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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: fine-tune-wav2vec2-large-xls-r-1b-sw
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: common_voice_11_0
config: sw
split: test[:1%]
args: sw
metrics:
- name: Wer
type: wer
value: 0.5834348355663824
---
<!-- 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. -->
# fine-tune-wav2vec2-large-xls-r-300m-sw
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_11_0 swahili dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2834
- Wer: 0.5834
## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 9
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.72 | 200 | 3.0092 | 1.0 |
| 4.1305 | 3.43 | 400 | 2.9159 | 1.0 |
| 4.1305 | 5.15 | 600 | 1.4301 | 0.7040 |
| 0.9217 | 6.87 | 800 | 1.3143 | 0.6529 |
| 0.9217 | 8.58 | 1000 | 1.2834 | 0.5834 |
### Framework versions
- Transformers 4.27.0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|