## TL;DR
Missing speaker labels on an earnings transcript usually means the diarization model could not separate the operator, CEO, and analysts, often because of phone-line audio quality. Re-run diarization with the expected speaker count hint (operator plus 2-4 executives plus analysts), or align the transcript against the published participant list from the press release.

## The error
```text
diarization: 1 speaker detected (expected 6+)
transcript: all segments labeled SPEAKER_00
Earnings call Q&A attribution impossible.
```

## Fix it
1. Check the audio quality first: sample the Q&A section and listen for crosstalk, hold music, or heavy compression that collapses speaker separation.
   Expected: Expected: you hear whether the audio itself defeats diarization or the model just needs tuning.
2. Re-run diarization with a speaker-count hint matching the call (1 operator, 2-4 company speakers, N analysts) instead of auto-detect.
   Expected: Expected: the output shows distinct speakers for the prepared remarks and the Q&A.
3. If diarization still collapses, split the audio into prepared-remarks and Q&A segments and diarize each separately; the Q&A has the hardest speaker mix.
   Expected: Expected: the Q&A segment diarizes better in isolation.
4. For the pipeline, fall back to role-based labeling: tag segments by matching known executive voiceprints or by aligning with the analyst question order in the press release.
   Expected: Expected: every segment gets a usable label even when pure diarization fails.

## When to use
- Your earnings transcript pipeline produces a single-speaker transcript for a multi-speaker call.
- You need analyst-level attribution for the Q&A section.
- You run diarization over phone-quality earnings audio.

## When not to use
- The transcript has no words at all - that is a transcription failure, not diarization.
- The call is a monologue (guidance update) - single speaker is correct.
- You need legal-grade attribution - then use the official transcript, not model diarization.

## Why it happens
Earnings calls run over compressed conference bridges with variable levels, crosstalk during Q&A handoffs, and hold music between segments. Diarization models trained on clean audio merge these into one or two speakers. Hinting the expected count gives the clustering a prior that matches the call structure.

## Variant phrasings
### earnings call diarization failed one speaker
Same failure and fix; some searchers phrase the query this way.
### speaker labels missing transcript earnings call
Same failure and fix; some searchers phrase the query this way.

## Edge cases
- The operator's 'next question' prompts are short and often get merged into adjacent speakers.
- Some providers strip stereo channels that diarization models use for separation cues.

## Provenance

Resolved from the public thread: https://vectle.com/posts/pst_dDTFtLVYV4XP1OIBf-8bzg