
|
|
|
|
|
|
with gr.Row():
|
|
|
label_device = gr.Dropdown(
|
|
|
- label="打标设备",
|
|
|
- info="建议使用cuda, 实在是低配置再用cpu",
|
|
|
+ label=i18n("Labeling Device"),
|
|
|
+ info=i18n(
|
|
|
+ "It is recommended to use CUDA, if you have low configuration, use CPU"
|
|
|
+ ),
|
|
|
choices=["cpu", "cuda"],
|
|
|
value="cuda",
|
|
|
interactive=True,
|
|
|
)
|
|
|
label_model = gr.Dropdown(
|
|
|
- label="打标模型大小",
|
|
|
- info="显存10G以上用large, 5G用medium, 2G用small",
|
|
|
+ label=i18n("Whisper Model"),
|
|
|
+ info=i18n(
|
|
|
+ "Use large for 10G+ GPU, medium for 5G, small for 2G"
|
|
|
+ ),
|
|
|
choices=["large", "medium", "small"],
|
|
|
value="small",
|
|
|
interactive=True,
|
|
|
)
|
|
|
label_radio = gr.Dropdown(
|
|
|
- label="(可选)打标语言",
|
|
|
- info="如果没有音频对应的文本，则进行辅助打标, 支持.txt或.lab格式",
|
|
|
- choices=["中文", "日文", "英文", "不打标"],
|
|
|
- value="不打标",
|
|
|
+ label=i18n("Optional Label Language"),
|
|
|
+ info=i18n(
|
|
|
+ "If there is no corresponding text for the audio, apply ASR for assistance, support .txt or .lab format"
|
|
|
+ ),
|
|
|
+ choices=[
|
|
|
+ (i18n("Chinese"), "ZH"),
|
|
|
+ (i18n("English"), "EN"),
|
|
|
+ (i18n("Japanese"), "JA"),
|
|
|
+ (i18n("Disabled"), "IGNORE"),
|
|
|
+ ],
|
|
|
+ value="IGNORE",
|
|
|
interactive=True,
|
|
|
)
|
|
|
|
|
|
- with gr.Tab("\U0001F6E0 训练配置项"): # hammer
|
|
|
+ with gr.Tab("\U0001F6E0 " + i18n("Training Configuration")):
|
|
|
with gr.Row():
|
|
|
model_type_radio = gr.Radio(
|
|
|
- label="选择要训练的模型类型",
|
|
|
+ label=i18n("Select the model to be trained"),
|
|
|
interactive=True,
|
|
|
choices=["VQGAN", "LLAMA", "all"],
|
|
|
value="all",
|
|
|
)
|
|
|
with gr.Row():
|
|
|
- with gr.Tab(label="VQGAN配置项"):
|
|
|
+ with gr.Tab(label=i18n("VQGAN Configuration")):
|
|
|
with gr.Row(equal_height=False):
|
|
|
vqgan_lr_slider = gr.Slider(
|
|
|
- label="初始学习率",
|
|
|
+ label=i18n("Initial Learning Rate"),
|
|
|
interactive=True,
|
|
|
minimum=1e-5,
|
|
|
maximum=1e-4,
|
|
|
@@ -617,7 +649,7 @@ with gr.Blocks(
|
|
|
value=init_vqgan_yml["model"]["optimizer"]["lr"],
|
|
|
)
|
|
|
vqgan_maxsteps_slider = gr.Slider(
|
|
|
- label="训练最大步数",
|
|
|
+ label=i18n("Maximum Training Steps"),
|
|
|
interactive=True,
|
|
|
minimum=1000,
|
|
|
maximum=100000,
|
|
|
@@ -627,7 +659,7 @@ with gr.Blocks(
|
|
|
|
|
|
with gr.Row(equal_height=False):
|
|
|
vqgan_data_num_workers_slider = gr.Slider(
|
|
|
- label="num_workers",
|
|
|
+ label=i18n("Number of Workers"),
|
|
|
interactive=True,
|
|
|
minimum=1,
|
|
|
maximum=16,
|
|
|
@@ -636,7 +668,7 @@ with gr.Blocks(
|
|
|
)
|
|
|
|
|
|
vqgan_data_batch_size_slider = gr.Slider(
|
|
|
- label="batch_size",
|
|
|
+ label=i18n("Batch Size"),
|
|
|
interactive=True,
|
|
|
minimum=1,
