metadata
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-hi-cv8-b2
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_8_0
name: Common Voice 7
args: hi
metrics:
- type: wer
value: 0.3891350503092403
name: Test WER
- name: Test CER
type: cer
value: 0.13016327327131985
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: hi
metrics:
- name: Test WER
type: wer
value: NA
- name: Test CER
type: cer
value: NA
wav2vec2-large-xls-r-300m-hi-cv8-b2
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HI dataset. It achieves the following results on the evaluation set:
- Loss: 0.7322
- Wer: 0.3469
Evaluation Commands
- To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8-b2 --dataset mozilla-foundation/common_voice_8_0 --config hi --split test --log_outputs
- To evaluate on speech-recognition-community-v2/dev_data
Hindi language isn't available in speech-recognition-community-v2/dev_data
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00025
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 700
- num_epochs: 35
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
9.6226 | 1.04 | 200 | 3.8855 | 1.0 |
3.4678 | 2.07 | 400 | 3.4283 | 1.0 |
2.3668 | 3.11 | 600 | 1.0743 | 0.7175 |
0.7308 | 4.15 | 800 | 0.7663 | 0.5498 |
0.4985 | 5.18 | 1000 | 0.6957 | 0.5001 |
0.3817 | 6.22 | 1200 | 0.6932 | 0.4866 |
0.3281 | 7.25 | 1400 | 0.7034 | 0.4983 |
0.2752 | 8.29 | 1600 | 0.6588 | 0.4606 |
0.2475 | 9.33 | 1800 | 0.6514 | 0.4328 |
0.219 | 10.36 | 2000 | 0.6396 | 0.4176 |
0.2036 | 11.4 | 2200 | 0.6867 | 0.4162 |
0.1793 | 12.44 | 2400 | 0.6943 | 0.4196 |
0.1724 | 13.47 | 2600 | 0.6862 | 0.4260 |
0.1554 | 14.51 | 2800 | 0.7615 | 0.4222 |
0.151 | 15.54 | 3000 | 0.7058 | 0.4110 |
0.1335 | 16.58 | 3200 | 0.7172 | 0.3986 |
0.1326 | 17.62 | 3400 | 0.7182 | 0.3923 |
0.1225 | 18.65 | 3600 | 0.6995 | 0.3910 |
0.1146 | 19.69 | 3800 | 0.7075 | 0.3875 |
0.108 | 20.73 | 4000 | 0.7297 | 0.3858 |
0.1048 | 21.76 | 4200 | 0.7413 | 0.3850 |
0.0979 | 22.8 | 4400 | 0.7452 | 0.3793 |
0.0946 | 23.83 | 4600 | 0.7436 | 0.3759 |
0.0897 | 24.87 | 4800 | 0.7289 | 0.3754 |
0.0854 | 25.91 | 5000 | 0.7271 | 0.3667 |
0.0803 | 26.94 | 5200 | 0.7378 | 0.3656 |
0.0752 | 27.98 | 5400 | 0.7488 | 0.3680 |
0.0718 | 29.02 | 5600 | 0.7185 | 0.3619 |
0.0702 | 30.05 | 5800 | 0.7428 | 0.3554 |
0.0653 | 31.09 | 6000 | 0.7447 | 0.3559 |
0.0638 | 32.12 | 6200 | 0.7327 | 0.3523 |
0.058 | 33.16 | 6400 | 0.7339 | 0.3488 |
0.0594 | 34.2 | 6600 | 0.7322 | 0.3469 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0