Update README.md

README.md

@@ -26,16 +26,16 @@ 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 but it works well for male voices too.
-**WER (Word Error Rate) on the Test Set**: 12.70 %  
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marathi using the [Open 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 the model works well for male voices too. Trained on Google Colab Pro on Tesla P100 16GB GPU.<br>
+**WER (Word Error Rate) 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
 import torch, torchaudio
-from datasets import load_dataset
 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 
-mr_test_dataset_new = all_data['test']
+#Since marathi is not present on Common Voice, script for reading the below dataset can be picked up from the eval script below
+mr_test_dataset = all_data['test']
 
 processor = Wav2Vec2Processor.from_pretrained("sumedh/wav2vec2-large-xlsr-marathi") 
 model = Wav2Vec2ForCTC.from_pretrained("sumedh/wav2vec2-large-xlsr-marathi") 
@@ -46,22 +46,31 @@ def speech_file_to_array_fn(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)
+mr_test_dataset = mr_test_dataset.map(speech_file_to_array_fn)
+inputs = processor(mr_test_dataset["speech"][:5], sampling_rate=16_000, return_tensors="pt", padding=True)
 with torch.no_grad():
   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])
+print("Reference:", mr_test_dataset["actual_text"][:5])
 ```
 ## Evaluation
 Evaluated on 10% of the Marathi data on Open SLR-64.
 ```python
 import re, torch, torchaudio
-from datasets import load_dataset, load_metric
+from datasets import load_metric
 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 
-mr_test_dataset_new = all_data['test']
+#below is a custom script to be used for reading marathi dataset since its not present on the Common Voice
+dataset_path = "./OpenSLR-64_Marathi/mr_in_female/" #TODO : include the path of the dataset extracted from http://openslr.org/64/
+audio_df = pd.read_csv(os.path.join(dataset_path,'line_index.tsv'),sep='\t',header=None)
+audio_df.columns = ['path_in_folder','actual_text']
+audio_df['path_in_folder'] = audio_df['path_in_folder'].apply(lambda x: dataset_path + x + '.wav')
+audio_df = audio_df.sample(frac=1, random_state=2020).reset_index(drop=True) #seed number is important for reproducibility of WER score
+all_data = Dataset.from_pandas(audio_df)
+all_data = all_data.train_test_split(test_size=0.10,seed=2020) #seed number is important for reproducibility of WER score
+
+mr_test_dataset = all_data['test']
 wer = load_metric("wer")
 
 processor = Wav2Vec2Processor.from_pretrained("sumedh/wav2vec2-large-xlsr-marathi")
@@ -76,7 +85,7 @@ def speech_file_to_array_fn(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)
+mr_test_dataset = mr_test_dataset.map(speech_file_to_array_fn)
 def evaluate(batch):
   inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
   with torch.no_grad():
@@ -84,13 +93,13 @@ def evaluate(batch):
     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)
+result = mr_test_dataset.map(evaluate, batched=True, batch_size=8)
 print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["actual_text"])))
 ```
 
 ## Training
 Train-Test ratio was 90:10.
-Colab training notebook can be found [here](https://colab.research.google.com/drive/1wX46fjExcgU5t3AsWhSPTipWg_aMDg2f?usp=sharing). 
+The training notebook Colab link [here](https://colab.research.google.com/drive/1wX46fjExcgU5t3AsWhSPTipWg_aMDg2f?usp=sharing). 
 
 ## Training Config and Summary 
 weights-and-biases run summary [here](https://wandb.ai/wandb/xlsr/runs/3itdhtb8/overview?workspace=user-sumedhkhodke)