{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/7"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/15"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"},{"label":"Speech","url":"https://skillfed.io/packages/category/multimedia-sound-audio-speech"}],"enrichment":{"capability":"Automatic Speech Recognition using ONNX models with minimal dependencies, supporting multiple modern ASR architectures and running on CPUs, GPUs, and edge devices.","skillfed_tags":["speech-recognition","edge-ml","onnx-inference"],"use_cases":["Transcribe audio files offline without cloud dependencies or heavy ML frameworks in production services.","Build speech-to-text features on edge devices or IoT hardware with constrained resources.","Process long-form audio recordings using Voice Activity Detection to split and recognize speech segments.","Integrate multilingual ASR (Parakeet v3, Canary, GigaAM Multilingual) into applications requiring non-English transcription.","Benchmark or compare ONNX-based ASR models across different hardware (CPU, CUDA, TensorRT, CoreML).","Develop custom ASR pipelines by loading quantized or fine-tuned ONNX models from Hugging Face."],"what_it_does":"onnx-asr is a Python library for speech-to-text inference using ONNX-format models. It wraps modern ASR architectures (NeMo Conformer/Parakeet/Canary, GigaAM, Kaldi Icefall Zipformer, T-Tech T-one, and OpenAI Whisper) with preprocessing and decoding logic, letting you load a model and transcribe audio in a few lines of code. The package is designed to be lightweight\u2014it requires only numpy and typing-extensions, avoiding heavy dependencies like PyTorch or Transformers\u2014and runs on diverse hardware from IoT devices to GPU servers.\n\nYou provide either a WAV file or NumPy array, and the package handles resampling, log-mel spectrogram computation, and greedy-search decoding. It supports batch processing, Voice Activity Detection for long-form audio, token-level timestamps, and log probabilities. Models load from Hugging Face or local directories, including quantized versions. The library is actively maintained, fully typed, and includes both a Python API and a command-line interface.","worth_installing":"Yes. The package is actively maintained, has low install friction, carries a permissive MIT license, and solves a real problem\u2014lightweight offline speech recognition without PyTorch or heavy dependencies. It supports modern models and diverse hardware. No known vulnerabilities. Install it if you need ASR inference in Python without framework overhead."},"id":"onnx-asr","links":{"html":"https://skillfed.io/packages/onnx-asr","md":"https://skillfed.io/packages/onnx-asr.md","pypi":"https://pypi.org/project/onnx-asr/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-15","license_spdx":"MIT","license_treatment":"permissive","name":"onnx-asr","python_support":"supports_current","summary":"A lightweight Python package for Automatic Speech Recognition using ONNX models"},"popularity":{"monthly_downloads":230295,"position":9113,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.12.0"}