Upload f5_tts/train/finetune_gradio.py with huggingface_hub

f5_tts/train/finetune_gradio.py

@@ -0,0 +1,1846 @@
+import threading
+import queue
+import re
+
+import gc
+import json
+import os
+import platform
+import psutil
+import random
+import signal
+import shutil
+import subprocess
+import sys
+import tempfile
+import time
+from glob import glob
+
+import click
+import gradio as gr
+import librosa
+import numpy as np
+import torch
+import torchaudio
+from datasets import Dataset as Dataset_
+from datasets.arrow_writer import ArrowWriter
+from safetensors.torch import save_file
+from scipy.io import wavfile
+from cached_path import cached_path
+from f5_tts.api import F5TTS
+from f5_tts.model.utils import convert_char_to_pinyin
+from f5_tts.infer.utils_infer import transcribe
+from importlib.resources import files
+
+
+training_process = None
+system = platform.system()
+python_executable = sys.executable or "python"
+tts_api = None
+last_checkpoint = ""
+last_device = ""
+last_ema = None
+
+
+path_data = str(files("f5_tts").joinpath("../../data"))
+path_project_ckpts = str(files("f5_tts").joinpath("../../ckpts"))
+file_train = str(files("f5_tts").joinpath("train/finetune_cli.py"))
+
+device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+
+
+# Save settings from a JSON file
+def save_settings(
+    project_name,
+    exp_name,
+    learning_rate,
+    batch_size_per_gpu,
+    batch_size_type,
+    max_samples,
+    grad_accumulation_steps,
+    max_grad_norm,
+    epochs,
+    num_warmup_updates,
+    save_per_updates,
+    last_per_steps,
+    finetune,
+    file_checkpoint_train,
+    tokenizer_type,
+    tokenizer_file,
+    mixed_precision,
+    logger,
+    ch_8bit_adam,
+):
+    path_project = os.path.join(path_project_ckpts, project_name)
+    os.makedirs(path_project, exist_ok=True)
+    file_setting = os.path.join(path_project, "setting.json")
+
+    settings = {
+        "exp_name": exp_name,
+        "learning_rate": learning_rate,
+        "batch_size_per_gpu": batch_size_per_gpu,
+        "batch_size_type": batch_size_type,
+        "max_samples": max_samples,
+        "grad_accumulation_steps": grad_accumulation_steps,
+        "max_grad_norm": max_grad_norm,
+        "epochs": epochs,
+        "num_warmup_updates": num_warmup_updates,
+        "save_per_updates": save_per_updates,
+        "last_per_steps": last_per_steps,
+        "finetune": finetune,
+        "file_checkpoint_train": file_checkpoint_train,
+        "tokenizer_type": tokenizer_type,
+        "tokenizer_file": tokenizer_file,
+        "mixed_precision": mixed_precision,
+        "logger": logger,
+        "bnb_optimizer": ch_8bit_adam,
+    }
+    with open(file_setting, "w") as f:
+        json.dump(settings, f, indent=4)
+    return "Settings saved!"
+
+
+# Load settings from a JSON file
+def load_settings(project_name):
+    project_name = project_name.replace("_pinyin", "").replace("_char", "")
+    path_project = os.path.join(path_project_ckpts, project_name)
+    file_setting = os.path.join(path_project, "setting.json")
+
+    if not os.path.isfile(file_setting):
+        settings = {
+            "exp_name": "F5TTS_Base",
+            "learning_rate": 1e-05,
+            "batch_size_per_gpu": 1000,
+            "batch_size_type": "frame",
+            "max_samples": 64,
+            "grad_accumulation_steps": 1,
+            "max_grad_norm": 1,
+            "epochs": 100,
+            "num_warmup_updates": 2,
+            "save_per_updates": 300,
+            "last_per_steps": 100,
+            "finetune": True,
+            "file_checkpoint_train": "",
+            "tokenizer_type": "pinyin",
+            "tokenizer_file": "",
+            "mixed_precision": "none",
+            "logger": "wandb",
+            "bnb_optimizer": False,
+        }
+        return (
+            settings["exp_name"],
+            settings["learning_rate"],
+            settings["batch_size_per_gpu"],
+            settings["batch_size_type"],
+            settings["max_samples"],
+            settings["grad_accumulation_steps"],
+            settings["max_grad_norm"],
+            settings["epochs"],
+            settings["num_warmup_updates"],
+            settings["save_per_updates"],
+            settings["last_per_steps"],
+            settings["finetune"],
+            settings["file_checkpoint_train"],
+            settings["tokenizer_type"],
+            settings["tokenizer_file"],
+            settings["mixed_precision"],
+            settings["logger"],
+            settings["bnb_optimizer"],
+        )
+
+    with open(file_setting, "r") as f:
+        settings = json.load(f)
+        if "logger" not in settings:
+            settings["logger"] = "wandb"
+        if "bnb_optimizer" not in settings:
+            settings["bnb_optimizer"] = False
+    return (
+        settings["exp_name"],
+        settings["learning_rate"],
+        settings["batch_size_per_gpu"],
+        settings["batch_size_type"],
+        settings["max_samples"],
+        settings["grad_accumulation_steps"],
+        settings["max_grad_norm"],
+        settings["epochs"],
+        settings["num_warmup_updates"],
+        settings["save_per_updates"],
+        settings["last_per_steps"],
+        settings["finetune"],
+        settings["file_checkpoint_train"],
+        settings["tokenizer_type"],
+        settings["tokenizer_file"],
+        settings["mixed_precision"],
+        settings["logger"],
+        settings["bnb_optimizer"],
+    )
+
+
+# Load metadata
+def get_audio_duration(audio_path):
+    """Calculate the duration mono of an audio file."""
+    audio, sample_rate = torchaudio.load(audio_path)
+    return audio.shape[1] / sample_rate
+
+
+def clear_text(text):
+    """Clean and prepare text by lowering the case and stripping whitespace."""
+    return text.lower().strip()
+
+
+def get_rms(
+    y,
+    frame_length=2048,
+    hop_length=512,
+    pad_mode="constant",
+):  # https://github.com/RVC-Boss/GPT-SoVITS/blob/main/tools/slicer2.py
+    padding = (int(frame_length // 2), int(frame_length // 2))
+    y = np.pad(y, padding, mode=pad_mode)
+
+    axis = -1
+    # put our new within-frame axis at the end for now
+    out_strides = y.strides + tuple([y.strides[axis]])
+    # Reduce the shape on the framing axis
+    x_shape_trimmed = list(y.shape)
+    x_shape_trimmed[axis] -= frame_length - 1
+    out_shape = tuple(x_shape_trimmed) + tuple([frame_length])
+    xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides)
+    if axis < 0:
+        target_axis = axis - 1
+    else:
+        target_axis = axis + 1
+    xw = np.moveaxis(xw, -1, target_axis)
+    # Downsample along the target axis
+    slices = [slice(None)] * xw.ndim
+    slices[axis] = slice(0, None, hop_length)
+    x = xw[tuple(slices)]
+
+    # Calculate power
+    power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True)
+
+    return np.sqrt(power)
+
+
+class Slicer:  # https://github.com/RVC-Boss/GPT-SoVITS/blob/main/tools/slicer2.py
+    def __init__(
+        self,
+        sr: int,
+        threshold: float = -40.0,
+        min_length: int = 2000,
+        min_interval: int = 300,
+        hop_size: int = 20,
+        max_sil_kept: int = 2000,
+    ):
+        if not min_length >= min_interval >= hop_size:
+            raise ValueError("The following condition must be satisfied: min_length >= min_interval >= hop_size")
+        if not max_sil_kept >= hop_size:
+            raise ValueError("The following condition must be satisfied: max_sil_kept >= hop_size")
+        min_interval = sr * min_interval / 1000
+        self.threshold = 10 ** (threshold / 20.0)
+        self.hop_size = round(sr * hop_size / 1000)
+        self.win_size = min(round(min_interval), 4 * self.hop_size)
+        self.min_length = round(sr * min_length / 1000 / self.hop_size)
+        self.min_interval = round(min_interval / self.hop_size)
+        self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
+
+    def _apply_slice(self, waveform, begin, end):
+        if len(waveform.shape) > 1:
+            return waveform[:, begin * self.hop_size : min(waveform.shape[1], end * self.hop_size)]
+        else:
+            return waveform[begin * self.hop_size : min(waveform.shape[0], end * self.hop_size)]
+
+    # @timeit
+    def slice(self, waveform):
+        if len(waveform.shape) > 1:
+            samples = waveform.mean(axis=0)
+        else:
+            samples = waveform
+        if samples.shape[0] <= self.min_length:
+            return [waveform]
+        rms_list = get_rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
+        sil_tags = []
+        silence_start = None
+        clip_start = 0
+        for i, rms in enumerate(rms_list):
+            # Keep looping while frame is silent.
