Sa1Krishna/sema-whisper-small-springlab-hindi-finetuned

Whisper Small — SPRINGLab Hindi Fine-tuned 🎙️

Fine-tuned version of openai/whisper-small for Hindi automatic speech recognition using LoRA (PEFT).

Model Details

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

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.