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| import os | |
| import numpy as np | |
| import gradio as gr | |
| #try: | |
| # import tensorflow # required in Colab to avoid protobuf compatibility issues | |
| #except ImportError: | |
| # pass | |
| #import torch | |
| #import pandas as pd | |
| import whisper | |
| #import torchaudio | |
| #from tqdm.notebook import tqdm | |
| #DEVICE = "cuda" if torch.cuda.is_available() else "CPU" | |
| model = whisper.load_model("base.en") | |
| def fun(audio): | |
| result = model.transcribe(audio) | |
| return result["text"] | |
| # predict without timestamps for short-form transcription | |
| #options = whisper.DecodingOptions(language="en", without_timestamps=True) | |
| #for mels, texts in tqdm(loader): | |
| # results = model.decode(mels, options) | |
| # hypotheses.extend([result.text for result in results]) | |
| # references.extend(texts) | |
| gr.Interface( | |
| title = 'Testing Whisper', | |
| fn=fun, | |
| inputs=[ | |
| gr.inputs.Audio(source="microphone", streaming = "True" ) #,type="filepath") | |
| ], | |
| outputs=[ | |
| "textbox" | |
| ], | |
| live=True).launch() | |