metadata
language:
- ar
license: apache-2.0
tags:
- ar
- 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: XLS-R-300M - Arabic
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: ar
metrics:
- name: Test WER
type: wer
value: 47.54
- name: Test CER
type: cer
value: 17.64
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: ar
metrics:
- name: Test WER
type: wer
value: 93.72
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: ar
metrics:
- name: Test WER
type: wer
value: 92.49
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AR dataset. It achieves the following results on the evaluation set:
- Loss: 0.4502
- Wer: 0.4783
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: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- num_epochs: 5.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 4.7972 | 0.43 | 500 | 5.1401 | 1.0 |
| 3.3241 | 0.86 | 1000 | 3.3220 | 1.0 |
| 3.1432 | 1.29 | 1500 | 3.0806 | 0.9999 |
| 2.9297 | 1.72 | 2000 | 2.5678 | 1.0057 |
| 2.2593 | 2.14 | 2500 | 1.1068 | 0.8218 |
| 2.0504 | 2.57 | 3000 | 0.7878 | 0.7114 |
| 1.937 | 3.0 | 3500 | 0.6955 | 0.6450 |
| 1.8491 | 3.43 | 4000 | 0.6452 | 0.6304 |
| 1.803 | 3.86 | 4500 | 0.5961 | 0.6042 |
| 1.7545 | 4.29 | 5000 | 0.5550 | 0.5748 |
| 1.7045 | 4.72 | 5500 | 0.5374 | 0.5743 |
| 1.6733 | 5.15 | 6000 | 0.5337 | 0.5404 |
| 1.6761 | 5.57 | 6500 | 0.5054 | 0.5266 |
| 1.655 | 6.0 | 7000 | 0.4926 | 0.5243 |
| 1.6252 | 6.43 | 7500 | 0.4946 | 0.5183 |
| 1.6209 | 6.86 | 8000 | 0.4915 | 0.5194 |
| 1.5772 | 7.29 | 8500 | 0.4725 | 0.5104 |
| 1.5602 | 7.72 | 9000 | 0.4726 | 0.5097 |
| 1.5783 | 8.15 | 9500 | 0.4667 | 0.4956 |
| 1.5442 | 8.58 | 10000 | 0.4685 | 0.4937 |
| 1.5597 | 9.01 | 10500 | 0.4708 | 0.4957 |
| 1.5406 | 9.43 | 11000 | 0.4539 | 0.4810 |
| 1.5274 | 9.86 | 11500 | 0.4502 | 0.4783 |
Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0