Parameter	Value
Base Model	openai/whisper-small
Dataset	SPRINGLab/IndicVoices-R_Hindi
Train Samples	25,002
Eval Samples	1,316
Training Epochs	3
Training Steps	2,346
Best Checkpoint	checkpoint-2346
Best Eval Loss	0.2637
Best Eval WER	26.52
20-sample Base WER	59.44
20-sample FT WER	20.85
LoRA Rank	16
LoRA Alpha	32
LoRA Dropout	0.05
LoRA Targets	q_proj, v_proj
Learning Rate	5e-5
Train Batch Size	8
Grad Accumulation	4
Effective Batch	32
Precision	bfloat16
Hardware	Google Colab A100
Method	LoRA fine-tuning with PEFT

Validation Summary

The final selected checkpoint was checkpoint-2346, which was manually evaluated after training and slightly outperformed checkpoint-2000 on the full validation split.

checkpoint-2000 eval WER: 26.6488
checkpoint-2346 eval WER: 26.52

Usage

python

from transformers import pipeline

asr = pipeline(
    task='automatic-speech-recognition',
    model='Sa1Krishna/sema-whisper-small-springlab-hindi-finetuned',
    device=0
)

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

print(result['text'])

Training Details

Trained on SPRINGLab/IndicVoices-R_Hindi using a 95/5 train-validation split and Hindi normalized transcripts.

Training Config

Framework: Hugging Face Transformers + PEFT
Fine-tuning method: LoRA
Precision: bfloat16
Learning rate: 5e-5
Batch size: 8
Gradient accumulation: 4
Evaluation cadence: every 500 steps
Sanity check: 20-sample qualitative comparison at step 200

Notes

Fine-tuned for Hindi speech recognition.
Uses the multilingual Whisper tokenizer and decoder for Hindi transcription.
Final checkpoint was chosen using full-validation WER plus qualitative review.

Limitations

Optimized for Hindi ASR only.
May still struggle with heavy accents, rare proper nouns, and unusual numerals.
Performance may vary on domains very different from IndicVoices-R.