One emphasis of psychointegrators is on psyche, meaning not only mind but also the soul of the spirit. Psychointegrators stimulate the integration of behavior, protomentation, and socioemotional dynamics with language based radiomentation, egoic representations, and personal identity. These physiological ...
For reference, the pre-trained Whisper small model achieves a WER of 63.5%, meaning we achieve an improvement of 31.5% absolute through fine-tuning. Not bad for just 8h of training data! We're now ready to share our fine-tuned model on the Hugging Face Hub. To make it more accessible...
In doing so, we only need to keep track of two objects during training: the processor and the model: from transformers import WhisperProcessor processor = WhisperProcessor.from_pretrained("openai/whisper-small", language="Hindi", task="transcribe") Prepare Data Let's print the firs...
Does that mean they are "wrong answers" or the "completion is not proper" meaning that the model answers but ends the answer in the middle without even completing the sentence? 1️⃣ Wrong answers If the answers are wrong, this simply means the context isn't passed properly or not in...
In doing so, we only need to keep track of two objects during training: the processor and the model: from transformers import WhisperProcessor processor = WhisperProcessor.from_pretrained("openai/whisper-small", language="Hindi", task="transcribe") Prepare Data Let's print the first examp...
We'll override these tokens to an empty list, meaning no tokens are suppressed: model.config.forced_decoder_ids = None model.config.suppress_tokens = [] Define the Training Arguments In the final step, we define all the parameters related to training. A subset of parameters are ex...
We'll override these tokens to an empty list, meaning no tokens are suppressed: model.config.forced_decoder_ids = None model.config.suppress_tokens = [] Define the Training Arguments In the final step, we define all the parameters related to training. A subset of parameters are e...
We'll override these tokens to an empty list, meaning no tokens are suppressed: model.config.forced_decoder_ids = None model.config.suppress_tokens = [] Define the Training Arguments In the final step, we define all the parameters related to training. A subset of parameters are expl...
For reference, the pre-trained Whisper small model achieves a WER of 63.5%, meaning we achieve an improvement of 31.5% absolute through fine-tuning. Not bad for just 8h of training data! We're now ready to share our fine-tuned model on the Hugging Face Hub. To make it ...
In doing so, we only need to keep track of two objects during training: the processor and the model: from transformers import WhisperProcessor processor = WhisperProcessor.from_pretrained("openai/whisper-small", language="Hindi", task="transcribe") Prepare Data Let's print the...