We participated in NAVER_AI_HACKTHON 2019 and ranked 9th(77.0155) as a two-man team(Hangbok Coding, 행복코딩).
- 이진호(Jinho Lee) github profile
- 박승일(Seungil Park) github profile
- Convolution feature extraction (deepspeech style)
- Seq2Seq (bidirectional GRU encoder, unidirectional GRU decoder with Bahdanau Attention)
- Augmentation with log mel spectrogram (SpecAugment)
- Beam-search
- Data preprocessing (deleted blank and special characters)
- Label Smoothing
- Multi step learning rate
- Ensemble (but not used for best model)
Naver has released approximately 100 hours of 16kHz Korean speech data.
- Download links : data part 1 / data part 2
- Audio data : 16bit, mono 16k sampling PCM, WAV audio
- Target script : Korean characters are converted to the indices according to the 'hackathon.labels' file
"네 괜찮습니다." => "715 662 127 76 396 337 669 662"
* DATASET-ROOT-FOLDER
|--train
|--train_data
+--data_list.csv
+--a.wav, b.wav, c.wav ...
+--train_label
-
data_list.csv
<wav-filename>,<script-filename> wav_001.wav,wav_001.label ... -
train_label
<filename>,<script labels> wav_001,628 9 625 662 408 690 2 125 71 662 220 630 610 749 62 661 123 662 ...
$ docker build -t model:0.0 .
$ docker run -i --name model model:0.0
$ docker exec -i -t model /bin/bash
$ ./run.shLogin with nsml first, and run commands as follows:
$ ./run.sh # for local training
$ ./run_nsml.sh # for NSML training
$ nsml submit (sessionName) (checkpoint) # for submit| Help | default | |
|---|---|---|
| hidden size of model | 256 | --hidden_size |
| size of embedding dimension | 64 | --embedding_size |
| number of layers of encoder | 4 | --encoder_layer_size |
| number of layers of decoder | 3 | --decoder_layer_size |
| batch size | 32 | --batch_size |
| initial learning rate | 1e-04 | --lr |
| teacher forcing | 0.5 | --teacher_forcing |
| maximum characters of sentence | 80 | --max_len |
Copyright 2019 Hangbok Coding.
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