@title 🛠️ Install Requirements - Ebook2audiobook

Welcome to the Universal TTS Fine-tune WebUI (XTTS & Piper)!

This Jupyter Notebook allows you to easily run the fine-tuning interface on Google Colab with GPU acceleration enabled. It supports fine-tuning both Coqui XTTS v2 and Piper models, speech dataset preprocessing, and inference testing.

For documentation and local installation guide, visit the repository at: https://github.com/DrewThomasson/Universal_TTS_Finetune

python

# @title 🛠️ Install Requirements
try:
    import google.colab
    in_colab = True
except ImportError:
    in_colab = False

if in_colab:
    print("Running on Google Colab. Installing system dependencies...")
    !sudo apt-get update
    !sudo apt-get -y install libegl1 libopengl0 libxcb-cursor0 espeak-ng libsndfile1
    !pip install -r https://raw.githubusercontent.com/DrewThomasson/Universal_TTS_Finetune/main/requirements.txt
else:
    print("Running locally. Make sure you install requirements.txt in your environment:")
    print("pip install -r requirements.txt")

python

# @title 🚀 Run Web Interface
try:
    import google.colab
    in_colab = True
except ImportError:
    in_colab = False

import os

if in_colab:
    # @markdown Specify whether you want to save datasets and models directly to Google Drive to prevent losing them when the session terminates.
    use_google_drive = True # @param {type:"boolean"}
    google_drive_path = "/content/drive/MyDrive/Universal_TTS_Finetune" # @param {type:"string"}

    if use_google_drive:
        from google.colab import drive
        print(" mounting Google Drive...")
        drive.mount('/content/drive')
        out_path = google_drive_path
        os.makedirs(out_path, exist_ok=True)
    else:
        out_path = "/content/Universal_TTS_Finetune/finetune_models"

    print(f"Outputs will be saved to: {out_path}")

    %cd /content/
    if not os.path.exists('/content/Universal_TTS_Finetune'):
        print("Cloning repository...")
        !git clone https://github.com/DrewThomasson/Universal_TTS_Finetune.git
    else:
        print("Repository already exists, pulling latest changes...")
        %cd /content/Universal_TTS_Finetune
        !git pull
    %cd /content/Universal_TTS_Finetune
else:
    out_path = "./finetune_models"
    print(f"Running locally. Outputs will be saved to: {out_path}")

!python web_gui.py --share --out_path "{out_path}"

python

# @title 📦 Zip and Upload Fine-tuned Models
# @markdown Note: If you used Google Drive, your files are already safe. Use this cell to create a quick shareable download link via file.io.
try:
    import google.colab
    in_colab = True
except ImportError:
    in_colab = False

import shutil
import requests
import os
from tqdm import tqdm

# Define the paths
try:
    base_out = out_path
except NameError:
    if in_colab:
        base_out = '/content/Universal_TTS_Finetune/finetune_models'
    else:
        base_out = './finetune_models'

finetune_dir = os.path.join(base_out, 'ready')  # @param {type:"string"}
dataset_dir = os.path.join(base_out, 'dataset')  # @param {type:"string"}

# Create a temporary directory to collect both folders before zipping
if in_colab:
    temp_dir = "/content/temp_finetune_dataset"
    zip_filename = "/content/finetune_and_dataset.zip"
else:
    temp_dir = "./temp_finetune_dataset"
    zip_filename = "./finetune_and_dataset.zip"

if os.path.exists(temp_dir):
    shutil.rmtree(temp_dir)
os.makedirs(temp_dir, exist_ok=True)

# Copy both directories into the temporary directory with a progress bar
def copy_with_progress(src, dst):
    if not os.path.exists(src):
        print(f"Warning: Path {src} does not exist. Skipping.")
        return
    total_files = sum(len(files) for _, _, files in os.walk(src))
    with tqdm(total=total_files, desc=f"Copying {os.path.basename(src)}") as pbar:
        for root, _, files in os.walk(src):
            rel_path = os.path.relpath(root, src)
            target_path = os.path.join(dst, rel_path)
            os.makedirs(target_path, exist_ok=True)
            for file in files:
                shutil.copy(os.path.join(root, file), target_path)
                pbar.update(1)

copy_with_progress(finetune_dir, os.path.join(temp_dir, "ready"))
copy_with_progress(dataset_dir, os.path.join(temp_dir, "dataset"))

# Create a zip file of the combined directories with progress
if os.path.exists(zip_filename):
    os.remove(zip_filename)

zip_base = os.path.splitext(zip_filename)[0]
with tqdm(total=100, desc="Zipping files") as pbar:
    shutil.make_archive(zip_base, 'zip', root_dir=temp_dir)
    pbar.update(100)

# Define a function to stream the upload with a progress bar
def upload_with_progress(file_path, url):
    file_size = os.path.getsize(file_path)
    with open(file_path, 'rb') as f, tqdm(
        total=file_size, unit='B', unit_scale=True, desc="Uploading"
    ) as progress:
        response = requests.post(
            url,
            files={"file": (file_path, f)},
            stream=True,
            headers={"Connection": "keep-alive"},
        )
        for chunk in response.iter_content(chunk_size=4096):
            if chunk:
                progress.update(len(chunk))
    return response

# Upload the zip file to file.io with a progress bar
print("Uploading archive to file.io...")
response = upload_with_progress(zip_filename, "https://file.io/?expires=1d")

# Parse the response and display the download link
if response.status_code == 200:
    download_link = response.json().get('link', 'Error: No link found.')
    print(f"Your file is ready: {download_link}")
else:
    print(f"Failed to upload: {response.status_code} - {response.text}")