AI Engineer for Custom OpenWakeWord Project

@nexvative

Hi, I can build and train a production-quality custom OpenWakeWord model focused on high detection accuracy, low latency, and especially minimizing false activations. My approach: • Build a diverse positive dataset covering multiple speakers, accents, speaking speeds, distances, microphones, and environments • Create a strong negative/Hard Negative dataset containing phonetically similar phrases, conversations, TV/audio, music, noise, and realistic background conditions • Train/fine-tune the OpenWakeWord pipeline directly in Python without using the prohibited OpenCollab training approach • Apply augmentation for noise, reverberation, volume variation, microphone differences, and distance • Perform threshold calibration and hard-negative mining to optimize the accuracy/false-positive tradeoff • Benchmark detection rate, false activations per hour, response latency, and robustness across environments • Export the final model in a lightweight deployment format such as ONNX/TFLite where appropriate Deliverables: 1. Fully trained custom OpenWakeWord model 2. Training/configuration scripts 3. Evaluation dataset methodology and performance report 4. Inference example and integration code 5. Retraining and deployment documentation I’ll establish measurable acceptance targets before training, particularly false positives per hour, so “high accuracy” is backed by real testing rather than a single validation score.

@FreeLancers360

Hi, Thanks for posting "AI Engineer for Custom OpenWakeWord Project" — it lands right in our wheelhouse, and we'd be thrilled to bring it to life. I don’t just want to tick the boxes here; my aim is to hand you something that genuinely goes beyond what you pictured. From your brief I can see this involves ai, machine learning — all areas we handle in-house. We specialise in Python, Audio Processing, Deep Learning, Data Collection, which lines up directly with what you need. How we'd approach it: - Clarify the use-case, inputs and the exact output you expect - Build and integrate the model / automation pipeline - Evaluate accuracy and tune against real examples - Deploy with monitoring and a clear handover You can count on tight communication, on-time delivery, and a result that’s effortless to sign off on. I can start right away and keep you updated at every step — let’s make this a great one. Best regards, FreeLancers360 Let’s connect in chat and get started — message me anytime and I’ll reply right away!

@alluankit01

Hi, I can train a custom OpenWakeWord model for your wake word with focus on high accuracy, fast response, noisy-environment reliability, and very low false positives. The best solution is to first review your target wake word, available positive samples, speaker/accent coverage, noise conditions, and deployment target. Then I’ll prepare the dataset strategy, train/fine-tune the OpenWakeWord pipeline locally, build a strong negative dataset with similar-sounding words, speech, music, TV/background noise, and test the model against real-world audio. I’m comfortable with Python, OpenWakeWord, keyword spotting, audio preprocessing, speech datasets, negative sample design, model evaluation, ONNX/TFLite-style inference, false-positive reduction, threshold tuning, and integration documentation. Deliverables will include: * Custom trained OpenWakeWord model * Dataset preparation guidance * Positive/negative sample strategy * Training scripts/configuration * Accuracy and false-positive testing * Threshold tuning notes * Inference/integration files * Real-world test results * Usage and retraining documentation I will not use OpenColab. I’ll focus on a reliable local/custom training workflow with proper testing, clear metrics, and practical steps to improve wake-word robustness across speakers, accents, distances, and noisy environments. Best regards Ankit

@NEHABHAT92

As a seasoned AI/ML engineer, I’ve acquired an extensive range of skills vital for your project. First and foremost, my proficiency in Python will prove invaluable in training the custom OpenWakeWord model you seek. Additionally, my expertise in Machine Learning and Deep Learning paired with my experience in Speech Recognition and Audio Processing will enable me to optimize the model for high accuracy, minimal false positive rate, fast response time and robust performance in noisy environments - all while maintaining its low-latency characteristic. Regarding deliverables, I assure you that I can handle the task at hand satisfactorily. My years of experience in various aspects of web development and mobile app development are well-aligned to this project’s unique requirements. Moreover, my familiarity with ONNX (although TFLite is preferred) suggests a smooth integration process during delivery - providing you with a ready-to-use, fully trained custom OpenWakeWord model alongside inference files and clear integration instructions. In terms of timeline and pricing, I am able to provide a fixed-price quote while working well within your given timeframe. My bid also includes an advantageous three-month free support post-delivery as a gesture to ensure long-term satisfaction with my service. My company is an adept provider of IT services; my particular inclination towards AI/ML makes me confid project!

