Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import torch
+import librosa
+import soundfile
+
+SAMPLE_RATE = 16000
+
+pipe = pipeline(model="openai/whisper-small")
+
+
+def transcribe(Microphone, File_Upload):
+    warn_output = ""
+    if (Microphone is not None) and (File_Upload is not None):
+        warn_output = "WARNING: You've uploaded an audio file and used the microphone. " \
+                      "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
+        file = Microphone
+
+    elif (Microphone is None) and (File_Upload is None):
+        return "ERROR: You have to either use the microphone or upload an audio file"
+
+    elif Microphone is not None:
+        file = Microphone
+    else:
+        file = File_Upload
+    
+    text = pipe(file)["text"]
+
+    return warn_output + text
+
+
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.inputs.Audio(source="microphone", type='filepath', optional=True),
+        gr.inputs.Audio(source="upload", type='filepath', optional=True),
+    ],
+    outputs="text",
+    layout="horizontal",
+    theme="huggingface",
+    title="Whisper Small",
+    description="Demo for multilingual speech recognition using the official OpenAI [Whisper small checkpoint](https://huggingface.co/openai/whisper-small).",
+    allow_flagging='never',
+)
+
+iface.launch(enable_queue=True)