Speculative Decoding doesn't work yet with Whisper-v3

In the example above we advertise using tiny in a ForCausalLM class which can't work since tiny does **not** share the same encoder as large-v3. We can advertise it as soon as distil-v3 is out.

README.md

@@ -258,57 +258,6 @@ result = pipe(sample, return_timestamps=True, generate_kwargs={"language": "fren
 print(result["chunks"])
 ```
 
-## Speculative Decoding
-
-Whisper `tiny` can be used as an assistant model to Whisper for speculative decoding. Speculative decoding mathematically
-ensures the exact same outputs as Whisper are obtained while being 2 times faster. This makes it the perfect drop-in 
-replacement for existing Whisper pipelines, since the same outputs are guaranteed.
-
-In the following code-snippet, we load the assistant Distil-Whisper model standalone to the main Whisper pipeline. We then
-specify it as the "assistant model" for generation:
-
-```python
-from transformers import pipeline, AutoModelForCausalLM, AutoModelForSpeechSeq2Seq, AutoProcessor
-import torch
-from datasets import load_dataset
-
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-
-assistant_model_id = "openai/whisper-tiny"
-
-assistant_model = AutoModelForCausalLM.from_pretrained(
-    assistant_model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
-)
-assistant_model.to(device)
-
-model_id = "openai/whisper-large-v3"
-
-model = AutoModelForSpeechSeq2Seq.from_pretrained(
-    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
-)
-model.to(device)
-
-processor = AutoProcessor.from_pretrained(model_id)
-
-pipe = pipeline(
-    "automatic-speech-recognition",
-    model=model,
-    tokenizer=processor.tokenizer,
-    feature_extractor=processor.feature_extractor,
-    max_new_tokens=128,
-    generate_kwargs={"assistant_model": assistant_model},
-    torch_dtype=torch_dtype,
-    device=device,
-)
-
-dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
-sample = dataset[0]["audio"]
-
-result = pipe(sample)
-print(result["text"])
-```
-
 ## Additional Speed & Memory Improvements
 
 You can apply additional speed and memory improvements to Whisper-large-v3 which we cover in the following.