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library_name: transformers
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---
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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### Results
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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library_name: transformers
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tags:
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- unsloth
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license: cc-by-nc-4.0
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language:
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- de
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base_model:
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- canopylabs/orpheus-3b-0.1-ft
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pipeline_tag: text-to-speech
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---
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# SauerkrautTTS-Preview-0.1
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**SauerkrautTTS-Preview-0.1** is a fine-tuned multilingual Text-to-Speech (TTS) model based on the powerful [canopylabs/orpheus-3b-0.1-ft](https://huggingface.co/canopylabs/orpheus-3b-0.1-ft).
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This preview model introduces four distinct German-speaking voices—**Lena**, **Anna**, **Max**, and **Tom**—crafted using original audio recordings captured with a Rhode Studio microphone and Mimic Studio, alongside carefully curated synthetic data. Despite the limited amount of original recordings, the high-quality synthetic data significantly enhances the clarity and naturalness of the generated speech.
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# Example Output and Comparison
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<video width="600" controls>
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<source src="https://vago-solutions.ai/wp-content/uploads/2025/03/SauerkrautTTS.mp4" type="video/mp4">
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Dein Browser unterstützt keine Videoanzeige.
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</video>
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## Model Details
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- **Base Model:** [canopylabs/orpheus-3b-0.1-ft](https://huggingface.co/canopylabs/orpheus-3b-0.1-ft)
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- **Languages Supported:** German
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- **License:** CC BY 4.0
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- **Model Version:** Preview 0.1 (initial release)
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## Speaker Data Breakdown
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| Speaker | Original Data (Hours) | Synthetic Data (Hours) | Total (Hours) |
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|---------|-----------------------|------------------------|---------------|
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| Tom | 1h | 3.8h | 4.8h |
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| Anna | 3h | 1.25h | 4.25h |
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| Max | - | 4.78h | 4.78h |
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| Lena | - | 4.87h | 4.87h |
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The synthetic audio data significantly enriches the model, resulting in versatile and expressive voice capabilities.
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## Usage
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For seamless inference and practical examples, check out our detailed instructions and ready-to-use scripts available on:
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- **Colab Notebook:** [Orpheus Colab](https://colab.research.google.com/drive/1KhXT56UePPUHhqitJNUxq63k-pQomz3N?usp=sharing)
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- **GitHub Repository:** [Orpheus GitHub](https://github.com/canopyai/Orpheus-TTS)
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To achieve optimal results, we recommend using a **lower temperature** for clear and stable outputs. Higher temperatures will enhance dynamism and expressiveness but might introduce instability.
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Example inference settings:
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```python
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temperature = 0.5 # Adjust lower for clearer output, higher for creativity
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```
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## Future Plans
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This model represents our first exploratory step into advanced German-language TTS. Expect significant improvements in upcoming versions, including:
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- Enhanced voice clarity
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- Expanded speaker diversity
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- Greater stability across temperature ranges
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Stay tuned for future releases and updates!
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## License
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SauerkrautTTS-Preview-0.1 is openly available under the [CC BY 4.0 License](https://creativecommons.org/licenses/by/4.0/), encouraging reuse, remixing, and improvements by the community.
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## Acknowledgments
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We thank [Unsloth](https://unsloth.ai) for their invaluable training script, which we utilized in a lightly modified form for training this model.
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