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...
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 ...
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 ...
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...
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")...