Speech-to-Text Timestamp Stagnation in ElevenLabs API

[haiewu]

Description

Description

When using the client.speech_to_text.convert API with diarization enabled, the returned word-level timestamps occasionally become "stuck" - multiple consecutive words are assigned identical start and end timestamps. This breaks the continuity of the transcription timeline.
Example of Problem:
json

{
"text": "休",
"start": 452.3,
"end": 452.3
},
{
"text": "息",
"start": 452.3,
"end": 452.3
}

Impact:

Timeline Accuracy: Renders timestamp information useless for applications requiring precise timing

Speaker Diarization: Prevents accurate speaker attribution when timestamps don't progress

Audio Alignment: Makes it impossible to sync transcription with original audio

Data Processing: Requires complex workarounds to handle corrupted timing data

Steps to Reproduce
Use a long audio file (>10 minutes) with multiple speaker changes

Call the API with parameters:
python

ElevenLabs.speech_to_text.convert(
file=audio_data,
model_id="scribe_v1",
diarize=True,
language_code="zh", # Also reproducible with other languages
tag_audio_events=True
)

Inspect word-level timestamps in the response

Observe duplicate timestamps for consecutive words, especially after speaker changes

Expected Behavior

Each word should have a unique timestamp range (start < end)

Timestamps should monotonically increase throughout the transcription

Speaker changes should not cause timestamp stagnation

Consecutive words should have increasing end timestamps

Code example

No response

Additional context

No response