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Orpheus (de, de/en)

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+ ---
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+ library_name: transformers
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+ tags:
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+ - unsloth
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+ datasets:
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+ - kadirnar/Emilia-DE
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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-pretrained
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+ ---
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+
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+ # Model Card for Model ID
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+
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+ ## Model Details
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+ ### Model Description
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+ <!-- Provide a longer summary of what this model is. -->
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+
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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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+
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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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+ - **Repository:** [More Information Needed]
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+ - **Demo [optional]:** [More Information Needed]
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+
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+ ## Uses
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+
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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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+
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+ ### Direct Use
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+ [More Information Needed]
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+ ### Downstream Use [optional]
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+ [More Information Needed]
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+
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+ ### Out-of-Scope Use
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+
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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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+ [More Information Needed]
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+ ### Recommendations
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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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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+
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+ [More Information Needed]
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+
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+ ## Training Details
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+ ### Training Data
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+ ### Training Procedure
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+
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+ #### Preprocessing [optional]
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+ #### Training Hyperparameters
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+
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+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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+
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+ #### Speeds, Sizes, Times [optional]
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+
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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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+
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+ ## Evaluation
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+
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+
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+ ### Testing Data, Factors & Metrics
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+
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+ #### Testing Data
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+ <!-- This should link to a Dataset Card if possible. -->
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+
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+ [More Information Needed]
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+
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+ #### Factors
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+
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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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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+ [More Information Needed]
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+ ### Results
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+ [More Information Needed]
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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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+ ## Environmental Impact
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+
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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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+
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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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+
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+ ## Technical Specifications [optional]
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+
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+ ### Model Architecture and Objective
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+
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+ [More Information Needed]
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+
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+ ### Compute Infrastructure
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+ [More Information Needed]
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+ #### Hardware
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+ [More Information Needed]
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+ #### Software
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+ ## Citation [optional]
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+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+ ## Glossary [optional]
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+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+ [More Information Needed]
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+ ## More Information [optional]
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+ ## Model Card Authors [optional]
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+ "use_cache": true,
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+ ---
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+ base_model: canopylabs/orpheus-tts-0.1-pretrained
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+ tags:
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+ - text-generation-inference
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+ - transformers
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+ - unsloth
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+ - llama
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+ - trl
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+ license: apache-2.0
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+ language:
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+ - en
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+ - de
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+ datasets:
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+ - kadirnar/Emilia-DE
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+ library_name: transformers
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+ ---
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+
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+ # Uploaded model
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+
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+ - **Developed by:** kadirnar
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+ - **License:** apache-2.0
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+ - **Finetuned from model :** canopylabs/orpheus-tts-0.1-pretrained
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+
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+
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+ [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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+ ---
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+ library_name: transformers
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+ tags:
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+ - unsloth
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+ datasets:
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+ - Thorsten-Voice/TV-44kHz-Full
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+ language:
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+ - en
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+ - de
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+ base_model:
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+ - canopylabs/orpheus-3b-0.1-pretrained
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+ ---
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+
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+ # Model Card for Model ID
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ <!-- Provide a longer summary of what this model is. -->
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+
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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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+
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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]
33
+ - **License:** [More Information Needed]
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+ - **Finetuned from model [optional]:** [More Information Needed]
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+
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+ ---
2
+ library_name: transformers
3
+ tags:
4
+ - unsloth
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+ - text-to-speech
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+ - tts
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+ - german
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+ - orpheus
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+ - llama-cpp
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+ - gguf-my-repo
11
+ language:
12
+ - de
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+ base_model: SebastianBodza/Kartoffel_Orpheus-3B_german_natural-v0.1
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+ license: llama3.2
15
+ ---
16
+
17
+ # TheVisitorX/Kartoffel_Orpheus-3B_german_natural-v0.1-Q5_K_M-GGUF
18
+ This model was converted to GGUF format from [`SebastianBodza/Kartoffel_Orpheus-3B_german_natural-v0.1`](https://huggingface.co/SebastianBodza/Kartoffel_Orpheus-3B_german_natural-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
19
+ Refer to the [original model card](https://huggingface.co/SebastianBodza/Kartoffel_Orpheus-3B_german_natural-v0.1) for more details on the model.
20
+
21
+ ## Use with llama.cpp
22
+ Install llama.cpp through brew (works on Mac and Linux)
23
+
24
+ ```bash
25
+ brew install llama.cpp
26
+
27
+ ```
28
+ Invoke the llama.cpp server or the CLI.
