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Simultaneous Speech to Speech Translation w/ Whisper

/assets/demo.gif

This repository contains code for the INTERSPEECH 2023 paper "Learning When to Speak: Latency and Quality Trade-offs for Simultaneous Speech-to-Speech Translation with Offline Models" (link). In our paper we introduce simple techniques for converting offline speech to text translation systems (like whisper) into real-time simultaneous translation systems.

Installation

Step 1: Install portaudio (required for PyAudio)

Windows

Pyaudio seems to compile portaudio upon installation automatically. No manual installation is needed.

Linux

Download the portaudio source from https://files.portaudio.com/download.html.

Extract the source into its own directory and compile portaudio using ./configure && make install.

Mac

brew install portaudio

Step 2: Clone Repository

Clone the repo and cd into the main directory

git clone https://github.com/liamdugan/speech-to-speech.git
cd speech-to-speech

Step 3: Create Environment and install dependencies

Conda (must be Python 3.8 or higher)

conda env create -n s2st python=3.8
conda activate s2st
pip install -r requirements.txt

Venv (must be Python 3.8 or higher)

python -m venv env
source env/bin/activate
pip install -r requirements.txt

and you're good to go!

Usage

First populate the api_keys.yml file with your API keys for both OpenAI and ElevenLabs. For OpenAI go to https://platform.openai.com/account/api-keys and for ElevenLabs go to https://api.elevenlabs.io/docs.

After you are finished use speech-to-speech.py to run the system. Add the --use_local flag to use the local version of whisper (model size and input language will be taken from the config.yml file). The full options are listed below

$ python speech-to-speech.py -h
  -h, --help           show this help message and exit
  --mic MIC            Integer ID for the input microphone (default: 0)
  --use_local          Whether to use local models instead of APIs
  --api_keys API_KEYS  The path to the api keys file (default: api_keys.yml)
  --config CONFIG      The path to the config file (default: keys.yml)

This script will default to using the Whisper API if --use_local is not specified. If it is specified, it will default to using the GPU for the whisper model if it is available. It is highly recommended to use the API if your machine does not have access to a large CUDA-capable GPU.

Note: Microphone Selection

If you run the speech-to-speech.py script with an invalid microphone ID then the following message will be printed

$ python speech-to-speech.py --mic 10
Microphone Error: please select a different mic. Available devices:
ID: 0 -- Liam’s AirPods
ID: 1 -- Sceptre Z27
ID: 2 -- MacBook Pro Microphone
ID: 3 -- MacBook Pro Speakers
ID: 4 -- ZoomAudioDevice
Please quit with Control-C and try again

This will allow you to pick the desired microphone from the list.

Note: Configuration

To edit the policy used for speaking output phrases, directly edit (or make copies of) the config.yml file. Fields of particular importance are the policy settings (policy, consensus_threshold, confidence_threshold, and frame_width), which can have a large impact on the final performance.

Evaluation

Below is a table showing the evaluation results (in BLEU and Average Lagging) for the four policies greedy, offline, confidence, and consensus using Whisper Medium on the CoVoST2 dataset - see the paper for more details on the evaluation setup.

assets/table.png

To reproduce these results run the evaluation/pipeline.py script. This script takes in elements from the CoVoST2 dataset and simulates a simultaneous environment by feeding the model portions of the audio in increments of frame_width.

Contribution

We appreciate any and all contributions but especially those containing implementations of new translators and vocalizers.

To implement a new translator simply add a new subfolder under /translators and create a class that implements the Translator interface. Likewise for vocalizers, simply add a new subfolder under /vocalizers and create a class that implements the Vocalizer interface. In particular we would love to have implementations for whisper.cpp and a local TTS system (possibly balacoon or some other fine-tuned tacotron).

Citation

If you use our code or findings in your research, please cite us as:

@inproceedings{dugan23_interspeech,
  author={Liam Dugan and 
          Anshul Wadhawan and 
          Kyle Spence and 
          Chris Callison-Burch and 
          Morgan McGuire and 
          Victor Zordan},
  title={{Learning When to Speak: Latency and Quality Trade-offs for Simultaneous Speech-to-Speech Translation with Offline Models}},
  year=2023,
  booktitle={Proc. INTERSPEECH 2023},
  pages={5265--5266}
}