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---
license: apache-2.0
datasets:
- ARTPARK-IISc/Vaani
language:
- hi
base_model:
- openai/whisper-tiny
pipeline_tag: automatic-speech-recognition
---
# Whisper-small-vaani-hindi
This is a fine-tuned version of [OpenAI's Whisper-tiny](https://huggingface.co/openai/whisper-tiny), trained on approximately 718 hours of transcribed Hindi speech from multiple datasets.
# Usage
This can be used with the pipeline function from the Transformers module.
```python
import torch
from transformers import pipeline
audio = "path to the audio file to be transcribed"
device = "cuda:0" if torch.cuda.is_available() else "cpu"
modelTags="ARTPARK-IISc/whisper-tiny-vaani-hindi"
transcribe = pipeline(task="automatic-speech-recognition", model=modelTags, chunk_length_s=30, device=device)
transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="hi", task="transcribe")
print('Transcription: ', transcribe(audio)["text"])
```
# Training and Evaluation
The models has finetuned using folllowing dataset [Vaani](https://huggingface.co/datasets/ARTPARK-IISc/Vaani) ,[Gramvaani](https://sites.google.com/view/gramvaaniasrchallenge/dataset)
[IndicVoices](https://huggingface.co/datasets/ai4bharat/IndicVoices), [Fleurs](https://huggingface.co/datasets/google/fleurs),[IndicTTS](https://huggingface.co/datasets/SPRINGLab/IndicTTS-Hindi)
and [Commonvoice](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0)
The performance of the model was evaluated using multiple datasets, and the evaluation results are provided below.
| Dataset | WER |
| :---: | :---: |
| Gramvaani | 42.34 |
| Fleurs | 26.39 |
| IndicTTS | 11.77 |
| MUCS | 39.00 |
|Commonvoice | 37.95 |
| Kathbath | 23.91 |
| Kathbath Noisy| 29.92 |
| Vaani | 33.33 |
|