---
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