Omnizart is a deep learning framework designed for automatic music transcription and music information retrieval. It functions as a toolkit for analyzing polyphonic audio recordings to extract structured musical information, including notes, chord progressions, drum events, and rhythmic patterns. The system provides a modular pipeline that orchestrates the entire lifecycle of audio analysis, from initial feature extraction and data preparation to model inference. Users can apply pre-trained models to transcribe audio directly or utilize the included utilities to train and fine-tune neural net
Vibe is a cross-platform transcription tool that converts spoken audio into text by running Whisper neural models directly on your device, with no cloud dependency. It can transcribe audio from files, microphones, system output, and network streams, and supports both batch processing of multiple files and real-time captioning from continuous input. Beyond basic transcription, Vibe identifies and labels different speakers through speaker diarization, and offers a choice of Command-Line Interface or HTTP API for automated and remote workflows. It also includes plugins to export transcripts to c
This project is a deep learning framework designed for end-to-end speech-to-text transcription. It utilizes the WaveNet neural network architecture to process spoken audio input and generate written text transcripts, leveraging connectionist temporal classification to map variable-length audio sequences to character-level outputs. The system distinguishes itself through a comprehensive training pipeline that supports distributed execution across multiple graphics processing units. It includes specialized utilities for audio data augmentation and the transformation of raw audio files into opti
ruby_llm is an LLM integration framework and AI agent orchestrator designed to connect applications to multiple large language model providers through a unified interface. It serves as a toolkit for building autonomous assistants with custom personas, managing structured output via JSON schemas, and implementing vector embedding engines for semantic search. The project distinguishes itself as an observability suite and multimodal toolkit. It provides specialized capabilities for tracking token usage, calculating model costs, and tracing workflows via OpenTelemetry, while supporting the proces
Basic-pitch هو ناسخ صوتي للشبكة العصبية وكاشف طبقة صوت متعدد الأصوات. يعمل كمحول صوت إلى MIDI يحول تسجيلات الصوت متعددة الأصوات إلى أحداث ملاحظات MIDI وبيانات انحناء طبقة الصوت.
الميزات الرئيسية لـ spotify/basic-pitch هي: Audio Transcription Models, Automated Music Transcription, Neural Audio Transcribers, AI Audio Analysis, Audio-to-MIDI Converters, Audio-to-MIDI Pipelines, Audio-to-MIDI Transcription, Pitch Bend Detection.
تشمل البدائل مفتوحة المصدر لـ spotify/basic-pitch: music-and-culture-technology-lab/omnizart — Omnizart is a deep learning framework designed for automatic music transcription and music information retrieval. It… thewh1teagle/vibe — Vibe is a cross-platform transcription tool that converts spoken audio into text by running Whisper neural models… buriburisuri/speech-to-text-wavenet — This project is a deep learning framework designed for end-to-end speech-to-text transcription. It utilizes the… jaakkopasanen/autoeq — AutoEq is a command-line tool that generates headphone equalization settings by comparing frequency response… crmne/ruby_llm — ruby_llm is an LLM integration framework and AI agent orchestrator designed to connect applications to multiple large… argmaxinc/whisperkit.