+            if rms < self.threshold:
+                # Record start of silent frames.
+                if silence_start is None:
+                    silence_start = i
+                continue
+            # Keep looping while frame is not silent and silence start has not been recorded.
+            if silence_start is None:
+                continue
+            # Clear recorded silence start if interval is not enough or clip is too short
+            is_leading_silence = silence_start == 0 and i > self.max_sil_kept
+            need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length
+            if not is_leading_silence and not need_slice_middle:
+                silence_start = None
+                continue
+            # Need slicing. Record the range of silent frames to be removed.
+            if i - silence_start <= self.max_sil_kept:
+                pos = rms_list[silence_start : i + 1].argmin() + silence_start
+                if silence_start == 0:
+                    sil_tags.append((0, pos))
+                else:
+                    sil_tags.append((pos, pos))
+                clip_start = pos
+            elif i - silence_start <= self.max_sil_kept * 2:
+                pos = rms_list[i - self.max_sil_kept : silence_start + self.max_sil_kept + 1].argmin()
+                pos += i - self.max_sil_kept
+                pos_l = rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start
+                pos_r = rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept
+                if silence_start == 0:
+                    sil_tags.append((0, pos_r))
+                    clip_start = pos_r
+                else:
+                    sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
+                    clip_start = max(pos_r, pos)
+            else:
+                pos_l = rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start
+                pos_r = rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept
+                if silence_start == 0:
+                    sil_tags.append((0, pos_r))
+                else:
+                    sil_tags.append((pos_l, pos_r))
+                clip_start = pos_r
+            silence_start = None
+        # Deal with trailing silence.
+        total_frames = rms_list.shape[0]
+        if silence_start is not None and total_frames - silence_start >= self.min_interval:
+            silence_end = min(total_frames, silence_start + self.max_sil_kept)
+            pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start
+            sil_tags.append((pos, total_frames + 1))
+        # Apply and return slices.
+        ####音频+起始时间+终止时间
+        if len(sil_tags) == 0:
+            return [[waveform, 0, int(total_frames * self.hop_size)]]
+        else:
+            chunks = []
+            if sil_tags[0][0] > 0:
+                chunks.append([self._apply_slice(waveform, 0, sil_tags[0][0]), 0, int(sil_tags[0][0] * self.hop_size)])
+            for i in range(len(sil_tags) - 1):
+                chunks.append(
+                    [
+                        self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0]),
+                        int(sil_tags[i][1] * self.hop_size),
+                        int(sil_tags[i + 1][0] * self.hop_size),
+                    ]
+                )
+            if sil_tags[-1][1] < total_frames:
+                chunks.append(
+                    [
+                        self._apply_slice(waveform, sil_tags[-1][1], total_frames),
+                        int(sil_tags[-1][1] * self.hop_size),
+                        int(total_frames * self.hop_size),
+                    ]
+                )
+            return chunks
+
+
+# terminal
+def terminate_process_tree(pid, including_parent=True):
+    try:
+        parent = psutil.Process(pid)
+    except psutil.NoSuchProcess:
+        # Process already terminated
+        return
+
+    children = parent.children(recursive=True)
+    for child in children:
+        try:
+            os.kill(child.pid, signal.SIGTERM)  # or signal.SIGKILL
+        except OSError:
+            pass
+    if including_parent:
+        try:
+            os.kill(parent.pid, signal.SIGTERM)  # or signal.SIGKILL
+        except OSError:
+            pass
+
+
+def terminate_process(pid):
+    if system == "Windows":
+        cmd = f"taskkill /t /f /pid {pid}"
+        os.system(cmd)
+    else:
+        terminate_process_tree(pid)
+
+
+def start_training(
+    dataset_name="",
+    exp_name="F5TTS_Base",
+    learning_rate=1e-4,
+    batch_size_per_gpu=400,
+    batch_size_type="frame",
+    max_samples=64,
+    grad_accumulation_steps=1,
+    max_grad_norm=1.0,
+    epochs=11,
+    num_warmup_updates=200,
+    save_per_updates=400,
+    last_per_steps=800,
+    finetune=True,
+    file_checkpoint_train="",
+    tokenizer_type="pinyin",
+    tokenizer_file="",
+    mixed_precision="fp16",
+    stream=False,
+    logger="wandb",
+    ch_8bit_adam=False,
+):
+    global training_process, tts_api, stop_signal
+
+    if tts_api is not None:
+        if tts_api is not None:
+            del tts_api
+
+        gc.collect()
+        torch.cuda.empty_cache()
+        tts_api = None
+
+    path_project = os.path.join(path_data, dataset_name)
+
+    if not os.path.isdir(path_project):
+        yield (
+            f"There is not project with name {dataset_name}",
+            gr.update(interactive=True),
+            gr.update(interactive=False),
+        )
+        return
+
+    file_raw = os.path.join(path_project, "raw.arrow")
+    if not os.path.isfile(file_raw):
+        yield f"There is no file {file_raw}", gr.update(interactive=True), gr.update(interactive=False)
+        return
+
+    # Check if a training process is already running
+    if training_process is not None:
+        return "Train run already!", gr.update(interactive=False), gr.update(interactive=True)
+
+    yield "start train", gr.update(interactive=False), gr.update(interactive=False)
+
+    # Command to run the training script with the specified arguments
+
+    if tokenizer_file == "":
+        if dataset_name.endswith("_pinyin"):
+            tokenizer_type = "pinyin"
+        elif dataset_name.endswith("_char"):
+            tokenizer_type = "char"
+    else:
+        tokenizer_type = "custom"
+
+    dataset_name = dataset_name.replace("_pinyin", "").replace("_char", "")
+
+    if mixed_precision != "none":
+        fp16 = f"--mixed_precision={mixed_precision}"
+    else:
+        fp16 = ""
+
+    cmd = (
+        f"accelerate launch {fp16} {file_train} --exp_name {exp_name} "
+        f"--learning_rate {learning_rate} "
+        f"--batch_size_per_gpu {batch_size_per_gpu} "
+        f"--batch_size_type {batch_size_type} "
+        f"--max_samples {max_samples} "
+        f"--grad_accumulation_steps {grad_accumulation_steps} "
+        f"--max_grad_norm {max_grad_norm} "
+        f"--epochs {epochs} "
+        f"--num_warmup_updates {num_warmup_updates} "
+        f"--save_per_updates {save_per_updates} "
+        f"--last_per_steps {last_per_steps} "
+        f"--dataset_name {dataset_name}"
+    )
+
+    cmd += f" --finetune {finetune}"
+
+    if file_checkpoint_train != "":
+        cmd += f" --pretrain {file_checkpoint_train}"
+
+    if tokenizer_file != "":
+        cmd += f" --tokenizer_path {tokenizer_file}"
+
+    cmd += f" --tokenizer {tokenizer_type} "
+
+    cmd += f" --log_samples True --logger {logger} "
+
+    if ch_8bit_adam:
+        cmd += " --bnb_optimizer True "
+
+    print("run command : \n" + cmd + "\n")
+
+    save_settings(
+        dataset_name,
+        exp_name,
+        learning_rate,
+        batch_size_per_gpu,
+        batch_size_type,
