|
--- |
|
language: |
|
- el |
|
license: apache-2.0 |
|
tags: |
|
- automatic-speech-recognition |
|
- generated_from_trainer |
|
- robust-speech-event |
|
- hf-asr-leaderboard |
|
- mozilla-foundation/common_voice_8_0 |
|
- robust-speech-event |
|
datasets: |
|
- mozilla-foundation/common_voice_8_0 |
|
model-index: |
|
- name: wav2vec2-large-xls-r-300m-el |
|
results: |
|
- task: |
|
name: Automatic Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: Common Voice 8 |
|
type: mozilla-foundation/common_voice_8_0 |
|
args: el |
|
metrics: |
|
- name: Test WER using LM |
|
type: wer |
|
value: 20.9 |
|
- name: Test CER using LM |
|
type: cer |
|
value: 6.0466 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# |
|
|
|
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_8_0 - EL dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3218 |
|
- Wer: 0.3095 |
|
|
|
## Training and evaluation data |
|
|
|
Evaluation is conducted in Notebook, you can see within the repo "notebook_evaluation_wav2vec2_el.ipynb" |
|
|
|
Test WER without LM |
|
wer = 31.1294 % |
|
cer = 7.9509 % |
|
|
|
Test WER using LM |
|
wer = 20.7340 % |
|
cer = 6.0466 % |
|
|
|
How to use eval.py |
|
``` |
|
huggingface-cli login #login to huggingface for getting auth token to access the common voice v8 |
|
#running with LM |
|
!python eval.py --model_id ayameRushia/wav2vec2-large-xls-r-300m-el --dataset mozilla-foundation/common_voice_8_0 --config el --split test |
|
# running without LM |
|
!python eval.py --model_id ayameRushia/wav2vec2-large-xls-r-300m-el --dataset mozilla-foundation/common_voice_8_0 --config el --split test --greedy |
|
``` |
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 5e-05 |
|
- train_batch_size: 32 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 2 |
|
- total_train_batch_size: 64 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 400 |
|
- num_epochs: 80.0 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:| |
|
| 6.3683 | 8.77 | 500 | 3.1280 | 1.0 | |
|
| 1.9915 | 17.54 | 1000 | 0.6600 | 0.6444 | |
|
| 0.6565 | 26.32 | 1500 | 0.4208 | 0.4486 | |
|
| 0.4484 | 35.09 | 2000 | 0.3885 | 0.4006 | |
|
| 0.3573 | 43.86 | 2500 | 0.3548 | 0.3626 | |
|
| 0.3063 | 52.63 | 3000 | 0.3375 | 0.3430 | |
|
| 0.2751 | 61.4 | 3500 | 0.3359 | 0.3241 | |
|
| 0.2511 | 70.18 | 4000 | 0.3222 | 0.3108 | |
|
| 0.2361 | 78.95 | 4500 | 0.3205 | 0.3084 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.17.0.dev0 |
|
- Pytorch 1.10.1+cu102 |
|
- Datasets 1.18.3 |
|
- Tokenizers 0.11.0 |
|
|
