Update README.md

README.md

@@ -11,7 +11,7 @@ tags:
 - xlsr-fine-tuning-week
 license: apache-2.0
 model-index:
-- name: GChhablani XLSR Wav2Vec2 Large 53 Marathi  #TODO: replace {human_readable_name} with a name of your model as it should appear on the leaderboard. It could be something like `Elgeish XLSR Wav2Vec2 Large 53`
+- name: GChhablani XLSR Wav2Vec2 Large 53 Marathi
   results:
   - task: 
       name: Speech Recognition
@@ -25,7 +25,7 @@ model-index:
          value: 14.53
 ---
 
-# Wav2Vec2-Large-XLSR-53-Marathi #TODO: replace language with your {language}, *e.g.* French
+# Wav2Vec2-Large-XLSR-53-Marathi
 
 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Marthi using the [OpenSLR SLR64](http://openslr.org/64/) dataset.
 When using this model, make sure that your speech input is sampled at 16kHz.
@@ -48,7 +48,7 @@ model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-mr")
 resampler = torchaudio.transforms.Resample(48_000, 16_000) # The original data was with 48,000 sampling rate. You can change it according to your input.
 
 # Preprocessing the datasets.
-# We need to read the aduio files as arrays
+# We need to read the audio files as arrays
 def speech_file_to_array_fn(batch):
 	speech_array, sampling_rate = torchaudio.load(batch["path"])
 	batch["speech"] = resampler(speech_array).squeeze().numpy()
@@ -120,6 +120,5 @@ print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"],
 
 ## Training
 
-90% of the OpenSLR Marathi dataset was used for training. # TODO: adapt to state all the datasets that were used for training.
-
+90% of the OpenSLR Marathi dataset was used for training.
 The colab notebook used for training can be found [here](https://colab.research.google.com/drive/1_BbLyLqDUsXG3RpSULfLRjC6UY3RjwME?usp=sharing)