Create app.py

app.py

@@ -0,0 +1,44 @@
+import whisper
+
+# You can choose your model from - see it on readme file and update the modelname
+modelname = "base"
+model = whisper.load_model(modelname)
+
+import gradio as gr 
+import time
+
+def SpeechToText(audio):
+    if audio == None : return "" 
+    time.sleep(1)
+
+    audio = whisper.load_audio(audio)
+    audio = whisper.pad_or_trim(audio)
+
+    # make log-Mel spectrogram and move to the same device as the model
+    mel = whisper.log_mel_spectrogram(audio).to(model.device)
+
+    # Detect the Max probability of language ?
+    _, probs = model.detect_language(mel)
+    language = max(probs, key=probs.get)
+
+    #  Decode audio to Text
+    options = whisper.DecodingOptions(fp16 = False)
+    result = whisper.decode(model, mel, options)
+    return (language , result.text)
+
+print("Starting the Gradio Web UI")
+gr.Interface(
+    title = 'OpenAI Whisper implementation on Gradio Web UI', 
+    fn=SpeechToText, 
+    
+    inputs=[
+        gr.Audio(source="microphone", type="filepath")
+    ],
+    outputs=[
+        "label",
+        "textbox",
+    ],
+    live=True
+).launch(
+    debug=False,
+)