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---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-large-960h
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-960h
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. -->
# wav2vec2-large-960h
This model is a fine-tuned version of [facebook/wav2vec2-large-960h](https://huggingface.co/facebook/wav2vec2-large-960h) on an [acc_dataset_v2](https://huggingface.co/datasets/monadical-labs/acc_dataset_v2)" dataset (commit: [a41c520](https://huggingface.co/datasets/monadical-labs/acc_dataset_v2/commit/a41c5204b95bcf293ac5ee1a0da94170374b1cb0)).
It achieves the following results on the evaluation set:
- Loss: 0.6286
- Wer: 0.1538
## Model description
This is a voice-2-text transcription model specialized for the acc dataset.
## Training and evaluation data
Training was based on the training set in [acc_dataset_v2](https://huggingface.co/datasets/monadical-labs/acc_dataset_v2) and evaluation based on
the validation and test sets in the same dataset.
## Training procedure
See the Jupyter notebook [Finetuning-notebook-wav2vec2-large-960h-on-acc-data](https://huggingface.co/ilokavat/wav2vec2-large-960h/blob/main/Finetuning-notebook-wav2vec2-large-960h-on-acc-data.ipynb)
for the full training procedure.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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: 15
- num_epochs: 128
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:--------:|:----:|:---------------:|:------:|
| 1.2379 | 8.3333 | 50 | 0.6501 | 0.3056 |
| 0.4865 | 16.6667 | 100 | 0.7069 | 0.2790 |
| 0.3054 | 25.0 | 150 | 0.6598 | 0.2369 |
| 0.2308 | 33.3333 | 200 | 0.6517 | 0.2215 |
| 0.1793 | 41.6667 | 250 | 0.6884 | 0.2103 |
| 0.1379 | 50.0 | 300 | 0.6418 | 0.1949 |
| 0.1253 | 58.3333 | 350 | 0.7004 | 0.1918 |
| 0.0988 | 66.6667 | 400 | 0.6059 | 0.1846 |
| 0.088 | 75.0 | 450 | 0.6507 | 0.1826 |
| 0.0773 | 83.3333 | 500 | 0.5473 | 0.1682 |
| 0.0686 | 91.6667 | 550 | 0.6027 | 0.1682 |
| 0.0643 | 100.0 | 600 | 0.6192 | 0.1713 |
| 0.0595 | 108.3333 | 650 | 0.6119 | 0.1703 |
| 0.0562 | 116.6667 | 700 | 0.5953 | 0.16 |
| 0.0507 | 125.0 | 750 | 0.6286 | 0.1538 |
### Framework versions
- Transformers 4.48.2
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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