Txt2Vid: Ultra-Low Bitrate Compression of Talking-Head Videos via Text

Repo containing code for txt2vid project. Gives a proof-of-concept for the following compression pipeline (for more details read the paper on arXiv):

.

Though the pipeline is flexible and can be replaced by appropriate software programs performing same function, the code currently uses and allows for Wav2Lip for lip-syncing, Resemble or Google APIs for personalized and general text-to-speech (TTS) synthesis respectively, and Google API for speech-to-text synthesis (STT). It uses ffmpeg-python to enable streaming.

Table of Contents

• Demo Videos
• Installation Instructions
  • Notes
  • Environment Setup
  • Wav2Lip Setup
  • Google STT and TTS Setup
  • Resemble TTS Setup
• Use Cases
  • Storing txt2vid video as file using text or audio file available at server
  • Streaming txt2vid video on a port using text or audio file available at server
  • Streaming txt2vid video on a port using streaming input from a sender

Demo Videos

Streaming txt2vid video on a port using streaming text input from terminal on a sender side

Click on the image below to play the demo video.

Streaming txt2vid video on a port using streaming text input from STT on a sender side

Click on the image below to play the demo video.

Installation Instructions

Notes

• This setup assumes three machines -- sender, server and receiver. Some of them can be same.

  • sender -- machine from where streaming inputs will be provided, typically will be your local machine.
  • server -- machine where decoding is happening (where main code runs), should have GPU.
  • receiver -- machine for watching output streaming content, typically will be your local machine.

• Server can be same as receiver if the server has access to GPU for running streamable lip-syncing code. In that case, port-forwarding for playing streaming video output using ffplay as described in sections below is not necessary.

• Sender and Receiver can be different local machines.

• Abbreviations:

  • TTS = Text-to-Speech
  • STT = Speech-to-Text

Environment Setup

Setup requirements using following steps on all machines (sender, server, receiver):

• Create conda environment:

  conda create --name myenv python=3.7
  conda activate myenv

  Note that 3.7 or above is integral for streaming to work, even though wav2lip repo uses 3.6. In particular, it is essential for audio-video sync (AV sync) in a streaming fashion as both audio and videos are being handled separately. myenv is the name of the environment you would like to keep.

• Ensure ffmpeg (and ffplay) are installed.

  sudo apt-get install ffmpeg

  (for macs use: brew install ffmpeg, this also installs ffplay required for streaming video content locally).

• Install python dependencies.

  pip install -r requirements.txt

Wav2Lip Setup

Make sure model files are downloaded and put in appropriate folder from the wav2lip repo, on the machine where the decoding code will run (server).

• GAN model: wav2lip_gan.pth should be present in Wav2Lip/checkpoints. Pretrained model can be downloaded from following link.
• Face detection model: s3fd.pth should be present in Wav2Lip/face_detection/detection/sfd/s3fd.pth. Pretrained model can be downloaded from link1 or link2.

Google STT and TTS Setup

To use Google API for TTS or STT, ensure following steps are executed:

• Follow instructions to create a service account (if it doesn't exist already), and download the json key.
• Follow instructions ("enable and disable APIs") to add the following APIs to this project
  1. Cloud Speech-to-Text API
  2. Cloud Text-to-Speech API
• Pass the path to the json key to the -gc parameter for the relevant script runs using Google as STT/TTS.
• Note that: Google APIs might ask for a billing account to enable them. But they allow free usage for reasonable number of characters. For pricing information you can see: here and here.

Resemble TTS Setup

To use Resemble API, ensure following steps are executed:

• Create an account on resemble.ai website and create your own voice by recording 50-100 samples of audio data. Resemble allows training one free voice clone with an account. Create a new project.

