# Load model directly
from transformers import AutoProcessor, AutoModelForTextToSpectrogram
processor = AutoProcessor.from_pretrained("Atrishi/speecht5_tts_voxpopuli_nl")
model = AutoModelForTextToSpectrogram.from_pretrained("Atrishi/speecht5_tts_voxpopuli_nl", device_map="auto")Quick Links
speecht5_tts
This model is a fine-tuned version of microsoft/speecht5_tts on the lj_speech dataset. It achieves the following results on the evaluation set:
- Loss: 0.3755
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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4371 | 2.7110 | 1000 | 0.3906 |
| 0.411 | 5.4206 | 2000 | 0.3799 |
| 0.4097 | 8.1303 | 3000 | 0.3765 |
| 0.4148 | 10.8412 | 4000 | 0.3755 |
Framework versions
- Transformers 4.51.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
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Model tree for Atrishi/speecht5_tts_voxpopuli_nl
Base model
microsoft/speecht5_tts
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="Atrishi/speecht5_tts_voxpopuli_nl")