Update README.md

README.md

@@ -10,7 +10,7 @@ tags:
 - speech
 license: mit
 model-index:
-- name: Wav2Vec2 Vakyansh Hindi Model by Harveen Chadha
+- name: Wav2Vec2 Hindi Model by Aditi sharma
   results:
   - task:
       name: Speech Recognition
@@ -25,27 +25,11 @@ model-index:
       value: 33.17
 ---
 
-## Spaces Demo
-Check the spaces demo [here](https://huggingface.co/spaces/Harveenchadha/wav2vec2-vakyansh-hindi/tree/main)
-
-## Pretrained Model
-
-Fine-tuned on Multilingual Pretrained Model [CLSRIL-23](https://arxiv.org/abs/2107.07402). The original fairseq checkpoint is present [here](https://github.com/Open-Speech-EkStep/vakyansh-models). When using this model, make sure that your speech input is sampled at 16kHz.
-
-**Note: The result from this model is without a language model so you may witness a higher WER in some cases.**
-
 ## Dataset
 
 This model was trained on 4200 hours of Hindi Labelled Data. The labelled data is not present in public domain as of now.
 
-## Training Script
 
-Models were trained using experimental platform setup by Vakyansh team at Ekstep. Here is the [training repository](https://github.com/Open-Speech-EkStep/vakyansh-wav2vec2-experimentation).
-
-In case you want to explore training logs on wandb they are [here](https://wandb.ai/harveenchadha/hindi_finetuning_multilingual?workspace=user-harveenchadha).
-
-
-## [Colab Demo](https://colab.research.google.com/github/harveenchadha/bol/blob/main/demos/hf/hindi/hf_hindi_him_4200_demo.ipynb)
 
 ## Usage
 
@@ -132,7 +116,5 @@ print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"],
 
 **Test Result**: 33.17 %
 
-[**Colab Evaluation**](https://colab.research.google.com/github/harveenchadha/bol/blob/main/demos/hf/hindi/hf_vakyansh_hindi_him_4200_evaluation_common_voice.ipynb) 
-
 ## Credits
-Thanks to Ekstep Foundation for making this possible. The vakyansh team will be open sourcing speech models in all the Indic Languages.
+Thanks to Deepmindz Innovations for making this possible.