---
language:
  - hi
  - ta
  - te
  - bn
  - mr
license: mit
library_name: transformers
pipeline_tag: automatic-speech-recognition
base_model: openai/whisper-small
tags:
  - polywhisper
  - indic-asr
  - hindi-asr
  - tamil-speech-recognition
  - telugu-stt
  - bengali-asr
  - marathi-speech-to-text
  - speech-recognition
  - multilingual
  - lora
  - whisper
  - hindi
  - tamil
  - telugu
  - bengali
  - marathi
  - indic-languages
  - indian-languages
  - automatic-speech-recognition
  - speech-to-text
  - low-resource-asr
  - fleurs
  - indicvoices
  - onnx
  - quantized
  - efficient-asr
  - edge-asr
  - peft
datasets:
  - ai4bharat/indicvoices-st
  - google/fleurs
model-index:
  - name: PolyWhisper v9 (Whisper-Small + Per-Language LoRA)
    results:
      - task:
          type: automatic-speech-recognition
          name: Hindi Speech Recognition
        dataset:
          name: FLEURS Hindi (hi_in)
          type: google/fleurs
        metrics:
          - type: wer
            value: 46.3
            name: WER (beam=1, normalized)
      - task:
          type: automatic-speech-recognition
          name: Tamil Speech Recognition
        dataset:
          name: FLEURS Tamil (ta_in)
          type: google/fleurs
        metrics:
          - type: wer
            value: 70.1
            name: WER (beam=1, normalized)
      - task:
          type: automatic-speech-recognition
          name: Telugu Speech Recognition
        dataset:
          name: FLEURS Telugu (te_in)
          type: google/fleurs
        metrics:
          - type: wer
            value: 100.1
            name: WER (beam=1, normalized)
      - task:
          type: automatic-speech-recognition
          name: Bengali Speech Recognition
        dataset:
          name: FLEURS Bengali (bn_in)
          type: google/fleurs
        metrics:
          - type: wer
            value: 130.2
            name: WER (beam=1, normalized)
      - task:
          type: automatic-speech-recognition
          name: Marathi Speech Recognition
        dataset:
          name: FLEURS Marathi (mr_in)
          type: google/fleurs
        metrics:
          - type: wer
            value: 116.9
            name: WER (beam=1, normalized)
---

![PolyWhisper — one frozen Whisper-Small backbone, five per-language LoRA adapters](https://raw.githubusercontent.com/eulogik/PolyWhisper/main/polywhisper-banner.png)

[![Model](https://img.shields.io/badge/%F0%9F%A4%97%20Model-eulogik%2Fpolywhisper-ffd21e)](https://huggingface.co/eulogik/polywhisper)
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[![PyTorch](https://img.shields.io/badge/PyTorch-2.0+-ee4c2c?logo=pytorch)](https://pytorch.org/)
[![ONNX](https://img.shields.io/badge/ONNX-Runtime-orange?logo=onnx)](https://onnxruntime.ai/)
![Hindi](https://img.shields.io/badge/Hindi-hi-138808) ![Tamil](https://img.shields.io/badge/Tamil-ta-FF9933) ![Telugu](https://img.shields.io/badge/Telugu-te-046A38) ![Bengali](https://img.shields.io/badge/Bengali-bn-006A4E) ![Marathi](https://img.shields.io/badge/Marathi-mr-FF9933)

# 🎙️ PolyWhisper v9 — Efficient Multilingual Indic ASR
> by [Eulogik](https://eulogik.com) — Frontier Edge AI · Vernacular Intelligence · [eulogik.com](https://eulogik.com)


> **TL;DR:** PolyWhisper v9 is a research-ready automatic speech recognition (ASR) system for **Hindi, Tamil, Telugu, Bengali, and Marathi**. It pairs a **frozen OpenAI Whisper-Small backbone (244M params)** with tiny **per-language LoRA adapters (~14MB each)**. Bengali WER drops **−28.2%** and Marathi **−75.4%** versus the no-augmentation baseline — at roughly **1% of the storage cost** of full fine-tuning.

