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import whisper
import gradio as gr
from pathlib import Path
from dotenv import load_dotenv
import os
transcription_value = ""
def transcribe_speech(filepath):
if filepath is None:
gr.Warning("No audio found, please retry.")
model = whisper.load_model("base")
result = model.transcribe(filepath, fp16=False)
return result["text"]
def store_transcription(output):
global transcription_value
transcription_value = output
return output
mic_transcribe = gr.Interface(
fn=lambda x: store_transcription(transcribe_speech(x)),
inputs=gr.Audio(sources=["microphone"], type="filepath"),
outputs=gr.Textbox(label="Transcription")
)
test_interface = gr.Blocks()
with test_interface:
gr.TabbedInterface(
[mic_transcribe],
["Transcribe Microphone"]
)
test_interface.launch(
share=True,
server_port=8000,
#prevent_thread_lock=True
)
print(transcription_value)
#test_interface.close() |