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---
language:
- el
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- el
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: XLS-R-300M - Greek
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: el
metrics:
- name: Test WER
type: wer
value: 102.23963133640552
- name: Test CER
type: cer
value: 146.28
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: el
metrics:
- name: Test WER
type: wer
value: 99.92
- name: Test CER
type: cer
value: 132.38
---
<!-- 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-large-xls-r-300m-greek
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - EL dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6592
- Wer: 0.4564
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.0928 | 4.42 | 500 | 3.0804 | 1.0073 |
| 1.4505 | 8.85 | 1000 | 0.9038 | 0.7330 |
| 1.2207 | 13.27 | 1500 | 0.7375 | 0.6045 |
| 1.0695 | 17.7 | 2000 | 0.7119 | 0.5441 |
| 1.0104 | 22.12 | 2500 | 0.6069 | 0.5296 |
| 0.9299 | 26.55 | 3000 | 0.6168 | 0.5206 |
| 0.8588 | 30.97 | 3500 | 0.6382 | 0.5171 |
| 0.7942 | 35.4 | 4000 | 0.6048 | 0.4988 |
| 0.7808 | 39.82 | 4500 | 0.6730 | 0.5084 |
| 0.743 | 44.25 | 5000 | 0.6749 | 0.5012 |
| 0.6652 | 48.67 | 5500 | 0.6491 | 0.4735 |
| 0.6386 | 53.1 | 6000 | 0.6928 | 0.4954 |
| 0.5945 | 57.52 | 6500 | 0.6359 | 0.4798 |
| 0.5561 | 61.95 | 7000 | 0.6409 | 0.4799 |
| 0.5464 | 66.37 | 7500 | 0.6452 | 0.4691 |
| 0.5119 | 70.8 | 8000 | 0.6376 | 0.4657 |
| 0.474 | 75.22 | 8500 | 0.6541 | 0.4700 |
| 0.45 | 79.65 | 9000 | 0.6374 | 0.4571 |
| 0.4315 | 84.07 | 9500 | 0.6568 | 0.4625 |
| 0.3967 | 88.5 | 10000 | 0.6636 | 0.4605 |
| 0.3937 | 92.92 | 10500 | 0.6537 | 0.4597 |
| 0.3788 | 97.35 | 11000 | 0.6614 | 0.4589 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
|