Parameter	Value
Base Model	openai/whisper-large-v3
Dataset	SPRINGLab/IndicVoices-R_Hindi
Train Samples	~14,800
Eval Samples	~740
Training Steps	4400
Best Val Loss	0.1167
LoRA Rank	8
LoRA Alpha	16
Baseline WER	32.10%
Fine-tuned WER	28.98%
Method	LoRA fine-tuning (PEFT)
Precision	bfloat16

Usage

python

from transformers import pipeline

asr = pipeline(
    task='automatic-speech-recognition',
    model='Sa1Krishna/sema-whisper-large-v3-hindi-finetuned',
    chunk_length_s=30,
    device=0
)

result = asr(
    'hindi_audio.wav',
    generate_kwargs={
        'language': 'hindi',
        'task': 'transcribe'
    }
)
print(result['text'])

Training Details

Trained on SPRINGLab/IndicVoices-R_Hindi — a large scale Hindi speech dataset with diverse speakers.

Training Config

Optimizer : AdamW
LR : 1e-4
Batch size : 16 (effective)
Precision : bfloat16
Framework : HuggingFace Transformers + PEFT

Limitations

Optimised for Hindi speech only
May struggle with heavy accents
Short utterances work best