| ```python | |
| import torch | |
| from faster_whisper import WhisperModel | |
| from datasets import load_dataset | |
| # define our torch configuration | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| compute_type = "float16" if torch.cuda.is_available() else "float32" | |
| # load model on GPU if available, else cpu | |
| model = WhisperModel("distil-whisper/distil-large-v3.5-ct2", device=device, compute_type=compute_type) | |
| # load toy dataset for example | |
| dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") | |
| sample = dataset[1]["audio"]["path"] | |
| segments, info = model.transcribe(sample, beam_size=5, language="en") | |
| for segment in segments: | |
| print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) | |
| ``` |