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---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: zlm_b32_le5_s12000
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. -->
# zlm_b32_le5_s12000
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3707
## 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: 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: 2000
- training_steps: 12000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.7211 | 0.2094 | 500 | 0.6148 |
| 0.6059 | 0.4188 | 1000 | 0.5140 |
| 0.5347 | 0.6283 | 1500 | 0.4725 |
| 0.4888 | 0.8377 | 2000 | 0.4612 |
| 0.4923 | 1.0471 | 2500 | 0.4283 |
| 0.466 | 1.2565 | 3000 | 0.4163 |
| 0.4535 | 1.4660 | 3500 | 0.4090 |
| 0.4442 | 1.6754 | 4000 | 0.4009 |
| 0.4423 | 1.8848 | 4500 | 0.3955 |
| 0.4539 | 2.0942 | 5000 | 0.3916 |
| 0.4416 | 2.3037 | 5500 | 0.3870 |
| 0.4306 | 2.5131 | 6000 | 0.3856 |
| 0.4242 | 2.7225 | 6500 | 0.3819 |
| 0.426 | 2.9319 | 7000 | 0.3814 |
| 0.4105 | 3.1414 | 7500 | 0.3787 |
| 0.4077 | 3.3508 | 8000 | 0.3750 |
| 0.4106 | 3.5602 | 8500 | 0.3748 |
| 0.4228 | 3.7696 | 9000 | 0.3728 |
| 0.4101 | 3.9791 | 9500 | 0.3719 |
| 0.4209 | 4.1885 | 10000 | 0.3707 |
| 0.4091 | 4.3979 | 10500 | 0.3712 |
| 0.4061 | 4.6073 | 11000 | 0.3715 |
| 0.4169 | 4.8168 | 11500 | 0.3700 |
| 0.4088 | 5.0262 | 12000 | 0.3707 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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