Create app.py

app.py

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+import whisper
+import gradio as gr 
+import time
+import warnings
+import json
+import openai
+import os
+from gtts import gTTS
+warnings.filterwarnings("ignore")
+with open('GPT_SECRET_KEY.json') as f:
+    data = json.load(f)
+openai.api_key = data["API_KEY"]
+model = whisper.load_model("base")
+model.device
+!ffmpeg -f lavfi -i anullsrc=r=44100:cl=mono -t 10 -q:a 9 -acodec libmp3lame Temp.mp3
+def chatgpt_api(input_text):
+    messages = [
+    {"role": "system", "content": "You are a helpful assistant."}]
+    
+    if input_text:
+        messages.append(
+            {"role": "user", "content": input_text},
+        )
+        chat_completion = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo", messages=messages
+        )
+    
+    reply = chat_completion.choices[0].message.content
+    return reply
+    def transcribe(audio):
+
+    language = 'en'
+
+    audio = whisper.load_audio(audio)
+    audio = whisper.pad_or_trim(audio)
+
+    mel = whisper.log_mel_spectrogram(audio).to(model.device)
+
+    _, probs = model.detect_language(mel)
+
+    options = whisper.DecodingOptions()
+    result = whisper.decode(model, mel, options)
+    result_text = result.text
+    
+    out_result = chatgpt_api(result_text)
+    
+    audioobj = gTTS(text = out_result, 
+                    lang = language, 
+                    slow = False)
+    
+    audioobj.save("Temp.mp3")
+
+    return [result_text, out_result, "Temp.mp3"]
+    output_1 = gr.Textbox(label="Speech to Text")
+output_2 = gr.Textbox(label="ChatGPT Output")
+output_3 = gr.Audio("Temp.mp3")
+
+gr.Interface(
+    title = 'OpenAI Whisper and ChatGPT ASR Gradio Web UI', 
+    fn=transcribe, 
+    inputs=[
+        gr.inputs.Audio(source="microphone", type="filepath")
+    ],
+
+    outputs=[
+        output_1,  output_2, output_3
+    ],
+    live=True).launch()