Speech to Phoneme, Bandwidth Extension and Speaker Verification using the Vibravox dataset.

Resources:

• 📝: The paper related to this project is available on arXiv on this link.
• 🤗: The dataset used in this project is hosted by Hugging Face. You can access it here.
• 🌐: For more information about the project, visit our project page.
• 🏆: Explore Leaderboards on Papers With Code.

Requirements

pip install -r requirements.txt

Available sensors

• 🟣:headset_microphone ( Not available for Bandwidth Extension as it is the reference mic )
• 🟡:throat_microphone
• 🟢:forehead_accelerometer
• 🔵:rigid_in_ear_microphone
• 🔴:soft_in_ear_microphone
• 🧊:temple_vibration_pickup

Run some models

• EBEN for Bandwidth Extension

  • Train and test on speech_clean, for recordings in a quiet environment:

    python run.py \
      lightning_datamodule=bwe \
      lightning_datamodule.sensor=throat_microphone \
      lightning_module=eben \
      lightning_module.generator.p=2 \
      +callbacks=[bwe_checkpoint] \
      ++trainer.check_val_every_n_epoch=15 \
      ++trainer.max_epochs=500
    

  • Train on speech_clean mixed with speechless_noisy and test on speech_noisy, for recordings in a noisy environment: (weights initialized from vibravox_EBEN_models )

    python run.py \
      lightning_datamodule=noisybwe \
      lightning_datamodule.sensor=throat_microphone \
      lightning_module=eben \
      lightning_module.description=from_pretrained-throat_microphone \
      ++lightning_module.generator=dummy \
      ++lightning_module.generator._target_=vibravox.torch_modules.dnn.eben_generator.EBENGenerator.from_pretrained \
      ++lightning_module.generator.pretrained_model_name_or_path=Cnam-LMSSC/EBEN_throat_microphone \
      ++lightning_module.discriminator=dummy \
      ++lightning_module.discriminator._target_=vibravox.torch_modules.dnn.eben_discriminator.DiscriminatorEBENMultiScales.from_pretrained \
      ++lightning_module.discriminator.pretrained_model_name_or_path=Cnam-LMSSC/DiscriminatorEBENMultiScales_throat_microphone \
      +callbacks=[bwe_checkpoint] \
      ++callbacks.checkpoint.monitor=validation/torchmetrics_stoi/synthetic \
      ++trainer.check_val_every_n_epoch=15 \
      ++trainer.max_epochs=200
    

• wav2vec2 for Speech to Phoneme

  • Train and test on speech_clean, for recordings in a quiet environment: (weights initialized from facebook/wav2vec2-base-fr-voxpopuli )

  python run.py \
    lightning_datamodule=stp \
    lightning_datamodule.sensor=throat_microphone \
    lightning_module=wav2vec2_for_stp \
    lightning_module.optimizer.lr=1e-5 \
    ++trainer.max_epochs=10
  

  • Train and test on speech_noisy, for recordings in a noisy environment: (weights initialized from vibravox_phonemizers )

  python run.py \
  lightning_datamodule=stp \
  lightning_datamodule.sensor=throat_microphone \
  lightning_datamodule.subset=speech_noisy \
  lightning_datamodule/data_augmentation=aggressive \
  lightning_module=wav2vec2_for_stp \
  lightning_module.wav2vec2_for_ctc.pretrained_model_name_or_path=Cnam-LMSSC/phonemizer_throat_microphone \
  lightning_module.optimizer.lr=1e-6 \
  ++trainer.max_epochs=30
  

• ECAPA2 for Speaker Verification:

  • Test the model on speech_clean:

  python run.py \
    lightning_datamodule=spkv \
    lightning_module=ecapa2 \
    logging=csv \
    ++trainer.limit_train_batches=0 \
    ++trainer.limit_val_batches=0
  

  • Test on speech_clean mixed with speechless_noisy, representative of speech_noisy with the exact same pairs that were used on speech_clean, allowing direct comparison of results:

  python run.py \
    lightning_datamodule=spkv \
    lightning_datamodule.dataset_name=Cnam-LMSSC/vibravox_mixed_for_spkv \
    lightning_datamodule.subset=speech_noisy_mixed \
    lightning_module=ecapa2 \
    logging=csv \
    ++trainer.limit_train_batches=0 \
    ++trainer.limit_val_batches=0