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README.md

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+---
+title: Butttler
+emoji: 🦀
+colorFrom: yellow
+colorTo: gray
+sdk: gradio
+sdk_version: 5.1.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+# This Gradio app creates a conversation pipeline that includes speech-to-text using the Whisper model,
+# GPT response generation, and text-to-speech using the Google Text-to-Speech API.
+# The app uses the microphone input to capture audio, processes it through the pipeline, and returns the GPT response as text and audio.
+
+import gradio as gr
+import openai
+import whisper
+import numpy as np
+import os
+
+# Load the Whisper model
+model = whisper.load_model("base")
+
+# OpenAI API key for GPT
+openai.api_key = 'your_openai_api_key'
+
+# Function to convert speech to text using Whisper
+def speech_to_text(audio):
+    result = model.transcribe(audio)
+    return result['text']
+
+# Function to get GPT response
+def gpt_response(text):
+    response = openai.Completion.create(
+        engine="gpt-3.5-turbo",
+        prompt=text,
+        max_tokens=100
+    )
+    return response.choices[0].text.strip()
+
+# Function to convert text to speech using Google Text-to-Speech API
+def text_to_speech_google(text):
+    from google.cloud import texttospeech
+    client = texttospeech.TextToSpeechClient()
+    input_text = texttospeech.SynthesisInput(text=text)
+    
+    voice = texttospeech.VoiceSelectionParams(
+        language_code="en-US",
+        ssml_gender=texttospeech.SsmlVoiceGender.NEUTRAL
+    )
+    
+    audio_config = texttospeech.AudioConfig(
+        audio_encoding=texttospeech.AudioEncoding.MP3
+    )
+    
+    response = client.synthesize_speech(
+        input=input_text, voice=voice, audio_config=audio_config
+    )
+    
+    # Save the audio response to a file
+    output_path = "output.mp3"
+    with open(output_path, "wb") as out:
+        out.write(response.audio_content)
+    return output_path
+
+# Function to handle the entire conversation pipeline
+def conversation_pipeline(audio):
+    # Step 1: Convert speech to text
+    text = speech_to_text(audio)
+    
+    # Step 2: Get GPT response
+    response_text = gpt_response(text)
+    
+    # Step 3: Convert GPT response to speech
+    response_audio = text_to_speech_google(response_text)
+    
+    return response_text, response_audio
+
+# Gradio interface
+demo = gr.Interface(
+    fn=conversation_pipeline,
+    inputs=gr.Audio(source="microphone", type="filepath"),
+    outputs=[gr.Textbox(label="GPT Response"), gr.Audio(label="GPT Response Audio", type="filepath", autoplay=True)]
+)
+
+demo.launch(show_error=True)

requirements.txt

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+numpy
+matplotlib