From 615c8398edf7a006a1dcb44b63b13c60d26661fd Mon Sep 17 00:00:00 2001 From: spicysama <122108331+AnyaCoder@users.noreply.github.com> Date: Wed, 1 May 2024 00:41:32 +0800 Subject: [PATCH] Gradio UI: Lora update (#142) * init package * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix spelling * Decorator * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Specify the backend on the command line * Fix Encoding Error * Add start configuration * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add lora config & GC * Label UI updated * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add llama base config * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * New Project Names & Remove ref audio * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Upgrade UI & Tensorboard * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add user custom path --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- fish_speech/webui/manage.py | 613 +++++++++++++++++++++++------------- tools/webui.py | 6 +- 2 files changed, 402 insertions(+), 217 deletions(-) diff --git a/fish_speech/webui/manage.py b/fish_speech/webui/manage.py index d6eb06c4..3fde3a7f 100644 --- a/fish_speech/webui/manage.py +++ b/fish_speech/webui/manage.py @@ -4,11 +4,11 @@ import json import os import platform -import random import shutil import signal import subprocess import sys +import webbrowser from pathlib import Path import gradio as gr @@ -26,7 +26,7 @@ print("You are in ", str(cur_work_dir)) config_path = cur_work_dir / "fish_speech" / "configs" vqgan_yml_path = config_path / "vqgan_finetune.yaml" -llama_yml_path = config_path / "text2semantic_sft.yaml" +llama_yml_path = config_path / "text2semantic_finetune.yaml" env = os.environ.copy() env["no_proxy"] = "127.0.0.1, localhost, 0.0.0.0" @@ -79,6 +79,7 @@ def kill_proc_tree(pid, including_parent=True): system = platform.system() p_label = None p_infer = None +p_tensorboard = None def kill_process(pid): @@ -92,18 +93,24 @@ def kill_process(pid): def change_label(if_label): global p_label - if if_label == True and p_label == None: - cmd = ["asr-label-win-x64.exe"] - yield f"打标工具WebUI已开启, 访问：http://localhost:{3000}" - p_label = subprocess.Popen(cmd, shell=True, env=env) - elif if_label == False and p_label != None: - kill_process(p_label.pid) + if if_label == True: + # 设置要访问的URL + url = "https://text-labeler.pages.dev/" + webbrowser.open(url) + yield f"已打开网址" + elif if_label == False: p_label = None - yield "打标工具WebUI已关闭" + yield "Nothing" def change_infer( - if_infer, host, port, infer_vqgan_model, infer_llama_model, infer_compile + if_infer, + host, + port, + infer_vqgan_model, + infer_llama_model, + infer_llama_config, + infer_compile, ): global p_infer if if_infer == True and p_infer == None: @@ -121,6 +128,8 @@ def change_infer( infer_vqgan_model, "--llama-checkpoint-path", infer_llama_model, + "--llama-config-name", + infer_llama_config, "--tokenizer", "checkpoints", ] @@ -193,12 +202,10 @@ def new_explorer(data_path, max_depth): ) -def add_item(folder: str, method: str, filelist: str, label_lang: str): +def add_item(folder: str, method: str, label_lang: str): folder = folder.strip(" ").strip('"') - filelist = filelist.strip(" ").strip('"') folder_path = Path(folder) - filelist_path = Path(filelist) if folder and folder not in items and data_pre_output not in folder_path.parents: if