Create app.py

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+from transformers import pipeline
+
+asr = pipeline(task = "automatic-speech-recognition",
+               model = "openai/whisper-large-v3")
+
+demo = gr.Blocks()
+
+def transcribe_speech(filepath):
+  if filepath is None:
+    gr.Warning("No audio file found, please retry!")
+    return ""
+  output = asr(filepath)
+  return output["text"]
+
+mic_transcribe = gr.Interface(
+    fn = transcribe_speech,
+    inputs = gr.Audio(sources = "microphone",
+                   type = "filepath"),
+    outputs = gr.Textbox(label = "Transcription",
+                         lines = 3),
+    allow_flagging = "never"
+)
+
+file_transcribe = gr.Interface (
+    fn = transcribe_speech,
+    inputs = gr.Audio(sources = "upload",
+                      type = "filepath"),
+    outputs = gr.Textbox(label = "Transcription",
+                         lines = 3),
+    allow_flagging = "never"
+)
+
+with demo:
+  gr.TabbedInterface(
+      [mic_transcribe,
+       file_transcribe],
+      ["Transcribe Microphone",
+       "Transcribe Audio File"],
+  )
+demo.launch()