transformers.WhisperProcessor).  The `WhisperProcessor` is used to: 1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model) 2. Post-process the model outputs (converting them from tokens to text)  The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens  are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order: 1. The transcription always starts with the `<|startoftranscript|>` token 2. The second token is the language token (e.g. `<|en|>` for English) 3. The third token is the "task token". It can take one of two values: `<|transcribe|>` for speech recognition or `<|translate|>` for speech translation 4. In addition, a `<|notimestamps|>` token is added if the model should not include timestamp prediction  Thus, a typical sequence of context tokens might look as follows: ``` <|startoftranscript|> <|en|> <|transcribe|> <|notimestamps|> ``` Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps.  These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at  each position. This allows one to control the output language and task for the Whisper model. If they are un-forced,  the Whisper model will automatically predict the output langauge and task itself.  The context tokens can be set accordingly:  ```python model.config.forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="english", task="transcribe") ```  Which forces the model to predict in English under the task of speech recognition.  