+        max_samples,
+        grad_accumulation_steps,
+        max_grad_norm,
+        epochs,
+        num_warmup_updates,
+        save_per_updates,
+        last_per_steps,
+        finetune,
+        file_checkpoint_train,
+        tokenizer_type,
+        tokenizer_file,
+        mixed_precision,
+        logger,
+        ch_8bit_adam,
+    )
+
+    try:
+        if not stream:
+            # Start the training process
+            training_process = subprocess.Popen(cmd, shell=True)
+
+            time.sleep(5)
+            yield "train start", gr.update(interactive=False), gr.update(interactive=True)
+
+            # Wait for the training process to finish
+            training_process.wait()
+        else:
+
+            def stream_output(pipe, output_queue):
+                try:
+                    for line in iter(pipe.readline, ""):
+                        output_queue.put(line)
+                except Exception as e:
+                    output_queue.put(f"Error reading pipe: {str(e)}")
+                finally:
+                    pipe.close()
+
+            env = os.environ.copy()
+            env["PYTHONUNBUFFERED"] = "1"
+
+            training_process = subprocess.Popen(
+                cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, bufsize=1, env=env
+            )
+            yield "Training started...", gr.update(interactive=False), gr.update(interactive=True)
+
+            stdout_queue = queue.Queue()
+            stderr_queue = queue.Queue()
+
+            stdout_thread = threading.Thread(target=stream_output, args=(training_process.stdout, stdout_queue))
+            stderr_thread = threading.Thread(target=stream_output, args=(training_process.stderr, stderr_queue))
+            stdout_thread.daemon = True
+            stderr_thread.daemon = True
+            stdout_thread.start()
+            stderr_thread.start()
+            stop_signal = False
+            while True:
+                if stop_signal:
+                    training_process.terminate()
+                    time.sleep(0.5)
+                    if training_process.poll() is None:
+                        training_process.kill()
+                    yield "Training stopped by user.", gr.update(interactive=True), gr.update(interactive=False)
+                    break
+
+                process_status = training_process.poll()
+
+                # Handle stdout
+                try:
+                    while True:
+                        output = stdout_queue.get_nowait()
+                        print(output, end="")
+                        match = re.search(
+                            r"Epoch (\d+)/(\d+):\s+(\d+)%\|.*\[(\d+:\d+)<.*?loss=(\d+\.\d+), step=(\d+)", output
+                        )
+                        if match:
+                            current_epoch = match.group(1)
+                            total_epochs = match.group(2)
+                            percent_complete = match.group(3)
+                            elapsed_time = match.group(4)
+                            loss = match.group(5)
+                            current_step = match.group(6)
+                            message = (
+                                f"Epoch: {current_epoch}/{total_epochs}, "
+                                f"Progress: {percent_complete}%, "
+                                f"Elapsed Time: {elapsed_time}, "
+                                f"Loss: {loss}, "
+                                f"Step: {current_step}"
+                            )
+                            yield message, gr.update(interactive=False), gr.update(interactive=True)
+                        elif output.strip():
+                            yield output, gr.update(interactive=False), gr.update(interactive=True)
+                except queue.Empty:
+                    pass
+
+                # Handle stderr
+                try:
+                    while True:
+                        error_output = stderr_queue.get_nowait()
+                        print(error_output, end="")
+                        if error_output.strip():
+                            yield f"{error_output.strip()}", gr.update(interactive=False), gr.update(interactive=True)
+                except queue.Empty:
+                    pass
+
+                if process_status is not None and stdout_queue.empty() and stderr_queue.empty():
+                    if process_status != 0:
+                        yield (
+                            f"Process crashed with exit code {process_status}!",
+                            gr.update(interactive=False),
+                            gr.update(interactive=True),
+                        )
+                    else:
+                        yield "Training complete!", gr.update(interactive=False), gr.update(interactive=True)
+                    break
+
+                # Small sleep to prevent CPU thrashing
+                time.sleep(0.1)
+
+            # Clean up
+            training_process.stdout.close()
+            training_process.stderr.close()
+            training_process.wait()
+
+        time.sleep(1)
+
+        if training_process is None:
+            text_info = "train stop"
+        else:
+            text_info = "train complete !"
+
+    except Exception as e:  # Catch all exceptions
+        # Ensure that we reset the training process variable in case of an error
+        text_info = f"An error occurred: {str(e)}"
+
+    training_process = None
+
+    yield text_info, gr.update(interactive=True), gr.update(interactive=False)
+
+
+def stop_training():
+    global training_process, stop_signal
+
+    if training_process is None:
+        return "Train not run !", gr.update(interactive=True), gr.update(interactive=False)
+    terminate_process_tree(training_process.pid)
+    # training_process = None
+    stop_signal = True
+    return "train stop", gr.update(interactive=True), gr.update(interactive=False)
+
+
+def get_list_projects():
+    project_list = []
+    for folder in os.listdir(path_data):
+        path_folder = os.path.join(path_data, folder)
+        if not os.path.isdir(path_folder):
+            continue
+        folder = folder.lower()
+        if folder == "emilia_zh_en_pinyin":
+            continue
+        project_list.append(folder)
+
+    projects_selelect = None if not project_list else project_list[-1]
+
+    return project_list, projects_selelect
+
+
+def create_data_project(name, tokenizer_type):
+    name += "_" + tokenizer_type
+    os.makedirs(os.path.join(path_data, name), exist_ok=True)
+    os.makedirs(os.path.join(path_data, name, "dataset"), exist_ok=True)
+    project_list, projects_selelect = get_list_projects()
+    return gr.update(choices=project_list, value=name)
+
+
+def transcribe_all(name_project, audio_files, language, user=False, progress=gr.Progress()):
+    path_project = os.path.join(path_data, name_project)
+    path_dataset = os.path.join(path_project, "dataset")
+    path_project_wavs = os.path.join(path_project, "wavs")
+    file_metadata = os.path.join(path_project, "metadata.csv")
+
+    if not user:
+        if audio_files is None:
+            return "You need to load an audio file."
+
+    if os.path.isdir(path_project_wavs):
+        shutil.rmtree(path_project_wavs)
+
+    if os.path.isfile(file_metadata):
+        os.remove(file_metadata)
+
+    os.makedirs(path_project_wavs, exist_ok=True)
+
+    if user:
+        file_audios = [
+            file
+            for format in ("*.wav", "*.ogg", "*.opus", "*.mp3", "*.flac")
+            for file in glob(os.path.join(path_dataset, format))
+        ]
+        if file_audios == []:
+            return "No audio file was found in the dataset."
