File size: 1,579 Bytes
6ae67d2
4ecf034
 
 
 
 
 
 
 
 
6ae67d2
 
4ecf034
 
6ae67d2
4ecf034
6ae67d2
4ecf034
 
 
 
6ae67d2
4ecf034
6ae67d2
4ecf034
6ae67d2
4ecf034
6ae67d2
4ecf034
6ae67d2
4ecf034
6ae67d2
4ecf034
6ae67d2
4ecf034
6ae67d2
4ecf034
6ae67d2
4ecf034
 
 
 
 
 
 
 
 
 
6ae67d2
4ecf034
6ae67d2
4ecf034
 
 
 
 
6ae67d2
 
4ecf034
6ae67d2
4ecf034
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: mr_sam_wav2vec2_nigerian_accent
  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. -->

# mr_sam_wav2vec2_nigerian_accent

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4369
- Wer: 0.3071

## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.3292        | 9.09  | 500  | 2.8449          | 0.9988 |
| 0.8545        | 18.18 | 1000 | 0.4708          | 0.3839 |
| 0.1545        | 27.27 | 1500 | 0.4369          | 0.3071 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2