Update README.md

README.md

@@ -26,7 +26,8 @@ model-index:
 ---
 
 # Wav2Vec2-Large-XLSR-53-Marathi
-Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using the [OpenSLR SLR64](http://openslr.org/64/) dataset. When using this model, make sure that your speech input is sampled at 16kHz. This data contains only female voices, although it works well for male voice too.
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using the [OpenSLR SLR64](http://openslr.org/64/) dataset. When using this model, make sure that your speech input is sampled at 16kHz. This data contains only female voices but it works well for male voices too.
+**WER on the Test Set**: 12.70 %  
 ## Usage
 The model can be used directly without a language model as follows, given that your dataset has Marathi `actual_text` and `path_in_folder` columns:
 ```python
@@ -42,13 +43,13 @@ model = Wav2Vec2ForCTC.from_pretrained("sumedh/wav2vec2-large-xlsr-marathi")
 resampler = torchaudio.transforms.Resample(48_000, 16_000) #first arg - input sample, second arg - output sample
 # Preprocessing the datasets. We need to read the aduio files as arrays
 def speech_file_to_array_fn(batch):
-\\tspeech_array, sampling_rate = torchaudio.load(batch["path_in_folder"])
-\\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
-\\treturn batch
+  speech_array, sampling_rate = torchaudio.load(batch["path_in_folder"])
+  batch["speech"] = resampler(speech_array).squeeze().numpy()
+  return batch
 mr_test_dataset_new = mr_test_dataset_new.map(speech_file_to_array_fn)
 inputs = processor(mr_test_dataset_new["speech"][:5], sampling_rate=16_000, return_tensors="pt", padding=True)
 with torch.no_grad():
-\\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
+  logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
 predicted_ids = torch.argmax(logits, dim=-1)
 print("Prediction:", processor.batch_decode(predicted_ids))
 print("Reference:", mr_test_dataset_new["actual_text"][:5])
@@ -67,26 +68,26 @@ processor = Wav2Vec2Processor.from_pretrained("sumedh/wav2vec2-large-xlsr-marath
 model = Wav2Vec2ForCTC.from_pretrained("sumedh/wav2vec2-large-xlsr-marathi") 
 model.to("cuda")
 
-chars_to_ignore_regex = '[\\\\,\\\\?\\\\.\\\\!\\\\-\\\\;\\\\:\\\\"\\\\“]' 
+chars_to_ignore_regex = '[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“]' 
 resampler = torchaudio.transforms.Resample(48_000, 16_000)
 # Preprocessing the datasets. We need to read the aduio files as arrays
 def speech_file_to_array_fn(batch):
-\\tbatch["actual_text"] = re.sub(chars_to_ignore_regex, '', batch["actual_text"]).lower()
-\\tspeech_array, sampling_rate = torchaudio.load(batch["path_in_folder"])
-\\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
-\\treturn batch
+  batch["actual_text"] = re.sub(chars_to_ignore_regex, '', batch["actual_text"]).lower()
+  speech_array, sampling_rate = torchaudio.load(batch["path_in_folder"])
+  batch["speech"] = resampler(speech_array).squeeze().numpy()
+  return batch
 mr_test_dataset_new = mr_test_dataset_new.map(speech_file_to_array_fn)
 def evaluate(batch):
-\\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
-\\twith torch.no_grad():
-\\t\\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
-\\t\\tpred_ids = torch.argmax(logits, dim=-1)
-\\t\\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
-\\treturn 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"), attention_mask=inputs.attention_mask.to("cuda")).logits
+    pred_ids = torch.argmax(logits, dim=-1)
+    batch["pred_strings"] = processor.batch_decode(pred_ids)
+  return batch
 result = mr_test_dataset_new.map(evaluate, batched=True, batch_size=8)
 print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["actual_text"])))
 ```
-**WER on the Test Set**: 12.70 %  
+
 ## Training
 Train-Test ratio was 90:10.
 The colab notebook used for training can be found [here](https://colab.research.google.com/drive/1wX46fjExcgU5t3AsWhSPTipWg_aMDg2f?usp=sharing).