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---
language:
- en
base_model: openai/wav2vec2
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: BANG WAV2VEC v1 (EN)
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Radio-Modified Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: en
split: test
args: 'config: en, split: test'
metrics:
- name: Wer
type: wer
value: 99.57036953835787
---
<!-- 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. -->
# BANG WAV2VEC v1 (EN)
This model is a fine-tuned version of [openai/wav2vec2](https://huggingface.co/openai/wav2vec2) on the Radio-Modified Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 261.3464
- Wer: 99.5704
## 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: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 6000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 206.0696 | 0.1667 | 1000 | 368.4753 | 98.1649 |
| 164.236 | 0.3333 | 2000 | 310.1212 | 98.1649 |
| 168.343 | 0.5 | 3000 | 299.5959 | 98.1649 |
| 165.9723 | 0.6667 | 4000 | 293.7289 | 98.1635 |
| 155.8457 | 0.8333 | 5000 | 265.2044 | 98.1635 |
| 155.6296 | 1.0 | 6000 | 261.3464 | 99.5704 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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