|
|
|
maximum=32,
|
|
|
@@ -645,7 +677,7 @@ with gr.Blocks(
|
|
|
)
|
|
|
with gr.Row(equal_height=False):
|
|
|
vqgan_data_val_batch_size_slider = gr.Slider(
|
|
|
- label="val_batch_size",
|
|
|
+ label=i18n("Validation Batch Size"),
|
|
|
interactive=True,
|
|
|
minimum=1,
|
|
|
maximum=32,
|
|
|
@@ -653,14 +685,17 @@ with gr.Blocks(
|
|
|
value=init_vqgan_yml["data"]["val_batch_size"],
|
|
|
)
|
|
|
vqgan_precision_dropdown = gr.Dropdown(
|
|
|
- label="训练精度",
|
|
|
+ label=i18n("Precision"),
|
|
|
interactive=True,
|
|
|
choices=["32", "bf16-true", "bf16-mixed"],
|
|
|
+ info=i18n(
|
|
|
+ "bf16-true is recommended for 30+ series GPU, 16-mixed is recommended for 10+ series GPU"
|
|
|
+ ),
|
|
|
value=str(init_vqgan_yml["trainer"]["precision"]),
|
|
|
)
|
|
|
with gr.Row(equal_height=False):
|
|
|
vqgan_check_interval_slider = gr.Slider(
|
|
|
- label="每n步保存一个模型",
|
|
|
+ label=i18n("Save model every n steps"),
|
|
|
interactive=True,
|
|
|
minimum=500,
|
|
|
maximum=10000,
|
|
|
@@ -668,15 +703,18 @@ with gr.Blocks(
|
|
|
value=init_vqgan_yml["trainer"]["val_check_interval"],
|
|
|
)
|
|
|
|
|
|
- with gr.Tab(label="LLAMA配置项"):
|
|
|
+ with gr.Tab(label=i18n("LLAMA Configuration")):
|
|
|
with gr.Row(equal_height=False):
|
|
|
llama_use_lora = gr.Checkbox(
|
|
|
- label="使用lora训练？",
|
|
|
+ label=i18n("Use LoRA"),
|
|
|
+ info=i18n(
|
|
|
+ "Use LoRA can save GPU memory, but may reduce the quality of the model"
|
|
|
+ ),
|
|
|
value=True,
|
|
|
)
|
|
|
with gr.Row(equal_height=False):
|
|
|
llama_lr_slider = gr.Slider(
|
|
|
- label="初始学习率",
|
|
|
+ label=i18n("Initial Learning Rate"),
|
|
|
interactive=True,
|
|
|
minimum=1e-5,
|
|
|
maximum=1e-4,
|
|
|
@@ -684,7 +722,7 @@ with gr.Blocks(
|
|
|
value=init_llama_yml["model"]["optimizer"]["lr"],
|
|
|
)
|
|
|
llama_maxsteps_slider = gr.Slider(
|
|
|
- label="训练最大步数",
|
|
|
+ label=i18n("Maximum Training Steps"),
|
|
|
interactive=True,
|
|
|
minimum=1000,
|
|
|
maximum=100000,
|
|
|
@@ -693,7 +731,7 @@ with gr.Blocks(
|
|
|
)
|
|
|
with gr.Row(equal_height=False):
|
|
|
llama_base_config = gr.Dropdown(
|
|
|
- label="模型基础属性",
|
|
|
+ label=i18n("Model Size"),
|
|
|
choices=[
|
|
|
"dual_ar_2_codebook_large",
|
|
|
"dual_ar_2_codebook_medium",
|
|
|
@@ -701,7 +739,7 @@ with gr.Blocks(
|
|
|
value="dual_ar_2_codebook_large",
|
|
|
)
|
|
|
llama_data_num_workers_slider = gr.Slider(
|
|
|
- label="num_workers",
|
|
|
+ label=i18n("Number of Workers"),
|
|
|
minimum=0,
|
|
|
maximum=16,
|
|
|
step=1,
|
|
|
@@ -711,7 +749,7 @@ with gr.Blocks(
|
|
|
)
|
|
|
with gr.Row(equal_height=False):
|
|
|
llama_data_batch_size_slider = gr.Slider(
|
|
|
- label="batch_size",
|
|
|
+ label=i18n("Batch Size"),
|
|
|