+    else:
+        file_audios = audio_files
+
+    alpha = 0.5
+    _max = 1.0
+    slicer = Slicer(24000)
+
+    num = 0
+    error_num = 0
+    data = ""
+    for file_audio in progress.tqdm(file_audios, desc="transcribe files", total=len((file_audios))):
+        audio, _ = librosa.load(file_audio, sr=24000, mono=True)
+
+        list_slicer = slicer.slice(audio)
+        for chunk, start, end in progress.tqdm(list_slicer, total=len(list_slicer), desc="slicer files"):
+            name_segment = os.path.join(f"segment_{num}")
+            file_segment = os.path.join(path_project_wavs, f"{name_segment}.wav")
+
+            tmp_max = np.abs(chunk).max()
+            if tmp_max > 1:
+                chunk /= tmp_max
+            chunk = (chunk / tmp_max * (_max * alpha)) + (1 - alpha) * chunk
+            wavfile.write(file_segment, 24000, (chunk * 32767).astype(np.int16))
+
+            try:
+                text = transcribe(file_segment, language)
+                text = text.lower().strip().replace('"', "")
+
+                data += f"{name_segment}|{text}\n"
+
+                num += 1
+            except:  # noqa: E722
+                error_num += 1
+
+    with open(file_metadata, "w", encoding="utf-8-sig") as f:
+        f.write(data)
+
+    if error_num != []:
+        error_text = f"\nerror files : {error_num}"
+    else:
+        error_text = ""
+
+    return f"transcribe complete samples : {num}\npath : {path_project_wavs}{error_text}"
+
+
+def format_seconds_to_hms(seconds):
+    hours = int(seconds / 3600)
+    minutes = int((seconds % 3600) / 60)
+    seconds = seconds % 60
+    return "{:02d}:{:02d}:{:02d}".format(hours, minutes, int(seconds))
+
+
+def get_correct_audio_path(
+    audio_input,
+    base_path="wavs",
+    supported_formats=("wav", "mp3", "aac", "flac", "m4a", "alac", "ogg", "aiff", "wma", "amr"),
+):
+    file_audio = None
+
+    # Helper function to check if file has a supported extension
+    def has_supported_extension(file_name):
+        return any(file_name.endswith(f".{ext}") for ext in supported_formats)
+
+    # Case 1: If it's a full path with a valid extension, use it directly
+    if os.path.isabs(audio_input) and has_supported_extension(audio_input):
+        file_audio = audio_input
+
+    # Case 2: If it has a supported extension but is not a full path
+    elif has_supported_extension(audio_input) and not os.path.isabs(audio_input):
+        file_audio = os.path.join(base_path, audio_input)
+
+    # Case 3: If only the name is given (no extension and not a full path)
+    elif not has_supported_extension(audio_input) and not os.path.isabs(audio_input):
+        for ext in supported_formats:
+            potential_file = os.path.join(base_path, f"{audio_input}.{ext}")
+            if os.path.exists(potential_file):
+                file_audio = potential_file
+                break
+        else:
+            file_audio = os.path.join(base_path, f"{audio_input}.{supported_formats[0]}")
+    return file_audio
+
+
+def create_metadata(name_project, ch_tokenizer, progress=gr.Progress()):
+    path_project = os.path.join(path_data, name_project)
+    path_project_wavs = os.path.join(path_project, "wavs")
+    file_metadata = os.path.join(path_project, "metadata.csv")
+    file_raw = os.path.join(path_project, "raw.arrow")
+    file_duration = os.path.join(path_project, "duration.json")
+    file_vocab = os.path.join(path_project, "vocab.txt")
+
+    if not os.path.isfile(file_metadata):
+        return "The file was not found in " + file_metadata, ""
+
+    with open(file_metadata, "r", encoding="utf-8-sig") as f:
+        data = f.read()
+
+    audio_path_list = []
+    text_list = []
+    duration_list = []
+
+    count = data.split("\n")
+    lenght = 0
+    result = []
+    error_files = []
+    text_vocab_set = set()
+    for line in progress.tqdm(data.split("\n"), total=count):
+        sp_line = line.split("|")
+        if len(sp_line) != 2:
+            continue
+        name_audio, text = sp_line[:2]
+
+        file_audio = get_correct_audio_path(name_audio, path_project_wavs)
+
+        if not os.path.isfile(file_audio):
+            error_files.append([file_audio, "error path"])
+            continue
+
+        try:
+            duration = get_audio_duration(file_audio)
+        except Exception as e:
+            error_files.append([file_audio, "duration"])
+            print(f"Error processing {file_audio}: {e}")
+            continue
+
+        if duration < 1 or duration > 25:
+            if duration > 25:
+                error_files.append([file_audio, "duration > 25 sec"])
+            if duration < 1:
+                error_files.append([file_audio, "duration < 1 sec "])
+            continue
+        if len(text) < 3:
+            error_files.append([file_audio, "very small text len 3"])
+            continue
+
+        text = clear_text(text)
+        text = convert_char_to_pinyin([text], polyphone=True)[0]
+
+        audio_path_list.append(file_audio)
+        duration_list.append(duration)
+        text_list.append(text)
+
+        result.append({"audio_path": file_audio, "text": text, "duration": duration})
+        if ch_tokenizer:
+            text_vocab_set.update(list(text))
+
+        lenght += duration
+
+    if duration_list == []:
+        return f"Error: No audio files found in the specified path : {path_project_wavs}", ""
+
+    min_second = round(min(duration_list), 2)
+    max_second = round(max(duration_list), 2)
+
+    with ArrowWriter(path=file_raw, writer_batch_size=1) as writer:
+        for line in progress.tqdm(result, total=len(result), desc="prepare data"):
+            writer.write(line)
+
+    with open(file_duration, "w") as f:
+        json.dump({"duration": duration_list}, f, ensure_ascii=False)
+
+    new_vocal = ""
+    if not ch_tokenizer:
+        if not os.path.isfile(file_vocab):
+            file_vocab_finetune = os.path.join(path_data, "Emilia_ZH_EN_pinyin/vocab.txt")
+            if not os.path.isfile(file_vocab_finetune):
+                return "Error: Vocabulary file 'Emilia_ZH_EN_pinyin' not found!", ""
+            shutil.copy2(file_vocab_finetune, file_vocab)
+
+        with open(file_vocab, "r", encoding="utf-8-sig") as f:
+            vocab_char_map = {}
+            for i, char in enumerate(f):
+                vocab_char_map[char[:-1]] = i
+        vocab_size = len(vocab_char_map)
+
+    else:
+        with open(file_vocab, "w", encoding="utf-8-sig") as f:
+            for vocab in sorted(text_vocab_set):
+                f.write(vocab + "\n")
+                new_vocal += vocab + "\n"
+        vocab_size = len(text_vocab_set)
+
+    if error_files != []:
+        error_text = "\n".join([" = ".join(item) for item in error_files])
+    else:
+        error_text = ""
+
+    return (
+        f"prepare complete \nsamples : {len(text_list)}\ntime data : {format_seconds_to_hms(lenght)}\nmin sec : {min_second}\nmax sec : {max_second}\nfile_arrow : {file_raw}\nvocab : {vocab_size}\n{error_text}",
+        new_vocal,
+    )
+
+
+def check_user(value):
+    return gr.update(visible=not value), gr.update(visible=value)
+
+
+def calculate_train(
+    name_project,
+    batch_size_type,
+    max_samples,
+    learning_rate,
+    num_warmup_updates,
+    save_per_updates,
+    last_per_steps,
+    finetune,
+):
+    path_project = os.path.join(path_data, name_project)
+    file_duraction = os.path.join(path_project, "duration.json")
+
+    if not os.path.isfile(file_duraction):
+        return (
+            1000,
+            max_samples,
+            num_warmup_updates,
+            save_per_updates,
+            last_per_steps,
+            "project not found !",
+            learning_rate,
+        )
+
+    with open(file_duraction, "r") as file:
+        data = json.load(file)
+
+    duration_list = data["duration"]
+    samples = len(duration_list)
+    hours = sum(duration_list) / 3600
+
+    # if torch.cuda.is_available():
+    # gpu_properties = torch.cuda.get_device_properties(0)
+    # total_memory = gpu_properties.total_memory / (1024**3)
+    # elif torch.backends.mps.is_available():
+    # total_memory = psutil.virtual_memory().available / (1024**3)
+
+    if torch.cuda.is_available():
+        gpu_count = torch.cuda.device_count()
+        total_memory = 0
+        for i in range(gpu_count):
+            gpu_properties = torch.cuda.get_device_properties(i)
+            total_memory += gpu_properties.total_memory / (1024**3)  # in GB
+
+    elif torch.backends.mps.is_available():
+        gpu_count = 1
+        total_memory = psutil.virtual_memory().available / (1024**3)
+
+    if batch_size_type == "frame":
+        batch = int(total_memory * 0.5)
+        batch = (lambda num: num + 1 if num % 2 != 0 else num)(batch)
+        batch_size_per_gpu = int(38400 / batch)
+    else:
+        batch_size_per_gpu = int(total_memory / 8)
+        batch_size_per_gpu = (lambda num: num + 1 if num % 2 != 0 else num)(batch_size_per_gpu)
+        batch = batch_size_per_gpu
+
+    if batch_size_per_gpu <= 0:
+        batch_size_per_gpu = 1
+
+    if samples < 64:
+        max_samples = int(samples * 0.25)
+    else:
+        max_samples = 64
+
+    num_warmup_updates = int(samples * 0.05)
+    save_per_updates = int(samples * 0.10)
+    last_per_steps = int(save_per_updates * 0.25)
+
+    max_samples = (lambda num: num + 1 if num % 2 != 0 else num)(max_samples)
+    num_warmup_updates = (lambda num: num + 1 if num % 2 != 0 else num)(num_warmup_updates)
+    save_per_updates = (lambda num: num + 1 if num % 2 != 0 else num)(save_per_updates)
+    last_per_steps = (lambda num: num + 1 if num % 2 != 0 else num)(last_per_steps)
+    if last_per_steps <= 0:
+        last_per_steps = 2
+
+    total_hours = hours
+    mel_hop_length = 256
+    mel_sampling_rate = 24000
+
+    # target
+    wanted_max_updates = 1000000
+
+    # train params
+    gpus = gpu_count
+    frames_per_gpu = batch_size_per_gpu  # 8 * 38400 = 307200
+    grad_accum = 1
+
+    # intermediate
+    mini_batch_frames = frames_per_gpu * grad_accum * gpus
+    mini_batch_hours = mini_batch_frames * mel_hop_length / mel_sampling_rate / 3600
+    updates_per_epoch = total_hours / mini_batch_hours
+    # steps_per_epoch = updates_per_epoch * grad_accum
+    epochs = wanted_max_updates / updates_per_epoch
+
+    if finetune:
+        learning_rate = 1e-5
+    else:
+        learning_rate = 7.5e-5
+
+    return (
+        batch_size_per_gpu,
+        max_samples,
+        num_warmup_updates,
+        save_per_updates,
+        last_per_steps,
+        samples,
+        learning_rate,
+        int(epochs),
+    )
+
+
+def extract_and_save_ema_model(checkpoint_path: str, new_checkpoint_path: str, safetensors: bool) -> str:
+    try:
+        checkpoint = torch.load(checkpoint_path)
+        print("Original Checkpoint Keys:", checkpoint.keys())
+
+        ema_model_state_dict = checkpoint.get("ema_model_state_dict", None)
+        if ema_model_state_dict is None:
+            return "No 'ema_model_state_dict' found in the checkpoint."
