whisper-small-hi-cv
This model is a fine-tuned version of 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
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"])))
WER: 23.3824
CER: 10.5288
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Base model
openai/whisper-smallDatasets used to train kingabzpro/whisper-small-hi-cv
Evaluation results
- Test WER on Common Voice 15self-reported13.991
- Test CER on Common Voice 15self-reported5.884
- Test WER on Common Voice 13self-reported23.382
- Test CER on Common Voice 13self-reported10.529