Link parkin’: whisperX
This repository provides fast automatic speech recognition (70x realtime with large-v2) with word-level timestamps and speaker diarization.
- ⚡️ Batched inference for 70x realtime transcription using whisper large-v2
- 🪶 faster-whisper backend, requires <8GB gpu memory for large-v2 with beam_size=5
- 🎯 Accurate word-level timestamps using wav2vec2 alignment
- 👯♂️ Multispeaker ASR using speaker diarization from pyannote-audio (speaker ID labels)
- 🗣️ VAD preprocessing, reduces hallucination & batching with no WER degradation
Whisper is an ASR model developed by OpenAI, trained on a large dataset of diverse audio. Whilst it does produces highly accurate transcriptions, the corresponding timestamps are at the utterance-level, not per word, and can be inaccurate by several seconds. OpenAI’s whisper does not natively support batching.
retrocast is my personal RAG “auditionware” project for investigating with podcast episodes. It’s meant to combine:
- agentic coding
- AI-powered search
- AI-powered UX
So far, the agentic coding has accelerated my ability to implement features. Recently, episode archiving was completed. Next up: generating transcripts for full-text and embedding search.
Looks like whisperX fits the bill for a first crack at ASR for retrocast.
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