transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: torgo_xlsr_finetune_M01_keep_all
  results: []
---

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# torgo_xlsr_finetune_M01_keep_all

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4135
- Wer: 0.2436

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5955        | 0.56  | 1000  | 3.3189          | 1.0    |
| 2.8346        | 1.11  | 2000  | 2.3479          | 0.9970 |
| 1.1249        | 1.67  | 3000  | 1.3541          | 0.6641 |
| 0.7802        | 2.23  | 4000  | 1.5194          | 0.5856 |
| 0.6145        | 2.79  | 5000  | 1.4507          | 0.5063 |
| 0.535         | 3.34  | 6000  | 1.4502          | 0.4964 |
| 0.5027        | 3.9   | 7000  | 1.4042          | 0.4167 |
| 0.4289        | 4.46  | 8000  | 1.4100          | 0.4091 |
| 0.4027        | 5.02  | 9000  | 1.3281          | 0.3538 |
| 0.3501        | 5.57  | 10000 | 1.5689          | 0.3862 |
| 0.3202        | 6.13  | 11000 | 1.2194          | 0.3416 |
| 0.3159        | 6.69  | 12000 | 1.3865          | 0.3366 |
| 0.2933        | 7.25  | 13000 | 1.3632          | 0.3222 |
| 0.2748        | 7.8   | 14000 | 1.6112          | 0.3408 |
| 0.2666        | 8.36  | 15000 | 1.5043          | 0.3267 |
| 0.2578        | 8.92  | 16000 | 1.3961          | 0.2974 |
| 0.2589        | 9.48  | 17000 | 1.1875          | 0.2939 |
| 0.2389        | 10.03 | 18000 | 1.3837          | 0.3100 |
| 0.2531        | 10.59 | 19000 | 1.3161          | 0.2978 |
| 0.2132        | 11.15 | 20000 | 1.3118          | 0.2848 |
| 0.1979        | 11.71 | 21000 | 1.3639          | 0.3012 |
| 0.1912        | 12.26 | 22000 | 1.4812          | 0.2798 |
| 0.2104        | 12.82 | 23000 | 1.4553          | 0.2642 |
| 0.1711        | 13.38 | 24000 | 1.3557          | 0.2592 |
| 0.2064        | 13.94 | 25000 | 1.3746          | 0.2737 |
| 0.1764        | 14.49 | 26000 | 1.3845          | 0.2665 |
| 0.1706        | 15.05 | 27000 | 1.3203          | 0.2570 |
| 0.1601        | 15.61 | 28000 | 1.4216          | 0.2592 |
| 0.1589        | 16.16 | 29000 | 1.3192          | 0.2440 |
| 0.15          | 16.72 | 30000 | 1.3713          | 0.2585 |
| 0.1464        | 17.28 | 31000 | 1.4574          | 0.2577 |
| 0.159         | 17.84 | 32000 | 1.3796          | 0.2516 |
| 0.1336        | 18.39 | 33000 | 1.4397          | 0.2474 |
| 0.1423        | 18.95 | 34000 | 1.3613          | 0.2459 |
| 0.1081        | 19.51 | 35000 | 1.4135          | 0.2436 |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.13.3
