File size: 3,130 Bytes
cdfd811 45ed7c7 cdfd811 fb235fd cdfd811 45ed7c7 fb235fd cdfd811 fb235fd cdfd811 fb235fd cdfd811 45ed7c7 cdfd811 fb235fd cdfd811 fb235fd cdfd811 45ed7c7 cdfd811 fb235fd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
---
language:
- bn
license: apache-2.0
tags:
- automatic-speech-recognition
- bn
- hf-asr-leaderboard
- openslr_SLR53
- robust-speech-event
datasets:
- openslr
- SLR53
- AI4Bharat/IndicCorp
metrics:
- wer
- cer
model-index:
- name: arijitx/wav2vec2-xls-r-300m-bengali
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: openslr
name: Open SLR
args: SLR53
metrics:
- type: wer
value: 0.21726385291857586
name: Test WER
- type: cer
value: 0.04725010353701041
name: Test CER
- type: wer
value: 0.15322879016421437
name: Test WER with lm
- type: cer
value: 0.03413696666806267
name: Test CER with lm
---
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. |