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---
language:
- pa-IN
license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
metrics:
- wer
- cer
model-index:
- name: wav2vec2-large-xlsr-53-punjabi
results:
- task:
type: automatic-speech-recognition # Required. Example: automatic-speech-recognition
name: Speech Recognition # Optional. Example: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_7_0 # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: Common Voice pa-IN # Required. Example: Common Voice zh-CN
args: pa-IN # Optional. Example: zh-CN
metrics:
- type: wer # Required. Example: wer
value: 39.42 # Required. Example: 20.90
name: Test WER # Optional. Example: Test WER
args:
- learning_rate: 0.0003
- 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: 200
- num_epochs: 30
- mixed_precision_training: Native AMP # Optional. Example for BLEU: max_order
- type: cer # Required. Example: wer
value: 12.99 # Required. Example: 20.90
name: Test CER # Optional. Example: Test WER
args:
- learning_rate: 0.0003
- 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: 200
- num_epochs: 30
- mixed_precision_training: Native AMP # Optional. Example for BLEU: max_order
---
<!-- 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-xlsr-53-punjabi
This model is a fine-tuned version of [manandey/wav2vec2-large-xlsr-punjabi](https://huggingface.co/manandey/wav2vec2-large-xlsr-punjabi) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6752
- Wer: 0.3942
- Cer: 0.1299
## Training procedure
### 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: 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: 200
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.8899 | 4.16 | 100 | 0.5338 | 0.4233 | 0.1394 |
| 0.3652 | 8.33 | 200 | 0.5759 | 0.4192 | 0.1349 |
| 0.248 | 12.49 | 300 | 0.6309 | 0.4102 | 0.1327 |
| 0.1898 | 16.65 | 400 | 0.6441 | 0.4007 | 0.1351 |
| 0.1486 | 20.82 | 500 | 0.6790 | 0.4044 | 0.1393 |
| 0.1245 | 24.98 | 600 | 0.6869 | 0.3987 | 0.1309 |
| 0.1085 | 29.16 | 700 | 0.6752 | 0.3942 | 0.1299 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
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