Update README.md

README.md

@@ -258,7 +258,7 @@ The Whisper model is intrinsically designed to work on audio samples of up to 30
 algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers 
 [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) 
 method. Chunking is enabled by setting `chunk_length_s=30` when instantiating the pipeline. With chunking enabled, the pipeline 
-can be run with batched inference. It can also be extended to predict sequence level timestamps by passing `return_timestamps=True`:
+can be run with batched inference. It can also be extended to predict word level timestamps by passing `return_timestamps="word"`:
 
 ```python
 >>> import torch