@mussaibb7

I'd love to dive into this OpenWakeWord project and deliver a high-accuracy, low-false positives custom wake word implementation. With my expertise in Python, audio processing, deep learning, data collection, and speech recognition, I'm confident in creating a clean, maintainable backend structure that works under real usage. As a seasoned AI engineer, I've successfully delivered similar work through Jarvis AI - Personal Automation Assistant, where I orchestrated 30+ automated workflows, and deployed a multi-client ML inference API with sub-200ms latency. This experience maps well to the delivery risk in this project, ensuring your custom wake word implementation meets the required accuracy and reliability. To achieve this, I'll focus on a robust API integration flow, error handling, and maintainable backend code. I'll also provide environment setup instructions and a comprehensive README to ensure seamless deployment. Before starting, I'd like to clarify the scope, first milestone, and most critical technical constraint to ensure we're aligned on the project's goals. Should the first milestone focus on a working backend flow or on hardening an existing service?

@citysceneglobal

As an AI/ML engineer, I have the experience and expertise to deliver on your project's goals of training an OpenWakeWord model with a custom wake word. Building highly accurate models is my specialization, and I am well-versed in OpenWakeWord, Machine Learning/Deep Learning techniques, and Speech Recognition/Audio Processing. To ensure optimal performance, I will employ real positive voice samples from a diverse range of speakers with different accents, speech speeds, and environments. Additionally, I will incorporate a substantial negative dataset consisting of similar-sounding words, random conversations, TV/audio samples etc. to minimize false positives. I am also knowledgeable in the deployment of models through ONNX or TFLite, further enhancing the speed and efficiency of the trained model. Having worked on several audio processing projects in the past, including keyword spotting and wake-word models specifically, I fully understand your requirements. I am ready to deliver not only the trained model but also comprehensive documentation on its usage and retraining if needed. Don't settle for less when it comes to wake word reliability - let me bring my skills to bear on this invaluable task for your project!

@rahulk0744

As an AI/ML engineer with a profound grip on Machine Learning, Deep Learning, Speech Recognition, and Audio Processing - truly, Python - I stand confident in my ability to train the ideal OpenWakeWord model for your project. Not only have I worked extensively with OpenWakeWord before, but I’ve also got experience training keyword spotting models - just like you require for this task. The combination of these skills will ensure that the custom wake word we train together is highly accurate, ultra-responsive, and critically - resistant to false activations. To make sure the model is flawlessly operable in real-life scenarios (both in terms of accuracy and latency), I propose thoroughly testing it with varied voice samples from multiple speakers, accents, speaking speeds and distances. To reduce false positive activations to an absolute minimum, I’ll introduce a broad array of negative samples simulating similar sounding words, random conversations as well as different types of background noise.

@prachia77

Hi, I went through your project, and training a high-accuracy OpenWakeWord model with low false activations is something we can definitely handle for you. I completely understand that Google Colab or open-collab scripts won't cut it for a production-grade model. To get real-world accuracy across different accents, room acoustics, and distances, we will train this locally using dedicated GPU setups and proper audio augmentation. Here is how we will approach your project: 1. Data Collection & Augmentation: We will generate real and diverse positive samples using multiple speaker voices, pitch variations, and room impulse responses. 2. Hard-Negative Training: To bring false activations down to near zero, we will train the model against hard negatives, including phonetically similar words, background TV audio, music, and everyday speech datasets like Common Voice and FSD50K. 3. Model Training & ONNX Export: We will train the classification layer on top of OpenWakeWord's speech embeddings and export the final model to lightweight ONNX or TFLite formats for fast, low-latency execution. 4. Testing & Metrics: We will run extensive false-activation-per-hour benchmarks and send you the complete evaluation report along with the clean training scripts. Let's connect on chat so we can get started right away. Best regards, Prachi Agrawal Webix Infotech

@ujjwalmahar01

Hi, I can build and train a production-ready custom OpenWakeWord model optimized for high accuracy, fast response, and very low false positives. The training pipeline will include diverse positive samples across multiple speakers, accents, speaking speeds, distances, microphones, and real-world environments. For negatives, I’ll use hard examples such as similar-sounding phrases, conversations, TV/radio, music, and background noise to improve robustness. My approach includes: * OpenWakeWord/KWS model training and optimization * Audio preprocessing, augmentation, and dataset balancing * Hard-negative mining to reduce false activations * Threshold/sensitivity tuning * Evaluation across real-world conditions * Metrics: precision, recall, F1, false accept/reject rates * ONNX/TFLite-compatible inference (if needed) * Clean Python training and inference scripts You will receive the trained model, full training pipeline, configs, evaluation results, and clear integration/retraining instructions. I will not use OpenCollab; the pipeline will be independently built and fully reproducible, focusing on measurable performance rather than just basic functionality. I can also help improve performance further through better speaker diversity, augmentation strategies, and threshold calibration to balance responsiveness and false wake-ups.

