Digital Speech Processing : Automatic Speech Recoginition with LibriSpeech dataset

How to train

# Set the environment
0. Virtual environment (ex: conda) is recommended 
1. ' pip install -r requirements.txt' or install : numpy, pandas, python-Levenshtein, librosa, numba, matplotlib
2. Install pytorch considering your CUDA version : https://pytorch.org/get-started/locally/
3. ' unzip Libri_data.zip -d ./DSP_project/ ' # unzip dataset
4. Set PATHs in train.py and test.py (+ set data path at ./Libri_Data/*.csv files)

# train and test
5. ' python DSP_ASR_LibriSpeech/train.py '
6. ' python DSP_ASR_LibriSpeech/test.py '

Recommended Environmet

Ubuntu 20.04.3 LTS
CUDA Version: 11.5
Python 3.8

Dataset

: Part of Libri Speech Clean data 360 (label length is under 150) - Download it from online

Base Model

: Location based Attention - CER 36.318% WER 73.181% at 160 Epochs - CER 31.254% WER 66.985% at 200 Epochs

Performance metric

: Word Error Rate = (S + I + D) / N - S : # of substitutions - I : # of deletions - D : # of insertions - N : # of words in the target label

Reference

specaugmentation code : https://github.com/SeanNaren/deepspeech.pytorch
Model (CLOVA Call) : https://github.com/clovaai/ClovaCall

For Beginners

Keywords, good to study

Listen, attend and spell
SpecAugment
Learning scheduler
teacher forcing

Good reference

Paperwithcode for LibriSpeech https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-clean

For students taking DSP class

submit as zip file, including ..

All code files you uesd for training
One best trained model(.pth) and test reult
Report as pdf (detail will be announced)

License

InhaDSP/ASR_tutorial

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