+
+        if safetensors:
+            new_checkpoint_path = new_checkpoint_path.replace(".pt", ".safetensors")
+            save_file(ema_model_state_dict, new_checkpoint_path)
+        else:
+            new_checkpoint_path = new_checkpoint_path.replace(".safetensors", ".pt")
+            new_checkpoint = {"ema_model_state_dict": ema_model_state_dict}
+            torch.save(new_checkpoint, new_checkpoint_path)
+
+        return f"New checkpoint saved at: {new_checkpoint_path}"
+
+    except Exception as e:
+        return f"An error occurred: {e}"
+
+
+def expand_model_embeddings(ckpt_path, new_ckpt_path, num_new_tokens=42):
+    seed = 666
+    random.seed(seed)
+    os.environ["PYTHONHASHSEED"] = str(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+    ckpt = torch.load(ckpt_path, map_location="cpu")
+
+    ema_sd = ckpt.get("ema_model_state_dict", {})
+    embed_key_ema = "ema_model.transformer.text_embed.text_embed.weight"
+    old_embed_ema = ema_sd[embed_key_ema]
+
+    vocab_old = old_embed_ema.size(0)
+    embed_dim = old_embed_ema.size(1)
+    vocab_new = vocab_old + num_new_tokens
+
+    def expand_embeddings(old_embeddings):
+        new_embeddings = torch.zeros((vocab_new, embed_dim))
+        new_embeddings[:vocab_old] = old_embeddings
+        new_embeddings[vocab_old:] = torch.randn((num_new_tokens, embed_dim))
+        return new_embeddings
+
+    ema_sd[embed_key_ema] = expand_embeddings(ema_sd[embed_key_ema])
+
+    torch.save(ckpt, new_ckpt_path)
+
+    return vocab_new
+
+
+def vocab_count(text):
+    return str(len(text.split(",")))
+
+
+def vocab_extend(project_name, symbols, model_type):
+    if symbols == "":
+        return "Symbols empty!"
+
+    name_project = project_name
+    path_project = os.path.join(path_data, name_project)
+    file_vocab_project = os.path.join(path_project, "vocab.txt")
+
+    file_vocab = os.path.join(path_data, "Emilia_ZH_EN_pinyin/vocab.txt")
+    if not os.path.isfile(file_vocab):
+        return f"the file {file_vocab} not found !"
+
+    symbols = symbols.split(",")
+    if symbols == []:
+        return "Symbols to extend not found."
+
+    with open(file_vocab, "r", encoding="utf-8-sig") as f:
+        data = f.read()
+        vocab = data.split("\n")
+    vocab_check = set(vocab)
+
+    miss_symbols = []
+    for item in symbols:
+        item = item.replace(" ", "")
+        if item in vocab_check:
+            continue
+        miss_symbols.append(item)
+
+    if miss_symbols == []:
+        return "Symbols are okay no need to extend."
+
+    size_vocab = len(vocab)
+    vocab.pop()
+    for item in miss_symbols:
+        vocab.append(item)
+
+    vocab.append("")
+
+    with open(file_vocab_project, "w", encoding="utf-8") as f:
+        f.write("\n".join(vocab))
+
+    if model_type == "F5-TTS":
+        ckpt_path = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.pt"))
+    else:
+        ckpt_path = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.pt"))
+
+    vocab_size_new = len(miss_symbols)
+
+    dataset_name = name_project.replace("_pinyin", "").replace("_char", "")
+    new_ckpt_path = os.path.join(path_project_ckpts, dataset_name)
+    os.makedirs(new_ckpt_path, exist_ok=True)
+    new_ckpt_file = os.path.join(new_ckpt_path, "model_1200000.pt")
+
+    size = expand_model_embeddings(ckpt_path, new_ckpt_file, num_new_tokens=vocab_size_new)
+
+    vocab_new = "\n".join(miss_symbols)
+    return f"vocab old size : {size_vocab}\nvocab new size : {size}\nvocab add : {vocab_size_new}\nnew symbols :\n{vocab_new}"
+
+
+def vocab_check(project_name):
+    name_project = project_name
+    path_project = os.path.join(path_data, name_project)
+
+    file_metadata = os.path.join(path_project, "metadata.csv")
+
+    file_vocab = os.path.join(path_data, "Emilia_ZH_EN_pinyin/vocab.txt")
+    if not os.path.isfile(file_vocab):
+        return f"the file {file_vocab} not found !", ""
+
+    with open(file_vocab, "r", encoding="utf-8-sig") as f:
+        data = f.read()
+        vocab = data.split("\n")
+        vocab = set(vocab)
+
+    if not os.path.isfile(file_metadata):
+        return f"the file {file_metadata} not found !", ""
+
+    with open(file_metadata, "r", encoding="utf-8-sig") as f:
+        data = f.read()
+
+    miss_symbols = []
+    miss_symbols_keep = {}
+    for item in data.split("\n"):
+        sp = item.split("|")
+        if len(sp) != 2:
+            continue
+
+        text = sp[1].lower().strip()
+
+        for t in text:
+            if t not in vocab and t not in miss_symbols_keep:
+                miss_symbols.append(t)
+                miss_symbols_keep[t] = t
+
+    if miss_symbols == []:
+        vocab_miss = ""
+        info = "You can train using your language !"
+    else:
+        vocab_miss = ",".join(miss_symbols)
+        info = f"The following symbols are missing in your language {len(miss_symbols)}\n\n"
+
+    return info, vocab_miss
+
+
+def get_random_sample_prepare(project_name):
+    name_project = project_name
+    path_project = os.path.join(path_data, name_project)
+    file_arrow = os.path.join(path_project, "raw.arrow")
+    if not os.path.isfile(file_arrow):
+        return "", None
+    dataset = Dataset_.from_file(file_arrow)
+    random_sample = dataset.shuffle(seed=random.randint(0, 1000)).select([0])
+    text = "[" + " , ".join(["' " + t + " '" for t in random_sample["text"][0]]) + "]"
+    audio_path = random_sample["audio_path"][0]
+    return text, audio_path
+
+
+def get_random_sample_transcribe(project_name):
+    name_project = project_name
+    path_project = os.path.join(path_data, name_project)
+    file_metadata = os.path.join(path_project, "metadata.csv")
+    if not os.path.isfile(file_metadata):
+        return "", None
+
+    data = ""
+    with open(file_metadata, "r", encoding="utf-8-sig") as f:
+        data = f.read()
+
+    list_data = []
+    for item in data.split("\n"):
+        sp = item.split("|")
+        if len(sp) != 2:
+            continue
+
+        # fixed audio when it is absolute
+        file_audio = get_correct_audio_path(sp[0], os.path.join(path_project, "wavs"))
+        list_data.append([file_audio, sp[1]])
+
+    if list_data == []:
+        return "", None
+
+    random_item = random.choice(list_data)
+
+    return random_item[1], random_item[0]
+
+
+def get_random_sample_infer(project_name):
+    text, audio = get_random_sample_transcribe(project_name)
+    return (
+        text,
+        text,
+        audio,
+    )
+
+
+def infer(
+    project, file_checkpoint, exp_name, ref_text, ref_audio, gen_text, nfe_step, use_ema, speed, seed, remove_silence
+):
+    global last_checkpoint, last_device, tts_api, last_ema
+
+    if not os.path.isfile(file_checkpoint):
+        return None, "checkpoint not found!"
