File size: 4,454 Bytes
4d5e92b 8a1f31f 4d5e92b 8a1f31f 4d5e92b ec31f53 c252d4f 4d5e92b 1319679 4d5e92b acadae5 4d5e92b 8a1f31f 4d5e92b 8a1f31f 4d5e92b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 |
---
language:
- ro
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: wav2vec2-xls-r-1b-ro
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7.0
type: mozilla-foundation/common_voice_7_0
args: ro
metrics:
- name: Test WER
type: wer
value: 99.99
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ro
metrics:
- name: Test WER
type: wer
value: 99.98
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ro
metrics:
- name: Test WER
type: wer
value: 99.99
---
<!-- 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-1b-ro
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - RO dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1113
- Wer: 0.4770
- Cer: 0.0306
## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 0.7844 | 1.67 | 1500 | 0.3412 | 0.8600 | 0.0940 |
| 0.7272 | 3.34 | 3000 | 0.1926 | 0.6409 | 0.0527 |
| 0.6924 | 5.02 | 4500 | 0.1413 | 0.5722 | 0.0401 |
| 0.6327 | 6.69 | 6000 | 0.1252 | 0.5366 | 0.0371 |
| 0.6363 | 8.36 | 7500 | 0.1235 | 0.5741 | 0.0389 |
| 0.6238 | 10.03 | 9000 | 0.1180 | 0.5542 | 0.0362 |
| 0.6018 | 11.71 | 10500 | 0.1192 | 0.5694 | 0.0369 |
| 0.583 | 13.38 | 12000 | 0.1216 | 0.5772 | 0.0385 |
| 0.5643 | 15.05 | 13500 | 0.1195 | 0.5419 | 0.0371 |
| 0.5399 | 16.72 | 15000 | 0.1240 | 0.5224 | 0.0370 |
| 0.5529 | 18.39 | 16500 | 0.1174 | 0.5555 | 0.0367 |
| 0.5246 | 20.07 | 18000 | 0.1097 | 0.5047 | 0.0339 |
| 0.4936 | 21.74 | 19500 | 0.1225 | 0.5189 | 0.0382 |
| 0.4629 | 23.41 | 21000 | 0.1142 | 0.5047 | 0.0344 |
| 0.4463 | 25.08 | 22500 | 0.1168 | 0.4887 | 0.0339 |
| 0.4671 | 26.76 | 24000 | 0.1119 | 0.5073 | 0.0338 |
| 0.4359 | 28.43 | 25500 | 0.1206 | 0.5479 | 0.0363 |
| 0.4225 | 30.1 | 27000 | 0.1122 | 0.5170 | 0.0345 |
| 0.4038 | 31.77 | 28500 | 0.1159 | 0.5032 | 0.0343 |
| 0.4271 | 33.44 | 30000 | 0.1116 | 0.5126 | 0.0339 |
| 0.3867 | 35.12 | 31500 | 0.1101 | 0.4937 | 0.0327 |
| 0.3674 | 36.79 | 33000 | 0.1142 | 0.4940 | 0.0330 |
| 0.3607 | 38.46 | 34500 | 0.1106 | 0.5145 | 0.0327 |
| 0.3651 | 40.13 | 36000 | 0.1172 | 0.4921 | 0.0317 |
| 0.3268 | 41.81 | 37500 | 0.1093 | 0.4830 | 0.0310 |
| 0.3345 | 43.48 | 39000 | 0.1131 | 0.4760 | 0.0314 |
| 0.3236 | 45.15 | 40500 | 0.1132 | 0.4864 | 0.0317 |
| 0.312 | 46.82 | 42000 | 0.1124 | 0.4861 | 0.0315 |
| 0.3106 | 48.49 | 43500 | 0.1116 | 0.4745 | 0.0306 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|