Update README.md

README.md

@@ -1,29 +1,100 @@
 ---
-language: Bengali
-datasets:
-- OpenSLR
-metrics:
-- wer
-tags:
+language: 
 - bn
-- audio
+license: apache-2.0
+tags:
 - automatic-speech-recognition
-- speech
+- openslr_SLR53
 - robust-speech-event
-license: cc-by-sa-4.0
+- bn
+datasets:
+- openslr
+- SLR53
+- AI4Bharat/IndicCorp
+metrics:
+- wer
+- cer
 model-index:
-- name: XLSR Wav2Vec2 Bengali by Arijit
+- name: arijitx/wav2vec2-xls-r-300m-bengali
   results:
-  - task:
-      name: Speech Recognition
+  - task: 
       type: automatic-speech-recognition
+      name: Speech Recognition
     dataset:
-      name: OpenSLR
-      type: OpenSLR
-      args: ben
+      type: openslr
+      name: Open SLR
+      args: SLR53
     metrics:
-    - name: Test WER
-      type: wer
-      value: 32.45
+      - type: wer    # Required. Example: wer
+        value: 0.21726385291857586  # Required. Example: 20.90
+        name: Test WER    # Optional. Example: Test WER
+      - type: cer
+        value: 0.04725010353701041
+        name: Test CER
+      - type: wer    # Required. Example: wer
+        value:  0.15322879016421437  # Required. Example: 20.90
+        name: Test WER with lm   # Optional. Example: Test WER
+      - type: cer
+        value: 0.03413696666806267
+        name: Test CER with lm
 ---
-# wav2vec2-xls-r-300m-bangla
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the OPENSLR_SLR53 - bengali dataset.
+It achieves the following results on the evaluation set. 
+
+Without language model : 
+- WER: 0.21726385291857586
+- CER: 0.04725010353701041
+
+With 5 gram language model trained on 30M sentences randomly chosen from [AI4Bharat IndicCorp](https://indicnlp.ai4bharat.org/corpora/) dataset : 
+- WER: 0.15322879016421437
+- CER: 0.03413696666806267
+
+  
+
+Note : 5% of a total 10935 samples have been used for evaluation. Evaluation set has 10935 examples which was not part of training training was done on first 95% and eval was done on last 5%. Training was stopped after 180k steps. Output predictions are available under files section.
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+
+- dataset_name="openslr"  	
+- model_name_or_path="facebook/wav2vec2-xls-r-300m"  	
+- dataset_config_name="SLR53"  	
+- output_dir="./wav2vec2-xls-r-300m-bengali"  	
+- overwrite_output_dir  	
+- num_train_epochs="50"  	
+- per_device_train_batch_size="32"  	
+- per_device_eval_batch_size="32"  	
+- gradient_accumulation_steps="1"  	
+- learning_rate="7.5e-5"  	
+- warmup_steps="2000"  	
+- length_column_name="input_length"  	
+- evaluation_strategy="steps"  	
+- text_column_name="sentence"  	
+- chars_to_ignore , ? . ! \- \; \: \" “ % ‘ ” � — ’ … –  	
+- save_steps="2000"  	
+- eval_steps="3000"  	
+- logging_steps="100"  	
+- layerdrop="0.0"  	
+- activation_dropout="0.1"  	
+- save_total_limit="3"  	
+- freeze_feature_encoder  	
+- feat_proj_dropout="0.0"  	
+- mask_time_prob="0.75"  	
+- mask_time_length="10"  	
+- mask_feature_prob="0.25"  	
+- mask_feature_length="64"      
+- preprocessing_num_workers 32  
+
+### Framework versions
+
+- Transformers 4.16.0.dev0
+- Pytorch 1.10.1+cu102
+- Datasets 1.17.1.dev0
+- Tokenizers 0.11.0
+
+Notes
+- Training and eval code modified from : https://github.com/huggingface/transformers/tree/master/examples/research_projects/robust-speech-event. 
+- Bengali speech data was not available from common voice or librispeech multilingual datasets, so OpenSLR53 has been used.
+- Minimum audio duration of 0.5s has been used to filter the training data which excluded may be 10-20 samples.
+- OpenSLR53 transcripts are *not* part of LM training and LM used to evaluate.