Hugging Face's logo

gokuls
/
whisper-base.en-speech-commands-h

TensorBoard
Safetensors
whisper
Generated from Trainer
Eval Results
Model card Files Metrics Community
whisper-base.en-speech-commands-h
File size: 2,483 Bytes 
0d9d0bf
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
 
---
license: apache-2.0
base_model: openai/whisper-base.en
tags:
- generated_from_trainer
datasets:
- speech_commands
metrics:
- accuracy
model-index:
- name: whisper-base.en-speech-commands-h
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: speech_commands
      type: speech_commands
      config: v0.02
      split: None
      args: v0.02
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7922661870503597
---

<!-- 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. -->

# whisper-base.en-speech-commands-h

This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on the speech_commands dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3313
- Accuracy: 0.7923

## 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: 0.0005
- train_batch_size: 96
- eval_batch_size: 96
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3859        | 1.0   | 412  | 1.3474          | 0.7707   |
| 0.2732        | 2.0   | 824  | 1.2471          | 0.7599   |
| 0.2373        | 3.0   | 1236 | 1.2114          | 0.7729   |
| 0.1694        | 4.0   | 1648 | 1.1600          | 0.7914   |
| 0.1495        | 5.0   | 2060 | 1.1535          | 0.7914   |
| 0.1931        | 6.0   | 2472 | 1.1446          | 0.7860   |
| 0.1329        | 7.0   | 2884 | 1.3313          | 0.7923   |
| 0.0731        | 8.0   | 3296 | 1.2812          | 0.7860   |
| 0.0702        | 9.0   | 3708 | 1.2134          | 0.7873   |
| 0.0828        | 10.0  | 4120 | 1.6292          | 0.7887   |
| 0.08          | 11.0  | 4532 | 1.4677          | 0.7797   |
| 0.0481        | 12.0  | 4944 | 1.3770          | 0.7909   |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1