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# speecht5_tts_vivos

This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the vivos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4145

## 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: 500
- training_steps: 4000

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4582        | 2.81  | 1000 | 0.4213          |
| 0.459         | 5.63  | 2000 | 0.4204          |
| 0.4489        | 8.44  | 3000 | 0.4149          |
| 0.4466        | 11.25 | 4000 | 0.4145          |


### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["vivos"], "model-index": [{"name": "speecht5_tts_vivos", "results": []}]}
transformers pytorch tensorboard speecht5 text-to-audio generated_from_trainer dataset:vivos license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #speecht5 #text-to-audio #generated_from_trainer #dataset-vivos #license-mit #endpoints_compatible #region-us 

speecht5\_tts\_vivos
====================


This model is a fine-tuned version of microsoft/speecht5\_tts on the vivos dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4145


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: 500
* training\_steps: 4000


### Training results



### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3



### 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: 500
* training\_steps: 4000 ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #speecht5 #text-to-audio #generated_from_trainer #dataset-vivos #license-mit #endpoints_compatible #region-us 
 ### 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: 500
* training\_steps: 4000 ### Training results ### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #speecht5 #text-to-audio #generated_from_trainer #dataset-vivos #license-mit #endpoints_compatible #region-us 
### 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: 500
* training\_steps: 4000### Training results### Framework versions


* Transformers 4.29.0.dev0
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3