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| # import gradio as gr | |
| # from googletrans import Translator | |
| # import torch | |
| # # Initialize Translator | |
| # from transformers import pipeline | |
| # translator = Translator() | |
| # MODEL_NAME = "openai/whisper-base" | |
| # device = 0 if torch.cuda.is_available() else "cpu" | |
| # pipe = pipeline( | |
| # task="automatic-speech-recognition", | |
| # model=MODEL_NAME, | |
| # chunk_length_s=30, | |
| # device=device, | |
| # ) | |
| # def transcribe_audio(audio): | |
| # text = pipe(audio)["text"] | |
| # return text | |
| # # return translated_text | |
| # audio_record = gr.inputs.Audio(source='microphone', label='Record Audio') | |
| # output_text = gr.outputs.Textbox(label='Transcription') | |
| # interface = gr.Interface(fn=transcribe_audio, inputs=audio_record, outputs=output_text) | |
| # interface.launch() | |
| import gradio as gr | |
| from transformers import pipeline | |
| modelo = pipeline("automatic-speech-recognition", model="openai/whisper-base") | |
| def transcribe(audio): | |
| text = modelo(audio)["text"] | |
| return text | |
| gr.Interface( | |
| fn=transcribe, | |
| inputs=[gr.Audio(source="microphone", type="filepath")], | |
| outputs=["textbox"] | |
| ).launch() | |