| Name | wyn-voice JSON |
| Version |
0.1.9
JSON |
| download |
| home_page | None |
| Summary | This library provides audio interface with OpenAI endpoint. |
| upload_time | 2024-09-08 00:11:31 |
| maintainer | None |
| docs_url | None |
| author | Yiqiao Yin |
| requires_python | <4.0,>=3.9 |
| license | None |
| keywords |
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| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# Wyn Voice: A Conversational AI and Audio Processing Library
## Introduction and Motivation
Wyn Voice is a Python library designed to simplify the process of creating conversational AI applications that leverage OpenAI's GPT models. The library provides an easy-to-use interface for generating responses to user inputs and includes functionality for recording and processing audio, making it suitable for building interactive voice-based applications.
## Directory Structure
The project directory is organized as follows:
```
.
├── pyproject.toml
├── README.md
└── wyn_voice
└── chat.py
```
- `pyproject.toml`: Contains the project's dependencies and other configuration settings.
- `README.md`: This file, providing an overview and usage instructions.
- `wyn_voice`: A folder containing the main library code.
- `chat.py`: The script defining the `ChatBot` and `AudioProcessor` classes.
## Example Usage
To get started with Wyn Voice, follow these steps:
### Installation
First, install the necessary packages using pip:
```bash
pip install wyn-voice pyautogen pydub openai
```
### Using the ChatBot Class
The `ChatBot` class allows you to interact with OpenAI's GPT models to generate responses based on user input.
```python
from wyn_voice.chat import ChatBot
# Initialize the ChatBot with your OpenAI API key
api_key = 'your-openai-api-key'
chatbot = ChatBot(api_key)
# Generate a response from the chatbot
prompt = "Hello, how are you?"
response = chatbot.generate_response(prompt)
print("ChatBot:", response)
# Retrieve the conversation history
history = chatbot.get_history()
print("Conversation History:", history)
```
### Using the AudioProcessor Class
The `AudioProcessor` class provides functionality to record audio, process it, and interact with the `ChatBot`.
```python
from wyn_voice.chat import ChatBot, AudioProcessor
# Initialize the ChatBot with your OpenAI API key
api_key = 'your-openai-api-key'
chatbot = ChatBot(api_key)
# Initialize the AudioProcessor with the ChatBot
audio_processor = AudioProcessor(chatbot)
# Record audio and generate a response
transcript = audio_processor.process_audio_and_generate_response()
print("Transcript:", transcript)
# Record audio and get the transcribed text
text = audio_processor.voice_to_text()
print("Transcribed Text:", text)
# Convert text to speech and save it as an mp3 file
response_text = "This is a test response."
output_file = audio_processor.text_to_voice(response_text)
print("Saved audio response to:", output_file)
# Play the saved audio file
audio_processor.play_audio(output_file)
```
## Author
Yiqiao Yin
## Site
[https://www.y-yin.io/](https://www.y-yin.io/)
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"description": "# Wyn Voice: A Conversational AI and Audio Processing Library\n\n## Introduction and Motivation\nWyn Voice is a Python library designed to simplify the process of creating conversational AI applications that leverage OpenAI's GPT models. The library provides an easy-to-use interface for generating responses to user inputs and includes functionality for recording and processing audio, making it suitable for building interactive voice-based applications.\n\n## Directory Structure\nThe project directory is organized as follows:\n\n```\n.\n\u251c\u2500\u2500 pyproject.toml\n\u251c\u2500\u2500 README.md\n\u2514\u2500\u2500 wyn_voice\n \u2514\u2500\u2500 chat.py\n```\n\n- `pyproject.toml`: Contains the project's dependencies and other configuration settings.\n- `README.md`: This file, providing an overview and usage instructions.\n- `wyn_voice`: A folder containing the main library code.\n - `chat.py`: The script defining the `ChatBot` and `AudioProcessor` classes.\n\n## Example Usage\nTo get started with Wyn Voice, follow these steps:\n\n### Installation\nFirst, install the necessary packages using pip:\n\n```bash\npip install wyn-voice pyautogen pydub openai\n```\n\n### Using the ChatBot Class\nThe `ChatBot` class allows you to interact with OpenAI's GPT models to generate responses based on user input.\n\n```python\nfrom wyn_voice.chat import ChatBot\n\n# Initialize the ChatBot with your OpenAI API key\napi_key = 'your-openai-api-key'\nchatbot = ChatBot(api_key)\n\n# Generate a response from the chatbot\nprompt = \"Hello, how are you?\"\nresponse = chatbot.generate_response(prompt)\nprint(\"ChatBot:\", response)\n\n# Retrieve the conversation history\nhistory = chatbot.get_history()\nprint(\"Conversation History:\", history)\n```\n\n### Using the AudioProcessor Class\nThe `AudioProcessor` class provides functionality to record audio, process it, and interact with the `ChatBot`.\n\n```python\nfrom wyn_voice.chat import ChatBot, AudioProcessor\n\n# Initialize the ChatBot with your OpenAI API key\napi_key = 'your-openai-api-key'\nchatbot = ChatBot(api_key)\n\n# Initialize the AudioProcessor with the ChatBot\naudio_processor = AudioProcessor(chatbot)\n\n# Record audio and generate a response\ntranscript = audio_processor.process_audio_and_generate_response()\nprint(\"Transcript:\", transcript)\n\n# Record audio and get the transcribed text\ntext = audio_processor.voice_to_text()\nprint(\"Transcribed Text:\", text)\n\n# Convert text to speech and save it as an mp3 file\nresponse_text = \"This is a test response.\"\noutput_file = audio_processor.text_to_voice(response_text)\nprint(\"Saved audio response to:\", output_file)\n\n# Play the saved audio file\naudio_processor.play_audio(output_file)\n```\n\n## Author\nYiqiao Yin\n\n## Site\n[https://www.y-yin.io/](https://www.y-yin.io/)\n",
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