Preprocessing the datasets.

We need to read the aduio files as arrays

def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

Preprocessing the datasets.

We need to read the aduio files as arrays

def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad(): logits = model(inputs.input_values.to("cuda")).logits

  pred_ids = torch.argmax(logits, dim=-1)
  batch["pred_strings"] = processor.batch_decode(pred_ids, skip_special_tokens=True)
  return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))

**Test Result**: 53.64 %

[**Colab Evaluation**](https://github.com/harveenchadha/bol/blob/main/demos/hf/tamil/hf_vakyansh_tamil_tnm_4200_evaluation_common_voice.ipynb) 

## Credits
Thanks to Ekstep Foundation for making this possible. The vakyansh team will be open sourcing speech models in all the Indic Languages.