MWirelabs/garo-asr

Garo ASR - Whisper Small Fine-tuned

Fine-tuned Whisper Small model for Automatic Speech Recognition in Garo language.

Model Details

• Base Model: openai/whisper-small (244M parameters)
• Language: Garo (Tibeto-Burman language family)
• Training Data: ARTPARK-IISc
• Training Samples: 26,784
• Test Samples: 3,348

Performance

Metric	Score
Word Error Rate (WER)	9.74%
Character Error Rate (CER)	3.82%

Baseline Comparison

Model	WER	CER
Whisper-small (zero-shot)	382.7%	-
Whisper-base (zero-shot)	-	203.46%
This model (fine-tuned)	9.74%	3.82%

Usage

from transformers import WhisperProcessor, WhisperForConditionalGeneration
import torch

# Load model and processor
processor = WhisperProcessor.from_pretrained("MWirelabs/garo-asr")
model = WhisperForConditionalGeneration.from_pretrained("MWirelabs/garo-asr")

# Load audio (16kHz)
# audio_array = your audio as numpy array

# Generate transcription
inputs = processor(audio_array, sampling_rate=16000, return_tensors="pt")
with torch.no_grad():
    predicted_ids = model.generate(inputs.input_features)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]

Training Details

• Training Steps: 4,000 (best checkpoint at 2,500)
• Batch Size: 16 per device
• Gradient Accumulation: 2 steps
• Learning Rate: 1e-5
• Warmup Steps: 500
• Precision: FP16

Limitations

• Performance degrades on English loanwords and code-switching
• 432 samples (12.9%) in test set contain annotation noise
• ~43% error rate on code-switched utterances with English words

Dataset Statistics

• Audio Duration: Mean 4.04s, Median 3.81s (range: 1.78-11.13s)
• Vocabulary: 3,621 unique words
• Type-Token Ratio: 0.148

Inference Speed

• Average: 0.252s per sample
• Real-time Factor: 0.05x (20x faster than real-time)

Citation

If you use this model, please cite:

@misc{garo-asr-2026,
  author = {MWire Labs},
  title = {Garo ASR: Fine-tuned Whisper for Garo Language},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/MWirelabs/garo-asr}
}

Acknowledgments

• Dataset: ARTPARK-IISc Vaani project
• Base Model: OpenAI Whisper """