import torchfrom transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipelinefrom datasets import load_datasetdevice = "cuda:0" if torch.cuda.is_available() else "cpu"torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32model_id = "primeline/whisper-large-v3-german"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, chunk_length_s=30, batch_size=16, return_timestamps=True, torch_dtype=torch_dtype, device=device,)dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")sample = dataset[0]["audio"]result = pipe(sample)print(result["text"])