
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: 0.3110
- Cer : 0.072

With 5 gram language model trained on [indic-text](https://huggingface.co/datasets/Harveenchadha/indic-text/tree/main) dataset : 
- Wer: 0.17776
- Cer : 0.04394
  

Note : 10% of a total 218703 samples have been used for evaluation. Evaluation set has 21871 examples. Training was stopped after 30k steps. Output predictions are available under files section.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0

Note : Training and evaluation script modified from https://huggingface.co/chmanoj/xls-r-300m-te and 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.

Note 2 : Minimum audio duration of 0.1s has been used to filter the training data which excluded may be 10-20 samples. 

# Citation

@misc {tahsin_mayeesha_2023,
	author       = { {Tahsin Mayeesha} },
	title        = { wav2vec2-bn-300m (Revision e10defc) },
	year         = 2023,
	url          = { https://huggingface.co/Tahsin-Mayeesha/wav2vec2-bn-300m },
	doi          = { 10.57967/hf/0939 },
	publisher    = { Hugging Face }
}
{"language": ["bn"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "openslr_SLR53", "robust-speech-event"], "datasets": ["openslr", "SLR53", "Harveenchadha/indic-text"], "metrics": ["wer", "cer"], "model-index": [{"name": "Tahsin-Mayeesha/wav2vec2-bn-300m", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Open SLR", "type": "openslr", "args": "SLR66"}, "metrics": [{"type": "wer", "value": 0.31104373941386626, "name": "Test WER"}, {"type": "cer", "value": 0.07263099973420006, "name": "Test CER"}, {"type": "wer", "value": 0.17776164652632478, "name": "Test WER with lm"}, {"type": "cer", "value": 0.04394092712884769, "name": "Test CER with lm"}]}]}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition hf-asr-leaderboard openslr_SLR53 robust-speech-event bn dataset:openslr dataset:SLR53 dataset:Harveenchadha/indic-text doi:10.57967/hf/0939 license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #openslr_SLR53 #robust-speech-event #bn #dataset-openslr #dataset-SLR53 #dataset-Harveenchadha/indic-text #doi-10.57967/hf/0939 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the OPENSLR_SLR53 - bengali dataset.
It achieves the following results on the evaluation set. 

Without language model : 
- Wer: 0.3110
- Cer : 0.072

With 5 gram language model trained on indic-text dataset : 
- Wer: 0.17776
- Cer : 0.04394
  

Note : 10% of a total 218703 samples have been used for evaluation. Evaluation set has 21871 examples. Training was stopped after 30k steps. Output predictions are available under files section.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0

Note : Training and evaluation script modified from URL and URL 
Bengali speech data was not available from common voice or librispeech multilingual datasets, so OpenSLR53 has been used.

Note 2 : Minimum audio duration of 0.1s has been used to filter the training data which excluded may be 10-20 samples. 

@misc {tahsin_mayeesha_2023,
	author       = { {Tahsin Mayeesha} },
	title        = { wav2vec2-bn-300m (Revision e10defc) },
	year         = 2023,
	url          = { URL },
	doi          = { 10.57967/hf/0939 },
	publisher    = { Hugging Face }
}
### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0

Note : Training and evaluation script modified from URL and URL 
Bengali speech data was not available from common voice or librispeech multilingual datasets, so OpenSLR53 has been used.

Note 2 : Minimum audio duration of 0.1s has been used to filter the training data which excluded may be 10-20 samples. 

@misc {tahsin_mayeesha_2023,
	author       = { {Tahsin Mayeesha} },
	title        = { wav2vec2-bn-300m (Revision e10defc) },
	year         = 2023,
	url          = { URL },
	doi          = { 10.57967/hf/0939 },
	publisher    = { Hugging Face }
}
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #openslr_SLR53 #robust-speech-event #bn #dataset-openslr #dataset-SLR53 #dataset-Harveenchadha/indic-text #doi-10.57967/hf/0939 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0

Note : Training and evaluation script modified from URL and URL 
Bengali speech data was not available from common voice or librispeech multilingual datasets, so OpenSLR53 has been used.

Note 2 : Minimum audio duration of 0.1s has been used to filter the training data which excluded may be 10-20 samples. 

@misc {tahsin_mayeesha_2023,
	author       = { {Tahsin Mayeesha} },
	title        = { wav2vec2-bn-300m (Revision e10defc) },
	year         = 2023,
	url          = { URL },
	doi          = { 10.57967/hf/0939 },
	publisher    = { Hugging Face }
}
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #openslr_SLR53 #robust-speech-event #bn #dataset-openslr #dataset-SLR53 #dataset-Harveenchadha/indic-text #doi-10.57967/hf/0939 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0

Note : Training and evaluation script modified from URL and URL 
Bengali speech data was not available from common voice or librispeech multilingual datasets, so OpenSLR53 has been used.

Note 2 : Minimum audio duration of 0.1s has been used to filter the training data which excluded may be 10-20 samples. 

@misc {tahsin_mayeesha_2023,
	author       = { {Tahsin Mayeesha} },
	title        = { wav2vec2-bn-300m (Revision e10defc) },
	year         = 2023,
	url          = { URL },
	doi          = { 10.57967/hf/0939 },
	publisher    = { Hugging Face }
}