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Satyam0077/
whisper-small-hindi-finetuned-7tym

Name: whisper-small-hindi-finetuned-7tym
Availability: InStock

safetensors

whisper

apache-2.0

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Whisper-Small Hindi Fine-Tuned Model

Overview

This is a fine-tuned version of OpenAI Whisper-small model for Hindi Automatic Speech Recognition (ASR).
The model is trained on ~10 hours of Hindi conversational speech data containing real-world noise, disfluencies, and natural speaking patterns.

Dataset

~10 hours Hindi conversational dataset
Includes real-world speech with fillers, pauses, and variations
Audio resampled to 16kHz mono
Transcriptions processed from segment-level JSON files

Training Details

Base model: Whisper-small
Framework: Hugging Face Transformers
Training environment: Google Colab (GPU)
Epochs: 2
Mixed precision training (fp16)

Results

Model	WER
Pretrained Whisper-small	0.8291
Fine-tuned Whisper-small	0.3184

👉 WER Reduction: ~61.6%

Additionally, on clean Hindi speech samples, the model achieves approximately ~0.15 WER, indicating strong performance in low-noise conditions.

Evaluation

Dataset: FLEURS Hindi (hi_in)
Hindi language enforced during decoding
Text normalization applied before WER computation
Evaluation performed on 418 samples

Usage

python

from transformers import pipeline

pipe = pipeline(
    "automatic-speech-recognition",
    model="Satyam0077/whisper-small-hindi-finetuned-7tym",
    generate_kwargs={"language": "hi"}
)

result = pipe("audio.wav")
print(result["text"])