README.md

metadata

language:
  - sa
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
  - automatic-speech-recognition
  - whisper
  - sanskrit
  - asr
  - deepspeed
  - fsdp
  - fine-tuned
datasets:
  - pavanmantha/sanskrit_asr
metrics:
  - wer
pipeline_tag: automatic-speech-recognition
model-index:
  - name: whisper-medium-sa
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: pavanmantha/sanskrit_asr
          type: pavanmantha/sanskrit_asr
          split: validation
        metrics:
          - type: wer
            value: TBD
            name: WER

Whisper Large-v2 — Sanskrit ASR (Fine-Tuned)

Fine-tuned version of openai/whisper-large-v2 on the pavanmantha/sanskrit_asr dataset for Sanskrit (sa) automatic speech recognition using HuggingFace Seq2SeqTrainer + DeepSpeed ZeRO-3 via Accelerate on 5× NVIDIA A10G GPUs.

---

Model Details

Property	Value
Base model	openai/whisper-large-v2
Language	Sanskrit (sa) — Devanagari script
Task	Automatic Speech Recognition (transcribe)
Fine-tuning framework	HuggingFace Transformers + DeepSpeed ZeRO-3
Precision	bf16 (Ampere / sm_86 native)
Parameters	~1.5B
License	Apache 2.0

---

Training Details

Dataset

	Value
Dataset	pavanmantha/sanskrit_asr
Train split	~95% of full dataset (5% held out for validation, seed=42)
Validation split	~5% of full dataset
Audio column	audio (resampled to 16 kHz)
Text column	sentence

Hardware & Infrastructure

	Value
GPUs	5× NVIDIA A10G (22.5 GB VRAM each)
Instance type	AWS G5 (or equivalent)
Distributed strategy	DeepSpeed ZeRO Stage 3 via Accelerate
ZeRO-3 flags	stage3_gather_16bit_weights_on_model_save: true, overlap_comm: true, contiguous_gradients: true

Hyperparameters

Parameter	Value
Epochs	3
Per-device batch size	8
Gradient accumulation steps	4
Effective batch size	160 (8 × 4 × 5 GPUs)
Learning rate	5e-6
LR scheduler	Linear with warmup
Warmup steps	200
Weight decay	0.01
Max grad norm	1.0
Eval beam size	5
Generation max length	225 tokens
Eval & save frequency	Every 500 steps
Best model metric	WER (lower is better)

Text Normalization (WER)

WER is computed after applying a custom Sanskrit/Devanagari normalizer:

• NFC Unicode recomposition (fixes encoding variants)
• Remove danda (।), double-danda (॥), and common ASCII punctuation
• Remove nukta (U+093C): normalizes ज़→ज, फ़→फ
• Chandrabindu → Anusvara (U+0901 → U+0902)
• Collapse whitespace

Key Training Flags

• gradient_checkpointing=True with use_reentrant=False (required for ZeRO-3 compatibility)
• forced_decoder_ids=None and suppress_tokens=[] to unblock Devanagari character tokens during generation
• predict_with_generate=True with beam search (num_beams=5) for evaluation

---

Usage

Quick Inference

from transformers import pipeline

asr = pipeline(
    "automatic-speech-recognition",
    model="pavanmantha/whisper-medium-sa",
    generate_kwargs={"language": "sanskrit", "task": "transcribe"}
)

result = asr("path/to/audio.mp3")
print(result["text"])

Manual Inference

import torch
from transformers import WhisperForConditionalGeneration, WhisperProcessor

model_id = "pavanmantha/whisper-medium-sa"

processor = WhisperProcessor.from_pretrained(model_id)
model = WhisperForConditionalGeneration.from_pretrained(model_id)
model.eval()

# Load your audio (must be 16 kHz mono)
# audio_array: np.ndarray, shape (N,), dtype float32

inputs = processor(
    audio_array,
    sampling_rate=16000,
    return_tensors="pt"
)

with torch.no_grad():
    predicted_ids = model.generate(
        inputs["input_features"],
        language="sa",
        task="transcribe",
        num_beams=5
    )

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

---

Evaluation

Split	WER
Validation	TBD

WER values are computed using the custom Devanagari normalizer described above.

---

Files

File	Description
model.safetensors	Fine-tuned model weights
config.json	Model architecture config
generation_config.json	Generation defaults (language=sa, task=transcribe)
tokenizer.json	Whisper tokenizer
tokenizer_config.json	Tokenizer configuration
processor_config.json	Processor configuration
vocab.json	Vocabulary file
merges.txt	BPE merge rules

---

Limitations

• Optimized specifically for Sanskrit (sa) in Devanagari script; performance on other languages or scripts is not guaranteed.
• Audio inputs longer than 30 seconds are truncated to the first 30 seconds by the Whisper feature extractor.
• Best results on clean, single-speaker audio sampled at 16 kHz.

---

Citation

If you use this model, please cite the original Whisper paper:

@misc{radford2022whisper,
  title={Robust Speech Recognition via Large-Scale Weak Supervision},
  author={Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
  year={2022},
  eprint={2212.04356},
  archivePrefix={arXiv}
}

---

Author

Fine-tuned by Pavan Kumar Mantha · GitHub