FLAW_TS

license: cc-by-4.0
language:
  - en
tags:
  - legal
  - appellate-court
  - audio-processing
  - speech-to-text
  - whisper-benchmark
pretty_name: Florida Appellate Court Oral Argument Transcription Dataset
size_categories:
  - 1K<n<10K

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: