Misspelled output

[gipt66]

I'm trying to transcribe greek audio and have some strange results.
Some common words are misspelled.
Example : "κόδικας" should have been written "κώδικας" ( κώδικας -> English means Code)
"Άγγυρα" spelled "Άγκυρα"
"Αυρωπαϊκή Ένωση" -> "Ευρωπαϊκή Ένωση".
I could write a post process script to correct this kind of errors but its not an elegant solution.
I have trained and used STT systems like kaldi and Deep Speech 2 with KENLM and they never output a word that does not exist in the dictionary of the system.
Is there any solution to fix that kind of errors, having the fact that we can not finetune the model?

Thanks
George

[EtienneAb3d]

I have a solution for you. It may be freely tested online, but it's a commercial product.
If your are interested, don't hesitate contact me (see links on my GitHub profile).

[jongwook]

This is unfortunately a limitation of the trained model, and our filters may not have been very effective at excluding ASR-generated transcripts for non-English languages. I suspect incorrectly spelled transcripts in the training data have probably caused this phenomenon. I've seen similar spelling errors in Korean as well, which wouldn't make sense if the training data only contained grammatically valid text in correct spelling.

Like you mentioned, integrating with an LM may improve the results; this is not directly supported, but one can extend the TokenDecoder class to select tokens according to a language model:

whisper/whisper/decoding.py

Lines 195 to 246 in 9e653bd

	class TokenDecoder:
	def reset(self):
	"""Initialize any stateful variables for decoding a new sequence"""
	def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
	"""Specify how to select the next token, based on the current trace and logits
	Parameters
	----------
	tokens : Tensor, shape = (n_batch, current_sequence_length)
	all tokens in the context so far, including the prefix and sot_sequence tokens
	logits : Tensor, shape = (n_batch, vocab_size)
	per-token logits of the probability distribution at the current step
	sum_logprobs : Tensor, shape = (n_batch)
	cumulative log probabilities for each sequence
	Returns
	-------
	tokens : Tensor, shape = (n_batch, current_sequence_length + 1)
	the tokens, appended with the selected next token
	completed : bool
	True if all sequences has reached the end of text
	"""
	raise NotImplementedError
	def finalize(
	self, tokens: Tensor, sum_logprobs: Tensor
	) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]:
	"""Finalize search and return the final candidate sequences
	Parameters
	----------
	tokens : Tensor, shape = (n_audio, n_group, current_sequence_length)
	all tokens in the context so far, including the prefix and sot_sequence
	sum_logprobs : Tensor, shape = (n_audio, n_group)
	cumulative log probabilities for each sequence
	Returns
	-------
	tokens : Sequence[Sequence[Tensor]], length = n_audio
	sequence of Tensors containing candidate token sequences, for each audio input
	sum_logprobs : List[List[float]], length = n_audio
	sequence of cumulative log probabilities corresponding to the above
	"""
	raise NotImplementedError

You may also consider fine-tuning if you have a speech corpus in Greek, like in this example.

[gipt66]

Thank you very much
I have the KENLM trained with greek dataset and I'll give it a try.

[R4ZZ3]

@gipt66 where you able to get it working? Were you able to add KenLM?

[gkarozis]

Did you find any answer to this question? I am intrerested in Greek too!

[skanda1005]

Any updates on this thread? To fix the spelling issues?