PIP Installable CLI Tool
PIP Installable CLI Tool README.md
## Technologies and Tools
- Python
- OpenAI (openai)
- Whisper (openai-whisper)
- Chromadb
- SoundFile
- SoundDevice
- Pydub
- Pyannote.audio
- Faiss-cpu
- gTTS
- spaCy
- BeautifulSoup
- googlesearch-python
- tiktoken
- geocoder
- Scrapy
## Functionality
- Conversational voice assistant named "Jarvis" powered by OpenAI's GPT models
- Real-time speech-to-text and text-to-speech capabilities
- Manages conversation history using Chromadb for storing and retrieving context
- Speaker recognition and diarization using Pyannote.audio
- Integrates with various APIs for weather information, geocoding, web search
- Customizable configuration and settings
- Command-line interface for user interaction
- Implements background processing and multiprocessing for performance
## Relevant Skills
- Advanced usage of OpenAI's chat completion API and GPT models for conversational AI
- Speech recognition and synthesis integration (Whisper, gTTS, etc.)
- Vector database management with Chromadb and Faiss for efficient storage and retrieval
- Web scraping and information extraction using BeautifulSoup and Scrapy
- API integration (OpenWeatherMap, geonames)
- Multiprocessing and multithreading for concurrent execution
- Logging and configuration management
- Unit testing with Python's unittest module
## Example Code
- Conversational flow management in `conversationalist.py`:
```python
def converse(memory, interrupt_event, start_event, stop_event):
audio_queue = multiprocessing.Queue()
text_queue = multiprocessing.Queue()
...
while not stop_event.is_set():
try:
text, ts = text_queue.get(timeout=1)
...
if wake_word in text.lower():
...
new_history = process_assistant_response(...)
...
```
- Speaker recognition using Pyannote.audio in `audio_identifier.py`:
```python
class SpeakerIdentifier:
def __init__(self, ...):
...
self.pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization")
def get_speakers(self, audio_data_io):
diarization = self.pipeline(audio_data_io)
...
for turn, _, speaker in diarization.itertracks(yield_label=True):
...
speaker_id = self.get_add_unknown_speaker(speakers[speaker])
...
```
## Notable Achievements
- Developed a comprehensive conversational AI system with voice interaction
- Implemented advanced techniques like speaker diarization and vector database indexing
- Integrated multiple APIs and libraries to enhance the assistant's capabilities
- Optimized performance through multiprocessing and background task handling
- Designed a modular and extensible architecture for easy customization and improvement
The code demonstrates strong proficiency in Python programming, particularly in the areas of natural language processing, speech technologies, and system design. The developer exhibits the ability to integrate various libraries and APIs to create a cohesive and functional application. The use of multiprocessing, configuration management, and testing also highlights good software engineering practices.