interactive=True,
|
|
|
minimum=1,
|
|
|
maximum=32,
|
|
|
@@ -719,7 +757,7 @@ with gr.Blocks(
|
|
|
value=init_llama_yml["data"]["batch_size"],
|
|
|
)
|
|
|
llama_data_max_length_slider = gr.Slider(
|
|
|
- label="max_length",
|
|
|
+ label=i18n("Maximum Length per Sample"),
|
|
|
interactive=True,
|
|
|
minimum=1024,
|
|
|
maximum=4096,
|
|
|
@@ -728,13 +766,16 @@ with gr.Blocks(
|
|
|
)
|
|
|
with gr.Row(equal_height=False):
|
|
|
llama_precision_dropdown = gr.Dropdown(
|
|
|
- label="训练精度",
|
|
|
+ label=i18n("Precision"),
|
|
|
+ info=i18n(
|
|
|
+ "bf16-true is recommended for 30+ series GPU, 16-mixed is recommended for 10+ series GPU"
|
|
|
+ ),
|
|
|
interactive=True,
|
|
|
choices=["32", "bf16-true", "16-mixed"],
|
|
|
value="bf16-true",
|
|
|
)
|
|
|
llama_check_interval_slider = gr.Slider(
|
|
|
- label="每n步保存一个模型",
|
|
|
+ label=i18n("Save model every n steps"),
|
|
|
interactive=True,
|
|
|
minimum=500,
|
|
|
maximum=10000,
|
|
|
@@ -743,7 +784,7 @@ with gr.Blocks(
|
|
|
)
|
|
|
with gr.Row(equal_height=False):
|
|
|
llama_grad_batches = gr.Slider(
|
|
|
- label="accumulate_grad_batches",
|
|
|
+ label=i18n("Accumulate Gradient Batches"),
|
|
|
interactive=True,
|
|
|
minimum=1,
|
|
|
maximum=20,
|
|
|
@@ -753,7 +794,7 @@ with gr.Blocks(
|
|
|
],
|
|
|
)
|
|
|
llama_use_speaker = gr.Slider(
|
|
|
- label="use_speaker_ratio",
|
|
|
+ label=i18n("Probability of applying Speaker Condition"),
|
|
|
interactive=True,
|
|
|
minimum=0.1,
|
|
|
maximum=1.0,
|
|
|
@@ -761,11 +802,11 @@ with gr.Blocks(
|
|
|
value=init_llama_yml["train_dataset"]["use_speaker"],
|
|
|
)
|
|
|
|
|
|
- with gr.Tab(label="LLAMA_lora融合"):
|
|
|
+ with gr.Tab(label=i18n("Merge LoRA")):
|
|
|
with gr.Row(equal_height=False):
|
|
|
llama_weight = gr.Dropdown(
|
|
|
- label="要融入的原模型",
|
|
|
- info="输入路径，或者下拉选择",
|
|
|
+ label=i18n("Base LLAMA Model"),
|
|
|
+ info=i18n("Type the path or select from the dropdown"),
|
|
|
choices=[init_llama_yml["ckpt_path"]],
|
|
|
value=init_llama_yml["ckpt_path"],
|
|
|
allow_custom_value=True,
|
|
|
@@ -773,8 +814,8 @@ with gr.Blocks(
|
|
|
)
|
|
|
with gr.Row(equal_height=False):
|
|
|
lora_weight = gr.Dropdown(
|
|
|
- label="要融入的lora模型",
|
|
|
- info="输入路径，或者下拉选择",
|
|
|
+ label=i18n("LoRA Model to be merged"),
|
|
|
+ info=i18n("Type the path or select from the dropdown"),
|
|
|
choices=[
|
|
|
str(p)
|
|
|
for p in Path("results").glob("text2*ar/**/*.ckpt")
|
|
|
@@ -784,8 +825,8 @@ with gr.Blocks(
|
|
|
)
|
|
|
with gr.Row(equal_height=False):
|
|
|
llama_lora_output = gr.Dropdown(
|
|
|
- label="输出的lora模型",
|
|
|
- info="输出路径",
|
|
|
+ label=i18n("Output Path"),
|
|
|
+ info=i18n("Type the path or select from the dropdown"),
|
|
|