+
+    if training_process is not None:
+        device_test = "cpu"
+    else:
+        device_test = None
+
+    if last_checkpoint != file_checkpoint or last_device != device_test or last_ema != use_ema or tts_api is None:
+        if last_checkpoint != file_checkpoint:
+            last_checkpoint = file_checkpoint
+
+        if last_device != device_test:
+            last_device = device_test
+
+        if last_ema != use_ema:
+            last_ema = use_ema
+
+        vocab_file = os.path.join(path_data, project, "vocab.txt")
+
+        tts_api = F5TTS(
+            model_type=exp_name, ckpt_file=file_checkpoint, vocab_file=vocab_file, device=device_test, use_ema=use_ema
+        )
+
+        print("update >> ", device_test, file_checkpoint, use_ema)
+
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
+        tts_api.infer(
+            gen_text=gen_text.lower().strip(),
+            ref_text=ref_text.lower().strip(),
+            ref_file=ref_audio,
+            nfe_step=nfe_step,
+            file_wave=f.name,
+            speed=speed,
+            seed=seed,
+            remove_silence=remove_silence,
+        )
+        return f.name, tts_api.device, str(tts_api.seed)
+
+
+def check_finetune(finetune):
+    return gr.update(interactive=finetune), gr.update(interactive=finetune), gr.update(interactive=finetune)
+
+
+def get_checkpoints_project(project_name, is_gradio=True):
+    if project_name is None:
+        return [], ""
+    project_name = project_name.replace("_pinyin", "").replace("_char", "")
+
+    if os.path.isdir(path_project_ckpts):
+        files_checkpoints = glob(os.path.join(path_project_ckpts, project_name, "*.pt"))
+        files_checkpoints = sorted(
+            files_checkpoints,
+            key=lambda x: int(os.path.basename(x).split("_")[1].split(".")[0])
+            if os.path.basename(x) != "model_last.pt"
+            else float("inf"),
+        )
+    else:
+        files_checkpoints = []
+
+    selelect_checkpoint = None if not files_checkpoints else files_checkpoints[0]
+
+    if is_gradio:
+        return gr.update(choices=files_checkpoints, value=selelect_checkpoint)
+
+    return files_checkpoints, selelect_checkpoint
+
+
+def get_audio_project(project_name, is_gradio=True):
+    if project_name is None:
+        return [], ""
+    project_name = project_name.replace("_pinyin", "").replace("_char", "")
+
+    if os.path.isdir(path_project_ckpts):
+        files_audios = glob(os.path.join(path_project_ckpts, project_name, "samples", "*.wav"))
+        files_audios = sorted(files_audios, key=lambda x: int(os.path.basename(x).split("_")[1].split(".")[0]))
+
+        files_audios = [item.replace("_gen.wav", "") for item in files_audios if item.endswith("_gen.wav")]
+    else:
+        files_audios = []
+
+    selelect_checkpoint = None if not files_audios else files_audios[0]
+
+    if is_gradio:
+        return gr.update(choices=files_audios, value=selelect_checkpoint)
+
+    return files_audios, selelect_checkpoint
+
+
+def get_gpu_stats():
+    gpu_stats = ""
+
+    if torch.cuda.is_available():
+        gpu_count = torch.cuda.device_count()
+        for i in range(gpu_count):
+            gpu_name = torch.cuda.get_device_name(i)
+            gpu_properties = torch.cuda.get_device_properties(i)
+            total_memory = gpu_properties.total_memory / (1024**3)  # in GB
+            allocated_memory = torch.cuda.memory_allocated(i) / (1024**2)  # in MB
+            reserved_memory = torch.cuda.memory_reserved(i) / (1024**2)  # in MB
+
+            gpu_stats += (
+                f"GPU {i} Name: {gpu_name}\n"
+                f"Total GPU memory (GPU {i}): {total_memory:.2f} GB\n"
+                f"Allocated GPU memory (GPU {i}): {allocated_memory:.2f} MB\n"
+                f"Reserved GPU memory (GPU {i}): {reserved_memory:.2f} MB\n\n"
+            )
+
+    elif torch.backends.mps.is_available():
+        gpu_count = 1
+        gpu_stats += "MPS GPU\n"
+        total_memory = psutil.virtual_memory().total / (
+            1024**3
+        )  # Total system memory (MPS doesn't have its own memory)
+        allocated_memory = 0
+        reserved_memory = 0
+
+        gpu_stats += (
+            f"Total system memory: {total_memory:.2f} GB\n"
+            f"Allocated GPU memory (MPS): {allocated_memory:.2f} MB\n"
+            f"Reserved GPU memory (MPS): {reserved_memory:.2f} MB\n"
+        )
+
+    else:
+        gpu_stats = "No GPU available"
+
+    return gpu_stats
+
+
+def get_cpu_stats():
+    cpu_usage = psutil.cpu_percent(interval=1)
+    memory_info = psutil.virtual_memory()
+    memory_used = memory_info.used / (1024**2)
+    memory_total = memory_info.total / (1024**2)
+    memory_percent = memory_info.percent
+
+    pid = os.getpid()
+    process = psutil.Process(pid)
+    nice_value = process.nice()
+
+    cpu_stats = (
+        f"CPU Usage: {cpu_usage:.2f}%\n"
+        f"System Memory: {memory_used:.2f} MB used / {memory_total:.2f} MB total ({memory_percent}% used)\n"
+        f"Process Priority (Nice value): {nice_value}"
+    )
+
+    return cpu_stats
+
+
+def get_combined_stats():
+    gpu_stats = get_gpu_stats()
+    cpu_stats = get_cpu_stats()
+    combined_stats = f"### GPU Stats\n{gpu_stats}\n\n### CPU Stats\n{cpu_stats}"
+    return combined_stats
+
+
+def get_audio_select(file_sample):
+    select_audio_ref = file_sample
+    select_audio_gen = file_sample
+
+    if file_sample is not None:
+        select_audio_ref += "_ref.wav"
+        select_audio_gen += "_gen.wav"
+
+    return select_audio_ref, select_audio_gen
+
+
+with gr.Blocks() as app:
+    gr.Markdown(
+        """
+# E2/F5 TTS Automatic Finetune
+
+This is a local web UI for F5 TTS with advanced batch processing support. This app supports the following TTS models:
+
+* [F5-TTS](https://arxiv.org/abs/2410.06885) (A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching)
+* [E2 TTS](https://arxiv.org/abs/2406.18009) (Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS)
+
+The checkpoints support English and Chinese.
+
+For tutorial and updates check here (https://github.com/SWivid/F5-TTS/discussions/143)
+"""
+    )
+
+    with gr.Row():
+        projects, projects_selelect = get_list_projects()
+        tokenizer_type = gr.Radio(label="Tokenizer Type", choices=["pinyin", "char", "custom"], value="pinyin")
+        project_name = gr.Textbox(label="Project Name", value="my_speak")
+        bt_create = gr.Button("Create a New Project")
+
+    with gr.Row():
+        cm_project = gr.Dropdown(
+            choices=projects, value=projects_selelect, label="Project", allow_custom_value=True, scale=6
+        )
+        ch_refresh_project = gr.Button("Refresh", scale=1)
+
+    bt_create.click(fn=create_data_project, inputs=[project_name, tokenizer_type], outputs=[cm_project])
+
+    with gr.Tabs():
+        with gr.TabItem("Transcribe Data"):
+            gr.Markdown("""```plaintext 
+Skip this step if you have your dataset, metadata.csv, and a folder wavs with all the audio files.                 
+```""")
+
+            ch_manual = gr.Checkbox(label="Audio from Path", value=False)
+
+            mark_info_transcribe = gr.Markdown(
+                """```plaintext    
+     Place your 'wavs' folder and 'metadata.csv' file in the '{your_project_name}' directory. 
+                 
+     my_speak/
+     │
+     └── dataset/
+         ├── audio1.wav
+         └── audio2.wav
+         ...
