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library_name: transformers
license: apache-2.0
base_model: facebook/data2vec-audio-base-960h
tags:
- generated_from_trainer
datasets:
- gigaspeech
metrics:
- wer
model-index:
- name: wav2vec_5e-5_3
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: gigaspeech
type: gigaspeech
config: xs
split: validation
args: xs
metrics:
- name: Wer
type: wer
value: 0.29402661714639433
---
<!-- 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. -->
# wav2vec_5e-5_3
This model is a fine-tuned version of [facebook/data2vec-audio-base-960h](https://huggingface.co/facebook/data2vec-audio-base-960h) on the gigaspeech dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6689
- Wer: 0.2940
## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 1.1835 | 1.0 | 1174 | 0.6329 | 0.3020 |
| 1.2218 | 2.0 | 2348 | 0.6741 | 0.2961 |
| 0.4211 | 2.9978 | 3519 | 0.6689 | 0.2940 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|