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Speech2RIR

This is the official implementation of reverberant speech to room impulse response estimator. We trained our network using Room Impulse Responses from SoundSpaces-NVAS dataset and clean speech from LibriSpeech dataset

Requirements

Python 3.8+
Cuda 11.0+
PyTorch 1.10+
numpy
pygsound
wavefile
tqdm
scipy
soundfile
librosa
cupy-cuda11x
torch_stoi
tensorboardX
pyyaml
gdown
sudo apt-get install p7zip-full

RIR and Clean Speech Dataset

Run the following script to download SoundSpaces-NVAS and LibriSpeech Dataset

bash download.sh

Reverberant Speech Augmentation

Run the following commands to augment Reverberant Speech to train and test.

./batch_flac2wav.sh data/LibriSpeech-wav
 python3 pickle_generator.py
python3 augment_speech_100.py --pickle train.pickle
python3 augment_speech_100.py --pickle val.pickle
python3 augment_speech_100.py --pickle test.pickle

Download Trained Model

To download our trained with checkpoint at 1,040,000 Run the following command

source download_model.sh

Testing

To test the trained model run the following command

bash submit_autoencoder.sh --start 2

Training

To train our network, run the following command

bash submit_autoencoder.sh --start 0 --stop 0 --tag_name "autoencoder/symAD_vctk_48000_hop300"

To resume training on a saved model at a particular step (e.g., 1,040,000 steps) run the following command

bash submit_autoencoder.sh --start 1 --stop 1 --resumepoint 1040000 --tag_name "autoencoder/symAD_vctk_48000_hop300"

Citations

Our model is built using the architectures from S2IR and AV-RIR. If you use our Speech2RIR, please consider citing

@INPROCEEDINGS{10094770,
  author={Ratnarajah, Anton and Ananthabhotla, Ishwarya and Ithapu, Vamsi Krishna and Hoffmann, Pablo and Manocha, Dinesh and Calamia, Paul},
  booktitle={ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Towards Improved Room Impulse Response Estimation for Speech Recognition}, 
  year={2023},
  volume={},
  number={},
  pages={1-5},
  keywords={Measurement;Error analysis;Estimation;Signal processing;Benchmark testing;Generative adversarial networks;Acoustics;room impulse response;blind estimation},
  doi={10.1109/ICASSP49357.2023.10094770}}

@InProceedings{Ratnarajah_2024_CVPR,
    author    = {Ratnarajah, Anton and Ghosh, Sreyan and Kumar, Sonal and Chiniya, Purva and Manocha, Dinesh},
    title     = {AV-RIR: Audio-Visual Room Impulse Response Estimation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {27164-27175}
}

If you use SoundSpaces-NVAS dataset, please consider citing

@INPROCEEDINGS{10204911,
  author={Chen, Changan and Richard, Alexander and Shapovalov, Roman and Ithapu, Vamsi Krishna and Neverova, Natalia and Grauman, Kristen and Vedaldi, Andrea},
  booktitle={2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Novel-View Acoustic Synthesis}, 
  year={2023},
  volume={},
  number={},
  pages={6409-6419},
  keywords={Location awareness;Visualization;Computational modeling;Transforms;Benchmark testing;Rendering (computer graphics);Pattern recognition;Multi-modal learning},
  doi={10.1109/CVPR52729.2023.00620}}

If you use LibriSpeech dataset. please consider citing

@INPROCEEDINGS{7178964,
  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
  booktitle={2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Librispeech: An ASR corpus based on public domain audio books}, 
  year={2015},
  volume={},
  number={},
  pages={5206-5210},
  keywords={Resource description framework;Genomics;Bioinformatics;Blogs;Information services;Electronic publishing;Speech Recognition;Corpus;LibriVox},
  doi={10.1109/ICASSP.2015.7178964}}