File size: 2,735 Bytes
1b22227 e6ec0a6 830a7d6 1b22227 962004a 1b22227 |
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 99 100 |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
- robust-speech-event
- fr
datasets:
- common_voice
model-index:
- name: wav2vec2-cls-r-300m-fr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: fr
metrics:
- name: Test WER
type: wer
value: 56.62
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: fr
metrics:
- name: Test WER
type: wer
value: 58.22
---
<!-- 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-cls-r-300m-fr
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 - FR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6521
- Wer: 0.4330
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.6773 | 0.8 | 500 | 1.3907 | 0.9864 |
| 0.9526 | 1.6 | 1000 | 0.7760 | 0.6448 |
| 0.6418 | 2.4 | 1500 | 0.7605 | 0.6194 |
| 0.5028 | 3.2 | 2000 | 0.6516 | 0.5322 |
| 0.4133 | 4.0 | 2500 | 0.6303 | 0.5097 |
| 0.3285 | 4.8 | 3000 | 0.6422 | 0.5062 |
| 0.2764 | 5.6 | 3500 | 0.5936 | 0.4748 |
| 0.2361 | 6.4 | 4000 | 0.6486 | 0.4683 |
| 0.2049 | 7.2 | 4500 | 0.6321 | 0.4532 |
| 0.176 | 8.0 | 5000 | 0.6230 | 0.4482 |
| 0.1393 | 8.8 | 5500 | 0.6595 | 0.4403 |
| 0.1141 | 9.6 | 6000 | 0.6552 | 0.4348 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|