@syedalihasnat929

Hi, I’m an AI/ML Engineer with strong experience in Python, machine learning, deep learning and audio/AI systems. I can build the custom OpenWakeWord pipeline with a strong focus on detection accuracy and minimizing false activations. My approach will include: - Preparing positive samples across speakers and conditions - Building a strong negative/noise dataset - Training and evaluating the wake-word model - Testing false-positive and detection rates - Optimizing inference performance - Delivering the trained model, scripts and clear integration documentation I will provide measurable test results so you can evaluate the model in real-world conditions. Best regards, Syed Ali AI/ML Engineer

@Geetam00

With years of experience in Artificial Intelligence and Machine Learning, I'm the perfect match for your custom OpenWakeWord project. My proficiency with Python, OpenWakeWord, and other related technologies such as ONNX and TFLite make me well-equipped to train a bespoke wake-word model that excels in terms of accuracy, responsiveness, and resistance to false activations. I understand that achieving these objectives requires more than just a proficiently trained model and that is why I shall go an extra mile in collecting an extensive dataset comprising real positive voice samples of different speakers, varied environments, speaking speeds, etc., as well as a large negative dataset containing similar-sounding words and other potential sources of false activations. I will then optimize the model for high detection accuracy, extremely low false positive rate, fast response time, and robust performance even in noisy situations. My previous experiences with similar projects have given me an understanding of the importance of thorough testing using real-world audio samples. Therefore, when I deliver the completed project to you, rest assured that it wouldn't be without extensive testing and performance metrics demonstrating the model's efficacy in various scenarios. Alongside the fully trained custom OpenWakeWord model and training scripts.

@ronakkataria1

First question, because it drives everything else: what is the wake word? Short or phonetically common words have a false-accept floor no amount of training removes. "Hey Jarvis" behaves very differently from a two-syllable everyday word. If yours sits in the risky category I'd rather tell you before we start than after. One correction to the brief. openWakeWord's own pipeline is synthetic-first: Piper TTS generates thousands of positive utterances across many voices, accents and speaking rates, then augments them with room impulse responses and noise. Recording real speakers gives far fewer samples at far higher cost and generalises worse. I'd generate positives synthetically at scale and keep your real recordings as a held-out test set — which is where they're genuinely valuable. Negatives are where the false-positive rate is won: hours of speech, TV, music, and phonetic near-misses of your specific wake word. On acceptance: "low false positives" isn't measurable, and I'd rather not hand over against a vague standard. The real metric is false accepts per hour on a fixed negative corpus. I'd agree a target up front — say under 0.5 FA/hour at a stated recall — and report against that same corpus at delivery, so it's pass/fail rather than opinion. Deliverables as listed, ONNX and TFLite, plus the retraining script so wake-word changes don't depend on me. Trained locally, not on Colab. Ronak — 8+ yrs; production speech pipelines, on-device deployment, quantization.

@mikes126

Hi there, let's have short meeting if you wanna discuss the wake word and target accuracy. I can build a custom OpenWakeWord model focused on high detection accuracy and very low false positives. I’ll handle the full pipeline: positive samples from different speakers/accents/speeds/distances, hard-negative audio with similar words, speech, TV, music and noise, then train and test the model with real-world audio. I’ll use Python/audio processing and optimize inference for fast response. I can also provide the trained model, training/config files, evaluation metrics, and clear integration/retraining docs. For reliability, I’d recommend hard-negative mining after initial testing and threshold tuning based on real usage conditions. I’ll avoid the OpenCollab training approach. Budget: $150 Timeline: 7 days

@caozhuyin

I specialize in training custom wake-word models using OpenWakeWord framework. For your project, I'll implement a robust pipeline: (1) Collect diverse positive samples across speakers, accents, and environments; (2) Generate synthetic samples via TTS + audio augmentation (noise, RIR, pitch shifting); (3) Build comprehensive negative dataset with similar-sounding words and conversational speech; (4) Train the model optimizing for high accuracy and extremely low false-positive rate; (5) Export to ONNX/TFLite for production deployment. Deliverables: fully trained custom wake-word model, test results with false-accept/false-reject metrics, integration guide. I'll provide a quick validation demo with a toy wake word before finalizing. 4-day turnaround, ₹7,000 fixed price. Ready to start immediately.

