whisper-medium-hindi

Whisper Medium — Hindi Fine-tuned

Fine-tuned version of openai/whisper-medium on the AI4Bharat Kathbath Hindi speech dataset for automatic speech recognition (ASR) in Hindi.

Model Details

• Developed by: ShaikhAnis007
• Model type: Automatic Speech Recognition (ASR)
• Language: Hindi (hi) — Devanagari script
• License: Apache 2.0
• Base model: openai/whisper-medium
• Fine-tuned on: AI4Bharat Kathbath — Hindi subset
• Training compute: Google Colab T4 GPU (free tier)

Results

Evaluated on 50 examples from the Kathbath valid split (never seen during training).

How to Use

from transformers import WhisperProcessor, WhisperForConditionalGeneration
import torch

processor = WhisperProcessor.from_pretrained("ShaikhAnis007/whisper-medium-hindi")
model = WhisperForConditionalGeneration.from_pretrained("ShaikhAnis007/whisper-medium-hindi")

# Load your audio (must be 16kHz mono)
# audio = load your audio array here

inputs = processor(audio, sampling_rate=16000, return_tensors="pt")
with torch.no_grad():
    predicted_ids = model.generate(
        inputs.input_features,
        language="hindi",
        task="transcribe"
    )

transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
print(transcription)

Training Details

Dataset

• Dataset: AI4Bharat Kathbath — Hindi subset
• Training samples: 1,000 examples (~1 hour of audio)
• Validation samples: 50 examples
• Speaker diversity: 1,218 unique speakers across 203 districts of India
• License: CC0 (fully open)

Hyperparameters

Training Hardware

• Hardware: Google Colab T4 GPU (16GB VRAM)
• Training time: ~60 minutes
• Cloud Provider: Google Colab (free tier)

Intended Use

• Hindi speech transcription from audio files
• Voice-to-text applications in Hindi
• Hindi ASR research and benchmarking

Limitations

• Trained on 1,000 samples — production use would benefit from the full Kathbath dataset (~140 hours)
• Optimised for read speech; may perform differently on conversational Hindi
• No data augmentation applied — robustness to noisy environments is limited

Citation

If you use this model, please cite the base model and dataset:

@misc{radford2022whisper,
  title={Robust Speech Recognition via Large-Scale Weak Supervision},
  author={Radford, Alec and others},
  year={2022}
}

@inproceedings{kathbath2022,
  title={Kathbath: A Robust Dataset for Hindi ASR},
  author={AI4Bharat},
  year={2022}
}