Update README.md

README.md

@@ -122,34 +122,4 @@ print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"],
 
 ## Training
 
-The Common Voice `train` and `validation` datasets were used for training. The script used for training can be found [here](https://github.com/jqueguiner/wav2vec2-sprint/blob/main/run_common_voice.py).
- The parameters passed were:
-
-```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 \\
-```
-
-Notebook containing the evaluation can be found [here](https://colab.research.google.com/drive/1pejk9gv9vMcUOjyVQ_vsV2ngW4NiWLWy?usp=sharing).
+The Common Voice `train` and `validation` datasets were used for training. The script used for training can be found [here](https://colab.research.google.com/drive/1pejk9gv9vMcUOjyVQ_vsV2ngW4NiWLWy?usp=sharing).