+     ```""",
+                visible=False,
+            )
+
+            audio_speaker = gr.File(label="Voice", type="filepath", file_count="multiple")
+            txt_lang = gr.Text(label="Language", value="English")
+            bt_transcribe = bt_create = gr.Button("Transcribe")
+            txt_info_transcribe = gr.Text(label="Info", value="")
+            bt_transcribe.click(
+                fn=transcribe_all,
+                inputs=[cm_project, audio_speaker, txt_lang, ch_manual],
+                outputs=[txt_info_transcribe],
+            )
+            ch_manual.change(fn=check_user, inputs=[ch_manual], outputs=[audio_speaker, mark_info_transcribe])
+
+            random_sample_transcribe = gr.Button("Random Sample")
+
+            with gr.Row():
+                random_text_transcribe = gr.Text(label="Text")
+                random_audio_transcribe = gr.Audio(label="Audio", type="filepath")
+
+            random_sample_transcribe.click(
+                fn=get_random_sample_transcribe,
+                inputs=[cm_project],
+                outputs=[random_text_transcribe, random_audio_transcribe],
+            )
+
+        with gr.TabItem("Vocab Check"):
+            gr.Markdown("""```plaintext 
+Check the vocabulary for fine-tuning Emilia_ZH_EN to ensure all symbols are included. For fine-tuning a new language.
+```""")
+
+            check_button = gr.Button("Check Vocab")
+            txt_info_check = gr.Text(label="Info", value="")
+
+            gr.Markdown("""```plaintext 
+Using the extended model, you can finetune to a new language that is missing symbols in the vocab. This creates a new model with a new vocabulary size and saves it in your ckpts/project folder.
+```""")
+
+            exp_name_extend = gr.Radio(label="Model", choices=["F5-TTS", "E2-TTS"], value="F5-TTS")
+
+            with gr.Row():
+                txt_extend = gr.Textbox(
+                    label="Symbols",
+                    value="",
+                    placeholder="To add new symbols, make sure to use ',' for each symbol",
+                    scale=6,
+                )
+                txt_count_symbol = gr.Textbox(label="New Vocab Size", value="", scale=1)
+
+            extend_button = gr.Button("Extend")
+            txt_info_extend = gr.Text(label="Info", value="")
+
+            txt_extend.change(vocab_count, inputs=[txt_extend], outputs=[txt_count_symbol])
+            check_button.click(fn=vocab_check, inputs=[cm_project], outputs=[txt_info_check, txt_extend])
+            extend_button.click(
+                fn=vocab_extend, inputs=[cm_project, txt_extend, exp_name_extend], outputs=[txt_info_extend]
+            )
+
+        with gr.TabItem("Prepare Data"):
+            gr.Markdown("""```plaintext 
+Skip this step if you have your dataset, raw.arrow, duration.json, and vocab.txt
+```""")
+
+            gr.Markdown(
+                """```plaintext    
+     Place all your "wavs" folder and your "metadata.csv" file in your project name directory.
+
+     Supported audio formats: "wav", "mp3", "aac", "flac", "m4a", "alac", "ogg", "aiff", "wma", "amr"
+
+     Example wav format:                               
+     my_speak/
+     │
+     ├── wavs/
+     │   ├── audio1.wav
+     │   └── audio2.wav
+     |   ...
+     │
+     └── metadata.csv
+      
+     File format metadata.csv:
+
+     audio1|text1 or audio1.wav|text1 or your_path/audio1.wav|text1 
+     audio2|text1 or audio2.wav|text1 or your_path/audio2.wav|text1 
+     ...
+
+     ```"""
+            )
+            ch_tokenizern = gr.Checkbox(label="Create Vocabulary", value=False, visible=False)
+
+            bt_prepare = bt_create = gr.Button("Prepare")
+            txt_info_prepare = gr.Text(label="Info", value="")
+            txt_vocab_prepare = gr.Text(label="Vocab", value="")
+
+            bt_prepare.click(
+                fn=create_metadata, inputs=[cm_project, ch_tokenizern], outputs=[txt_info_prepare, txt_vocab_prepare]
+            )
+
+            random_sample_prepare = gr.Button("Random Sample")
+
+            with gr.Row():
+                random_text_prepare = gr.Text(label="Tokenizer")
+                random_audio_prepare = gr.Audio(label="Audio", type="filepath")
+
+            random_sample_prepare.click(
+                fn=get_random_sample_prepare, inputs=[cm_project], outputs=[random_text_prepare, random_audio_prepare]
+            )
+
+        with gr.TabItem("Train Data"):
+            gr.Markdown("""```plaintext 
+The auto-setting is still experimental. Please make sure that the epochs, save per updates, and last per steps are set correctly, or change them manually as needed.
+If you encounter a memory error, try reducing the batch size per GPU to a smaller number.
+```""")
+            with gr.Row():
+                bt_calculate = bt_create = gr.Button("Auto Settings")
+                lb_samples = gr.Label(label="Samples")
+                batch_size_type = gr.Radio(label="Batch Size Type", choices=["frame", "sample"], value="frame")
+
+            with gr.Row():
+                ch_finetune = bt_create = gr.Checkbox(label="Finetune", value=True)
+                tokenizer_file = gr.Textbox(label="Tokenizer File", value="")
+                file_checkpoint_train = gr.Textbox(label="Path to the Pretrained Checkpoint", value="")
+
+            with gr.Row():
+                exp_name = gr.Radio(label="Model", choices=["F5TTS_Base", "E2TTS_Base"], value="F5TTS_Base")
+                learning_rate = gr.Number(label="Learning Rate", value=1e-5, step=1e-5)
+
+            with gr.Row():
+                batch_size_per_gpu = gr.Number(label="Batch Size per GPU", value=1000)
+                max_samples = gr.Number(label="Max Samples", value=64)
+
+            with gr.Row():
+                grad_accumulation_steps = gr.Number(label="Gradient Accumulation Steps", value=1)
+                max_grad_norm = gr.Number(label="Max Gradient Norm", value=1.0)
+
+            with gr.Row():
+                epochs = gr.Number(label="Epochs", value=10)
+                num_warmup_updates = gr.Number(label="Warmup Updates", value=2)
+
+            with gr.Row():
+                save_per_updates = gr.Number(label="Save per Updates", value=300)
+                last_per_steps = gr.Number(label="Last per Steps", value=100)
+
+            with gr.Row():
+                ch_8bit_adam = gr.Checkbox(label="Use 8-bit Adam optimizer")
+                mixed_precision = gr.Radio(label="mixed_precision", choices=["none", "fp16", "bf16"], value="none")
+                cd_logger = gr.Radio(label="logger", choices=["wandb", "tensorboard"], value="wandb")
+                start_button = gr.Button("Start Training")
+                stop_button = gr.Button("Stop Training", interactive=False)
+
+            if projects_selelect is not None:
+                (
+                    exp_namev,
+                    learning_ratev,
+                    batch_size_per_gpuv,
+                    batch_size_typev,
+                    max_samplesv,
+                    grad_accumulation_stepsv,
+                    max_grad_normv,
+                    epochsv,
+                    num_warmupv_updatesv,
+                    save_per_updatesv,
+                    last_per_stepsv,
+                    finetunev,
+                    file_checkpoint_trainv,
+                    tokenizer_typev,
+                    tokenizer_filev,
+                    mixed_precisionv,
+                    cd_loggerv,
+                    ch_8bit_adamv,
+                ) = load_settings(projects_selelect)
+                exp_name.value = exp_namev
+                learning_rate.value = learning_ratev
+                batch_size_per_gpu.value = batch_size_per_gpuv
+                batch_size_type.value = batch_size_typev
+                max_samples.value = max_samplesv
+                grad_accumulation_steps.value = grad_accumulation_stepsv
+                max_grad_norm.value = max_grad_normv
+                epochs.value = epochsv
+                num_warmup_updates.value = num_warmupv_updatesv
+                save_per_updates.value = save_per_updatesv
+                last_per_steps.value = last_per_stepsv
+                ch_finetune.value = finetunev
+                file_checkpoint_train.value = file_checkpoint_trainv
+                tokenizer_type.value = tokenizer_typev
+                tokenizer_file.value = tokenizer_filev
+                mixed_precision.value = mixed_precisionv
+                cd_logger.value = cd_loggerv
+                ch_8bit_adam.value = ch_8bit_adamv
+
+            ch_stream = gr.Checkbox(label="Stream Output Experiment", value=True)
+            txt_info_train = gr.Text(label="Info", value="")
+
+            list_audios, select_audio = get_audio_project(projects_selelect, False)
+
