Alternatives
Products that do what CryDecoder – On-device ML for classifying baby cries (Swift, Core ML) does
Hi HN, I’m the developer behind CryDecoder. I built this after too many nights at 3am staring at a crying infant, completely exhausted, trying to guess whether it was hunger, gas, or just general fussiness. I realized I was essentially running a mental decision tree on very little sleep, so I decided to see if I could automate some of that signal processing. What it does: CryDecoder analyzes short audio clips of a baby’s cry and classifies them into categories like hunger, discomfort/gas, tiredness, or general fussiness. How it works: • Tech: On-device audio feature extraction paired…
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Free online parenting tools and baby care guides.
28d ago · ai.mombabycaretips.com
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We needed a speaker diarization solution that could run every few seconds alongside transcription on iOS and macOS. But native Swift support was either limited or locked behind paid licenses. Since diarization is a common need in speech-to-text workflows, we decided to open source our work and give back to the community. We initially tried sherpa-onnx, which works, but running both diarization and transcription models slowed down older devices. CPU-only inference just isn’t ideal for near real-time workloads, so we wanted the option to offload segmentation and speaker embedding to the GPU or…
2025 · github.com
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I've made a small Python library, designed for quick-and-easy prototyping of machine learning models. It's built on top of scikit-learn, to serialize and deserialize data from the forms you're likely to have, to the format used in scikit-learn. https://github.com/madman-bob/Smart-Fruit It's pretty bare-bones at the moment, but I thought I'd see if there was any interest before spending too much time on it. Let me know what you think.
2018
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Hi HN, I’m excited to share SpeechAnalyzerDylib, a tiny C-compatible dynamic library that wraps Apple’s upcoming SpeechAnalyzer API in macOS 26.0 (Tahoe). If you’re building a Swift, Objective-C, or plain C project and want to add on-device transcription with virtually no boilerplate, this might help. ## Key Features - *C-FFI wrapper* Exposes a simple, C-compatible API so you can call from Swift, Objective-C or plain C. - *Asynchronous File Transcription* `sw_transcribeFile(const char filePath, const char locale, TranscriptionCallback callback, void userData)` Transcribe any audio file on…
2025 · github.com
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title basically covers it. project #2 from my paternity leave. My partner & I got frustrated with hearing Brahm's Lullaby and Wheels On The Bus repeatedly. I also stand by that babies can enjoy adult music just as easily as they can enjoy "kids music". But most adult music is too dense/complex for babies to understand, so I created this to generate baby-friendly, slowed down versions of songs that we like to play for our baby & sing along with. Uses BS-roformer for stemming out the input audio + spotify Basic Pitch for transposing to midi. Still needs some work, kinda sounds like a…
19d ago · lullabyconverter.com
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WaveletLM is a wavelet-based, attention-free architecture that replaces self-attention with learned lifting wavelet decomposition, a Fast Walsh-Hadamard Transform, per-scale gated spectral mixing with SwiGLU activation, an inverse FWHT, and wavelet reconstruction. Combined with expanded MLPs and sparse product-key memory, this yields a model with O(n log n) scaling in sequence length. With 23.8 PPL on WikiText-103, WaveletLM beats both GPT-2 Medium, which was trained on 80× more data, and Transformer-XL Standard, which uses recurrence to extend its effective context. It is undertrained and…
Apr 2026 · github.com
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Hey HN, when building ML systems for industrial AI, we have learned that data inspection is critical during the ML development process. We are also big fans of the Hugging Face ecosystem. That is why we built an integration to our data exploration tool Spotlight that allows you to interactively explore Hugging Face datasets with one line of code. Spotlight lets you leverage model results such as predictions and embeddings to gain a deeper understanding in data segments and model failure modes. Currently, many many NLP, CV, Audio and multimodal datasets are supported both locally and on the…
2023 · huggingface.co
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Hey HN, I wanted to share a project I’ve been working on called Convoxa. It’s a native iOS transcriber/summarizer. I had two main goals: keep it efficient and keep it private. THE TECH STACK 100% Swift & SwiftUI: No heavy cross-platform wrappers or bloated dependencies. Binary Size: The final build is only 4.8 MB. Transcription: Uses Apple's latest speech APIs for maximum privacy and efficiency. THE CHALLENGE: BYPASSING THE 4K CONTEXT LIMIT The biggest technical hurdle was working with Apple’s foundation models. The default context window is capped at 4096 tokens, which is practically…
Jan 2026 · apps.apple.com
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Spotify has 7 features for each of their music tracks (acousticness, danceability, energy, instrumentalness, liveness, speechiness, valence) which describe the perceptual/emotional content of the song. I wanted to tag my own offline music library with these features so that I could sort my songs into playlists for different occasions (working out, driving, etc.), but unfortunately Spotify doesn't share how they calculate these features. So, I trained my own lightweight neural network to predict these features!
2025 · github.com
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Hey HN — I’m Gaurav, one of the founders of Captions. We work on applied AI research for talking videos. Our foundation model, Lipdub, captures how humans speak, and matches full face movement to what’s being said. The model is zero-shot and can generate videos in under a minute, without person-specific training. Building on Lipdub, we’re releasing a few APIs that can generate and translate talking videos in bulk. Here are some ways they could be used: * Translating videos with matching lip movement * Creating personalized videos that include someone’s name or company, like what’s shown in…
2024 · captions.ai
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Hi HN, I’m Ben, long time web developer but this is my first time building a macOS app. I film a lot of tutorial and talking-head content and wanted to make it easier to chop out mistakes. So I built CutWord, a macOS app that lets you mark cue points with voice commands while you’re recording. Once the app downloads a Whisper model everything runs locally, so nothing leaves your machine. You can export a trimmed video directly from the app or export a FCPXML file that can be imported into Final Cut or DaVinci Resolve for finishing. Built with SwiftUI and AVFoundation. TestFlight:…
2025 · cutword.com
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Hello HackerNews, I am Paul, and I would like to get some feedback on the tool we are releasing as beta today. 3LC is an ML tool that gives detailed insights, real-time data-centric iterative workflows for training/finetuning, and data quality improvements for your Machine Learning datasets and models. 3LC serves as a visualizer, editor, and debugger, focusing on how models learn from the training data. Key Features of 3LC: • Detailed Data Analysis: 3LC enables users to dive into model performance beyond typical labeling errors. It offers the capability to analyze intricate false…
2024 · pypi.org
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We developed a simple mobile app to monitor ML model training on mobile phones, with two lines of code to push training statistics. Client library (GitHub): https://github.com/lab-ml/labml App (Github): https://github.com/lab-ml/app Sample: https://web.lab-ml.com/run?run_uuid=d4722546f58411ea8addd3f9...
2020
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