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Florida Appellate Court Oral Argument Transcription Dataset
Version: 0.1
Released: June 2026
Entries: 1,440 Court Cases
Engine Stack: yt-dlp / ffmpeg / OpenAI Whisper-1 (Deterministic Configuration)
What This Is
A high-fidelity, structurally verified dataset comprising the oral argument records pulled from regional Florida Appellate Court dockets. Each entry in this repository consists of two permanently linked data modules:
- A compressed reference audio track (
.m4a). - A completely un-hallucinated, sub-second timestamped timeline matrix transcript (
.srt).
This dataset serves as an authentic acoustic baseline for evaluating how speech-to-text models perform when confronted with dense legal proper nouns, echoing courtroom acoustics, and rapid judge-to-attorney cross-talk interruptions.
Technical Pipeline Architecture
To bypass the typical text looping and data loss bugs associated with consumer-grade cloud transcription, all assets are processed through a hardened, zero-stochastic automation loop:
Dynamic Guardrails Applied:
temperature=0.0(Anti-Stochastic Lock): Eliminates text variance. Forces the model to operate as a direct phonetic typewriter, ensuring stutters, broken legal syntax, and case caption numbers are recorded exactly as spoken without semantic smoothing.response_format="srt"(Anti-Loop Pacing): Binds text blocks tightly to sub-second timeline arrays (HH:MM:SS,mmm). This completely suppresses the model's tendency to generate ghost text loops during long spells of ambient courtroom silence or microphone friction.- Lossless Slicing Framework: Files crossing the 25MB server limit are parsed into 15-minute segments using
ffmpeg, processed sequentially, and mathematically realigned via a localized time-offset alignment wrapper.
Ingestion Cost & Infrastructure Benchmarks
Processing raw courtroom acoustics at scale requires careful infrastructure balancing. The baseline benchmarks below illustrate real-world operational tradeoffs for this dataset volume:
| Processing Stack / Provider | API Cost Rate | Projected Dataset Expense (1,440 Cases) | Structural Integrity Tradeoff |
|---|---|---|---|
| OpenAI Whisper-1 API (As Deployed) | $0.0060 / min | ~$302.40 | Hyper-Rigid Phonetic Accuracy: Exceptional proper noun retrieval. Highly resilient against loop errors at zero temperature. Constrained by rigid 25MB limits. |
| Groq Cloud Ecosystem | ~$0.0030 / min | ~$151.20 | Ultra-Low Latency Acceleration: Executes file passes nearly instantly via hardware LPUs. Bound by highly restrictive per-minute API rate throttles. |
| Deepgram Engine (Nova-2) | $0.0043 / min | ~$216.72 | Advanced Diarization: Excellent at distinguishing speech patterns when judges interrupt counsel. Prone to dropping words entirely in heavy echo conditions. |
Dataset Layout Schema
Each case asset is housed within a self-contained, isolated directory structure to ensure easy parsing, sorting, and indexing:
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