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app.py

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+import os
+os.system("pip install git+https://github.com/openai/whisper.git")
+
+import gradio as gr
+import whisper
+model = whisper.load_model("base")
+
+import time
+
+def transcribe(audio):
+    # load audio for 30 seconds
+    audio = whisper.load_audio(audio)
+    audio = whisper.pad_or_trim(audio)
+
+    # make log-Mel spectrogram and move to device as the model
+    mel = whisper.log_mel_spectrogram(audio).to(model.device)
+
+    # detect the spoken language
+    _, probs = model.detect_language(mel)
+    print(f"Detected language: {max(probs, key=probs.get)}")
+
+    # decoding the audio
+    options = whisper.DecodingOptions(fp16 = False)
+    result = whisper.decode(model, mel, options)
+    print(result.text)
+    return result.text
+    
+gr.Interface(
+    title = 'Speech to Text with OpenAI (base)', 
+    fn=transcribe, 
+    inputs=[
+        gr.inputs.Audio(source="microphone", type="filepath")
+    ],
+    outputs=[
+        "textbox"
+    ],
+    live=True).launch()