| But if it works in your use case, you can use: | |
| py | |
| transcriber = pipeline(model="openai/whisper-large-v2", device=0, batch_size=2) | |
| audio_filenames = [f"https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/{i}.flac" for i in range(1, 5)] | |
| texts = transcriber(audio_filenames) | |
| This runs the pipeline on the 4 provided audio files, but it will pass them in batches of 2 | |
| to the model (which is on a GPU, where batching is more likely to help) without requiring any further code from you. |