folder_path.is_dir(): @@ -212,22 +219,6 @@ def add_item(folder: str, method: str, filelist: str, label_lang: str): f"添加文件夹路径无效: {err}" ) - if ( - filelist - and filelist not in items - and data_pre_output not in filelist_path.parents - ): - if filelist_path.is_file(): - items.append(filelist) - dict_items[filelist] = dict( - type="file", method=method, label_lang=label_lang - ) - elif filelist: - err = filelist - return gr.Checkboxgroup(choices=items), build_html_error_message( - f"添加文件路径无效: {err}" - ) - formatted_data = json.dumps(dict_items, ensure_ascii=False, indent=4) logger.info(formatted_data) return gr.Checkboxgroup(choices=items), build_html_ok_message("添加文件(夹)路径成功!") @@ -350,9 +341,9 @@ def train_process( vqgan_precision, vqgan_check_interval, # llama config + llama_base_config, llama_lr, llama_maxsteps, - llama_limit_val_batches, llama_data_num_workers, llama_data_batch_size, llama_data_max_length, @@ -360,8 +351,21 @@ def train_process( llama_check_interval, llama_grad_batches, llama_use_speaker, + llama_use_lora, ): + import datetime + + def generate_folder_name(): + now = datetime.datetime.now() + folder_name = now.strftime("%Y%m%d_%H%M%S") + return folder_name + backend = "nccl" if sys.platform == "linux" else "gloo" + + new_project = generate_folder_name() + + print("New Project Name: ", new_project) + if option == "VQGAN" or option == "all": subprocess.run( [ @@ -375,6 +379,7 @@ def train_process( "fish_speech/train.py", "--config-name", "vqgan_finetune", + f"project={'vqgan_' + new_project}", f"trainer.strategy.process_group_backend={backend}", f"model.optimizer.lr={vqgan_lr}", f"trainer.max_steps={vqgan_maxsteps}", @@ -418,20 +423,25 @@ def train_process( "16", ] ) - + ckpt_path = ( + "text2semantic-pretrain-medium-2k-v1.pth" + if llama_base_config == "dual_ar_2_codebook_medium" + else "text2semantic-sft-large-v1-4k.pth" + ) train_cmd = [ PYTHON, "fish_speech/train.py", "--config-name", - "text2semantic_sft", + "text2semantic_finetune", + f"project={'text2semantic_' + new_project}", + f"ckpt_path=checkpoints/{ckpt_path}", f"trainer.strategy.process_group_backend={backend}", - "model@model.model=dual_ar_2_codebook_medium", + f"model@model.model={llama_base_config}", "tokenizer.pretrained_model_name_or_path=checkpoints", f"train_dataset.proto_files={str(['data/quantized-dataset-ft'])}", f"val_dataset.proto_files={str(['data/quantized-dataset-ft'])}", f"model.optimizer.lr={llama_lr}", f"trainer.max_steps={llama_maxsteps}", - f"trainer.limit_val_batches={llama_limit_val_batches}", f"data.num_workers={llama_data_num_workers}", f"data.batch_size={llama_data_batch_size}", f"max_length={llama_data_max_length}", @@ -439,13 +449,91 @@ def train_process( f"trainer.val_check_interval={llama_check_interval}", f"trainer.accumulate_grad_batches={llama_grad_batches}", f"train_dataset.use_speaker={llama_use_speaker}", - ] + ] + ([f"+lora@model.lora_config=r_8_alpha_16"] if llama_use_lora else []) logger.info(train_cmd) subprocess.run(train_cmd) return build_html_ok_message("训练终止") +def tensorboard_process( + if_tensorboard: bool, + tensorboard_dir: str, + host: str, + port: str, +): + global p_tensorboard + if if_tensorboard == True and p_tensorboard == None: + yield build_html_ok_message(f"Tensorboard界面已开启, 访问 http://{host}:{port}") + p_tensorboard = subprocess.Popen( + [ + "fishenv/python.exe", + "fishenv/Scripts/tensorboard.exe" + if