![PolyWhisper architecture: frozen Whisper-Small backbone with swappable per-language LoRA adapters](https://raw.githubusercontent.com/eulogik/PolyWhisper/main/paper/figures/fig1_architecture.png)

## ✨ Why PolyWhisper?

| | Full fine-tune (per language) | **PolyWhisper v9** |
|---|---|---|
| Storage per language | ~1.5 GB | **~14 MB (100× smaller)** |
| Backbone | retrained each time | **frozen once, shared by all 5** |
| Bengali (bn) FLEURS WER | 181.3 (baseline) | **130.2 (−28.2%)** |
| Marathi (mr) FLEURS WER | 474.9 (baseline) | **116.9 (−75.4%)** |
| Telugu (te) FLEURS WER | 103.0 (baseline) | **100.1 (−2.8%)** |
| Hindi (hi) FLEURS WER | 43.0 (baseline) | **46.3** |
| Tamil (ta) FLEURS WER | 68.2 (baseline) | **70.1** |
| CPU deployment | heavy | **ONNX INT8, no GPU needed** |

*WER = word error rate (lower is better). FLEURS test set, beam=1, punctuation-normalized scoring.*

## 📊 Benchmarks (FLEURS, beam=1, normalized WER)

| Language | Code | Script | v7 (no augment) | **v9 final** | Δ vs v7 |
|---|---|---|---|---|---|
| Hindi | `hi` | Devanagari | 43.0 | **46.3** | +7.7% |
| Tamil | `ta` | Tamil | 68.2 | **70.1** | +2.8% |
| Telugu | `te` | Telugu | 103.0 | **100.1** | ✅ **−2.8%** |
| Bengali | `bn` | Bengali | 181.3 | **130.2** | ✅ **−28.2%** |
| Marathi | `mr` | Devanagari | 474.9 | **116.9** | ✅ **−75.4%** |

![FLEURS WER by language for v7, v8, and v9 variants](https://raw.githubusercontent.com/eulogik/PolyWhisper/main/paper/figures/fig2_wer_variants.png)

### 🧪 The v9 finding: augment per language, not globally

Training with SpecAugment + speed perturbation on **all** languages damaged Hindi/Tamil (token-loop degeneration) while massively helping Bengali/Marathi. The v9 recipe augments **only `bn`/`mr`** and trains `hi`/`ta`/`te` clean:

| Language | Augmentation | Result |
|---|---|---|
| Hindi, Tamil, Telugu | none (clean) | avoids global-augment damage; stays near the no-augment baseline |
| Bengali, Marathi | SpecAugment + 0.9×/1.1× speed perturb | large gains on hard languages |

![Relative WER change from selective v9 versus global v8 augmentation](https://raw.githubusercontent.com/eulogik/PolyWhisper/main/paper/figures/fig3_augment_delta.png)

### 🎯 Decoding: per-language beam widths (measured, full FLEURS test)

Beam-5 + repetition penalty 1.3 helps every language **except Telugu**, where beam search collapses into repeated-token loops (0/472 perfect samples, 326/472 over 100% WER). The library/CLI defaults encode this (`num_beams=None` → per-language optimal):

| Language | beam-1 | beam-5 + rep 1.3 | Shipped default |
|---|---|---|---|
| Hindi | 46.3 | **45.0** (−2.8%) | beam-5 |
| Tamil | 70.1 | **68.6** (−2.2%) | beam-5 |
| Telugu | **100.1** | 120.5 (+20.4% ⚠️) | **beam-1** |
| Bengali | 130.2 | **126.4** (−2.9%) | beam-5 |
| Marathi | 116.9 | **91.5** (−21.7%) | beam-5 |

