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hear-2021-neurips-challenge---ntu's Introduction

HEAR-2021-NeurIPS-Challenge---NTU

Description

  • We try many methods (cat, average, fusion) on three models (Hubert, Wav2vec2, Torchcrepe).
  • We adopt two methods on the relationship between our scene-embedding and timestamp-embedding models. In "fusion_cat_xwc_time", every certain time inverted is averaged and concatenated. In other models, we simply average three models'(Hubert, Wav2vec2, Torchcrepe) embeddings.
  • Notice that Hubert models contain xlarge and large, Wav2vec2 model contains only large.
  • The pretrained models we use are:
    • facebook/hubert-large-ll60k
    • facebook/hubert-xlarge-ll60k
    • facebook/wav2vec2-large-960h-lv60-self
    • torchcrepe

Installation of the package

pip install \
git+https://github.com/tony10101105/HEAR-2021-NeurIPS-Challenge---NTU.git

Usage

# In python code:
from GURA import fusion_wav2vec2
from GURA import cat_wc
.
.
.

Python Version

  • python3.8

CUDA Version

  • CUDA: 11.4

Torch Version

  • torch: 1.9.1+cu111

Transformer Version

  • 4.11.3

Torchcrepe Version

  • 0.0.15

hear-2021-neurips-challenge---ntu's People

Contributors

hank0316 avatar lca0503 avatar hsutim047 avatar tony10101105 avatar

Stargazers

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