Path("fishenv").exists() + else "tensorboard", + "--logdir", + tensorboard_dir, + "--host", + host, + "--port", + port, + "--reload_interval", + "120", + ] + ) + elif if_tensorboard == False and p_tensorboard != None: + kill_process(p_tensorboard.pid) + p_tensorboard = None + yield build_html_error_message("Tensorboard界面已关闭") + + +def fresh_tb_dir(): + return gr.Dropdown( + choices=[str(p) for p in Path("results").glob("**/tensorboard/version_*/")] + ) + + +def fresh_vqgan_model(): + return gr.Dropdown( + choices=[init_vqgan_yml["ckpt_path"]] + + [str(p) for p in Path("results").glob("vqgan*/**/*.ckpt")] + ) + + +def fresh_llama_model(): + return gr.Dropdown( + choices=[init_llama_yml["ckpt_path"]] + + [str(p) for p in Path("results").glob("text2sem*/**/*.ckpt")] + ) + + +def llama_lora_merge(llama_weight, lora_weight, llama_lora_output): + if ( + lora_weight is None + or not Path(lora_weight).exists() + 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") + + merge_cmd = [ + PYTHON, + "tools/llama/merge_lora.py", + "--llama-config", + "dual_ar_2_codebook_large", + "--lora-config", + "r_8_alpha_16", + "--llama-weight", + llama_weight, + "--lora-weight", + lora_weight, + "--output", + llama_lora_output, + ] + logger.info(merge_cmd) + subprocess.run(merge_cmd) + return build_html_ok_message("融合终止") + + init_vqgan_yml = load_yaml_data_in_fact(vqgan_yml_path) init_llama_yml = load_yaml_data_in_fact(llama_yml_path) @@ -465,11 +553,6 @@ def train_process( info="音频装在一个以说话人命名的文件夹内作为区分", interactive=True, ) - transcript_path = gr.Textbox( - label="\U0001F4DD 转写文本filelist所在路径", - info="支持 Bert-Vits2 / GPT-SoVITS 格式", - interactive=True, - ) with gr.Row(equal_height=False): with gr.Column(): output_radio = gr.Radio( @@ -511,173 +594,227 @@ def train_process( ) with gr.Tab("\U0001F6E0 训练配置项"): # hammer - with gr.Column(): - with gr.Row(): - model_type_radio = gr.Radio( - label="选择要训练的模型类型", - interactive=True, - choices=["VQGAN", "LLAMA", "all"], - value="all", - ) - with gr.Row(): - with gr.Accordion("VQGAN配置项", open=False): - with gr.Row(equal_height=False): - vqgan_lr_slider = gr.Slider( - label="初始学习率", - interactive=True, - minimum=1e-5, - maximum=1e-4, - step=1e-5, - value=init_vqgan_yml["model"]["optimizer"]["lr"], - ) - vqgan_maxsteps_slider = gr.Slider( - label="训练最大步数", - interactive=True, - minimum=1000, - maximum=100000, - step=1000, - value=init_vqgan_yml["trainer"]["max_steps"], - ) - - with gr.Row(equal_height=False): - vqgan_data_num_workers_slider = gr.Slider( - label="num_workers", - interactive=True, - minimum=1, - maximum=16, - step=1, - value=init_vqgan_yml["data"]["num_workers"], - ) - - vqgan_data_batch_size_slider = gr.Slider( - label="batch_size", - interactive=True, - minimum=1, - maximum=32, - step=1, - value=init_vqgan_yml["data"]["batch_size"], - ) - with gr.Row(equal_height=False): - vqgan_data_val_batch_size_slider = gr.Slider( - label="val_batch_size", - interactive=True, - minimum=1, - maximum=32, - step=1, - value=init_vqgan_yml["data"]["val_batch_size"], - ) - vqgan_precision_dropdown = gr.Dropdown( - label="训练精度", - interactive=True, - choices=["32", "bf16-true", "bf16-mixed"], - 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", - interactive=True, - minimum=500, - maximum=10000, - step=500, - value=init_vqgan_yml["trainer"][ - "val_check_interval" - ], - ) - - with gr.Row(): - with