| --- |
| 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. |
| |
