Datasets:
Tasks:
Automatic Speech Recognition
Modalities:
Audio
Formats:
soundfolder
Languages:
English
Size:
< 1K
License:
Search is not available for this dataset
audio audioduration (s) 3.51 56.8 |
|---|
STT Calibration Dataset
Tiny calibration dataset for PersonalAssistant STT service. Used on first run to auto-tune speculative pre-transcription parameters.
Contents
| File | Duration | Size | Purpose |
|---|---|---|---|
short.wav |
3.5s | 110KB | RTF measurement + VAD onset latency |
long.wav |
23.3s | 729KB | Split quality calibration (whole vs split comparison) |
very_long.wav |
56.8s | 1.8MB | Multi-split calibration (find minimum safe split interval) |
manifest.json |
- | 2KB | Sample metadata + reference transcriptions |
Total: ~2.6MB
Source
All audio from LibriSpeech test-clean (CC BY 4.0). very_long.wav is 4 samples concatenated with 0.5s silence gaps.
Usage
Downloaded automatically on first STT run via huggingface_hub.snapshot_download:
from huggingface_hub import snapshot_download
path = snapshot_download("cvxhull/stt-calibration")
Cached in ~/.cache/huggingface/hub/. No re-download on subsequent runs.
Calibration Parameters
| Parameter | How it's calibrated |
|---|---|
| ASR RTF | Transcribe short.wav, measure wall time / audio duration |
| Split interval | Derived from RTF: clamp(target_latency / rtf, min_safe_split, buffer_timeout) |
| Min safe split | Transcribe long.wav whole vs split at [5s, 8s, 12s], find minimum with similarity >= 0.95 |
| VAD onset latency | Feed short.wav through VAD, measure chunks until first detection |
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