Precise “Hey Bobo” Wake-Word

@avinashdubeyg209

Hi there, I am an ML engineer. I can start right away and deliver within the deadline. So, Let’s have a quick conversation. I can be more specific once we get all the requirements and information required to execute the project. Thank you!!

@abdf2010

Hey, I liked your project, Precise “Hey Bobo” Wake-Word and believe I can help you with the project. With my background in Python, Linux, Electronics, I'm confident I can meet your requirements. Would be glad to go over specifics if you're interested.

@Hemantt841

You’re looking to develop a production-ready wake-word model that reliably recognizes “Hey Bobo” and its phonetic variants on both ESP32 and Raspberry Pi, with strict accuracy and latency requirements in moderately noisy environments. You need a fully offline solution running on ESP-IDF and Raspbian/Ubuntu, complete with demo code, performance metrics, and clear instructions for ongoing data collection and sensitivity tuning. With over 15 years of experience and 200+ projects completed, I specialize in Python, Linux, embedded systems, audio processing, and machine learning—skills that align perfectly with your need for a lightweight, real-time wake-word detection system on constrained hardware. I have previously delivered similar embedded audio models optimized for low latency and minimal false positives. I will start by designing a balanced dataset capturing your target phrase and approved variants, then train and optimize a TensorFlow Lite Micro or Porcupine-based model tailored to the ESP32 and Raspberry Pi platforms. The deliverables will include fully tested firmware, demo applications for streaming audio and event signaling, detailed resource usage reports, and clear documentation to enable you to refine the model further. I expect to complete this within 4–5 weeks. Let’s discuss your exact requirements and how I can help bring this wake-word system to life.

@kratika0510

Drawing from my expertise in Electrical Engineering, Electronics and Python programming, I propose an outstanding solution to one of your critical needs: a reliable wake-word model that performs at optimal levels on both the ESP32 and Raspberry Pi boards. In particular, I am skilled in the use of tools like TensorFlow Lite Micro, Porcupine and Picovoice DIY which would be highly beneficial to this project. Importantly, I have experience with delivering edge AI solutions with little or no reliance on cloud services; this means I can provide you with a wake-word model that runs on ESP-IDF for ESP32 and Raspbian/Ubuntu for Raspberry Pi without the need for cloud calls, simultaneously ensuring low latency. In addition to meeting all your requirements for this project, my approach would be comprehensive. Alongside delivering an optimized and efficient wake-word model that boasts a ≥ 95 % wake-up rate in moderate background noise, with less than 2 false activations per hour, my deliverables will also include clear instructions on collecting additional audio samples. You will be empowered to continue fine-tuning the model if needed while maintaining high precision.

@rafaellDev

Hello, I can build a production-ready wake-word detection system for “Hey Bobo” that runs locally on both ESP32 and Raspberry Pi without any cloud dependency. I have extensive experience with embedded Linux systems, audio pipelines, and lightweight AI models for edge devices. My approach focuses on reliability, low latency, and efficient CPU/memory usage. Technical approach I will implement a custom wake-word detection pipeline using a lightweight neural network optimized for edge devices. The system will include: • Real-time audio capture from microphone • DSP feature extraction (MFCC) • Wake-word classification model • Event callback when the phrase is detected The solution will run entirely offline and will be optimized for both Raspberry Pi and ESP32 environments. Key features • Wake-word detection for “Hey Bobo” with phonetic tolerance (hello bo, hey bebo, etc.) • Low latency response (<250 ms) • High accuracy in moderately noisy environments • Low CPU and memory footprint suitable for embedded hardware • Fully reproducible build and source code