ashish19goyal/F5TTS-Hindi

Automated Data preparation and training

Repository Setup for Data and model files

• Install git-xet on your local machine. This is huggingface's tool to manage large files
  • Mac

brew install git-xet - Ubuntu curl --proto '=https' --tlsv1.2 -sSf https://raw.githubusercontent.com/huggingface/xet-core/refs/heads/main/git_xet/install.sh | sh

• Install huggingface_cli for authentication
  • Ubuntu

curl -LsSf https://hf.co/cli/install.sh | bash

• Login to huggingface using your account credentials

hf auth login

• Initialize git xet in the repo

git xet install

• Force Git LFS to only use Hugging Face Run this command in your local repo.

git config xet.url "https://huggingface.co/datasets/ashish19goyal/F5TTS-Hindi"

• Tell git-xet to manage model and data files

git xet track "*.pt" "*.wav" "*.arrow" "*.jsonl" "*.json" "*.png" "*.pdf"

• Commit attributes file to git

git add .gitattributes git commit -m "Add git-xet attributes"

• Workflow
  • Push code + pointers to GitHub

git push origin main

- Push the actual large data files to Hugging Face

git push hf main

Configuration

This setup uses mutliple tools for different purposes as listed below

• Apache airflow: To run pipelines for each of the objectives listed above.
• Apache spark: To run each of the tasks in the pipelines in a distributed manner. This helps in partitioning the data and avoiding out-of-memory errors
• MLFlow: To track multiple training runs with different configurations
• Git xet: To store processed data and model checkpoints on huggingface.
• docker: To deploy Apache airflow, spark, mlflow clusters.

Additional setup

• Create a variables.json file in the root of the repo with following content. This file is not committed to prevent secrets from being exposed.
• If you use Gmail SMTP, do not use your normal Google account password. Use a Google App Password (requires 2-Step Verification) from https://myaccount.google.com/apppasswords.

{
    "hf_token": "<huggingface_token>",
    "smtp_username": "<smtp_username>",
    "smtp_password": "<smtp_app_password>",
    "notify_email_to": "<recipient_email>"
}

Usage

This folder contains run.sh script. This script can be used to trigger

• Data analytics: ./run.sh analyse
  • It produces the analysis report at data/analysis/report.html
• Data cleanup: ./run.sh cleanup
  • It produces the cleaned data at data/cleanup
• Data preaparation: ./run.sh prepare
  • This produces the vocabulary at data/prepare/text/vocab.txt
  • Audio and text for training are stored as arrow files at data/prepare/arrow
• Hyperparameter tuning: ./run.sh tune
  • This produces the best hyperparameters at data/hyperparameters/best_params.json
• Training: ./run.sh train
• Evaluation: ./run.sh eval
• Inferencing: ./run.sh inference

Visualizing performance profiles

Use prefetto to visualize the training profile dumps generated in data/artifacts/*_profile.json

• Open https://ui.perfetto.dev in chrome
• Click Open trace file and pick the profile dump for visualizing