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| # How to prepare dataset for training | |
| 1. Download Ukrainian dataset from [https://github.com/egorsmkv/speech-recognition-uk](https://github.com/egorsmkv/speech-recognition-uk). | |
| 2. Delete Common Voice folder in dataset | |
| 3. Download [import_ukrainian.py](scripts/import_ukrainian.py) and put into DeepSpeech/bin folder. | |
| 4. Run import script | |
| 5. Download Common Voice 6.1 Ukrainian dataset | |
| 6. Convert to DeepSpeech format | |
| 7. Merge train.csv from dataset and from DeepSpeech into one file | |
| 8. Put CV files into dataset files folder | |
| 9. Put dev.csv and test.csv into folder | |
| Note: you can also specify dataset with "," e.g. dataset1/train.csv,dataset2/train.csv. | |
| You have a reproducible dataset! | |
| # Scorer | |
| 1. Refer to DeepSpeech guide for further explanations. | |
| 2. Generate scorer package. | |
| ``` | |
| python3 generate_lm.py --input_txt ../../../voice-recognition-ua/data/all_text.txt --output_dir . \ | |
| --top_k 500000 --kenlm_bins ../../../voice-recognition-ua/kenlm/build/bin \ | |
| --arpa_order 5 --max_arpa_memory "85%" --arpa_prune "0|0|1" \ | |
| --binary_a_bits 255 --binary_q_bits 8 --binary_type trie | |
| ``` | |
| 3. Run lm_optimizer to find the best scorer value. | |
| 4. Rerun step 2 to generate new scorer. | |
| Caution: scorer is very model-dependant, so you'll likely need to adjust it to each model. |