README.md

---
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
- mozilla-foundation/common_voice_15_0
- mozilla-foundation/common_voice_13_0
language:
- hi
metrics:
- cer
- wer
library_name: transformers
pipeline_tag: automatic-speech-recognition
model-index:
  - name: whisper-small-hi-cv
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 15
          type: mozilla-foundation/common_voice_15_0
          args: hi
        metrics:
          - name: Test WER
            type: wer
            value: 13.9913
          - name: Test CER
            type: cer
            value: 5.8844

      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 13
          type: mozilla-foundation/common_voice_13_0
          args: hi
        metrics:
          - name: Test WER
            type: wer
            value: 23.3824
          - name: Test CER
            type: cer
            value: 10.5288
---

# whisper-small-hi-cv

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 15 dataset.
It achieves the following results on the evaluation set:
- Wer: 13.9913
- Cer: 5.8844

View the results on Kaggle Notebook: https://www.kaggle.com/code/kingabzpro/whisper-hindi-eval

## Evaluation

```python
from datasets import load_dataset,load_metric,Audio
from transformers import WhisperForConditionalGeneration, WhisperProcessor
import torch
import torchaudio

test_dataset = load_dataset("mozilla-foundation/common_voice_13_0", "hi", split="test")
wer = load_metric("wer")
cer = load_metric("cer")

processor = WhisperProcessor.from_pretrained("kingabzpro/whisper-small-hi-cv")
model = WhisperForConditionalGeneration.from_pretrained("kingabzpro/whisper-small-hi-cv").to("cuda")
test_dataset = test_dataset.cast_column("audio", Audio(sampling_rate=16000))

def map_to_pred(batch):
    audio = batch["audio"]
    input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features
    batch["reference"] = processor.tokenizer._normalize(batch['sentence'])

    with torch.no_grad():
        predicted_ids = model.generate(input_features.to("cuda"))[0]
    transcription = processor.decode(predicted_ids)
    batch["prediction"] = processor.tokenizer._normalize(transcription)
    return batch

result = test_dataset.map(map_to_pred)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["prediction"], references=result["reference"])))
print("CER: {:2f}".format(100 * cer.compute(predictions=result["prediction"], references=result["reference"])))
```
```bash
WER: 23.3824
CER: 10.5288
```