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binqiangliu
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Upload folder using huggingface_hub
Browse files- .env +1 -0
- .gitattributes +1 -0
- README.md +0 -1
- app.py +3 -6
- flagged/log.csv +2 -0
- requirements.txt +1 -3
- response.mp3 +3 -0
.env
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OPENAI_API_KEY="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"
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.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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response.mp3 filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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title: VoiceChatbot
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python_version: 3.11.4
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app_file: app.py
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sdk: gradio
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sdk_version: 3.34.0
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---
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title: VoiceChatbot
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app_file: app.py
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sdk: gradio
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sdk_version: 3.34.0
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app.py
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#pip install pyttsx3
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#pip install pydantic
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#pip install openai gradio pyttsx3 pydantic
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import gradio as gr
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import openai
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import pyttsx3
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#import python-espeak
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#from gtts import gTTS
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#import pydantic
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from dotenv import load_dotenv
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# Global variable to hold the chat history, initialise with system role
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conversation = [
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{"role": "system", "content": "You are
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]
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# transcribe function to record the audio input
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# Text to speech
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engine = pyttsx3.init()
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# engine = pyttsx3.init('espeak')
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# engine.setProperty('engine', 'gTTS')
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engine.setProperty("rate", 150)
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engine.setProperty("voice", "english-us")
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engine.save_to_file(system_message, "response.mp3")
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# Gradio output
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bot = gr.Interface(fn=transcribe, inputs=gr.Audio(source="microphone", type="filepath"), outputs="audio")
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bot.launch(share=
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iface.share()
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#pip install pyttsx3
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#pip install pydantic
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#pip install openai gradio pyttsx3 pydantic
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#pip install python-dotenv
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import gradio as gr
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import openai
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import pyttsx3
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#import pydantic
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from dotenv import load_dotenv
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# Global variable to hold the chat history, initialise with system role
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conversation = [
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{"role": "system", "content": "You are an intelligent professor."}
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]
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# transcribe function to record the audio input
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# Text to speech
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engine = pyttsx3.init()
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engine.setProperty("rate", 150)
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engine.setProperty("voice", "english-us")
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engine.save_to_file(system_message, "response.mp3")
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# Gradio output
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bot = gr.Interface(fn=transcribe, inputs=gr.Audio(source="microphone", type="filepath"), outputs="audio")
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bot.launch(share=True)
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iface.share()
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flagged/log.csv
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audio,output,flag,username,timestamp
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,,,,2023-07-04 03:30:51.207212
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requirements.txt
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openai
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gradio
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#pyttsx
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pyttsx3
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python-dotenv
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#python-espeak
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openai
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gradio
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pyttsx3
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python-dotenv
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response.mp3
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version https://git-lfs.github.com/spec/v1
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oid sha256:9f11924e8a95e28a8e376cf00cff3c4edb19c6f96243d9d0e22b6cb625bf54b6
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size 1304548
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