{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"}],"enrichment":{"capability":"WhisperX performs fast automatic speech recognition with word-level timestamps and speaker diarization, using batched inference and forced phoneme alignment to improve accuracy over standard Whisper.","skillfed_tags":["speech-recognition","speaker-diarization","audio-processing"],"use_cases":["Transcribe meeting recordings with speaker labels and accurate word timestamps for searchable meeting notes.","Generate subtitle files (.srt) with precise timing and speaker identification for video content.","Process podcast or interview audio to extract speaker segments and create chapter markers.","Batch-process large audio datasets for research or content indexing with minimal GPU memory.","Transcribe multilingual audio (supported languages: en, fr, de, es, it, and others via Hugging Face)."],"what_it_does":"WhisperX is a speech-to-text system that wraps OpenAI's Whisper model with improvements for timestamp accuracy and speaker identification. It uses batched inference via faster-whisper for speed, forced phoneme alignment via wav2vec2 for word-level timing precision, and pyannote-audio for speaker diarization\u2014assigning speaker labels to segments of audio. The package includes voice activity detection to reduce hallucinations and supports multiple languages through language-specific alignment models.\n\nIt is designed for transcription workflows where accurate timing and speaker identity matter: meeting notes, podcast chapters, video subtitles, or research datasets. The main trade-off is a large dependency tree (torch, transformers, torchaudio, and others) and GPU memory requirements, though CPU-only and reduced-precision modes are available. Setup requires CUDA 12.8 for GPU use and a Hugging Face token for speaker diarization.","worth_installing":"Yes, if you need word-level timestamps and speaker diarization for transcription. The package is actively maintained, permissively licensed, and popular (top 5000 PyPI). Install friction is low but dependency footprint is substantial (torch, transformers, etc.); ensure GPU memory or CPU fallback fits your deployment. No known vulnerabilities as of 2026-08-14."},"id":"whisperx","links":{"html":"https://skillfed.io/packages/whisperx","md":"https://skillfed.io/packages/whisperx.md","pypi":"https://pypi.org/project/whisperx/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-25","license_spdx":null,"license_treatment":"permissive","name":"whisperx","python_support":"supports_current","summary":"Time-Accurate Automatic Speech Recognition using Whisper."},"popularity":{"monthly_downloads":1465012,"position":3877,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"3.8.6"}