2

README.md

@@ -1,5 +1,5 @@
 ---
-language: {pa-IN} 
+language: pa-IN
 datasets:
 - common_voice 
 metrics:
@@ -11,23 +11,23 @@ tags:
 - xlsr-fine-tuning-week
 license: apache-2.0
 model-index:
-- name: {danurahul/wav2vec2-large-xlsr-pa-IN} 
+- name: danurahul/wav2vec2-large-xlsr-pa-IN
   results:
   - task: 
       name: Speech Recognition
       type: automatic-speech-recognition
     dataset:
-      name: Common Voice {pa-IN} 
+      name: Common Voice pa-IN
       type: common_voice
-      args: {pa-IN} 
+      args: pa-IN
     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: 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
 ---
 
-# Wav2Vec2-Large-XLSR-53-{Punjabi} 
-Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on {Punjabi} using the [Common Voice](https://huggingface.co/datasets/common_voice).
+# Wav2Vec2-Large-XLSR-53-Punjabi
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Punjabi using the [Common Voice](https://huggingface.co/datasets/common_voice).
 When using this model, make sure that your speech input is sampled at 16kHz.
 
 ## Usage
@@ -40,10 +40,10 @@ import torchaudio
 from datasets import load_dataset
 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 
-test_dataset = load_dataset("common_voice", "{pa-IN}", split="test[:2%]") 
+test_dataset = load_dataset("common_voice", "pa-IN", split="test[:2%]") 
 
-processor = Wav2Vec2Processor.from_pretrained("{danurahul/wav2vec2-large-xlsr-pa-IN}") 
-model = Wav2Vec2ForCTC.from_pretrained("{danurahul/wav2vec2-large-xlsr-pa-IN}") 
+processor = Wav2Vec2Processor.from_pretrained("danurahul/wav2vec2-large-xlsr-pa-IN") 
+model = Wav2Vec2ForCTC.from_pretrained("danurahul/wav2vec2-large-xlsr-pa-IN") 
 
 resampler = torchaudio.transforms.Resample(48_000, 16_000)
 
@@ -69,7 +69,7 @@ print("Reference:", test_dataset["sentence"][:2])
 
 ## Evaluation
 
-The model can be evaluated as follows on the {Punjabi} test data of Common Voice. 
+The model can be evaluated as follows on the Punjabi test data of Common Voice. 
 
 
 ```python
@@ -79,13 +79,13 @@ from datasets import load_dataset, load_metric
 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 import re
 
-test_dataset = load_dataset("common_voice", "{pa-IN}", split="test") 
+test_dataset = load_dataset("common_voice", "pa-IN", split="test") 
 
 wer = load_metric("wer")
 
-processor = Wav2Vec2Processor.from_pretrained("{danurahul/wav2vec2-large-xlsr-pa-IN}") 
+processor = Wav2Vec2Processor.from_pretrained("danurahul/wav2vec2-large-xlsr-pa-IN") 
 
-model = Wav2Vec2ForCTC.from_pretrained("{danurahul/wav2vec2-large-xlsr-pa-IN}") 
+model = Wav2Vec2ForCTC.from_pretrained("danurahul/wav2vec2-large-xlsr-pa-IN") 
 
 model.to("cuda")