File size: 1,683 Bytes
6d270be
a092395
 
 
ef35013
a092395
 
 
 
 
6d270be
 
a092395
 
6d270be
a092395
6d270be
a092395
 
 
 
6d270be
a092395
6d270be
a092395
6d270be
a092395
6d270be
a092395
6d270be
a092395
6d270be
a092395
6d270be
a092395
6d270be
a092395
6d270be
a092395
 
 
 
 
 
 
 
 
 
 
 
6d270be
a092395
6d270be
a092395
 
 
 
 
 
6d270be
 
a092395
6d270be
a092395
 
 
 
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
66
67
68
---
license: apache-2.0
tags:
- generated_from_trainer
base_model: facebook/wav2vec2-xls-r-300m
metrics:
- wer
model-index:
- name: asr_2500
  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. -->

# asr_2500

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4736
- Wer: 0.3085

## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 3.6647        | 7.3059  | 400  | 0.6404          | 0.5682 |
| 0.2773        | 14.6119 | 800  | 0.4814          | 0.4010 |
| 0.0989        | 21.9178 | 1200 | 0.4779          | 0.3385 |
| 0.0534        | 29.2237 | 1600 | 0.4736          | 0.3085 |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1