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| from transformers import pipeline | |
| import gradio as gr | |
| import torch | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| asr = pipeline( | |
| "automatic-speech-recognition", | |
| model="MaximilianChen/Casper", | |
| chunk_length_s=30, | |
| device=device, | |
| ) | |
| def transcribe_audio(mic=None, file=None): | |
| if mic is not None: | |
| audio = mic | |
| elif file is not None: | |
| audio = file | |
| else: | |
| return "You must either provide a mic recording or a file" | |
| transcription = asr(audio)["text"] | |
| return transcription | |
| gr.Interface( | |
| fn=transcribe_audio, | |
| inputs=[ | |
| gr.Audio(source="microphone", type="filepath", optional=True), | |
| gr.Audio(source="upload", type="filepath", optional=True), | |
| ], | |
| outputs="text", | |
| ).launch() |