|
--- |
|
language: |
|
- hsb |
|
license: apache-2.0 |
|
tags: |
|
- automatic-speech-recognition |
|
- mozilla-foundation/common_voice_8_0 |
|
- generated_from_trainer |
|
- hsb |
|
- robust-speech-event |
|
- model_for_talk |
|
datasets: |
|
- common_voice |
|
model-index: |
|
- name: wav2vec2-large-xls-r-300m-hsb-v2 |
|
results: |
|
- task: |
|
name: Automatic Speech Recognition |
|
type: automatic-speech-recognition |
|
dataset: |
|
name: Common Voice 8 |
|
type: mozilla-foundation/common_voice_8_0 |
|
args: hsb |
|
metrics: |
|
- name: Test WER |
|
type: wer |
|
value: [] |
|
- name: Test CER |
|
type: cer |
|
value: [] |
|
--- |
|
|
|
<!-- 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-hsb-v2 |
|
|
|
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 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.5328 |
|
- Wer: 0.4596 |
|
|
|
## 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: 0.00045 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 2 |
|
- 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: 500 |
|
- num_epochs: 50 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:| |
|
| 8.5979 | 3.23 | 100 | 3.5602 | 1.0 | |
|
| 3.303 | 6.45 | 200 | 3.2238 | 1.0 | |
|
| 3.2034 | 9.68 | 300 | 3.2002 | 0.9888 | |
|
| 2.7986 | 12.9 | 400 | 1.2408 | 0.9210 | |
|
| 1.3869 | 16.13 | 500 | 0.7973 | 0.7462 | |
|
| 1.0228 | 19.35 | 600 | 0.6722 | 0.6788 | |
|
| 0.8311 | 22.58 | 700 | 0.6100 | 0.6150 | |
|
| 0.717 | 25.81 | 800 | 0.6236 | 0.6013 | |
|
| 0.6264 | 29.03 | 900 | 0.6031 | 0.5575 | |
|
| 0.5494 | 32.26 | 1000 | 0.5656 | 0.5309 | |
|
| 0.4781 | 35.48 | 1100 | 0.5289 | 0.4996 | |
|
| 0.4311 | 38.71 | 1200 | 0.5375 | 0.4768 | |
|
| 0.3902 | 41.94 | 1300 | 0.5246 | 0.4703 | |
|
| 0.3508 | 45.16 | 1400 | 0.5382 | 0.4696 | |
|
| 0.3199 | 48.39 | 1500 | 0.5328 | 0.4596 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.16.1 |
|
- Pytorch 1.10.0+cu111 |
|
- Datasets 1.18.2 |
|
- Tokenizers 0.11.0 |
|
|
