Code to train a custom time-domain autoencoder to dereverb audio
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Updated
Nov 30, 2023 - Python
Code to train a custom time-domain autoencoder to dereverb audio
Dual-model speech AI toolkit for speaker verification and speaker-aware diarization, with streaming inference, meeting analysis, long-audio monitoring, and speaker-bank integration.
Local MCP server + CLI turning YouTube & local audio into rich sonic signatures. Extracts BPM, section-by-section key, vocal presence, transient punch, and 512-dim CLAP vibe embeddings. Powered by Demucs stem separation & librosa. 100% private, offline-first, and GPU-accelerated with graceful CPU/HPSS degradation.
Unified dual-teacher distillation (ReDimNet + ASSIST) into Wav2Vec2 for speaker verification and deepfake detection
This repository consists of projects where GANs were used to work with audio data like music generator etc.
Real-time speech enhancement pipeline — custom-trained U-Net denoising model, ONNX inference, Overlap-Add synthesis, and virtual audio routing for Teams, Zoom, and DAW use. CPU-only, no cloud dependency.
Stable Audio LoRA Trainer of salty goodness
Engine identification using acoustic signal analysis and machine learning to classify 8 vehicle types. Audio signals are processed using FFT and feature extraction, and a multi-class model predicts vehicle categories based on their unique sound patterns.
Edge-deployable keyword spotter: INT8-quantized DS-CNN on Google Speech Commands, exported to ONNX, with fp32 vs INT8 benchmarks, a live mic demo, and a C++ inference harness.
Audio analysis in javascript/typescript
Convert Meta's HTDemucs (Hybrid Transformer Demucs) to Apple Core ML. Real-valued STFT/ISTFT wrapper, manual MHA decomposition, pre-computed overlap-add. Includes Swift example.
AI-generated audio summarisation pipeline — Whisper transcription, LLM key-insight extraction, and structured spoken summaries with TTS playback and Streamlit interface.
Machine learning system for music genre classification using feature engineering, stratified evaluation, SVC/XGBoost modeling, and reproducible prediction export.
Audio file processing pipeline with GPT-4-powered error diagnosis — detects codec issues, sample rate mismatches, and corruption artefacts with automated remediation suggestions.
Neural TTS and voice-cloning application using XTTS/VITS. Supports 3–30 s reference audio for speaker adaptation, real-time pitch/speed control, and WAV/MP3 export.
A lightweight audio-event classifier built around auditable extracted features and confidence-based review.
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