+            select_audio_ref = select_audio
+            select_audio_gen = select_audio
+
+            if select_audio is not None:
+                select_audio_ref += "_ref.wav"
+                select_audio_gen += "_gen.wav"
+
+            with gr.Row():
+                ch_list_audio = gr.Dropdown(
+                    choices=list_audios,
+                    value=select_audio,
+                    label="Audios",
+                    allow_custom_value=True,
+                    scale=6,
+                    interactive=True,
+                )
+                bt_stream_audio = gr.Button("Refresh", scale=1)
+                bt_stream_audio.click(fn=get_audio_project, inputs=[cm_project], outputs=[ch_list_audio])
+                cm_project.change(fn=get_audio_project, inputs=[cm_project], outputs=[ch_list_audio])
+
+            with gr.Row():
+                audio_ref_stream = gr.Audio(label="Original", type="filepath", value=select_audio_ref)
+                audio_gen_stream = gr.Audio(label="Generate", type="filepath", value=select_audio_gen)
+
+            ch_list_audio.change(
+                fn=get_audio_select,
+                inputs=[ch_list_audio],
+                outputs=[audio_ref_stream, audio_gen_stream],
+            )
+
+            start_button.click(
+                fn=start_training,
+                inputs=[
+                    cm_project,
+                    exp_name,
+                    learning_rate,
+                    batch_size_per_gpu,
+                    batch_size_type,
+                    max_samples,
+                    grad_accumulation_steps,
+                    max_grad_norm,
+                    epochs,
+                    num_warmup_updates,
+                    save_per_updates,
+                    last_per_steps,
+                    ch_finetune,
+                    file_checkpoint_train,
+                    tokenizer_type,
+                    tokenizer_file,
+                    mixed_precision,
+                    ch_stream,
+                    cd_logger,
+                    ch_8bit_adam,
+                ],
+                outputs=[txt_info_train, start_button, stop_button],
+            )
+            stop_button.click(fn=stop_training, outputs=[txt_info_train, start_button, stop_button])
+
+            bt_calculate.click(
+                fn=calculate_train,
+                inputs=[
+                    cm_project,
+                    batch_size_type,
+                    max_samples,
+                    learning_rate,
+                    num_warmup_updates,
+                    save_per_updates,
+                    last_per_steps,
+                    ch_finetune,
+                ],
+                outputs=[
+                    batch_size_per_gpu,
+                    max_samples,
+                    num_warmup_updates,
+                    save_per_updates,
+                    last_per_steps,
+                    lb_samples,
+                    learning_rate,
+                    epochs,
+                ],
+            )
+
+            ch_finetune.change(
+                check_finetune, inputs=[ch_finetune], outputs=[file_checkpoint_train, tokenizer_file, tokenizer_type]
+            )
+
+            def setup_load_settings():
+                output_components = [
+                    exp_name,
+                    learning_rate,
+                    batch_size_per_gpu,
+                    batch_size_type,
+                    max_samples,
+                    grad_accumulation_steps,
+                    max_grad_norm,
+                    epochs,
+                    num_warmup_updates,
+                    save_per_updates,
+                    last_per_steps,
+                    ch_finetune,
+                    file_checkpoint_train,
+                    tokenizer_type,
+                    tokenizer_file,
+                    mixed_precision,
+                    cd_logger,
+                ]
+
+                return output_components
+
+            outputs = setup_load_settings()
+
+            cm_project.change(
+                fn=load_settings,
+                inputs=[cm_project],
+                outputs=outputs,
+            )
+
+            ch_refresh_project.click(
+                fn=load_settings,
+                inputs=[cm_project],
+                outputs=outputs,
+            )
+
+        with gr.TabItem("Test Model"):
+            gr.Markdown("""```plaintext 
+SOS: Check the use_ema setting (True or False) for your model to see what works best for you. use seed -1 from random
+```""")
+            exp_name = gr.Radio(label="Model", choices=["F5-TTS", "E2-TTS"], value="F5-TTS")
+            list_checkpoints, checkpoint_select = get_checkpoints_project(projects_selelect, False)
+
+            with gr.Row():
+                nfe_step = gr.Number(label="NFE Step", value=32)
+                speed = gr.Slider(label="Speed", value=1.0, minimum=0.3, maximum=2.0, step=0.1)
+                seed = gr.Number(label="Seed", value=-1, minimum=-1)
+                remove_silence = gr.Checkbox(label="Remove Silence")
+
+            ch_use_ema = gr.Checkbox(label="Use EMA", value=True)
+            with gr.Row():
+                cm_checkpoint = gr.Dropdown(
+                    choices=list_checkpoints, value=checkpoint_select, label="Checkpoints", allow_custom_value=True
+                )
+                bt_checkpoint_refresh = gr.Button("Refresh")
+
+            random_sample_infer = gr.Button("Random Sample")
+
+            ref_text = gr.Textbox(label="Ref Text")
+            ref_audio = gr.Audio(label="Audio Ref", type="filepath")
+            gen_text = gr.Textbox(label="Gen Text")
+
+            random_sample_infer.click(
+                fn=get_random_sample_infer, inputs=[cm_project], outputs=[ref_text, gen_text, ref_audio]
+            )
+
+            with gr.Row():
+                txt_info_gpu = gr.Textbox("", label="Device")
+                seed_info = gr.Text(label="Seed :")
+                check_button_infer = gr.Button("Infer")
+
+            gen_audio = gr.Audio(label="Audio Gen", type="filepath")
+
+            check_button_infer.click(
+                fn=infer,
+                inputs=[
+                    cm_project,
+                    cm_checkpoint,
+                    exp_name,
+                    ref_text,
+                    ref_audio,
+                    gen_text,
+                    nfe_step,
+                    ch_use_ema,
+                    speed,
+                    seed,
+                    remove_silence,
+                ],
+                outputs=[gen_audio, txt_info_gpu, seed_info],
+            )
+
+            bt_checkpoint_refresh.click(fn=get_checkpoints_project, inputs=[cm_project], outputs=[cm_checkpoint])
+            cm_project.change(fn=get_checkpoints_project, inputs=[cm_project], outputs=[cm_checkpoint])
+
+        with gr.TabItem("Reduce Checkpoint"):
+            gr.Markdown("""```plaintext 
+Reduce the model size from 5GB to 1.3GB. The new checkpoint can be used for inference or fine-tuning afterward, but it cannot be used to continue training.
+```""")
+            txt_path_checkpoint = gr.Text(label="Path to Checkpoint:")
+            txt_path_checkpoint_small = gr.Text(label="Path to Output:")
+            ch_safetensors = gr.Checkbox(label="Safetensors", value="")
+            txt_info_reduse = gr.Text(label="Info", value="")
+            reduse_button = gr.Button("Reduce")
+            reduse_button.click(
+                fn=extract_and_save_ema_model,
+                inputs=[txt_path_checkpoint, txt_path_checkpoint_small, ch_safetensors],
+                outputs=[txt_info_reduse],
+            )
+
+        with gr.TabItem("System Info"):
+            output_box = gr.Textbox(label="GPU and CPU Information", lines=20)
+
+            def update_stats():
+                return get_combined_stats()
+
+            update_button = gr.Button("Update Stats")
+            update_button.click(fn=update_stats, outputs=output_box)
+
+            def auto_update():
+                yield gr.update(value=update_stats())
+
+            gr.update(fn=auto_update, inputs=[], outputs=output_box)
+
+
+@click.command()
+@click.option("--port", "-p", default=None, type=int, help="Port to run the app on")
+@click.option("--host", "-H", default=None, help="Host to run the app on")
+@click.option(
+    "--share",
+    "-s",
+    default=False,
+    is_flag=True,
+    help="Share the app via Gradio share link",
+)
+@click.option("--api", "-a", default=True, is_flag=True, help="Allow API access")
+def main(port, host, share, api):
+    global app
+    print("Starting app...")
+    app.queue(api_open=api).launch(server_name=host, server_port=port, share=share, show_api=api)
+
+
+if __name__ == "__main__":
+    main()