Introducing Whisper-TikTok 🤖🎥

Discover Whisper-TikTok, an innovative AI-powered tool that leverages the prowess of Edge TTS, OpenAI-Whisper, and FFMPEG to craft captivating TikTok videos. Harnessing the capabilities of OpenAI's Whisper model, Whisper-TikTok effortlessly generates an accurate transcription from provided audio files, laying the foundation for the creation of mesmerizing TikTok videos through the utilization of FFMPEG. Additionally, the program seamlessly integrates the Microsoft Edge Cloud Text-to-Speech (TTS) API to lend a vibrant voiceover to the video. Opting for Microsoft Edge Cloud TTS API's voiceover is a deliberate choice, as it delivers a remarkably natural and authentic auditory experience, setting it apart from the often monotonous and artificial voiceovers prevalent in numerous TikTok videos.

Demo Video 🎬

demo.mp4

Operating Principle

Employing Whisper-TikTok is a breeze: simply modify the video.json. The JSON file contains the following fields:

• series: The name of the series.
• part: The part number of the video.
• text: The text to be spoken in the video.
• outro: The outro text to be spoken in the video.

Summarizing the program's functionality:

Furnished with a structured JSON dataset containing details such as the series name, video part number, video text and outro text, the program orchestrates the synthesis of a video incorporating the provided text and outro. Subsequently, the generated video is stored within the designated output folder.

In-Depth Insights

The program conducts the sequence of actions outlined below:

1. Retrieve environment variables from the optional .env file.
2. Validate the presence of PyTorch with CUDA installation. If the requisite dependencies are absent, the program will use the CPU instead of the GPU.
3. Download a random video from platforms like YouTube, e.g., a Minecraft parkour gameplay clip.
4. Load the OpenAI Whisper model into memory.
5. Extract the video text from the provided JSON file and initiate a Text-to-Speech request to the Microsoft Edge Cloud TTS API, preserving the response as an .mp3 audio file.
6. Utilize the OpenAI Whisper model to generate a detailed transcription of the .mp3 file, available in .srt format.
7. Select a random background video from the dedicated folder.
8. Integrate the srt file into the chosen video using FFMPEG, creating a final .mp4 output.
9. Voila! In a matter of minutes, you've crafted a captivating TikTok video while sipping your favorite coffee ☕️.

Prerequisites 🛠️

Whisper-TikTok has undergone rigorous testing on Windows 10, Windows 11 and Ubuntu 23.04 systems equipped with Python versions 3.8, 3.9 and 3.11. To streamline the installation of necessary dependencies, execute the following command within your terminal:

pip install -U -r requirements.txt

It also requires the command-line tool ffmpeg to be installed on your system, which is available from most package managers:

# on Ubuntu or Debian

sudo apt update && sudo apt install ffmpeg

# on Arch Linux

sudo pacman -S ffmpeg

# on MacOS using Homebrew (<https://brew.sh/>)

brew install ffmpeg

# on Windows using Chocolatey (<https://chocolatey.org/>)

choco install ffmpeg

# on Windows using Scoop (<https://scoop.sh/>)

scoop install ffmpeg

Please note that for optimal performance, it's advisable to have a GPU when using the OpenAI Whisper model for speech recognition. However, the program will work without a GPU, but it will run more slowly. This performance difference is because GPUs efficiently handle fp16 computation, while CPUs use fp32 or fp64 (depending on your machine), which are slower.

Usage Guidelines 📝

To embark on your Whisper-TikTok journey, initiate the following command within your terminal:

python main.py

Command-Line Options

Whisper-TikTok supports the following command-line options:

python main.py [OPTIONS]

Options:
  --model TEXT        Model to use
                      [tiny|base|small|medium|large] (Default: small)
  --non_english       Don't use the English model. (Flag)
  --url TEXT          YouTube URL to download as background video.
                      (Default: <https://www.youtube.com/watch?v=intRX7BRA90>)
  --tts TEXT          Voice to use for TTS (Default: en-US-ChristopherNeural)
  --list-voices       Use `edge-tts --list-voices` to list all voices.
  --random_voice      Random voice for TTS (Flag)
  --gender TEXT       Gender of the random TTS voice [Male|Female].
  --language TEXT     Language of the random TTS voice
                      (e.g., en-US)
  -v, --verbose       Verbose (Flag)

If you use the --random_voice option, please specify both --gender and --language arguments. Also you will need to specify the --non_english argument if you want to use a non-English voice otherwise the program will use the English model. Whisper model will auto-detect the language of the audio file and use the corresponding model.

Usage Examples

• Generate a TikTok video using a specific TTS model and voice:

python main.py --model medium --tts en-US-EricNeural

• Generate a TikTok video without using the English model:

python main.py --non_english --tts de-DE-KillianNeural

• Use a custom YouTube video as the background video:

python main.py --url https://www.youtube.com/watch?v=dQw4w9WgXcQ --tts en-US-JennyNeural

• Generate a TikTok video with a random TTS voice:

python main.py --random_voice --gender Male --language en-US

• List all available voices:

edge-tts --list-voices

Code of Conduct

Please review our Code of Conduct before contributing to Whisper-TikTok.

Contributing

We welcome contributions from the community! Please see our Contributing Guidelines for more information.

Upcoming Features 🔮

• Integration with the OpenAI API to generate more advanced responses.
• Integration with the TikTok Developer API to upload videos directly to the platform.
• Improved user interface for a more seamless experience.

OpenAI Whisper Forum Discussion

• openai/whisper#223

Acknowledgments

• We'd like to give a huge thanks to @rany2 for their edge-tts package, which made it possible to use the Microsoft Edge Cloud TTS API with Whisper-TikTok.
• We also acknowledge the contributions of the Whisper model by @OpenAI for robust speech recognition via large-scale weak supervision