File size: 2,749 Bytes
85c12bc
d599aa1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
85c12bc
 
d599aa1
 
85c12bc
d599aa1
85c12bc
d599aa1
 
 
 
 
85c12bc
d599aa1
85c12bc
d599aa1
85c12bc
d599aa1
85c12bc
d599aa1
85c12bc
d599aa1
85c12bc
d599aa1
85c12bc
d599aa1
85c12bc
d599aa1
85c12bc
d599aa1
 
 
 
 
 
 
 
 
 
 
 
85c12bc
d599aa1
85c12bc
d599aa1
 
 
 
 
 
 
 
 
 
 
 
 
 
85c12bc
 
d599aa1
85c12bc
d599aa1
 
 
 
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
90
91
92
93
94
95
---
language:
- sw
license: apache-2.0
base_model: openai/whisper-medium
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_14_0
metrics:
- wer
model-index:
- name: Whisper Medium  - Denis Musinguzi
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 14.0
      type: mozilla-foundation/common_voice_14_0
      config: lg
      split: None
      args: 'config: sw, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 0.2354584169666847
---

<!-- 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 Medium  - Denis Musinguzi

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 14.0 dataset.
It achieves the following results on the evaluation set:
- Cer: 0.0622
- Loss: 0.2969
- Wer: 0.2355

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- 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
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Cer    | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:------:|:---------------:|:------:|
| 0.9513        | 0.3   | 800  | 0.0998 | 0.4428          | 0.4067 |
| 0.313         | 0.61  | 1600 | 0.0913 | 0.3519          | 0.3427 |
| 0.2593        | 0.91  | 2400 | 0.0628 | 0.3160          | 0.2689 |
| 0.1887        | 1.22  | 3200 | 0.0633 | 0.3049          | 0.2574 |
| 0.1642        | 1.52  | 4000 | 0.0752 | 0.2906          | 0.2655 |
| 0.1595        | 1.82  | 4800 | 0.0737 | 0.2807          | 0.2617 |
| 0.1288        | 2.13  | 5600 | 0.0643 | 0.2889          | 0.2416 |
| 0.0928        | 2.43  | 6400 | 0.0629 | 0.2860          | 0.2387 |
| 0.0887        | 2.74  | 7200 | 0.0572 | 0.2838          | 0.2309 |
| 0.0836        | 3.04  | 8000 | 0.0575 | 0.2897          | 0.2338 |
| 0.0466        | 3.34  | 8800 | 0.0572 | 0.2968          | 0.2322 |
| 0.045         | 3.65  | 9600 | 0.0622 | 0.2969          | 0.2355 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.2.1
- Datasets 2.17.0
- Tokenizers 0.15.2