## 📦 Which adapter should I use?

| Language | Adapter file | Backbone | WER |
|---|---|---|---|
| Hindi (`hi`) | [`polywhisper_output_hi/adapters_v3/hi_best_clean.pt`](https://huggingface.co/eulogik/polywhisper/resolve/main/polywhisper_output_hi/adapters_v3/hi_best_clean.pt) | `openai/whisper-small` | 46.3 |
| Tamil (`ta`) | [`polywhisper_output_ta/adapters_v3/ta_best_clean.pt`](https://huggingface.co/eulogik/polywhisper/resolve/main/polywhisper_output_ta/adapters_v3/ta_best_clean.pt) | `openai/whisper-small` | 70.1 |
| Telugu (`te`) | [`polywhisper_output_gpu0/adapters_v3/te_best_prod.pt`](https://huggingface.co/eulogik/polywhisper/resolve/main/polywhisper_output_gpu0/adapters_v3/te_best_prod.pt) | `openai/whisper-small` | 100.1 |
| Bengali (`bn`) | [`polywhisper_output_gpu0/adapters_v3/bn_best_prod.pt`](https://huggingface.co/eulogik/polywhisper/resolve/main/polywhisper_output_gpu0/adapters_v3/bn_best_prod.pt) | `openai/whisper-small` | 130.2 |
| Marathi (`mr`) | [`polywhisper_output_gpu1/adapters_v3/mr_best_prod.pt`](https://huggingface.co/eulogik/polywhisper/resolve/main/polywhisper_output_gpu1/adapters_v3/mr_best_prod.pt) | `openai/whisper-small` | 116.9 |

All adapters are rank-16 LoRA (decoder + encoder attention), ~14MB each. Backbone weights are **not** included — they load from `openai/whisper-small` at runtime. The `_prod` suffix is the v9 production-run tag, not an augmentation marker: Telugu was trained clean in the selective v9 recipe.

## 🚀 Quickstart

```bash
pip install -e .
```

```bash
# Hindi speech to text
polywhisper transcribe audio.wav --lang hi

# Tamil with JSON output
polywhisper transcribe audio.wav --lang ta --format json

# Auto-detect language, SRT subtitles
polywhisper transcribe audio.wav --format srt > subs.srt

# Batch a folder
polywhisper batch ./audio_folder/ --lang bn --output results.json
```

```python
from polywhisper import transcribe

result = transcribe("audio.wav", lang="mr")
print(result.text)
print(result.segments)  # timestamped segments
```

## 🖥️ CPU-only inference (ONNX Runtime)

Export INT8-quantized ONNX graphs (no PyTorch, no GPU needed at inference):

```bash
polywhisper export --lang hi --variant prod --int8
```

Pre-exported v9 graphs live under `export/onnx/` on the [Hub](https://huggingface.co/eulogik/polywhisper/tree/main/export/onnx) — per language, fp32 + INT8:

| Lang | Encoder (fp32 / INT8) | Decoder (fp32 / INT8) |
|---|---|---|
| hi | 358MB / 97MB | 784MB / 204MB |
| ta | 358MB / 97MB | 784MB / 204MB |
| te | 358MB / 97MB | 784MB / 204MB |
| bn | 358MB / 97MB | 784MB / 204MB |
| mr | 358MB / 97MB | 784MB / 204MB |

Files are named `{lang}_{lang}_best_prod_{encoder,decoder}{,_int8}.onnx`. INT8 is ~4× smaller.

![ONNX encoder/decoder sizes for fp32 versus INT8](https://raw.githubusercontent.com/eulogik/PolyWhisper/main/paper/figures/fig4_onnx_sizes.png)

**Verification:** fp32 ONNX vs PyTorch max diff < 1e-3 on all five languages (encoder + decoder). End-to-end greedy spot-checks (FLEURS audio, beam=1):

| Lang | torch WER | ONNX INT8 WER |
|---|---|---|
| hi (10 samples) | 43.4% | 48.3% |
| ta (5 samples) | 100.0% | 100.0% |
| te (5 samples) | 100.0% | 101.6% |
| bn (5 samples) | 104.9% | 118.7% |
| mr (5 samples) | 82.9% | 89.4% |

