| import gradio as gr | |
| import torch | |
| from transformers import pipeline | |
| from datasets import load_dataset | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| def convert_audio(): | |
| pipe = pipeline( | |
| "automatic-speech-recognition", | |
| model="openai/whisper-small", | |
| chunk_length_s=30, | |
| device=device, | |
| ) | |
| ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") | |
| sample = ds[0]["audio"] | |
| print("Using predefined audio sample:") | |
| audio_data = sample['array'] | |
| prediction = pipe(audio_data)["text"] | |
| print(prediction) | |
| return prediction | |
| demo = gr.Interface( | |
| fn = convert_audio, | |
| inputs = None, | |
| outputs = "text", | |
| ) | |
| demo.launch(share=True) | |