whisper-medium · Supreme Court hearings (hybrid dataset)
A full fine-tune of openai/whisper-medium
(all 763M parameters) on the kirandevraj/supreme-court-hearings-asr
dataset — 46.9 h of Indian Supreme Court hearing audio, sentence-aligned by a best-of-both
(MMS-CTC + Whisper) forced-alignment pipeline.
Results
Whisper BasicTextNormalizer applied to both sides; WER/CER via jiwer. Trained on the
by-case train split (24,422 clips); both eval sets are held out (the gold hearing is never
trained on). Best checkpoint selected by validation WER.
| Eval set | Model | WER | CER |
|---|---|---|---|
| Held-out test (3,799) | whisper-medium · zero-shot | 12.6 | 7.2 |
| Held-out test (3,799) | whisper-medium · fine-tuned (this) | 9.2 | 5.2 |
| Human-verified gold (109) | whisper-medium · zero-shot | 25.0 | 18.1 |
| Human-verified gold (109) | whisper-medium · fine-tuned (this) | 14.6 | 10.3 |
Validation WER during training reached 9.53 (CER 5.99). Fine-tuning cuts test WER ~27% relative and gold WER ~42% relative over zero-shot whisper-medium.
Training
- Base:
openai/whisper-medium(769M), full fine-tune (no LoRA) - Optimizer: AdamW, lr
1e-5, 50-step warmup, bf16, gradient checkpointing - Batch: effective 16 (per-device 8 × grad-accum 2)
- Steps: 3000 (~2 epochs); decoding forced to English
Usage
import torch
from transformers import pipeline
asr = pipeline(
"automatic-speech-recognition",
model="kirandevraj/whisper-medium-supreme-court-hybrid",
torch_dtype=torch.float16,
device=0, # GPU; use -1 for CPU
)
out = asr("hearing_clip.wav", generate_kwargs={"language": "en", "task": "transcribe"})
print(out["text"])
Lower-level (processor + model):
import torch, soundfile as sf
from transformers import WhisperForConditionalGeneration, WhisperProcessor
repo = "kirandevraj/whisper-medium-supreme-court-hybrid"
proc = WhisperProcessor.from_pretrained(repo)
model = WhisperForConditionalGeneration.from_pretrained(repo, torch_dtype=torch.float16).to("cuda").eval()
audio, sr = sf.read("hearing_clip.wav") # 16 kHz mono
feats = proc(audio, sampling_rate=16000, return_tensors="pt").input_features.to("cuda", torch.float16)
ids = model.generate(feats, language="en", task="transcribe")
print(proc.batch_decode(ids, skip_special_tokens=True)[0])
Notes
- Audio must be 16 kHz mono; clips ≤ 30 s (the Whisper encoder window).
- Domain: Indian Supreme Court oral hearings — English with occasional romanized Hindi, legal vocabulary, case citations, and speaker names.