Voice Cloning and AI Lip Sync: How to Dub Videos Like a Pro with Open Source Tools

Voice cloning and lip syncing technology has made huge strides in recent years, largely driven by advances in artificial intelligence and deep learning. With a short audio sample of someone‘s voice, it‘s now possible to generate an AI model that can synthesize speech in that same voice. When combined with AI-powered lip syncing of video, you can create highly realistic dubbed videos where it looks like the person is speaking the new audio.

While much of this technology is complex and inaccessible to the average user, a growing number of open source tools and pre-trained models have emerged to make voice cloning and lip syncing more accessible for creative projects, entertainment, and dubbing applications. In this guide, we‘ll walk through how you can leverage some of the top open source AI tools to voice clone and lip sync videos yourself.

How Voice Cloning and AI Lip Sync Works

At a high level, the process of voice cloning and lip syncing a video involves several key AI models working in tandem:

1. A speech recognition model to convert the original audio into text
2. A text-to-speech (TTS) model to synthesize the new audio based on the voice clone
3. A lip sync model to modify the mouth movements in the video to match the new audio

Speech recognition has become highly accurate in recent years, even for arbitrary speakers and accents. Open source models like Whisper from OpenAI provide near state-of-the-art accuracy on par with the best commercial offerings.

For text-to-speech, a number of open source implementations of advanced TTS models have been released, making it possible to train models to synthesize highly natural sounding speech that mimics a real person‘s voice. We‘ll be using one called Coqui-AI which has an easy-to-use Python API.

Finally, the lip sync model takes in the video and new audio and generates a modified version of the video with mouth movements that match the audio. This is a complex task, as it requires detecting and tracking lip movements, identifying the corresponding mouth shapes for each speech sound, and realistically merging the generated mouth with the original video. Fortunately, there are open source implementations that achieve impressive results, like Wav2Lip which we‘ll use here.

Open Source Tools You‘ll Need

Here are the key open source tools we‘ll be using:

• Whisper: An open source speech recognition model from OpenAI. We‘ll use this to transcribe the original video‘s audio to text.

• Coqui-AI TTS: An open source Python library for training and using text-to-speech models. It provides an easy API for synthesizing speech based on various voices. We‘ll use a pre-trained multilingual model called xTTS.

• Wav2Lip: An open source lip sync model and implementation based on the paper "A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild". It modifies lip movements in existing videos to match a new audio track.

We‘ll tie all of these together in a Python environment, using ffmpeg to handle audio and video processing. The following tutorial assumes basic proficiency with Python and using the command line.

Step-by-Step Tutorial

1. Install dependencies

We highly recommend using a cloud GPU environment like Google Colab to run this process, as the AI models are quite computationally intensive. Once your Python environment is set up, install the required dependencies:

!pip install TTS
!pip install git+https://github.com/openai/whisper.git

2. Prepare the video

Download the video you want to dub and lip sync. For best results, it should be in MP4 format and scaled to 720p resolution. You can do this programmatically with ffmpeg:

from google.colab import files
import subprocess

uploaded = files.upload()

for filename in uploaded.keys():
    print(f‘Uploaded {filename}‘) 
    output_filename = f"resized_{filename}"
    cmd = f"ffmpeg -i {filename} -vf ‘scale=-1:720‘ {output_filename}"  
    subprocess.run(cmd, shell=True)
    print(f‘Resized video saved as {output_filename}‘)
    video_path = output_filename

3. Transcribe the audio

Next, we use ffmpeg to extract the audio from the video and then transcribe it using OpenAI‘s Whisper model:

import whisper

ffmpeg_command = f"ffmpeg -i ‘{video_path}‘ -acodec pcm_s24le -ar 48000 -q:a 0 -map a  -y ‘output_audio.wav‘"                    
subprocess.run(ffmpeg_command, shell=True)

model = whisper.load_model("base") 
result = model.transcribe("output_audio.wav")
whisper_text = result["text"]
print("Transcribed text:", whisper_text)

4. Synthesize speech with the voice clone

Now we load up the TTS model and synthesize the speech using a voice clone. Coqui-AI provides a multilingual model called xTTS that supports cloning voices in multiple languages:

from TTS.api import TTS

tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2")  