• Make resemble_tts/resemble_config.json with your data. This json has following structure:

  {
  "voices": { 
  <voice_name>: {
    "token": <api_token>,
    "voice_id": <voice_id>
    },
  } 
  "project_uuid": <project_uuid>
  }

  where all the variables are strings. api_token is the token used for API access (found here) and voice_id is 8 character resemble voice ID (can be found here by executing the interactive example and copying the uuid). For default resemble voices, voice_id is same as the name of the default voice instead of unique 8 character ID. The voice_name can be any identifier string for the voice. Pass the voice_name to --voice parameter for the relevant script runs using Resemble as TTS. project_uuid is 8 character ID of the project where the voice will be created using the API (can be found here by executing the interactive example and copying the uuid of the project to contain voice clips generated via API).

• Callback Server setup
  

  • For the Resemble TTS to work via API, we use a callback server to receive the voice output generated by Resemble. On server (machine where decoding will happen), launch the callback server by

    cd resemble_tts
    export FLASK_APP=tts_callback_file.py
    python -m flask run -p <callback_port>

    This runs a callback server on <callback_port> port (default is 5000) on the server.

  • If the server's port <callback_port> is publicly accessible, then the callback from resemble can be received at http://localhost:<callback_port>. If server is inside a network, we need to provide a publicly accessible port to the resemble for sending voice data. One way to do so could be to use https://ngrok.com for creating an HTTP tunnel. Create a ngrok account and follow instructions to install it on your server. Launch tunnel forwarding to local port <callback_port> where the callback server is listening by running:

    ./ngrok http <callback_port>

    This port forwards a publicly accessible link to our callback server. The publicly accessible link (<link>) is available as output where the ngrok command was run in Forwarding section as <link> -> http://localhost:<callback_port>.

  • Pass the <link> variable or appropriate publicly accessible callback server address to the --resemble_callback_url parameter for the relevant script runs using Resemble as TTS.

Use-Cases

Currently, the repo allows following use cases:

The main scripts are

1. Wav2Lip/inference_streaming_pipeline.py for handing the decoding by appropriately handling inputs through various pipes and queues in a multiprocess framework
2. input_stream_socket.py for handling streaming input handling.
   

Below we describe a subset of these use-cases with an example from all store/stream modalities, in increasing order of complexity. See all available argument flags by:

cd Wav2Lip
python inference_streaming_pipeline.py -h

and

python input_stream_socket.py -h

Ensure Google or Resemble TTS setup is done for all use-cases involving text as described in Google STT and TTS Setup and Resemble TTS Setup.

Storing txt2vid video as file using text or audio file available at server

server (AV-synced streamed video)
^
|
pre-recorded audio/text + driving picture/video

Example Code:

On server launch the streaming inference script, and save the generated video.

• from pre-recorded audio:

  python inference_streaming_pipeline.py -it audio \
                                       --checkpoint_path checkpoints/wav2lip_gan.pth \
                                       --face sample_data/einstein.jpg \
                                       -aif file \
                                       --audio_file_path sample_data/einstein_audio_resemble.wav \
                                       -vot file \
                                       --video_file_out results/test_video.mp4

• from pre-recorded text and Resemble TTS:

  python inference_streaming_pipeline.py -it text \
                                       -TTS Resemble \
                                       --checkpoint_path checkpoints/wav2lip_gan.pth \
                                       --face sample_data/einstein.jpg \
                                       -tif file \
                                       --text_file_path sample_data/einstein_text.txt \
                                       -vot file \
                                       --video_file_out results/test_video.mp4 \
                                       --voice <voice_name> \
                                       --resemble_callback_url <resemble_callback_url>

  Arguments <voice_name>, <resemble_callback_url> depend on the resemble setup and instructions to get them are provided in "Installation Instructions - Resemble TTS".
  Resemble also allows for voices generated in specific emotions, instead of default personalized TTS output. For accessing them use -e flag.

