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Create app.py
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app.py
ADDED
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1 |
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import base64
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import tempfile
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import os
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import requests
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import gradio as gr
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from openai import OpenAI
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# Available voices for audio generation
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VOICES = ["alloy", "ash", "ballad", "coral", "echo", "fable", "onyx", "nova", "sage", "shimmer", "verse"]
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def process_text_input(api_key, text_prompt, selected_voice):
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"""Generate audio response from text input"""
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try:
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# Initialize OpenAI client with the provided API key
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client = OpenAI(api_key=api_key)
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completion = client.chat.completions.create(
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model="gpt-4o-audio-preview",
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modalities=["text", "audio"],
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audio={"voice": selected_voice, "format": "wav"},
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messages=[
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{
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"role": "user",
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"content": text_prompt
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}
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]
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)
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# Save the audio to a temporary file
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wav_bytes = base64.b64decode(completion.choices[0].message.audio.data)
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temp_path = tempfile.mktemp(suffix=".wav")
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with open(temp_path, "wb") as f:
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f.write(wav_bytes)
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# Get the text response
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text_response = completion.choices[0].message.content
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return text_response, temp_path
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except Exception as e:
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return f"Error: {str(e)}", None
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def process_audio_input(api_key, audio_path, text_prompt, selected_voice):
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"""Process audio input and generate a response"""
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try:
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if not audio_path:
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return "Please upload or record audio first.", None
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# Initialize OpenAI client with the provided API key
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client = OpenAI(api_key=api_key)
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# Read audio file and encode to base64
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with open(audio_path, "rb") as audio_file:
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audio_data = audio_file.read()
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encoded_audio = base64.b64encode(audio_data).decode('utf-8')
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# Create message content with both text and audio
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message_content = []
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if text_prompt:
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message_content.append({
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"type": "text",
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"text": text_prompt
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})
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message_content.append({
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"type": "input_audio",
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"input_audio": {
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"data": encoded_audio,
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"format": "wav"
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}
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})
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# Call OpenAI API
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completion = client.chat.completions.create(
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model="gpt-4o-audio-preview",
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modalities=["text", "audio"],
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audio={"voice": selected_voice, "format": "wav"},
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messages=[
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{
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"role": "user",
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"content": message_content
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}
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]
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)
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# Save the audio response
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wav_bytes = base64.b64decode(completion.choices[0].message.audio.data)
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temp_path = tempfile.mktemp(suffix=".wav")
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with open(temp_path, "wb") as f:
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f.write(wav_bytes)
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# Get the text response
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text_response = completion.choices[0].message.content
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return text_response, temp_path
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except Exception as e:
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return f"Error: {str(e)}", None
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def download_example_audio():
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"""Download an example audio file for testing"""
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try:
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url = "https://cdn.openai.com/API/docs/audio/alloy.wav"
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response = requests.get(url)
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response.raise_for_status()
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# Save to a temporary file
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temp_path = tempfile.mktemp(suffix=".wav")
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with open(temp_path, "wb") as f:
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f.write(response.content)
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return temp_path
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except Exception as e:
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return None
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def use_example_audio():
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"""Load example audio for the interface"""
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audio_path = download_example_audio()
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return audio_path
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# Create Gradio Interface
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with gr.Blocks(title="OpenAI Audio Chat App") as app:
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gr.Markdown("# OpenAI Audio Chat App")
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gr.Markdown("Interact with GPT-4o audio model through text and audio inputs")
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# API Key input (used across all tabs)
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api_key = gr.Textbox(
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label="OpenAI API Key",
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placeholder="Enter your OpenAI API key here",
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type="password"
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)
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with gr.Tab("Text to Audio"):
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with gr.Row():
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with gr.Column():
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text_input = gr.Textbox(
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label="Text Prompt",
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placeholder="Enter your question or prompt here...",
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lines=3
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)
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text_voice = gr.Dropdown(
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choices=VOICES,
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value="alloy",
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label="Voice"
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)
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text_submit = gr.Button("Generate Response")
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with gr.Column():
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text_output = gr.Textbox(label="AI Response (Text)", lines=5)
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audio_output = gr.Audio(label="AI Response (Audio)")
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text_submit.click(
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fn=process_text_input,
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inputs=[api_key, text_input, text_voice],
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outputs=[text_output, audio_output]
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)
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with gr.Tab("Audio Input to Audio Response"):
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with gr.Row():
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with gr.Column():
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audio_input = gr.Audio(
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label="Audio Input",
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type="filepath",
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sources=["microphone", "upload"]
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)
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example_btn = gr.Button("Use Example Audio")
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+
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accompanying_text = gr.Textbox(
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label="Accompanying Text (Optional)",
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placeholder="Add any text context or question about the audio...",
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lines=2
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)
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audio_voice = gr.Dropdown(
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choices=VOICES,
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value="alloy",
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label="Response Voice"
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)
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audio_submit = gr.Button("Process Audio & Generate Response")
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+
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with gr.Column():
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audio_text_output = gr.Textbox(label="AI Response (Text)", lines=5)
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audio_audio_output = gr.Audio(label="AI Response (Audio)")
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+
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audio_submit.click(
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fn=process_audio_input,
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inputs=[api_key, audio_input, accompanying_text, audio_voice],
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outputs=[audio_text_output, audio_audio_output]
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)
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example_btn.click(
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fn=use_example_audio,
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inputs=[],
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outputs=[audio_input]
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)
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with gr.Tab("Voice Samples"):
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gr.Markdown("## Listen to samples of each voice")
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def generate_voice_sample(api_key, voice_type):
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try:
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if not api_key:
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return "Please enter your OpenAI API key first.", None
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+
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client = OpenAI(api_key=api_key)
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completion = client.chat.completions.create(
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model="gpt-4o-audio-preview",
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modalities=["text", "audio"],
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audio={"voice": voice_type, "format": "wav"},
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messages=[
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{
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"role": "user",
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"content": f"This is a sample of the {voice_type} voice. It has its own unique tone and character."
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}
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]
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)
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# Save the audio to a temporary file
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wav_bytes = base64.b64decode(completion.choices[0].message.audio.data)
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temp_path = tempfile.mktemp(suffix=".wav")
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with open(temp_path, "wb") as f:
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f.write(wav_bytes)
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return f"Sample generated with voice: {voice_type}", temp_path
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except Exception as e:
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return f"Error: {str(e)}", None
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+
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with gr.Row():
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sample_voice = gr.Dropdown(
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choices=VOICES,
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value="alloy",
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label="Select Voice Sample"
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)
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sample_btn = gr.Button("Generate Sample")
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with gr.Row():
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sample_text = gr.Textbox(label="Status")
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sample_audio = gr.Audio(label="Voice Sample")
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sample_btn.click(
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fn=generate_voice_sample,
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inputs=[api_key, sample_voice],
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outputs=[sample_text, sample_audio]
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)
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gr.Markdown("""
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## Notes:
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- You must provide your OpenAI API key in the field above
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- The model used is `gpt-4o-audio-preview`
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- Audio inputs should be in WAV format
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- Available voices: alloy, ash, ballad, coral, echo, fable, onyx, nova, sage, shimmer, and verse
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""")
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if __name__ == "__main__":
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app.launch()
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