File size: 3,123 Bytes
96e8020 ef46c45 eac3a78 61f2f82 3c01bec 96e8020 61f2f82 3c01bec ef46c45 3c01bec ef46c45 3c01bec ef46c45 96e8020 61f2f82 96e8020 61f2f82 96e8020 3c01bec 96e8020 3c01bec 61f2f82 96e8020 61f2f82 96e8020 48bc185 61f2f82 96e8020 3c01bec 96e8020 61f2f82 |
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 96 97 98 |
---
license: apache-2.0
tags:
- generated_from_trainer
base_model: facebook/wav2vec2-xls-r-300m
datasets:
- common_voice_15_0
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-br
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: common_voice_15_0
type: common_voice_15_0
config: br
split: None
args: br
metrics:
- type: wer
value: 49.79811574697174
name: Wer
---
<!-- 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-br
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co./facebook/wav2vec2-xls-r-300m) on the common_voice_15_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8887
- Wer: 49.7981
- Cer: 17.3877
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 5.1153 | 2.18 | 1000 | 2.8854 | 100.0 | 100.0 |
| 1.4117 | 4.36 | 2000 | 0.9161 | 71.2786 | 25.3180 |
| 0.7888 | 6.54 | 3000 | 0.7753 | 62.7456 | 22.0767 |
| 0.6316 | 8.71 | 4000 | 0.7550 | 58.1786 | 20.5383 |
| 0.5434 | 10.89 | 5000 | 0.7508 | 56.5096 | 20.1168 |
| 0.4672 | 13.07 | 6000 | 0.7844 | 54.9125 | 19.3835 |
| 0.4237 | 15.25 | 7000 | 0.7786 | 53.2705 | 18.5765 |
| 0.3899 | 17.43 | 8000 | 0.8050 | 53.0552 | 18.6105 |
| 0.3607 | 19.61 | 9000 | 0.8280 | 51.9874 | 18.3024 |
| 0.3355 | 21.79 | 10000 | 0.7967 | 51.5388 | 17.9811 |
| 0.3098 | 23.97 | 11000 | 0.8296 | 51.2876 | 17.9547 |
| 0.2937 | 26.14 | 12000 | 0.8544 | 50.9915 | 17.7827 |
| 0.2793 | 28.32 | 13000 | 0.8909 | 51.5478 | 18.1286 |
| 0.2641 | 30.5 | 14000 | 0.8740 | 50.4800 | 17.6561 |
| 0.2552 | 32.68 | 15000 | 0.8832 | 49.9776 | 17.4463 |
| 0.2467 | 34.86 | 16000 | 0.8753 | 50.3096 | 17.4765 |
| 0.2378 | 37.04 | 17000 | 0.8895 | 49.8789 | 17.3952 |
| 0.2337 | 39.22 | 18000 | 0.8887 | 49.7981 | 17.3877 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2
|