MLP-based robust speech quantizers trained with CTC loss and iterative pseudo-labeling on augmented audio, following
Algayres et al., Interspeech 2023. Evaluated on K ∈ {100, 200, 500} vocabulary sizes.
1from huggingface_hub import hf_hub_download
2
3model_path = hf_hub_download(
4 repo_id="iliasslasri/robust_speech_quantizer",
5 filename="500_vocab_size/round_1/E1_best.pt"
6)
7config_path = hf_hub_download(
8 repo_id="iliasslasri/robust_speech_quantizer",
9 filename="500_vocab_size/config.yaml"
10)
We trained quantizers across different encoders, codebook sizes, and augmentation strategies. The augmentation configurations are:
