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| import gradio as gr | |
| import openai | |
| import config | |
| import os | |
| import azure.cognitiveservices.speech as speechsdk | |
| openai.api_key = os.environ['OPEN_AI_KEY'] | |
| speech_config = speechsdk.SpeechConfig(subscription=os.environ['AZURE_SPEECH_KEY'], region="westeurope") | |
| #audio_config = speechsdk.audio.AudioOutputConfig(use_default_speaker=True) | |
| speech_config.speech_synthesis_voice_name = "nl-NL-ColetteNeural" | |
| speech_synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config, audio_config=None) | |
| context = [{"role": "system", "content": 'You are a clever and helpfull conversationalist. \ | |
| Respond to all input in 25 words or less. \ | |
| Do not mention that you are an AI or service of any kind.'}] | |
| def transcribe(model: str, audio: str): | |
| audio_file = open(audio, "rb") | |
| transcript = openai.Audio.transcribe(model, audio_file) | |
| return transcript | |
| def gen_response(model: str, context: list): | |
| response = openai.ChatCompletion.create(model=model, messages=context) | |
| return response["choices"][0]["message"] | |
| def gen_voice(response, response_filename): | |
| reponse_audio = speech_synthesizer.speak_text_async(response['content']).get() | |
| stream = speechsdk.AudioDataStream(reponse_audio) | |
| stream.save_to_wav_file(response_filename) | |
| def conversation(audio:str): | |
| transcript = transcribe("whisper-1", audio) | |
| context.append({"role": "user", "content": transcript['text']}) | |
| response = gen_response("gpt-3.5-turbo", context) | |
| context.append(response) | |
| gen_voice(response, "voice.wav") | |
| chat_transcript = "" | |
| for message in context: | |
| if message['role'] != 'system': | |
| chat_transcript += message['role'] + ": " + message['content'] + "\n\n" | |
| return "voice.wav" | |
| # set a custom theme | |
| theme = gr.themes.Default().set( | |
| body_background_fill="#000000", | |
| ) | |
| with gr.Blocks(theme=theme) as ui: | |
| # advisor image input and microphone input | |
| #advisor = gr.Image(value=config.TARS_LOGO).style(width=config.LOGO_IMAGE_WIDTH, height=config.LOGO_IMAGE_HEIGHT) | |
| audio_input = gr.Audio(source="microphone", type="filepath") | |
| audio_output = gr.Audio() | |
| # text transcript output and audio | |
| # text_output = gr.Textbox(label="Transcript") | |
| btn = gr.Button("Run") | |
| btn.click(fn=conversation, inputs=audio_input, outputs=[audio_output]) | |
| ui.launch() | |