File size: 2,713 Bytes
e52eea1 4159395 e52eea1 9374fee 7db2956 e4fbef4 e52eea1 9374fee e52eea1 9374fee e52eea1 9374fee e52eea1 9374fee e52eea1 06b0fe2 e52eea1 a597ed7 e4fbef4 e52eea1 7db2956 e4fbef4 7db2956 e52eea1 4159395 |
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 |
---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_finetuned_emirhan_tr
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. -->
# speecht5_finetuned_emirhan_tr
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on [erenfazlioglu/turkishvoicedataset](https://huggingface.co/datasets/erenfazlioglu/turkishvoicedataset).
It achieves the following results on the evaluation set:
- Loss: 0.3135
## Model description
The base model uses a transformer-based approach, specifically Transfer Transformer, to generate high-quality speech from text. The fine-tuning process on the Turkish Voice Dataset enables the model to produce more natural and accurate speech in Turkish.
## Intended uses & limitations
This model is intended for text-to-speech (TTS) applications specifically tailored for the Turkish language. It can be used in various scenarios, such as voice assistants, automated announcements, and accessibility tools for Turkish speakers.
## Training and evaluation data
The model's performance is optimized for Turkish and may not generalize well to other languages.
The model might not handle rare or domain-specific vocabulary as effectively as more common words.
## Training procedure
The model was fine-tuned on the Turkish Voice Dataset, which consists of high-quality synthetic Turkish voice recordings from Microsoft Azure. The dataset was split into training and evaluation subsets, with the evaluation set used to measure the model's loss and overall performance.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- 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: 100
- training_steps: 660
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.514 | 0.4545 | 100 | 0.4372 |
| 0.4226 | 0.9091 | 200 | 0.3626 |
| 0.3771 | 1.3636 | 300 | 0.3417 |
| 0.3562 | 1.8182 | 400 | 0.3278 |
| 0.3472 | 2.2727 | 500 | 0.3217 |
| 0.3402 | 2.7273 | 600 | 0.3135 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|