value="checkpoints/merged.ckpt",
|
|
|
choices=["checkpoints/merged.ckpt"],
|
|
|
allow_custom_value=True,
|
|
|
@@ -793,20 +834,20 @@ with gr.Blocks(
|
|
|
)
|
|
|
with gr.Row(equal_height=False):
|
|
|
llama_lora_merge_btn = gr.Button(
|
|
|
- value="开始融合", variant="primary"
|
|
|
+ value=i18n("Merge"), variant="primary"
|
|
|
)
|
|
|
|
|
|
with gr.Tab(label="Tensorboard"):
|
|
|
with gr.Row(equal_height=False):
|
|
|
tb_host = gr.Textbox(
|
|
|
- label="Tensorboard Host", value="127.0.0.1"
|
|
|
+ label=i18n("Tensorboard Host"), value="127.0.0.1"
|
|
|
)
|
|
|
tb_port = gr.Textbox(
|
|
|
- label="Tensorboard Port", value="11451"
|
|
|
+ label=i18n("Tensorboard Port"), value="11451"
|
|
|
)
|
|
|
with gr.Row(equal_height=False):
|
|
|
tb_dir = gr.Dropdown(
|
|
|
- label="Tensorboard 日志文件夹",
|
|
|
+ label=i18n("Tensorboard Log Path"),
|
|
|
allow_custom_value=True,
|
|
|
choices=[
|
|
|
str(p)
|
|
|
@@ -817,24 +858,30 @@ with gr.Blocks(
|
|
|
)
|
|
|
with gr.Row(equal_height=False):
|
|
|
if_tb = gr.Checkbox(
|
|
|
- label="是否打开tensorboard?",
|
|
|
+ label=i18n("Open Tensorboard"),
|
|
|
)
|
|
|
|
|
|
- with gr.Tab("\U0001F9E0 进入推理界面"):
|
|
|
+ with gr.Tab("\U0001F9E0 " + i18n("Inference Configuration")):
|
|
|
with gr.Column():
|
|
|
with gr.Row():
|
|
|
- with gr.Accordion(label="\U0001F5A5 推理服务器配置", open=False):
|
|
|
+ with gr.Accordion(
|
|
|
+ label="\U0001F5A5 "
|
|
|
+ + i18n("Inference Server Configuration"),
|
|
|
+ open=False,
|
|
|
+ ):
|
|
|
with gr.Row():
|
|
|
infer_host_textbox = gr.Textbox(
|
|
|
- label="Webui启动服务器地址", value="127.0.0.1"
|
|
|
+ label=i18n("WebUI Host"), value="127.0.0.1"
|
|
|
)
|
|
|
infer_port_textbox = gr.Textbox(
|
|
|
- label="Webui启动服务器端口", value="7862"
|
|
|
+ label=i18n("WebUI Port"), value="7862"
|
|
|
)
|
|
|
with gr.Row():
|
|
|
infer_vqgan_model = gr.Dropdown(
|
|
|
- label="VQGAN模型位置",
|
|
|
- info="填写pth/ckpt文件路径",
|
|
|
+ label=i18n("VQGAN Model Path"),
|
|
|
+ info=i18n(
|
|
|
+ "Type the path or select from the dropdown"
|
|
|
+ ),
|
|
|
value=init_vqgan_yml["ckpt_path"],
|
|
|
choices=[init_vqgan_yml["ckpt_path"]]
|
|
|
+ [
|
|
|
@@ -847,8 +894,10 @@ with gr.Blocks(
|
|
|
)
|
|
|
with gr.Row():
|
|
|
infer_llama_model = gr.Dropdown(
|
|
|
- label="LLAMA模型位置",
|
|
|
- info="填写pth/ckpt文件路径",
|
|
|
+ label=i18n("LLAMA Model Path"),
|
|
|
+ info=i18n(
|
|
|
+ "Type the path or select from the dropdown"
|
|
|
+ ),
|
|
|
value=init_llama_yml["ckpt_path"],
|
|
|
choices=[init_llama_yml["ckpt_path"]]
|
|
|
+ [
|
|
|
@@ -861,10 +910,15 @@ with gr.Blocks(
|
|
|
)
|
|
|
with gr.Row():
|
|
|
infer_compile = gr.Radio(
|
|
|
- label="是否编译模型？", choices=["Yes", "No"], value="Yes"
|
|
|
+ label=i18n("Compile Model"),
|
|
|
+ info=i18n(
|
|
