# WhisperX alignment gaps: numbers and symbols may lack word timestamps

WhisperX can leave recognized words without reliable alignment when its alignment model cannot map their characters or sounds.

Author: Retrivium team
Updated: 2026-11-03
Canonical URL: https://retrivium.ai/journal/whisperx-forced-alignment-missing-words/
Language: en

WhisperX can leave recognized words without reliable alignment when its alignment model cannot map their characters or sounds. Preserve those words and their uncertainty instead of deleting them, because missing timestamps do not establish that the transcription itself contains no meaningful evidence.

## Distinguish recognition from forced alignment

The [WhisperX paper](https://arxiv.org/abs/2303.00747) explains alignment-based timing, while the [project repository](https://github.com/m-bain/whisperX) documents limitations. Transcription predicts words from audio; forced alignment places a supplied transcript against acoustic evidence. These stages can fail differently. A recognized number may be useful text while lacking a timestamp, and a precisely aligned word can still be the wrong recognized word. Neither output automatically validates the other.

## Inspect language and dictionary coverage

WhisperX documents language-specific alignment models and difficulty with words outside the alignment dictionary, including some numeric or currency forms. Check the actual transcript representation and selected aligner before blaming the recording. A wrong language model or unsupported characters can create systematic gaps. Do not replace every missing time with a neighboring timestamp, because that turns a known uncertainty into false precision.

## Build a numeric-word fixture

Choose a short recording containing a product identifier, a currency amount and ordinary surrounding speech. Keep the original [Whisper transcription](https://github.com/openai/whisper) and compare it with the aligned output. Mark whether each token survives and whether a time is present. The fixture should distinguish missing text from missing timing; otherwise a cleaner-looking subtitle export can silently erase the most important technical detail.

| Observation | Likely investigation |
| --- | --- |
| Text survives, time missing | Alignment coverage or representation |
| Wrong text with a time | Recognition error before alignment |
| Many gaps in one language | Alignment-model suitability |
| Overlapping speakers | Acoustic ambiguity and segmentation |

## Keep timing uncertainty explicit

Maintain a source token record with text, any available timing and an alignment-status field. A sentence-level span may remain usable when an individual word lacks timing, but its precision is different. If a downstream format requires complete timing, use a documented approximation policy and retain the original status. Never present interpolated positions as direct word-level measurements from the alignment model.

## Review the evidence before downstream use

Test search, quotation and subtitle exports against the same uncertain-word fixture. A search index can retain a product identifier even when exact playback positioning is unavailable. A citation can point to the surrounding audio span with an explicit timing limitation. The acceptance criterion is preservation of meaningful text and honest temporal resolution, not the disappearance of every null field. This keeps alignment repair separate from unsupported changes to the recognized transcript.

Related reading: [Long audio with Whisper: segments, queues and durable results](/journal/whisper-long-audio/); [Choosing GPU memory for Whisper transcription](/journal/whisper-gpu-requirements/).