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---
language:
- te
license: apache-2.0
tags:
- automatic-speech-recognition
- openslr_SLR66
- generated_from_trainer
- robust-speech-event
- hf-asr-leaderboard
datasets:
- openslr
- SLR66
metrics:
- wer
model-index:
- name: xls-r-1B-te
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: openslr
name: Open SLR
args: SLR66
metrics:
- type: wer
value: 20.624
name: Test WER
- type: cer
value: 3.979
name: Test CER
- type: wer
value: 26.14777618364419
name: Test WER (without LM)
- type: cer
value: 4.932543184970369
name: Test CER (without LM)
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the OPENSLR_SLR66 - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3119
- Wer: 0.2613
### Evaluation metrics
| Metric | Split | Decode with LM | Value |
|:------:|:------:|:--------------:|:---------:|
| WER | Train | No | 5.36 |
| CER | Train | No | 1.11 |
| WER | Test | No | 26.14 |
| CER | Test | No | 4.93 |
| WER | Train | Yes | 5.04 |
| CER | Train | Yes | 1.07 |
| WER | Test | Yes | 20.69 |
| CER | Test | Yes | 3.986 |
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- 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: 2000
- num_epochs: 150.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 2.9038 | 4.8 | 500 | 3.0125 | 1.0 |
| 1.3777 | 9.61 | 1000 | 0.8681 | 0.8753 |
| 1.1436 | 14.42 | 1500 | 0.6256 | 0.7961 |
| 1.0997 | 19.23 | 2000 | 0.5244 | 0.6875 |
| 1.0363 | 24.04 | 2500 | 0.4585 | 0.6276 |
| 0.7996 | 28.84 | 3000 | 0.4072 | 0.5295 |
| 0.825 | 33.65 | 3500 | 0.3590 | 0.5222 |
| 0.8018 | 38.46 | 4000 | 0.3678 | 0.4671 |
| 0.7545 | 43.27 | 4500 | 0.3474 | 0.3962 |
| 0.7375 | 48.08 | 5000 | 0.3224 | 0.3869 |
| 0.6198 | 52.88 | 5500 | 0.3233 | 0.3630 |
| 0.6608 | 57.69 | 6000 | 0.3029 | 0.3308 |
| 0.645 | 62.5 | 6500 | 0.3195 | 0.3722 |
| 0.5249 | 67.31 | 7000 | 0.3004 | 0.3202 |
| 0.4875 | 72.11 | 7500 | 0.2826 | 0.2992 |
| 0.5171 | 76.92 | 8000 | 0.2962 | 0.2976 |
| 0.4974 | 81.73 | 8500 | 0.2990 | 0.2933 |
| 0.4387 | 86.54 | 9000 | 0.2834 | 0.2755 |
| 0.4511 | 91.34 | 9500 | 0.2886 | 0.2787 |
| 0.4112 | 96.15 | 10000 | 0.3093 | 0.2976 |
| 0.4064 | 100.96 | 10500 | 0.3123 | 0.2863 |
| 0.4047 | 105.77 | 11000 | 0.2968 | 0.2719 |
| 0.3519 | 110.57 | 11500 | 0.3106 | 0.2832 |
| 0.3719 | 115.38 | 12000 | 0.3030 | 0.2737 |
| 0.3669 | 120.19 | 12500 | 0.2964 | 0.2714 |
| 0.3386 | 125.0 | 13000 | 0.3101 | 0.2714 |
| 0.3137 | 129.8 | 13500 | 0.3063 | 0.2710 |
| 0.3008 | 134.61 | 14000 | 0.3082 | 0.2617 |
| 0.301 | 139.42 | 14500 | 0.3121 | 0.2628 |
| 0.3291 | 144.23 | 15000 | 0.3105 | 0.2612 |
| 0.3133 | 149.04 | 15500 | 0.3114 | 0.2624 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
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