|
|
--- |
|
|
license: mit |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
model-index: |
|
|
- name: speecht5_finetuned_voxpopuli_de_Merkel |
|
|
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. --> |
|
|
|
|
|
# speecht5_finetuned_voxpopuli_de_Merkel |
|
|
|
|
|
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the None dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.4112 |
|
|
|
|
|
## 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: 1e-05 |
|
|
- train_batch_size: 4 |
|
|
- eval_batch_size: 2 |
|
|
- seed: 42 |
|
|
- gradient_accumulation_steps: 8 |
|
|
- 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 |
|
|
- training_steps: 10000 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|
|:-------------:|:-----:|:-----:|:---------------:| |
|
|
| 0.4831 | 4.06 | 1000 | 0.4406 | |
|
|
| 0.4583 | 8.12 | 2000 | 0.4271 | |
|
|
| 0.4482 | 12.18 | 3000 | 0.4177 | |
|
|
| 0.4435 | 16.24 | 4000 | 0.4148 | |
|
|
| 0.433 | 20.3 | 5000 | 0.4142 | |
|
|
| 0.4333 | 24.37 | 6000 | 0.4128 | |
|
|
| 0.4306 | 28.43 | 7000 | 0.4111 | |
|
|
| 0.4288 | 32.49 | 8000 | 0.4110 | |
|
|
| 0.4262 | 36.55 | 9000 | 0.4109 | |
|
|
| 0.4228 | 40.61 | 10000 | 0.4112 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.30.2 |
|
|
- Pytorch 2.0.1+cu118 |
|
|
- Datasets 2.13.0 |
|
|
- Tokenizers 0.13.3 |
|
|
|
