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---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: Marathi_ASR_using_xlsr_wav2vec
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: mr
split: test
args: mr
metrics:
- name: Wer
type: wer
value: 0.7180765086206896
---
<!-- 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. -->
# Marathi_ASR_using_xlsr_wav2vec
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7582
- Wer: 0.7181
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| No log | 2.6667 | 200 | 0.7381 | 0.7263 |
| 0.336 | 5.3333 | 400 | 0.7472 | 0.7289 |
| 0.336 | 8.0 | 600 | 0.7452 | 0.7215 |
| 0.3237 | 10.6667 | 800 | 0.7449 | 0.7212 |
| 0.3237 | 13.3333 | 1000 | 0.7546 | 0.7192 |
| 0.3104 | 16.0 | 1200 | 0.7565 | 0.7210 |
| 0.3104 | 18.6667 | 1400 | 0.7550 | 0.7193 |
| 0.3089 | 21.3333 | 1600 | 0.7551 | 0.7186 |
| 0.3089 | 24.0 | 1800 | 0.7572 | 0.7185 |
| 0.2993 | 26.6667 | 2000 | 0.7571 | 0.7175 |
| 0.2993 | 29.3333 | 2200 | 0.7582 | 0.7181 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.1.2
- Datasets 2.19.0
- Tokenizers 0.19.1
|