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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: wav2vec2-large-xlsr-mvc-swahili
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: sw
split: test
args: sw
metrics:
- name: Wer
type: wer
value: 0.32237526397075045
---
<!-- 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-mvc-swahili
This model is a fine-tuned version of [eddiegulay/wav2vec2-large-xlsr-mvc-swahili](https://huggingface.co/eddiegulay/wav2vec2-large-xlsr-mvc-swahili) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 0.3224
## 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: 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: 500
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.17 | 100 | inf | 1.0 |
| No log | 0.34 | 200 | inf | 1.0 |
| No log | 0.5 | 300 | inf | 0.3420 |
| 3.3446 | 0.67 | 400 | inf | 0.3431 |
| 3.3446 | 0.84 | 500 | inf | 0.3500 |
| 3.3446 | 1.01 | 600 | inf | 0.3433 |
| 3.3446 | 1.17 | 700 | inf | 0.3347 |
| 0.1975 | 1.34 | 800 | inf | 0.3340 |
| 0.1975 | 1.51 | 900 | inf | 0.3307 |
| 0.1975 | 1.68 | 1000 | inf | 0.3233 |
| 0.1975 | 1.84 | 1100 | inf | 0.3224 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|