pytorch tensorboard wav2vec2 automatic-speech-recognition dataset:common_voice transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-finetuned-hindi-common-voice-9-0
  results: []
---

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# wav2vec2-large-xls-r-300m-finetuned-hindi-common-voice-9-0

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: 0.7392
- Wer: 1.0141

## 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: 4.42184e-05
- 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: 500
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 9.2217        | 3.03  | 400  | 4.0314          | 1.0    |
| 3.2902        | 6.06  | 800  | 2.1356          | 1.0001 |
| 0.9858        | 9.09  | 1200 | 0.8566          | 1.0037 |
| 0.5131        | 12.12 | 1600 | 0.7481          | 1.0074 |
| 0.3781        | 15.15 | 2000 | 0.7437          | 1.008  |
| 0.2998        | 18.18 | 2400 | 0.7310          | 1.0162 |
| 0.2553        | 21.21 | 2800 | 0.7384          | 1.0159 |
| 0.2216        | 24.24 | 3200 | 0.7537          | 1.0100 |
| 0.2048        | 27.27 | 3600 | 0.7392          | 1.0141 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 2.2.2
- Tokenizers 0.10.3