gr.Accordion("LLAMA配置项", open=False): - with gr.Row(equal_height=False): - llama_lr_slider = gr.Slider( - label="初始学习率", - interactive=True, - minimum=1e-5, - maximum=1e-4, - step=1e-5, - value=init_llama_yml["model"]["optimizer"]["lr"], - ) - llama_maxsteps_slider = gr.Slider( - label="训练最大步数", - interactive=True, - minimum=1000, - maximum=100000, - step=1000, - value=init_llama_yml["trainer"]["max_steps"], - ) - with gr.Row(equal_height=False): - llama_limit_val_batches_slider = gr.Slider( - label="limit_val_batches", - interactive=True, - minimum=1, - maximum=20, - step=1, - value=init_llama_yml["trainer"][ - "limit_val_batches" - ], - ) - llama_data_num_workers_slider = gr.Slider( - label="num_workers", - minimum=0, - maximum=16, - step=1, - value=init_llama_yml["data"]["num_workers"] - if sys.platform == "linux" - else 0, - ) - with gr.Row(equal_height=False): - llama_data_batch_size_slider = gr.Slider( - label="batch_size", - interactive=True, - minimum=1, - maximum=32, - step=1, - value=init_llama_yml["data"]["batch_size"], - ) - llama_data_max_length_slider = gr.Slider( - label="max_length", - interactive=True, - minimum=1024, - maximum=4096, - step=128, - value=init_llama_yml["max_length"], - ) - with gr.Row(equal_height=False): - llama_precision_dropdown = gr.Dropdown( - label="训练精度", - 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", - interactive=True, - minimum=500, - maximum=10000, - step=500, - value=init_llama_yml["trainer"][ - "val_check_interval" - ], - ) - with gr.Row(equal_height=False): - llama_grad_batches = gr.Slider( - label="accumulate_grad_batches", - interactive=True, - minimum=1, - maximum=20, - step=1, - value=init_llama_yml["trainer"][ - "accumulate_grad_batches" - ], - ) - llama_use_speaker = gr.Slider( - label="use_speaker_ratio", - interactive=True, - minimum=0.1, - maximum=1.0, - step=0.05, - value=init_llama_yml["train_dataset"][ - "use_speaker" - ], - ) + with gr.Row(): + model_type_radio = gr.Radio( + label="选择要训练的模型类型", + interactive=True, + choices=["VQGAN", "LLAMA", "all"], + value="all", + ) + with gr.Row(): + with gr.Tab(label="VQGAN配置项"): + with gr.Row(equal_height=False): + vqgan_lr_slider = gr.Slider( + label="初始学习率", + interactive=True, + minimum=1e-5, + maximum=1e-4, + step=1e-5, + value=init_vqgan_yml["model"]["optimizer"]["lr"], + ) + vqgan_maxsteps_slider = gr.Slider( + label="训练最大步数", + interactive=True, + minimum=1000, + maximum=100000, + step=1000, + value=init_vqgan_yml["trainer"]["max_steps"], + ) + + with gr.Row(equal_height=False): + vqgan_data_num_workers_slider = gr.Slider( + label="num_workers", + interactive=True, + minimum=1, + maximum=16, + step=1, + value=init_vqgan_yml["data"]["num_workers"], + ) + + vqgan_data_batch_size_slider = gr.Slider( + label="batch_size", + interactive=True, + minimum=1, + maximum=32, + step=1, + value=init_vqgan_yml["data"]["batch_size"], + ) + with gr.Row(equal_height=False): + vqgan_data_val_batch_size_slider = gr.Slider( + label="val_batch_size", + interactive=True, + minimum=1, + maximum=32, + step=1, + value=init_vqgan_yml["data"]["val_batch_size"], + ) + vqgan_precision_dropdown = gr.Dropdown( + label="训练精度", + interactive=True, + choices=["32", "bf16-true", "bf16-mixed"], + 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", + interactive=True, + minimum=500, + maximum=10000, + step=500, + value=init_vqgan_yml["trainer"]["val_check_interval"], + ) + + with