• from pre-recorded text and Google TTS:

  python inference_streaming_pipeline.py -it text \
                                       -TTS Google \
                                       --checkpoint_path checkpoints/wav2lip_gan.pth \
                                       --face sample_data/einstein.jpg \
                                       -tif file \
                                       --text_file_path sample_data/einstein_text.txt \
                                       -vot file \
                                       --video_file_out results/test_video.mp4 \
                                       -gc <google_credential>

  The argument <google_credential> depends on the google setup and instructions to get it are provided in "Installation Instructions - Google TTS and STT".
  

Streaming txt2vid video on a port using text or audio file available at server

server (AV-synced streamed video) -----> receiver (view AV stream)
^
|
pre-recorded audio/text + driving picture/video

On server launch the streaming inference script, and port forward to stream the generated txt2vid video.

Example Code:

Note: We show use case with text as input and Resemble as TTS. Other use cases for Google TTS or audio file can be generated by changing appropriate flags.

• On receiver, ssh into the server with port-forwarding enabled.

  ssh -Y -L localhost:8080:localhost:8080 xyz@abc.com

  8080 is the default port but any port should work with appropriately modified <output_port> argument in inference_streaming_pipeline below. This step is not needed if server = receiver (i.e. receiver is the local machine and has GPU access).

• On server launch the streaming inference script, and write txt2vid video to a port.

  python inference_streaming_pipeline.py -it text \
                                       -TTS Resemble \
                                       --checkpoint_path checkpoints/wav2lip_gan.pth \
                                       --face sample_data/einstein.jpg \
                                       -tif file \
                                       --text_file_path sample_data/einstein_text.txt \
                                       -vot socket \
                                       --output_port 8080 \
                                       --voice <voice_name> \
                                       --resemble_callback_url <resemble_callback_url>

  Wait till it says Model Loaded. The code will halt here waiting for the ffplay command to ask for streaming content.

• View streaming output on the receiver. Ensure ffplay is installed.

  ffplay -f avi http://localhost:8080

Streaming txt2vid video on a port using streaming input from a sender

sender -----> server (AV-synced streamed video) -----> receiver (view AV stream)
^             ^
|             |
audio/video   driving picture/video

Example Code:

Note: We show use case with streaming text as input from terminal and Google STT, with Resemble as TTS. Other use cases for Google TTS or audio from microphone can be generated by changing appropriate flags.

• On receiver, ssh into the server with port-forwarding enabled.

  ssh -Y -L localhost:8080:localhost:8080 xyz@abc.com

  8080 is default port but any port should work with appropriately modified <output_port> argument in inference_streaming_pipeline below. This step is not needed if server = receiver (i.e. receiver is the local machine and has GPU access).

• On server launch the streaming inference script, and write txt2vid video to a port.

  python inference_streaming_pipeline.py -it text \
                                       -TTS Resemble \
                                       --checkpoint_path checkpoints/wav2lip_gan.pth \
                                       --face sample_data/einstein.jpg \
                                       -tif socket \
                                       --text_port 50007 \
                                       -vot socket \
                                       --output_port 8080 \
                                       --voice <voice_name> \
                                       --resemble_callback_url <resemble_callback_url>

  Wait till it says Model Loaded. The code will halt here waiting for the ffplay command to ask for streaming content.
  50007 is default port where the Server will stream the text. Can be specified to be something else, but also change PORT in below command.

• On sender launch the input streaming via socket. 2 examples are shown (use one, not both).

  • for text stream from terminal:

    python input_stream_socket.py -it text \
                              -tif terminal \
                              --HOST abc.com \
                              --PORT 50007

  • for text stream using microphone and then Google STT:

    python input_stream_socket.py -it text \
                              -tif Google \
                              --HOST abc.com \
                              --PORT 50007
                              --gc <google_credential>

    Here, <google_credential> is the credential required for Google STT described in installation instructions.

  Note: HOST needs to be the server machine where the wav2lip inference code will run (in our example: server = xyz@abc.com)

• View streaming output on the receiver. Ensure ffplay is installed.

  ffplay -f avi http://localhost:8080

  Might need to wait a few secs (~4s) before running this command after the previous one due to latency of the system. If it throws error, just try running it again.