Update README.md

README.md

@@ -87,7 +87,7 @@ processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt
 model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt")
 model.to("cuda")
 
-chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\'\�]'
+chars_to_ignore_regex = '[\,\?\.\!\-\;\;\"\“\'\�]'
 resampler = torchaudio.transforms.Resample(48_000, 16_000)
 
 # Preprocessing the datasets.
@@ -126,29 +126,29 @@ The Common Voice `train` and `validation` datasets were used for training. The s
 
 ```bash
 #!/usr/bin/env bash
-python run_common_voice.py \\
-    --model_name_or_path="facebook/wav2vec2-large-xlsr-53" \\
-    --dataset_config_name="pt" \\
-    --output_dir=/workspace/output_models/pt/wav2vec2-large-xlsr-pt \\
-    --cache_dir=/workspace/data \\
-    --overwrite_output_dir \\
-    --num_train_epochs="30" \\
-    --per_device_train_batch_size="32" \\
-    --per_device_eval_batch_size="32" \\
-    --evaluation_strategy="steps" \\
-    --learning_rate="3e-4" \\
-    --warmup_steps="500" \\
-    --fp16 \\
-    --freeze_feature_extractor \\
-    --save_steps="500" \\
-    --eval_steps="500" \\
-    --save_total_limit="1" \\
-    --logging_steps="500" \\
-    --group_by_length \\
-    --feat_proj_dropout="0.0" \\
-    --layerdrop="0.1" \\
-    --gradient_checkpointing \\
-    --do_train --do_eval \\
+python run_common_voice.py \
+    --model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
+    --dataset_config_name="pt" \
+    --output_dir=/workspace/output_models/pt/wav2vec2-large-xlsr-pt \
+    --cache_dir=/workspace/data \
+    --overwrite_output_dir \
+    --num_train_epochs="30" \
+    --per_device_train_batch_size="32" \
+    --per_device_eval_batch_size="32" \
+    --evaluation_strategy="steps" \
+    --learning_rate="3e-4" \
+    --warmup_steps="500" \
+    --fp16 \
+    --freeze_feature_extractor \
+    --save_steps="500" \
+    --eval_steps="500" \
+    --save_total_limit="1" \
+    --logging_steps="500" \
+    --group_by_length \
+    --feat_proj_dropout="0.0" \
+    --layerdrop="0.1" \
+    --gradient_checkpointing \
+    --do_train --do_eval \
 ```
 
 Notebook containing the evaluation can be found [here](https://colab.research.google.com/drive/14e-zNK_5pm8EMY9EbeZerpHx7WsGycqG?usp=sharing).