---
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