Instructions to use elijahross/csm-german with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use elijahross/csm-german with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="elijahross/csm-german")# Load model directly from transformers import AutoFeatureExtractor, AutoModelForTextToWaveform extractor = AutoFeatureExtractor.from_pretrained("elijahross/csm-german") model = AutoModelForTextToWaveform.from_pretrained("elijahross/csm-german") - Notebooks
- Google Colab
- Kaggle
csm-german
This model was trained from scratch on an unknown dataset.
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: 3e-05
- train_batch_size: 82
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- 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: 50
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.54.0
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.4
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