

The dataset examples contain raw_text and normalized_text features. When deciding which feature to use as the text input, 
consider that the SpeechT5 tokenizer doesn't have any tokens for numbers. In normalized_text the numbers are written 
out as text. Thus, it is a better fit, and we recommend using    normalized_text as input text.
Because SpeechT5 was trained on the English language, it may not recognize certain characters in the Dutch dataset. If 
left as is, these characters will be converted to <unk> tokens. However, in Dutch, certain characters like à are 
used to stress syllables. In order to preserve the meaning of the text, we can replace this character with a regular a.
To identify unsupported tokens, extract all unique characters in the dataset using the SpeechT5Tokenizer which 
works with characters as tokens. To do this, write the extract_all_chars mapping function that concatenates 
the transcriptions from all examples into one string and converts it to a set of characters. 
Make sure to set batched=True and batch_size=-1 in dataset.map() so that all transcriptions are available at once for 
the mapping function.