File size: 2,831 Bytes
caa1790 7181897 6325747 caa1790 7181897 3f7fb84 7181897 caa1790 7181897 caa1790 3f7fb84 caa1790 3f7fb84 caa1790 3f7fb84 caa1790 3f7fb84 caa1790 |
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 |
---
license: mit
tags:
- automatic-speech-recognition
- asr
- pytorch
- wav2vec2
- wolof
- wo
model-index:
- name: wav2vec2-xls-r-300m-wolof
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
metrics:
- name: Test WER
type: wer
value: 21.25
- name: Validation Loss
type: Loss
value: 0.36
---
<!-- 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-xls-r-300m-wolof
Wolof is a language spoken in Senegal and neighbouring countries, this language is not too well represented, there are few resources in the field of Text en speech
In this sense we aim to bring our contribution to this, it is in this sense that enters this repo.
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) , that is trained with the largest available speech dataset of the [ALFFA_PUBLIC project](https://github.com/besacier/ALFFA_PUBLIC/tree/master/ASR/WOLOF)
It achieves the following results on the evaluation set:
- Loss: 0.367826
- Wer: 0.212565
## Model description
The duration of the training data is 16.8 hours, which we have divided into 10,000 audio files for the training and 3,339 for the test.
## Training and evaluation data
We eval the model at every 1500 step , and log it . and save at every 33340 step
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-4
- train_batch_size: 3
- eval_batch_size : 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10.0
### Training results
| Step | Training Loss | Validation Loss | Wer |
|:-------:|:-------------:|:---------------:|:------:|
| 1500 | 2.854200 |0.642243 |0.543964 |
| 3000 | 0.599200 | 0.468138 | 0.429549|
| 4500 | 0.468300 | 0.433436 | 0.405644|
| 6000 | 0.427000 | 0.384873 | 0.344150|
| 7500 | 0.377000 | 0.374003 | 0.323892|
| 9000 | 0.337000 | 0.363674 | 0.306189|
| 10500 | 0.302400 | 0.349884 |0 .283908 |
| 12000 | 0.264100 | 0.344104 |0.277120|
| 13500 |0 .254000 |0.341820 |0.271316|
| 15000 | 0.208400| 0.326502 | 0.260695|
| 16500 | 0.203500| 0.326209 | 0.250313|
| 18000 |0.159800 |0.323539 | 0.239851|
| 19500 | 0.158200 | 0.310694 | 0.230028|
| 21000 | 0.132800 | 0.338318 | 0.229283|
| 22500 | 0.112800 | 0.336765 | 0.224145|
| 24000 | 0.103600 | 0.350208 | 0.227073 |
| 25500 | 0.091400 | 0.353609 | 0.221589 |
| 27000 | 0.084400 | 0.367826 | 0.212565 |
### Framework versions
- Transformers 4.11
- Pytorch 1.10.0
- Datasets 1.13 |