*Spot-checks are tiny (5–10 utterances) so single-sentence flips move the numbers; fp32 ONNX is at parity with torch. INT8 trades a few points for 4× smaller files.*

## 🏋️ Training recipe (reproducible)

- **Data:** [IndicVoices-ST](https://huggingface.co/datasets/ai4bharat/indicvoices-st) (~19–20k clips/language) · **Eval:** [FLEURS](https://huggingface.co/datasets/google/fleurs)
- **Backbone:** `openai/whisper-small`, frozen · **Adapters:** LoRA rank-16, encoder + decoder attention
- **Schedule:** 3–5 epochs/language, batch 4, AdamW, cosine LR (peak 1e-4), 2× NVIDIA T4
- **Augmentation (v9):** SpecAugment + speed perturb for `bn`/`mr` only; `hi`/`ta`/`te` clean
- **Selection:** WER-gated checkpoints (`*_best_*.pt`) on FLEURS dev slices
- **Code:** [`train_v3.py`](https://github.com/eulogik/PolyWhisper/blob/main/train_v3.py) · orchestrator [`kaggle_train_resumable.py`](https://github.com/eulogik/PolyWhisper/blob/main/kaggle_train_resumable.py) · scoring [`normalize_ortho.py`](https://github.com/eulogik/PolyWhisper/blob/main/normalize_ortho.py)

## ❓ FAQ

**What is PolyWhisper?**
PolyWhisper is an open-source Indic ASR toolkit: one frozen Whisper-Small backbone plus five small per-language LoRA adapters covering Hindi, Tamil, Telugu, Bengali, and Marathi.

**How is it different from fine-tuning Whisper?**
Full fine-tuning rewrites ~244M–1.5B weights per language. PolyWhisper freezes the backbone and trains ~3.5M LoRA parameters per language (~14MB), so five languages ship for the storage cost of a rounding error.

**Which languages are usable?**
All five ship working adapters. Hindi (46.3 WER) and Tamil (70.1) are strongest; Telugu, Bengali, and Marathi remain high-WER research adapters, useful for assistive/search/subtitle-draft workflows rather than verbatim transcription.

**Can I run it on CPU?**
Yes — export to ONNX INT8 and run with ONNX Runtime, no GPU required.

**Can I run it on a Mac?**
Yes — PyTorch MPS is supported (`Device: mps`), plus CPU via ONNX.

**What data was it trained/evaluated on?**
Trained on IndicVoices-ST conversational speech, evaluated on FLEURS read speech with punctuation-normalized, script-aware scoring.

## ⚠️ Limitations

- Absolute WER on Telugu/Bengali/Marathi is still high — usable for assistive/search/subtitle-draft workflows, not verbatim legal/medical transcription.
- Evaluated on read speech (FLEURS); spontaneous conversational accuracy will differ.
- Beam=1 numbers in the benchmark table above (paper parity); shipped defaults use beam-5 + repetition penalty 1.3 except Telugu (beam-1), see decoding table.

## 📄 License & citation

MIT. Whisper weights © OpenAI. Training data: IndicVoices-ST (CC-BY) · Eval: FLEURS (CC-BY).

```bibtex
@misc{polywhisper2026,
  title  = {PolyWhisper: Efficient Multilingual Indic ASR via Frozen Backbone and Per-Language LoRA Adapters},
  author = {Kishore, Gautam},
  year   = {2026},
  publisher = {HuggingFace},
  url    = {https://huggingface.co/eulogik/polywhisper}
}
```

## 🔗 Links

- 🌍 Eulogik: [eulogik.com](https://eulogik.com)
- 🤗 Model: [huggingface.co/eulogik/polywhisper](https://huggingface.co/eulogik/polywhisper)
- 💻 Code: [github.com/eulogik/PolyWhisper](https://github.com/eulogik/PolyWhisper)
- 🗣️ Train data: [ai4bharat/indicvoices-st](https://huggingface.co/datasets/ai4bharat/indicvoices-st)
- 🧪 Eval data: [google/fleurs](https://huggingface.co/datasets/google/fleurs)