# Convert text to the target language 
# whisper_text = translate(whisper_text)

# Break text into chunks if too long
text_chunks = whisper_text.split(sep = "")

for i, chunk in enumerate(text_chunks):
    tts.tts_to_file(chunk, speaker_wav=‘output_audio.wav‘, 
                    file_path=f"synth_{i}.wav", language="en")

# Combine the audio files  
with open("files.txt", "w") as f:
    for i in range(len(text_chunks)):
        f.write(f"file ‘synth_{i}.wav‘\n")

cmd = "ffmpeg -f concat -safe 0 -i files.txt -c copy synth_audio.wav"
subprocess.run(cmd, shell=True)

This will generate synth_audio.wav containing the audio clone speaking the transcribed text. You can tweak the model settings or provide a different speaker reference file to modify the output voice.

5. Generate the lip synced video

Finally, we use Wav2Lip to generate a lip synced version of the original video to match the new synthesized audio:

%cd /Wav2Lip
!git clone https://github.com/Rudrabha/Wav2Lip
%cd Wav2Lip
!pip install -r requirements.txt

!wget ‘https://github.com/Rudrabha/Wav2Lip/releases/\
        download/models/wav2lip.pth‘ -O ‘checkpoints/wav2lip.pth‘

!python inference.py --checkpoint_path checkpoints/wav2lip.pth \
        --face ../video.mp4 --audio ../synth_audio.wav \
        --resize_factor 1 --outfile /content/output_video.mp4

This will generate output_video.mp4 with the lip movements synced to the new audio. You can experiment with the different hyperparameters to tweak the output.

And that‘s it! You‘ve now successfully dubbed a video in a new language using an AI-generated voice clone and automatic lip syncing. The quality of the output will depend on a number of factors, including the quality of the input video, the accuracy of the speech recognition and lip sync models, and how well the voice clone matches the original speaker. But with some iteration and fine-tuning, it‘s possible to achieve highly realistic dubbed videos.

Potential Use Cases

So what can you do with this technology? Some potential applications include:

• Translating and dubbing videos in new languages to reach international audiences
• Generating personalized videos with custom voices for marketing or virtual assistants
• Lip syncing videos for music production, movies, and other entertainment
• Creating accessible content by automatically generating captions and sign language
• Mitigating accent and pronunciation barriers for educational content
• Developing new user interfaces that incorporate realistic visual speech

Ethical Considerations

As with any technology that enables the creation of highly realistic synthetic media, there are important ethical considerations to keep in mind. Voice cloning and lip syncing can potentially be used to create misleading deepfakes that spread misinformation or harass individuals.

It‘s critical that this technology is used responsibly and transparently. Synthesized media should be clearly disclosed to avoid deception. And it should go without saying that you should only clone someone‘s voice and appearance with their explicit consent.

Researchers are actively working on techniques to detect synthesized speech and video to help combat misuse. Much like how watermarks are used to identify copyrighted images, AI-generated media may soon incorporate signatures to distinguish them as synthetic. Provisions are also being developed to restrict the synthesis of voices for public figures and celebrities.

Ultimately, voice cloning and lip sync technology is a powerful creative tool that will allow for compelling new applications in entertainment, education, accessibility, and UI design. But it needs to be balanced with ethical judgment and safeguards against malicious use. By making the technology more open and transparent, the open source community can actively shape the norms and regulations around its use.

Conclusion

We‘ve seen how open source AI tools are putting powerful voice cloning and lip sync capabilities in the hands of creators and developers. With just a few lines of code, you can now synthesize highly realistic speech in someone else‘s voice and automatically synchronize it to their lip movements in a video.

While the details of the AI models get complex, the high-level process is fairly straightforward:

1. Use a speech recognition model like Whisper to transcribe the audio
2. Feed the text into a TTS model like Coqui-AI to generate a voice clone
3. Use a lip sync model like Wav2Lip to animate mouth movements to the audio

Experiment with the different open source implementations to see what works best for your use case. And consider how this technology can open up new possibilities for creative projects and applications.

Just remember to use these tools responsibly and ethically. Voice cloning and lip sync are an incredible feat of human ingenuity – let‘s make sure we harness them as a positive force for art, knowledge, and understanding.