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2025 02 28 dhwani basic features v 0 0 01

Week 1 - project dhwani

Project Roadmap: Advanced Voice Interaction System Development

1. Architecture and Design

  1. Design the architecture with scalability and modularity in mind
  2. Write comprehensive benchmarks for performance evaluation
  3. Develop robust code evaluation processes
  4. Implement GitHub Actions for continuous integration and automated testing
  5. Design error handling and recovery mechanisms

2. Natural Language Understanding (NLU) and API Development

  1. Implement advanced NLU capabilities
  2. Standardize API format for consistency across the system
  3. Update function calls with actual inputs and responses
  4. Expand support for all Alexa-like functions
  5. Develop context awareness and personalization features

3. Language Processing and Model Optimization

  1. Add auto-detection of language
  2. Switch and optimize ASR model
  3. Fix bug with repeated words
  4. Implement lazy loading of models
  5. Reduce latency and response times for all interactions

4. Documentation and Testing

  1. Improve documentation for clarity and completeness
  2. Test with various compute options (beyond T4 GPU)
  3. Write parser to show daily speed improvements
  4. Implement comprehensive logging for all steps

5. Gradio Demo and Workflow Development

  1. Enhance Gradio demo with language ASR model loading button
  2. Focus on workflow verification (Month 1)
  3. Implement key workflows: a. Two-way translation for tourists b. Question-answering in source language c. Call center analytics and automation d. Develop 7 additional use-cases (total 10)

6. Component Integration and Optimization

  1. Refine and optimize the component chain:
  2. ASR -> NLU -> Translate -> TTS
  3. Text -> NLU -> TTS -> ASR
  4. Ensure seamless integration between all components

Top 3 Priority Items

  1. Natural Language Understanding (NLU) Implementation
  2. Enhance accuracy in comprehending user intent
  3. Integrate context awareness and personalization features

  4. Improve overall interaction quality and relevance

  5. Error Handling and Recovery Mechanism

  6. Design clear error messages and alternative options
  7. Implement user guidance for error situations

  8. Minimize user frustration and improve system robustness

  9. Performance Optimization and Benchmarking

  10. Focus on reducing latency and response times
  11. Implement comprehensive logging and performance tracking
  12. Conduct regular benchmarks to guide optimization efforts

Summary of Tasks

This project aims to develop an advanced voice interaction system with state-of-the-art natural language understanding, personalization, and error handling capabilities. Key focus areas include architectural design, API standardization, workflow implementation, and continuous performance optimization. The system will support multiple use-cases such as translation, question-answering, and call center analytics. Development will prioritize NLU implementation, robust error handling, and performance optimization to ensure a highly efficient, user-friendly, and adaptable voice interaction platform.

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initial idea !!!

Basic Features For Dhwani - v.0.0.1 for user Acceptance Testing second phase

Standardize Api format,
Updatw the function calls, with actual inputs and response

Support all Alexa functions.

Fix bug with repeated words

Add auto detection of language,
Switch model fur for asr

Fix docs, make everything clear

Hf load time for gpu restart 10 mins with t4.

Should test with other compute.

Write benchmarks

Design the architecture now, don't blindly build and let it fail for lack of testing.

Write evaluation for for code,
Add github actions, trigger tests for all commits.

Gradio demo,
Add button, to load languages ASR .

Do lazy loading of models

Month 1 - Use only the gradio demo for verification and designing of workflows for voice mode.

Don't spend time on UX development.

We should reduce the Latency and response times for every interactions.

Logs every steps, write a psrser to show speed improvements every day.

Workflows 1. Simple translation flow . Source language to target language and reverse flow for two way conversations. Tourists use cases

  1. Answer machine - ask a question in source language, get response in source language with llm geherated response.

  2. Call center analytics and automation automation. Large scale audio input, llm parsers and report creation.

Consider 10 use- cases.

Identify components and steps in order of function call.

ASR -> translate -> TTS ,

Text -> TTS ->ASR ,