Update README.md

README.md

@@ -1,30 +1,33 @@
-language: cs
-datasets:
-- common_voice
-metrics:
-- wer
+---
+language:
+- cs
+license: apache-2.0
 tags:
-- audio
 - automatic-speech-recognition
-- speech
+- common_voice
+- generated_from_trainer
+- robust-speech-event
 - xlsr-fine-tuning-week
-license: apache-2.0
+datasets:
+- common_voice
 model-index:
-- name: {Czech Wav2Vec2 XLSR 300M}
+- name: Czech comodoro Wav2Vec2 XLSR 300M CV6.1
   results:
   - task: 
-      name: Speech Recognition
+      name: Automatic Speech Recognition 
       type: automatic-speech-recognition
     dataset:
-      name: Common Voice cs
+      name: Common Voice 6.1
       type: common_voice
       args: cs
     metrics:
        - name: Test WER
          type: wer
-         value: {wer_result_on_test} #TODO (IMPORTANT): replace {wer_result_on_test} with the WER error rate you achieved on the common_voice test set. It should be in the format XX.XX (don't add the % sign here). **Please** remember to fill out this value after you evaluated your model, so that your model appears on the leaderboard. If you fill out this model card before evaluating your model, please remember to edit the model card afterward to fill in your value
+         value: 22.20
+       - name: Test CER
+         type: cer
+         value: 5.1
 ---
-
 # Wav2Vec2-Large-XLSR-53-Czech
 
 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Czech using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
@@ -67,9 +70,9 @@ print("Reference:", test_dataset[:2]["sentence"])
 ```
 
 
-## Evaluation#TODO
+## Evaluation
 
-The model can be evaluated as follows on the Czech test data of Common Voice. 
+The model can be evaluated as follows on the Czech test data of Common Voice 6.1 
 
 
 ```python
@@ -79,14 +82,14 @@ from datasets import load_dataset, load_metric
 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 import re
 
-test_dataset = load_dataset("common_voice", "{lang_id}", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
+test_dataset = load_dataset("common_voice", "cs", split="test") 
 wer = load_metric("wer")
 
-processor = Wav2Vec2Processor.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
-model = Wav2Vec2ForCTC.from_pretrained("{model_id}") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
+processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs")
+model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs") 
 model.to("cuda")
 
-chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]'  # TODO: adapt this list to include all special characters you removed from the data
+chars_to_ignore_regex = '[\,\?\.\!\-\;\:\/\"\“\„\%\”\�\–\'\`\«\»\—\’\…]'
 resampler = torchaudio.transforms.Resample(48_000, 16_000)
 
 # Preprocessing the datasets.
@@ -116,11 +119,11 @@ result = test_dataset.map(evaluate, batched=True, batch_size=8)
 print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
 ```
 
-**Test Result**: XX.XX %  # TODO: write output of print here. IMPORTANT: Please remember to also replace {wer_result_on_test} at the top of with this value here. tags.
+**Test Result**: 22.20 % 
 
 
 ## Training
 
 The Common Voice `train` and `validation` datasets were used for training
 
-The script used for training can be found [here](...) # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here.
+# TODO The script used for training can be found [here](...)