| ##Created from ted-multi dataset | |
| adding processing steps here if you want another language | |
| #using Turkish as target | |
| target_lang="tr" # change to your target lang | |
| from datasets import load_dataset | |
| #ted-multi is a multiple language translated dataset | |
| #fits for our case , not to big and curated | |
| dataset = load_dataset("ted_multi") | |
| #there is no Turkish lanugage in europarl, so will need to choose one | |
| dataset.cleanup_cache_files() | |
| #chars_to_ignore_regex = '[,?.!\-\;\:\"β%ββοΏ½βββ¦β]' # change to the ignored characters of your fine-tuned model | |
| #will use cahya/wav2vec2-base-turkish-artificial-cv | |
| #checking inside model repository to find which chars removed (no run.sh) | |
| chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\β\β\β\'\`β¦\β»«]' | |
| cols_to_remove = ['translations', 'talk_name'] | |
| dataset = dataset.map(extract_target_lang_entries, remove_columns=cols_to_remove) | |
| dataset_cleaned = dataset.filter(lambda x: x['text'] is not None) | |
| dataset_cleaned | |
| from huggingface_hub import notebook_login | |
| notebook_login() | |
| dataset_cleaned.push_to_hub(f"{target_lang}_ted_talk_translated") | |