Update README.md

README.md

@@ -42,13 +42,13 @@ 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
+\\tspeech_array, sampling_rate = torchaudio.load(batch["path_in_folder"])
+\\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
+\\treturn 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
+\\tlogits = 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])
@@ -67,22 +67,22 @@ 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
+\\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
 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
+\\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
 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"])))
 ```
@@ -90,3 +90,6 @@ print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"],
 ## 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). 
+
+## Training Config and Summary 
+weights-and-biases run profile [here](https://wandb.ai/wandb/xlsr/runs/3itdhtb8/overview?workspace=user-sumedhkhodke)