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---
language:
- ur
license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
- cer
model-index:
- name: wav2vec2-large-xls-r-300m-Urdu
results:
- task:
type: automatic-speech-recognition # Required. Example: automatic-speech-recognition
name: Speech Recognition # Optional. Example: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_8_0 # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: Common Voice ur # Required. Example: Common Voice zh-CN
args: ur # Optional. Example: zh-CN
metrics:
- type: wer # Required. Example: wer
value: 51.96 # Required. Example: 20.90
name: Test WER With LM # Optional. Example: Test WER
# Optional. Example for BLEU: max_order
- type: cer # Required. Example: wer
value: 22.69 # Required. Example: 20.90
name: Test CER # Optional. Example: Test WER
---
<!-- 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. -->
# wav2vec2-large-xls-r-300m-Urdu
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5867
- Wer: 0.6240
- Cer: 0.2579
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 11.1231 | 8.31 | 100 | 3.5275 | 1.0 | 1.0 |
| 3.4028 | 16.63 | 200 | 3.1164 | 1.0 | 1.0 |
| 3.1973 | 24.94 | 300 | 2.5590 | 1.0 | 1.0 |
| 1.3774 | 33.31 | 400 | 1.4729 | 0.7646 | 0.3325 |
| 0.5427 | 41.63 | 500 | 1.4140 | 0.6939 | 0.3001 |
| 0.3541 | 49.94 | 600 | 1.4532 | 0.6580 | 0.2815 |
| 0.26 | 58.31 | 700 | 1.5309 | 0.6403 | 0.2726 |
| 0.2025 | 66.63 | 800 | 1.5230 | 0.6310 | 0.2655 |
| 0.171 | 74.94 | 900 | 1.5578 | 0.6336 | 0.2632 |
| 0.1511 | 83.31 | 1000 | 1.5733 | 0.6321 | 0.2635 |
| 0.1352 | 91.63 | 1100 | 1.6022 | 0.6255 | 0.2608 |
| 0.1192 | 99.94 | 1200 | 1.5867 | 0.6240 | 0.2579 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
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