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language:
- hi
library_name: transformers
pipeline_tag: automatic-speech-recognition
license: other
tags:
- whisper
- speech-recognition
- hindi
- hinglish
---
# MME_STT_Model
Hindi speech to Roman-script Hinglish transcription (lowercase, no punctuation). The model was fine-tuned
with LoRA and the adapter was merged into the weights, so it loads as a normal Transformers model with no PEFT.
## Usage
```python
import torch, librosa
from transformers import WhisperForConditionalGeneration, WhisperProcessor
model = WhisperForConditionalGeneration.from_pretrained("MmeOG/MME_STT_Model", torch_dtype=torch.float16).to("cuda")
processor = WhisperProcessor.from_pretrained("MmeOG/MME_STT_Model")
audio, _ = librosa.load("sample.wav", sr=16000) # 16 kHz mono, up to 30 seconds
feats = processor(audio, sampling_rate=16000, return_tensors="pt").input_features.to("cuda", torch.float16)
ids = model.generate(feats, language="hi", task="transcribe")
print(processor.batch_decode(ids, skip_special_tokens=True)[0])
```
## Evaluation
Phonetic WER (pWER, lower is better) on six public Hindi sets (1,100 clips). It is a lenient metric used
to compare systems on the same clips, not an absolute accuracy figure.
| Dataset | This model | Before fine-tuning |
|---|---|---|
| fleurs | 18.45 | 20.15 |
| commonvoice | 26.42 | 29.16 |
| indictts | 13.32 | 13.72 |
| kathbath | 19.79 | 21.73 |
| kathbath_noisy | 21.91 | 23.37 |
| mucs | 19.65 | 20.84 |
| **all** | **19.64** | **21.21** |
## Limitations
- Not validated on real phone calls.
- Not a streaming model; it transcribes after the speaker finishes.
- Accuracy drops in noise: about 5 to 7 points at 10 dB, and strong noise (0 dB) breaks it.
- Occasional repetition loops (about 0.5% of clips); a repetition guard at decoding time is recommended.
- Very short clips (under 2 seconds) have high error rates.
- Outputs have no punctuation or capitalisation, and there is no speaker diarisation.
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