Openai Whisper Finetune checkpoint in local directory
Hello, I had a question regarding the openai whisper model prediction process. Suppose I finetuned the model using Trainer and saved the model and tokenizer using.save_pretrained()in my local directory, now when I create the pipeline for prediction or transcription will the pipeline work correctly? What I mean, we can save models locally by usingtrainer.save_model("local/dir/path")but for ASR models which require feature extractors, how to save the trainer so that I can later create a pipeline just by uploading the local folder?Also, according to my understanding ASR pipeline has 3 components:Feature extractorModelTokenizerSo my question is, why don’t we have to save pass a feature extractor argument in trainer like model and pass something like this:trainer = Seq2SeqTrainer(model=model, 
                         feature_extractor=processor.feature_extractor, 
                         tokenizer=processor.tokenizer, ....)Edit:We can save the model and other required components by;trainer.save_model("local/dir/path")
processor.save_pretrained("local/dir/path")