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# Let's get pipelines from transformers | |
from transformers import pipeline | |
# Let's import Gradio | |
import gradio as gr | |
# Let's set up the model | |
model = pipeline("automatic-speech-recognition", model="moraxgiga/audio_test") | |
title = "Audio2Text" | |
description = "Record your audio in English and send it in order to received a transcription" | |
# Function | |
def transcribe(audio): | |
# Let's invoke "model" defined above | |
text = model(audio)["text"] | |
return text | |
# Interface Set-Up | |
'''gr.Interface( | |
fn=transcribe, | |
inputs=[gr.Audio(source="microphone", type="filepath")], | |
title="Audio-to-text", | |
description="text-to-speech model demo", | |
outputs=["textbox"] | |
).launch() | |
''' | |
demo = gr.Interface(fn=transcribe , | |
inputs=[gr.Audio(source="microphone", type="filepath")], | |
outputs=[gr.Textbox(label="Result", lines=3)], | |
title="Audio-to-text", | |
description="text-to-speech model demo" | |
) | |
demo.launch() |