File size: 2,771 Bytes
e52eea1 4159395 e52eea1 4f5cf08 e52eea1 4f5cf08 e52eea1 9374fee 7db2956 e4fbef4 e52eea1 9374fee e52eea1 9374fee e52eea1 9374fee e52eea1 9374fee e52eea1 06b0fe2 e52eea1 a597ed7 e4fbef4 e52eea1 7db2956 e4fbef4 7db2956 e52eea1 4f5cf08 |
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 |
---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
- turkish
- tr
model-index:
- name: speecht5_finetuned_emirhan_tr
results: []
language:
- tr
pipeline_tag: text-to-speech
---
<!-- 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 |