gr.Tab(label="LLAMA配置项"): + with gr.Row(equal_height=False): + llama_use_lora = gr.Checkbox( + label="使用lora训练？", + value=True, + ) + with gr.Row(equal_height=False): + llama_lr_slider = gr.Slider( + label="初始学习率", + interactive=True, + minimum=1e-5, + maximum=1e-4, + step=1e-5, + value=init_llama_yml["model"]["optimizer"]["lr"], + ) + llama_maxsteps_slider = gr.Slider( + label="训练最大步数", + interactive=True, + minimum=1000, + maximum=100000, + step=1000, + value=init_llama_yml["trainer"]["max_steps"], + ) + with gr.Row(equal_height=False): + llama_base_config = gr.Dropdown( + label="模型基础属性", + choices=[ + "dual_ar_2_codebook_large", + "dual_ar_2_codebook_medium", + ], + value="dual_ar_2_codebook_large", + ) + llama_data_num_workers_slider = gr.Slider( + label="num_workers", + minimum=0, + maximum=16, + step=1, + value=init_llama_yml["data"]["num_workers"] + if sys.platform == "linux" + else 0, + ) + with gr.Row(equal_height=False): + llama_data_batch_size_slider = gr.Slider( + label="batch_size", + interactive=True, + minimum=1, + maximum=32, + step=1, + value=init_llama_yml["data"]["batch_size"], + ) + llama_data_max_length_slider = gr.Slider( + label="max_length", + interactive=True, + minimum=1024, + maximum=4096, + step=128, + value=init_llama_yml["max_length"], + ) + with gr.Row(equal_height=False): + llama_precision_dropdown = gr.Dropdown( + label="训练精度", + 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", + interactive=True, + minimum=500, + maximum=10000, + step=500, + value=init_llama_yml["trainer"]["val_check_interval"], + ) + with gr.Row(equal_height=False): + llama_grad_batches = gr.Slider( + label="accumulate_grad_batches", + interactive=True, + minimum=1, + maximum=20, + step=1, + value=init_llama_yml["trainer"][ + "accumulate_grad_batches" + ], + ) + llama_use_speaker = gr.Slider( + label="use_speaker_ratio", + interactive=True, + minimum=0.1, + maximum=1.0, + step=0.05, + value=init_llama_yml["train_dataset"]["use_speaker"], + ) + + with gr.Tab(label="LLAMA_lora融合"): + 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", + choices=[init_llama_yml["ckpt_path"]], + value=init_llama_yml["ckpt_path"], + allow_custom_value=True, + interactive=True, + ) + 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", + choices=[ + str(p) + for p in Path("results").glob("text2*ar/**/*.ckpt") + ], + allow_custom_value=True, + interactive=True, + ) + with gr.Row(equal_height=False): + llama_lora_output = gr.Dropdown( + label="输出的lora模型", + info="输出路径", + value="checkpoints/merged.ckpt", + choices=["checkpoints/merged.ckpt"], + allow_custom_value=True, + interactive=True, + ) + with gr.Row(equal_height=False): + llama_lora_merge_btn = gr.Button( + value="开始融合", 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" + ) + tb_port = gr.Textbox( + label="Tensorboard Port", value="11451" + ) + with gr.Row(equal_height=False): + tb_dir = gr.Dropdown( + label="Tensorboard 日志文件夹", + allow_custom_value=True, + choices=[ + str(p) + for p in Path("results").glob( + "**/tensorboard/version_*/" + ) + ], + ) + with gr.Row(equal_height=False): + if_tb = gr.Checkbox( + label="是否打开tensorboard?", + ) with gr.Tab("\U0001F9E0 进入推理界面"): with gr.Column(): @@ -691,21 +828,46 @@ def train_process( label="Webui启动服务器端口", value="7862" ) with gr.Row(): - infer_vqgan_model = gr.Textbox( + infer_vqgan_model = gr.Dropdown( label="VQGAN模型位置", - placeholder="填写pth/ckpt文件路径", - value="checkpoints/vq-gan-group-fsq-2x1024.pth", + info="填写pth/ckpt文件路径", + value=init_vqgan_yml["ckpt_path"], + choices=[init_vqgan_yml["ckpt_path"]] + + [ + str(p) + for p in Path("results").glob( + "vqgan*/**/*.ckpt" + ) + ], + allow_custom_value=True, ) with gr.Row(): - infer_llama_model = gr.Textbox( + infer_llama_model = gr.Dropdown( label="LLAMA模型位置", - placeholder="填写pth/ckpt文件路径", - value="checkpoints/text2semantic-medium-v1-2k.pth", + info="填写pth/ckpt文件路径", + value=init_llama_yml["ckpt_path"], + choices=[init_llama_yml["ckpt_path"]] + + [ + str(p) + for p in Path("results").glob( + "text2sem*/**/*.ckpt" + ) + ], + allow_custom_value=True, ) with gr.Row(): infer_compile = gr.Radio( label="是否编译模型？", choices=["Yes", "No"], value="Yes" ) + infer_llama_config = gr.Dropdown( + label="LLAMA模型基础属性", + choices=[ + "dual_ar_2_codebook_large", + "dual_ar_2_codebook_medium", + ], + value="dual_ar_2_codebook_large", + allow_custom_value=True, + ) with gr.Row(): infer_checkbox = gr.Checkbox(label="是否打开推理界面") @@ -758,7 +920,7 @@ def train_process( add_button.click( fn=add_item, - inputs=[textbox, output_radio, transcript_path, label_radio], + inputs=[textbox, output_radio, label_radio], outputs=[checkbox_group, error], ) remove_button.click( @@ -785,9 +947,9 @@ def train_process( vqgan_precision_dropdown, vqgan_check_interval_slider, # llama config + llama_base_config, llama_lr_slider, llama_maxsteps_slider, - llama_limit_val_batches_slider, llama_data_num_workers_slider, llama_data_batch_size_slider, llama_data_max_length_slider, @@ -795,9 +957,23 @@ def train_process( llama_check_interval_slider, llama_grad_batches, llama_use_speaker, + llama_use_lora, ], outputs=[train_error], ) + if_tb.change( + fn=tensorboard_process, + inputs=[if_tb, tb_dir, tb_host, tb_port], + outputs=[train_error], + ) + tb_dir.change(fn=fresh_tb_dir, inputs=[], outputs=[tb_dir]) + infer_vqgan_model.change( + fn=fresh_vqgan_model, inputs=[], outputs=[infer_vqgan_model] + ) + infer_llama_model.change( + fn=fresh_llama_model, inputs=[], outputs=[infer_llama_model] + ) + llama_weight.change(fn=fresh_llama_model, inputs=[], outputs=[llama_weight]) admit_btn.click( fn=check_files, inputs=[train_box, tree_slider, label_model, label_device], @@ -806,7 +982,11 @@ def train_process( fresh_btn.click( fn=new_explorer, inputs=[train_box, tree_slider], outputs=[file_markdown] ) - + llama_lora_merge_btn.click( + fn=llama_lora_merge, + inputs=[llama_weight, lora_weight, llama_lora_output], + outputs=[train_error], + ) infer_checkbox.change( fn=change_infer, inputs=[ @@ -815,6 +995,7 @@ def train_process( infer_port_textbox, infer_vqgan_model, infer_llama_model, + infer_llama_config, infer_compile, ], outputs=[infer_error], diff --git a/tools/webui.py b/tools/webui.py index 5d3c2049..7c41d845 100644 --- a/tools/webui.py +++ b/tools/webui.py @@ -1,3 +1,4 @@ +import gc import html import os import threading @@ -138,6 +139,10 @@ def inference( fake_audios = fake_audios.float().cpu().numpy() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + gc.collect() + return (vqgan_model.sampling_rate, fake_audios), None @@ -217,7 +222,6 @@ def build_app(): ) reference_audio = gr.Audio( label="Reference Audio / 参考音频", - value="docs/assets/audios/0_input.wav", type="filepath", ) reference_text = gr.Textbox(