|
+ "Compile the model can significantly reduce the inference time, but will increase cold start time"
|
|
|
+ ),
|
|
|
+ choices=["Yes", "No"],
|
|
|
+ value="Yes",
|
|
|
)
|
|
|
infer_llama_config = gr.Dropdown(
|
|
|
- label="LLAMA模型基础属性",
|
|
|
+ label=i18n("LLAMA Model Config"),
|
|
|
choices=[
|
|
|
"dual_ar_2_codebook_large",
|
|
|
"dual_ar_2_codebook_medium",
|
|
|
@@ -874,27 +928,35 @@ with gr.Blocks(
|
|
|
)
|
|
|
|
|
|
with gr.Row():
|
|
|
- infer_checkbox = gr.Checkbox(label="是否打开推理界面")
|
|
|
- infer_error = gr.HTML(label="推理界面错误信息")
|
|
|
+ infer_checkbox = gr.Checkbox(
|
|
|
+ label=i18n("Open Inference Server")
|
|
|
+ )
|
|
|
+ infer_error = gr.HTML(label=i18n("Inference Server Error"))
|
|
|
|
|
|
with gr.Column():
|
|
|
- train_error = gr.HTML(label="训练时的报错信息")
|
|
|
+ train_error = gr.HTML(label=i18n("Training Error"))
|
|
|
checkbox_group = gr.CheckboxGroup(
|
|
|
- label="\U0001F4CA 数据源列表",
|
|
|
- info="左侧输入文件夹所在路径或filelist。无论是否勾选，在此列表中都会被用以后续训练。",
|
|
|
+ label="\U0001F4CA " + i18n("Data Source"),
|
|
|
+ info=i18n(
|
|
|
+ "The path of the input folder on the left or the filelist. Whether checked or not, it will be used for subsequent training in this list."
|
|
|
+ ),
|
|
|
elem_classes=["data_src"],
|
|
|
)
|
|
|
train_box = gr.Textbox(
|
|
|
- label="数据预处理文件夹路径", value=str(data_pre_output), interactive=False
|
|
|
+ label=i18n("Data Preprocessing Path"),
|
|
|
+ value=str(data_pre_output),
|
|
|
+ interactive=False,
|
|
|
)
|
|
|
model_box = gr.Textbox(
|
|
|
- label="\U0001F4BE 模型输出路径",
|
|
|
+ label="\U0001F4BE " + i18n("Model Output Path"),
|
|
|
value=str(default_model_output),
|
|
|
interactive=False,
|
|
|
)
|
|
|
|
|
|
with gr.Accordion(
|
|
|
- "查看预处理文件夹状态 (滑块为显示深度大小)",
|
|
|
+ i18n(
|
|
|
+ "View the status of the preprocessing folder (use the slider to control the depth of the tree)"
|
|
|
+ ),
|
|
|
elem_classes=["scrollable-component"],
|
|
|
elem_id="file_accordion",
|
|
|
):
|
|
|
@@ -909,11 +971,14 @@ with gr.Blocks(
|
|
|
file_markdown = new_explorer(str(data_pre_output), 0)
|
|
|
with gr.Row(equal_height=False):
|
|
|
admit_btn = gr.Button(
|
|
|
- "\U00002705 文件预处理", scale=0, min_width=160, variant="primary"
|
|
|
+ "\U00002705 " + i18n("File Preprocessing"),
|
|
|
+ scale=0,
|
|
|
+ min_width=160,
|
|
|
+ variant="primary",
|
|
|
)
|
|
|
fresh_btn = gr.Button("\U0001F503", scale=0, min_width=80)
|
|
|
help_button = gr.Button("\U00002753", scale=0, min_width=80) # question
|
|
|
- train_btn = gr.Button("训练启动!", variant="primary")
|
|
|
+ train_btn = gr.Button(i18n("Start Training"), variant="primary")
|
|
|
|
|
|
footer = load_data_in_raw("fish_speech/webui/html/footer.html")
|
|
|
footer = footer.format(
|
Lengyue