metadata

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:
          type: openslr
          name: Open SLR
          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

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 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 } }