metadata
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_9_0
- generated_from_trainer
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_9_0
metrics:
- wer
model-index:
- name: XLS-R-300M - Hindi
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_9_0
name: Common Voice 9
args: hi
metrics:
- type: wer
value: 21.145
name: Test WER
- name: Test CER
type: cer
value: 7.709
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - HI dataset. It achieves the following results on the evaluation set:
- Loss: 0.5164
- Wer: 0.3349
- Cer: 0.1082
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 9815
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 3.9471 | 8.16 | 400 | 3.7109 | 1.0 | 1.0 |
| 3.274 | 16.32 | 800 | 3.1582 | 0.9917 | 0.9573 |
| 1.5889 | 24.48 | 1200 | 0.7763 | 0.6030 | 0.1990 |
| 1.3647 | 32.65 | 1600 | 0.6051 | 0.5135 | 0.1687 |
| 1.2532 | 40.81 | 2000 | 0.5423 | 0.4712 | 0.1539 |
| 1.1905 | 48.97 | 2400 | 0.5180 | 0.4532 | 0.1490 |
| 1.1193 | 57.14 | 2800 | 0.4906 | 0.4248 | 0.1393 |
| 1.0584 | 65.3 | 3200 | 0.4854 | 0.4069 | 0.1332 |
| 1.0095 | 73.46 | 3600 | 0.4780 | 0.3926 | 0.1287 |
| 0.9759 | 81.63 | 4000 | 0.4666 | 0.3925 | 0.1269 |
| 0.9593 | 89.79 | 4400 | 0.4808 | 0.3830 | 0.1247 |
| 0.909 | 97.95 | 4800 | 0.4798 | 0.3765 | 0.1212 |
| 0.8788 | 106.12 | 5200 | 0.4906 | 0.3608 | 0.1162 |
| 0.8471 | 114.28 | 5600 | 0.4759 | 0.3604 | 0.1166 |
| 0.8116 | 122.44 | 6000 | 0.5080 | 0.3627 | 0.1176 |
| 0.7881 | 130.61 | 6400 | 0.4868 | 0.3489 | 0.1135 |
| 0.766 | 138.77 | 6800 | 0.4955 | 0.3492 | 0.1136 |
| 0.7333 | 146.93 | 7200 | 0.5019 | 0.3461 | 0.1125 |
| 0.709 | 155.1 | 7600 | 0.5084 | 0.3468 | 0.1117 |
| 0.6911 | 163.26 | 8000 | 0.5144 | 0.3412 | 0.1106 |
| 0.6683 | 171.42 | 8400 | 0.5219 | 0.3409 | 0.1117 |
| 0.659 | 179.59 | 8800 | 0.5230 | 0.3376 | 0.1096 |
| 0.6475 | 187.75 | 9200 | 0.5229 | 0.3398 | 0.1097 |
| 0.6419 | 195.91 | 9600 | 0.5200 | 0.3337 | 0.1084 |
Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.1.1.dev0
- Tokenizers 0.12.1