# Load model directly
from transformers import AutoProcessor, AutoModelForTextToSpectrogram
processor = AutoProcessor.from_pretrained("Salama1429/TTS_German_Speecht5_finetuned_voxpopuli_nl")
model = AutoModelForTextToSpectrogram.from_pretrained("Salama1429/TTS_German_Speecht5_finetuned_voxpopuli_nl", device_map="auto")Quick Links
TTS_German_Speecht5_finetuned_voxpopuli_nl
This model is a fine-tuned version of microsoft/speecht5_tts on the facebook/voxpopuli dataset. It achieves the following results on the evaluation set:
- Loss: 0.4593
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
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.5248 | 4.3 | 1000 | 0.4792 |
| 0.5019 | 8.61 | 2000 | 0.4663 |
| 0.4937 | 12.91 | 3000 | 0.4609 |
| 0.4896 | 17.21 | 4000 | 0.4593 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="Salama1429/TTS_German_Speecht5_finetuned_voxpopuli_nl")