Model Description

Standard ASR models trained on monolingual data degrade significantly on code-switched speech — sentences where Tamil and English are mixed mid-utterance. This model targets that gap through targeted fine-tuning: training data is weighted to oversample code-switched segments and high switch-point samples, guided by a structured failure taxonomy.

Intended Use

• Transcription of Tamil-English code-switched (Tanglish) speech
• Voice interfaces and STT pipelines for urban Indian users
• Research baseline for code-switched Indic ASR

Out of scope: Clean monolingual Tamil or English at scale — use openai/whisper-medium or ai4bharat/indicwav2vec for monolingual speech.

Evaluation Results

WER on held-out test set (synthetic Tamil-English code-switched corpus), stratified by segment type:

41.5% relative WER reduction on code-switched speech vs. Whisper-small baseline. 36% improvement over the best pre-trained Tamil-specialized model.

CS Penalty = code-switched WER ÷ average(mono-Tamil WER, mono-English WER). A value below 1.0 means the model handles code-switched speech better than monolingual speech — the opposite of all three baselines.

See full results in the training repository.

Failure Taxonomy

The fine-tuning strategy was derived from a structured analysis of 5 failure categories observed across all baselines:

Only SUBSTITUTION_SWITCH and LANGUAGE_CONFUSION were observed — both are systemic architectural blind spots shared across all models, not model-specific bugs. Fine-tuning reduced LANGUAGE_CONFUSION from 54% → 41% but did not eliminate either category.

Training Procedure

Data sampling (targeted oversampling):

• Code-switched segments: ×3
• Segments with >2 language switch points: ×2
• Monolingual segments: ×0.5 (undersampled)

Hyperparameters:

• Epochs: 3
• Batch size: 4 (effective: 16 with gradient accumulation ×4)
• Learning rate: 1e-3 with 50 warmup steps
• Optimizer: AdamW 8-bit
• Precision: FP16
• Early stopping: patience 3, metric WER

How to Use

python
Copy code
import torch
import numpy as np
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from peft import PeftModel

base_model_id = "openai/whisper-small"
adapter_model_id = "Dhanush66-rv/whisper-small-tanglish-lora"

processor = WhisperProcessor.from_pretrained(adapter_model_id)
base = WhisperForConditionalGeneration.from_pretrained(base_model_id)
model = PeftModel.from_pretrained(base, adapter_model_id)
model.eval()

# audio: np.ndarray, mono float32, 16kHz
def transcribe(audio: np.ndarray) -> str:
    inputs = processor(audio, sampling_rate=16000, return_tensors="pt").input_features
    with torch.no_grad():
        ids = model.generate(inputs, language="ta", task="transcribe")
    return processor.batch_decode(ids, skip_special_tokens=True)[0].strip()

Or via the FastAPI endpoint (see api/app.py in the training repo):

bash
Copy code
uvicorn api.app:app --port 8000
curl -X POST http://localhost:8000/transcribe -F "audio=@speech.wav"

Limitations

• Trained on 1500 samples — a small corpus. Performance on diverse speakers, accents, and domains will vary.
• Language detection for segment tagging uses langdetect, which can misclassify short Tamil-script words.
• Numbers and proper nouns (especially transliterated names) remain a known weak point — see DELETION_PROPER_NOUN and SUBSTITUTION_NUMBER failure categories.
• Not evaluated on spontaneous conversational speech; training data is read-speech from IndicVoices.

Citation

bibtex
Copy code
@misc{whisper-small-tanglish-lora,
  author    = {Dhanush, R V},
  title     = {Whisper-small fine-tuned for Tamil-English code-switched ASR},
  year      = {2025},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/Dhanush66-rv/whisper-small-tanglish-lora}
}