---
library_name: transformers
base_model: syvai/tts-v1-pretrained
tags:
- axolotl
- generated_from_trainer
datasets:
- syvai/zac-coral-tts
model-index:
- name: tts-v1-finetuned
  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. -->

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.8.0`
```yaml
base_model: syvai/tts-v1-pretrained
# Automatically upload checkpoint and final model to HF
hub_model_id: syvai/tts-v1-finetuned

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true

datasets:
  - path: syvai/zac-coral-tts
    type: 
dataset_prepared_path: last_run_prepared
val_set_size: 0.01
eval_sample_packing: False
output_dir: ./outputs/finetuned

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

wandb_project: orph
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 2e-5

bf16: auto
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_steps: 3
evals_per_epoch: 5
saves_per_epoch: 5
weight_decay: 0.05

special_tokens:
  pad_token: <custom_token_7>

```

</details><br>

# tts-v1-finetuned

This model is a fine-tuned version of [syvai/tts-v1-pretrained](https://huggingface.co/syvai/tts-v1-pretrained) on the syvai/zac-coral-tts dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2860

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 3
- num_epochs: 3.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.9492        | 0.0246 | 1    | 4.8478          |
| 4.7181        | 0.1969 | 8    | 4.5872          |
| 4.5871        | 0.3938 | 16   | 4.4631          |
| 4.557         | 0.5908 | 24   | 4.3972          |
| 4.4965        | 0.7877 | 32   | 4.3521          |
| 4.4697        | 0.9846 | 40   | 4.3258          |
| 4.4525        | 1.1723 | 48   | 4.3083          |
| 4.4301        | 1.3692 | 56   | 4.2980          |
| 4.4459        | 1.5662 | 64   | 4.2915          |
| 4.4382        | 1.7631 | 72   | 4.2893          |
| 4.4315        | 1.96   | 80   | 4.2866          |
| 4.4178        | 2.1477 | 88   | 4.2861          |
| 4.4501        | 2.3446 | 96   | 4.2859          |
| 4.4121        | 2.5415 | 104  | 4.2856          |
| 4.4164        | 2.7385 | 112  | 4.2859          |
| 4.4264        | 2.9354 | 120  | 4.2860          |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1