@hematarboun

Hi, my name is Ibrahim , an AI/ML Engineer specializing in applied deep learning and audio/speech systems. I've built production AI pipelines including RAG systems, predictive models, and deployed inference services with ONNX/TFLite for real-world environments — so I understand the gap between a model that "works in testing" and one that's reliable in production, which is exactly the challenge with wake-word detection. Here's how I'd approach your custom OpenWakeWord model: Data Strategy First — Before training, I'd build a diverse positive dataset (multiple speakers, accents, distances, speaking speeds) using both real recordings and synthetic TTS augmentation to cover edge cases cheaply. Hard Negative Mining — Rather than just generic negative audio, I'd specifically target phonetically similar words,media speech, and background chatter — this is usually where false-positive rates actually get reduced. Training & Fine-tune OpenWakeWord's architecture on your target word, iterating with feature-embedding augmentation for robustness across noise conditions. Validation Loop — Test against a held-out real-world audio set (not just synthetic), measuring false-accept/false-reject rates before final delivery — not opencollab-based, fully local/custom training as required. Delivery — Trained model + inference files (ONNX/TFLite) + training config + documentation for retraining. Happy to share a fixed price once we align on target false-positive tolerance and deployment environment.

@phpabby

Hi, I can train a custom OpenWakeWord model with a strong focus on high accuracy and very low false positives. I have worked on Python-based AI models, audio processing, model optimization, and ONNX deployment, and I understand that wake-word systems fail when negative data is not handled correctly. My approach would be to build a balanced training pipeline using diverse positive samples (different speakers, accents, distances, microphones, and noisy environments) and a large negative dataset with similar-sounding words, conversations, TV audio, music, and background noise. I would then tune the detection threshold, confidence scoring, and post-processing to reduce false activations while maintaining fast response time. The model will be tested with real-world audio, and I will provide accuracy, precision/recall, and false positive evaluation results. I can also export the model in ONNX/TFLite format and provide integration and retraining documentation. I understand that OpenCollab-based training is not acceptable, so the training will be performed in a proper local or cloud ML environment with reproducible scripts. I can start immediately and deliver a production-ready model within the agreed timeline. Regards, Abhishek

@sunilmeena992814

Hi, I can develop and train a production-quality **custom OpenWakeWord model** focused on high accuracy, fast detection, and extremely low false positives. My approach includes: * Training with positive samples from multiple speakers, accents, speeds, distances, and environments. * Building a strong negative dataset with similar-sounding phrases, conversations, TV/audio, music, and background noise. * Python-based OpenWakeWord training without using OpenCollab. * Audio augmentation for noise, reverberation, volume, and microphone variation. * Threshold optimization to balance detection accuracy and false activations. * Testing with unseen speakers and real-world environments. * Measuring precision, recall, false-positive rate, and detection latency. * Providing ONNX/TFLite integration where appropriate. ### Deliverables • Trained custom wake-word model • Training scripts/configuration • Inference files • Performance/test report • Deployment and retraining documentation I recommend creating **hard-negative samples** specifically around words and phrases that sound similar to your wake word, as this is critical for minimizing false activations. **Timeline:** 1–2 weeks I’m ready to review your wake word and existing dataset and begin immediately.

@Waqas12301

Hi, I can build a custom OpenWakeWord model focused on high detection accuracy and, most importantly, very low false activations. My approach would include: Collecting/curating positive samples across speakers, accents, distances, speeds and environments Building a challenging negative dataset with similar-sounding phrases, conversations, TV/audio, music and background noise Training/fine-tuning the OpenWakeWord pipeline with iterative evaluation Testing across clean and noisy real-world recordings Optimizing detection threshold, latency and false-positive rate Exporting the final model with the required inference files and integration support Providing reproducible Python training/configuration scripts and documentation I’ll provide measurable results including accuracy, false-positive rate, false-negative rate and detection latency, rather than relying only on subjective testing. I can also structure the dataset and training pipeline so the wake word can be retrained or improved later with additional recordings. Timeline: 7–10 days Fixed price: ₹12,500 Deliverables: trained model, source/training scripts, test results, requirements, and complete usage/retraining documentation. I’m ready to start with the wake-word specification and sample recordings.

@LehloM

Your project to train a custom OpenWakeWord model is both exciting and critical in enhancing user interaction while minimizing false activations. I understand the importance of high accuracy and low false positives, especially in diverse environments and with various accents. Having successfully developed similar wake-word models, I can ensure robust performance tailored to your specifications. My approach includes utilizing real voice samples and a comprehensive negative dataset, which is essential for minimizing false triggers. I’ll provide clear documentation and performance metrics, ensuring you have all the tools needed for integration and potential retraining. Could you clarify the specific timeline you have in mind for this project? Looking forward to collaborating on this innovative solution!