|
--- |
|
license: mit |
|
base_model: facebook/w2v-bert-2.0 |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: wav2vec-read_aloud |
|
results: [] |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# wav2vec-read_aloud |
|
|
|
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 973.4864 |
|
- Pcc Accuracy: 0.7547 |
|
- Pcc Fluency: 0.7664 |
|
- Pcc Total Score: 0.8143 |
|
- Pcc Content: nan |
|
|
|
## 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: 5.5e-05 |
|
- train_batch_size: 2 |
|
- eval_batch_size: 6 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 12 |
|
- total_train_batch_size: 24 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: cosine |
|
- lr_scheduler_warmup_ratio: 0.4 |
|
- num_epochs: 15 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Pcc Accuracy | Pcc Fluency | Pcc Total Score | Pcc Content | |
|
|:-------------:|:-----:|:----:|:---------------:|:------------:|:-----------:|:---------------:|:-----------:| |
|
| 2390.3109 | 1.95 | 500 | 2342.6951 | nan | 0.4815 | nan | nan | |
|
| 2164.6891 | 3.9 | 1000 | 2318.7217 | nan | 0.6461 | nan | nan | |
|
| 1078.8019 | 5.85 | 1500 | 1029.2085 | 0.6188 | 0.7014 | 0.6845 | nan | |
|
| 974.6556 | 7.8 | 2000 | 985.5543 | 0.7117 | 0.7355 | 0.7743 | nan | |
|
| 1002.623 | 9.75 | 2500 | 989.1628 | 0.7401 | 0.7533 | 0.7995 | nan | |
|
| 947.5643 | 11.7 | 3000 | 972.3806 | 0.7507 | 0.7628 | 0.8103 | nan | |
|
| 995.6286 | 13.65 | 3500 | 973.4864 | 0.7547 | 0.7664 | 0.8143 | nan | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.37.0 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.1 |
|
|
