---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: vc
  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. -->

# vc

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: 0.5712

## 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: 5e-06
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.9951        | 2.0851  | 50   | 0.9152          |
| 0.9256        | 4.1702  | 100  | 0.8480          |
| 0.9001        | 6.2553  | 150  | 0.8114          |
| 0.8567        | 8.3404  | 200  | 0.7857          |
| 0.8139        | 10.4255 | 250  | 0.7477          |
| 0.7437        | 12.5106 | 300  | 0.6724          |
| 0.6937        | 14.5957 | 350  | 0.6352          |
| 0.6686        | 16.6809 | 400  | 0.6194          |
| 0.6487        | 18.7660 | 450  | 0.6070          |
| 0.6411        | 20.8511 | 500  | 0.6009          |
| 0.643         | 22.9362 | 550  | 0.5970          |
| 0.6158        | 25.0    | 600  | 0.5893          |
| 0.632         | 27.0851 | 650  | 0.5871          |
| 0.6152        | 29.1702 | 700  | 0.5855          |
| 0.6066        | 31.2553 | 750  | 0.5833          |
| 0.615         | 33.3404 | 800  | 0.5817          |
| 0.6011        | 35.4255 | 850  | 0.5812          |
| 0.598         | 37.5106 | 900  | 0.5788          |
| 0.6023        | 39.5957 | 950  | 0.5764          |
| 0.6031        | 41.6809 | 1000 | 0.5775          |
| 0.5976        | 43.7660 | 1050 | 0.5764          |
| 0.597         | 45.8511 | 1100 | 0.5772          |
| 0.5923        | 47.9362 | 1150 | 0.5727          |
| 0.5793        | 50.0    | 1200 | 0.5746          |
| 0.5879        | 52.0851 | 1250 | 0.5757          |
| 0.5908        | 54.1702 | 1300 | 0.5727          |
| 0.5838        | 56.2553 | 1350 | 0.5745          |
| 0.5852        | 58.3404 | 1400 | 0.5709          |
| 0.5869        | 60.4255 | 1450 | 0.5753          |
| 0.585         | 62.5106 | 1500 | 0.5720          |
| 0.5875        | 64.5957 | 1550 | 0.5715          |
| 0.5807        | 66.6809 | 1600 | 0.5729          |
| 0.5886        | 68.7660 | 1650 | 0.5730          |
| 0.5831        | 70.8511 | 1700 | 0.5753          |
| 0.5812        | 72.9362 | 1750 | 0.5711          |
| 0.5736        | 75.0    | 1800 | 0.5768          |
| 0.5761        | 77.0851 | 1850 | 0.5735          |
| 0.5767        | 79.1702 | 1900 | 0.5759          |
| 0.5777        | 81.2553 | 1950 | 0.5720          |
| 0.5759        | 83.3404 | 2000 | 0.5712          |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0