transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer dataset:librispeech_asr base_model:facebook/wav2vec2-xls-r-300m license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- librispeech_asr
model-index:
- name: trainer_compile_test
  results: []
---

<!-- 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. -->

# trainer_compile_test

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6771
- Cer: 0.8309

## 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: 16
- 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: cosine
- lr_scheduler_warmup_steps: 75
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.2503        | 1.2   | 300  | 3.2171          | 1.0    |
| 2.953         | 2.4   | 600  | 2.9743          | 1.0    |
| 2.8961        | 3.6   | 900  | 2.9325          | 1.0    |
| 2.8357        | 4.8   | 1200 | 2.8215          | 1.0    |
| 2.2379        | 6.0   | 1500 | 2.1781          | 0.9481 |
| 1.8339        | 7.2   | 1800 | 1.8275          | 0.8780 |
| 1.6769        | 8.4   | 2100 | 1.7017          | 0.8374 |
| 1.642         | 9.6   | 2400 | 1.6771          | 0.8309 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.11.0
