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--- |
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language: |
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- bn |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- hf-asr-leaderboard |
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- openslr_SLR53 |
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- robust-speech-event |
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datasets: |
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- openslr |
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- SLR53 |
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- Harveenchadha/indic-text |
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metrics: |
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- wer |
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- cer |
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model-index: |
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- name: Tahsin-Mayeesha/wav2vec2-bn-300m |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Speech Recognition |
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dataset: |
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type: openslr |
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name: Open SLR |
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args: SLR66 |
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metrics: |
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- type: wer |
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value: 0.31104373941386626 |
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name: Test WER |
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- type: cer |
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value: 0.07263099973420006 |
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name: Test CER |
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- type: wer |
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value: 0.17776164652632478 |
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name: Test WER with lm |
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- type: cer |
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value: 0.04394092712884769 |
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name: Test CER with lm |
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--- |
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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. |
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It achieves the following results on the evaluation set. |
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Without language model : |
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- Wer: 0.3110 |
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- Cer : 0.072 |
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With 5 gram language model trained on [indic-text](https://huggingface.co/datasets/Harveenchadha/indic-text/tree/main) dataset : |
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- Wer: 0.17776 |
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- Cer : 0.04394 |
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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. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 7.5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- gradient_accumulation_steps: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 2000 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.16.0.dev0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 1.17.1.dev0 |
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- Tokenizers 0.11.0 |
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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. |
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Bengali speech data was not available from common voice or librispeech multilingual datasets, so OpenSLR53 has been used. |
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Note 2 : Minimum audio duration of 0.1s has been used to filter the training data which excluded may be 10-20 samples. |
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# Citation |
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@misc {tahsin_mayeesha_2023, |
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author = { {Tahsin Mayeesha} }, |
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title = { wav2vec2-bn-300m (Revision e10defc) }, |
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year = 2023, |
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url = { https://huggingface.co/Tahsin-Mayeesha/wav2vec2-bn-300m }, |
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doi = { 10.57967/hf/0939 }, |
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publisher = { Hugging Face } |
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} |
