File size: 4,145 Bytes
3e5c93e 267862e 3e5c93e 267862e 3e5c93e 267862e 2c107e8 267862e 3e5c93e 2c107e8 3e5c93e 152aebd 3e5c93e 267862e 3e5c93e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
---
library_name: transformers
license: mit
base_model: Sana1207/Hindi_SpeechT5_finetuned
tags:
- generated_from_trainer
model-index:
- name: Sindhi-TTS
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. -->
# Sindhi-TTS
This model is a fine-tuned version of [Sana1207/Hindi_SpeechT5_finetuned](https://huggingface.co/Sana1207/Hindi_SpeechT5_finetuned) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4887
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- 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: 100
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.5769 | 0.3992 | 100 | 0.5673 |
| 0.5602 | 0.7984 | 200 | 0.5603 |
| 0.553 | 1.1986 | 300 | 0.5489 |
| 0.5413 | 1.5978 | 400 | 0.5433 |
| 0.5361 | 1.9970 | 500 | 0.5336 |
| 0.5391 | 2.3972 | 600 | 0.5414 |
| 0.524 | 2.7964 | 700 | 0.5290 |
| 0.5253 | 3.1966 | 800 | 0.5317 |
| 0.5272 | 3.5958 | 900 | 0.5234 |
| 0.5258 | 3.9950 | 1000 | 0.5280 |
| 0.5173 | 4.3952 | 1100 | 0.5180 |
| 0.518 | 4.7944 | 1200 | 0.5207 |
| 0.5119 | 5.1946 | 1300 | 0.5157 |
| 0.5114 | 5.5938 | 1400 | 0.5169 |
| 0.5138 | 5.9930 | 1500 | 0.5140 |
| 0.5048 | 6.3932 | 1600 | 0.5122 |
| 0.5059 | 6.7924 | 1700 | 0.5173 |
| 0.4991 | 7.1926 | 1800 | 0.5057 |
| 0.5038 | 7.5918 | 1900 | 0.5053 |
| 0.4994 | 7.9910 | 2000 | 0.5071 |
| 0.4989 | 8.3912 | 2100 | 0.5080 |
| 0.4951 | 8.7904 | 2200 | 0.5099 |
| 0.4941 | 9.1906 | 2300 | 0.5022 |
| 0.493 | 9.5898 | 2400 | 0.5039 |
| 0.4915 | 9.9890 | 2500 | 0.5014 |
| 0.4911 | 10.3892 | 2600 | 0.5066 |
| 0.4861 | 10.7884 | 2700 | 0.4987 |
| 0.4875 | 11.1886 | 2800 | 0.5042 |
| 0.4892 | 11.5878 | 2900 | 0.4980 |
| 0.4909 | 11.9870 | 3000 | 0.5007 |
| 0.4886 | 12.3872 | 3100 | 0.4980 |
| 0.4857 | 12.7864 | 3200 | 0.4952 |
| 0.4868 | 13.1866 | 3300 | 0.4972 |
| 0.482 | 13.5858 | 3400 | 0.4957 |
| 0.479 | 13.9850 | 3500 | 0.5029 |
| 0.48 | 14.3852 | 3600 | 0.4954 |
| 0.4819 | 14.7844 | 3700 | 0.4982 |
| 0.4791 | 15.1846 | 3800 | 0.4936 |
| 0.4776 | 15.5838 | 3900 | 0.4947 |
| 0.4736 | 15.9830 | 4000 | 0.4930 |
| 0.4744 | 16.3832 | 4100 | 0.4937 |
| 0.4735 | 16.7824 | 4200 | 0.4895 |
| 0.4789 | 17.1826 | 4300 | 0.4936 |
| 0.4729 | 17.5818 | 4400 | 0.4920 |
| 0.4742 | 17.9810 | 4500 | 0.4915 |
| 0.4721 | 18.3812 | 4600 | 0.4887 |
| 0.4733 | 18.7804 | 4700 | 0.4933 |
| 0.4849 | 19.1806 | 4800 | 0.4879 |
| 0.4692 | 19.5798 | 4900 | 0.4889 |
| 0.4747 | 19.9790 | 5000 | 0.4887 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|