Text-to-Speech
Transformers
TensorBoard
Safetensors
Croatian
speecht5
text-to-audio
Generated from Trainer
Instructions to use derek-thomas/speecht5_finetuned_voxpopuli_hr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use derek-thomas/speecht5_finetuned_voxpopuli_hr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="derek-thomas/speecht5_finetuned_voxpopuli_hr")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToSpectrogram processor = AutoProcessor.from_pretrained("derek-thomas/speecht5_finetuned_voxpopuli_hr") model = AutoModelForTextToSpectrogram.from_pretrained("derek-thomas/speecht5_finetuned_voxpopuli_hr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from derek-thomas/speecht5_finetuned_voxpopuli_hr: direct link, hf CLI and curl.
- Browser
- Download file 1.72 kB
-
https://huggingface.co/derek-thomas/speecht5_finetuned_voxpopuli_hr/resolve/main/README.md
- Command line
-
hf download hf://derek-thomas/speecht5_finetuned_voxpopuli_hr/README.md
-
curl -L -o README.md https://huggingface.co/derek-thomas/speecht5_finetuned_voxpopuli_hr/resolve/main/README.md
1.72 kB
metadata
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- facebook/voxpopuli
- voxpopuli/hr
model-index:
- name: speecht5_finetuned_voxpopuli_hr
results: []
pipeline_tag: text-to-speech
language:
- hr
speecht5_finetuned_voxpopuli_it
This model is a fine-tuned version of microsoft/speecht5_tts on the voxpopuli/it dataset. It achieves the following results on the evaluation set:
- Loss: 0.4497
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: 1e-05
- 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4773 | 32.52 | 1000 | 0.4502 |
| 0.4545 | 65.04 | 2000 | 0.4462 |
| 0.4488 | 97.56 | 3000 | 0.4502 |
| 0.4517 | 130.08 | 4000 | 0.4497 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0