Otter.ai
AI meeting assistant for transcription and automated note-taking
WhisperX
Fast, word-level ASR with speaker diarization and 70× realtime speed
WhisperX delivers rapid automatic speech recognition with precise word-level timestamps, speaker diarization, and GPU-efficient batching, supporting large-v2 models on modest hardware.
Overview
Highlights
70× realtime transcription with large‑v2 model via batched inference
Word‑level timestamps using wav2vec2 forced alignment
Multispeaker diarization with pyannote‑audio and speaker ID labels
GPU‑efficient runtime requiring <8 GB memory for large‑v2
Pros
- Extremely fast inference suitable for real‑time applications
- High timestamp precision improves subtitle and analysis quality
- Built‑in speaker diarization provides named speaker segments
- Low GPU memory footprint enables use on consumer‑grade GPUs
Considerations
- Requires CUDA‑12.8 and compatible GPU for optimal performance
- Speaker diarization depends on external Hugging Face models and tokens
- Alignment model adds extra memory and compute overhead
- Limited to languages supported by the underlying Whisper model
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Fit guide
Great for
- Real‑time meeting transcription and captioning
- Podcast or lecture indexing with accurate timestamps
- Multilingual content where speaker attribution is needed
- Developers integrating ASR into custom pipelines
Not ideal when
- Environments without GPU or CUDA support
- Low‑resource devices lacking 8 GB GPU memory
- Use cases requiring on‑device inference on mobile phones
- Languages not covered by Whisper’s training data
How teams use it
Live meeting transcription
Generate near‑real‑time captions with speaker names for Zoom, Teams, or Google Meet recordings.
Podcast post‑production
Create word‑accurate transcripts and speaker labels to streamline editing and searchable archives.
Academic lecture indexing
Produce timestamped transcripts for automatic subtitle generation and topic navigation.
AI research data preparation
Batch‑process large audio corpora with precise alignment for training downstream models.
Tech snapshot
Python100%
Frequently asked questions
What hardware is needed for optimal performance?
A GPU with CUDA 12.8 and at least 8 GB of memory; CPU‑only works but is much slower.
How does WhisperX achieve word‑level timestamps?
It uses forced phoneme alignment with a wav2vec2 ASR model after the initial Whisper transcription.
Is speaker diarization automatic?
Yes, when you provide a Hugging Face token for the required pyannote‑audio models; the system assigns speaker IDs.
Can I run WhisperX without installing CUDA?
You can run on CPU, but you will lose the 70× speed advantage and experience longer processing times.
Which Whisper models are supported?
WhisperX works with the standard Whisper models (small, base, large, large‑v2); larger models improve accuracy but require more GPU memory.
Project at a glance
Active- Stars
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LicenseBSD-2-Clause
Repo age3 years old
Last commit3 months ago
Primary languagePython
Last synced yesterday