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kirandevraj
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Model Details
Model Provider
kirandevraj
Model Tree
openai/whisper-medium
Input Modalities
Output Modalities
Supported Functionality
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.
openai/whisper-medium (769M), full fine-tune (no LoRA)1e-5, 50-step warmup, bf16, gradient checkpointingpython
import torchfrom 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):
python
import torch, soundfile as sffrom 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 monofeats = 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])import torchfrom 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"])import torch, soundfile as sffrom 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 monofeats = 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])