29
+
30
+ ### CLI:
31
+ ```bash
32
+ llama-cli --hf-repo TheVisitorX/Kartoffel_Orpheus-3B_german_natural-v0.1-Q5_K_M-GGUF --hf-file kartoffel_orpheus-3b_german_natural-v0.1-q5_k_m.gguf -p "The meaning to life and the universe is"
33
+ ```
34
+
35
+ ### Server:
36
+ ```bash
37
+ llama-server --hf-repo TheVisitorX/Kartoffel_Orpheus-3B_german_natural-v0.1-Q5_K_M-GGUF --hf-file kartoffel_orpheus-3b_german_natural-v0.1-q5_k_m.gguf -c 2048
38
+ ```
39
+
40
+ Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
41
+
42
+ Step 1: Clone llama.cpp from GitHub.
43
+ ```
44
+ git clone https://github.com/ggerganov/llama.cpp
45
+ ```
46
+
47
+ Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
48
+ ```
49
+ cd llama.cpp && LLAMA_CURL=1 make
50
+ ```
51
+
52
+ Step 3: Run inference through the main binary.
53
+ ```
54
+ ./llama-cli --hf-repo TheVisitorX/Kartoffel_Orpheus-3B_german_natural-v0.1-Q5_K_M-GGUF --hf-file kartoffel_orpheus-3b_german_natural-v0.1-q5_k_m.gguf -p "The meaning to life and the universe is"
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+ ```
56
+ or
57
+ ```
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+ ./llama-server --hf-repo TheVisitorX/Kartoffel_Orpheus-3B_german_natural-v0.1-Q5_K_M-GGUF --hf-file kartoffel_orpheus-3b_german_natural-v0.1-q5_k_m.gguf -c 2048
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+ ```
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de/Kartoffel_Orpheus-3B_german_natural-v0.1/README.md ADDED
@@ -0,0 +1,182 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: transformers
3
+ tags:
4
+ - unsloth
5
+ - text-to-speech
6
+ - tts
7
+ - german
8
+ - orpheus
9
+ language:
10
+ - de
11
+ base_model:
12
+ - amuvarma/3b-de-pretrain
13
+ - canopylabs/orpheus-3b-0.1-ft
14
+ license: llama3.2
15
+ ---
16
+
17
+ <!-- <img src="https://huggingface.co/SebastianBodza/Kartoffel_Orpheus-3B_german_natural-v0.1/resolve/main/kartoffel.jpeg" alt="Kartoffel German Logo" width="400" style="margin-left:'auto' margin-right:'auto' display:'block'"/> -->
18
+
19
+
20
+ # Kartoffel-3B (Based on Orpheus-3B) - Natural
21
+ <!-- <a target="_blank" href="https://huggingface.co/spaces/YourUsername/Kartoffel-3B-Demo"> # <-- Update with your Space link
22
+ <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/>
23
+ </a> -->
24
+
25
+ <video src="https://huggingface.co/SebastianBodza/Kartoffel_Orpheus-3B_german_natural-v0.1/resolve/main/demo_kartoffel.mp4" alt="Demo Video" width="400" controls autoplay></video>
26
+
27
+ ## Updates
28
+
29
+ - I have added a blog post that includes code on how to use Kartoffel for near real-time streaming. Check it out here: [Blog Post](https://bitbasti.com/blog/audio-streaming-with-orpheus).
30
+
31
+ ## Model Overview
32
+
33
+ This is a German text-to-speech (TTS) model family based on Orpheus-3B.
34
+
35
+ Two main versions are available:
36
+
37
+ 1. **[Kartoffel-3B-Natural](https://huggingface.co/SebastianBodza/Kartoffel_Orpheus-3B_german_natural-v0.1):** Fine-tuned primarily on natural human speech recordings, aiming for realistic voices. The dataset is based on, high-quality German audio, including permissive podcasts, lectures, and other OER data that were processed with an Emilia styled pipeline.
38
+ 2. **[Kartoffel-3B-Synthetic](https://huggingface.co/SebastianBodza/Kartoffel_Orpheus-3B_german_synthetic-v0.1):** Fine-tuned using synthetic speech data, with emotions and different outbursts. The dataset consists of a diverse set of emotions with 4 different speeakers.
39
+
40
+ This is currently the natural version for natural sounding speakers.
41
+
42
+ Both versions support:
43
+ * **Multiple Speakers:** The model can generate speech using various speaker identities from predefined speakers.
44
+ * **Varied Expressions:** Capable of generating speech with different emotional tones and expressions based on the input text. The natural version has limited support for expressions and emotions.
45
+
46
+ ## Available Speakers & Expressions for the Natural Version:
47
+ ### Speakers:
48
+ There are a couple of speakers, however not all are stable. Therefore I will only list following the speakers that are at least partially stable:
49
+ * Jakob
50
+ * Anton
51
+ * Julian
52
+ * Jan
53
+ * Alexander
54
+ * Emil
55
+ * Ben
56
+ * Elias
57
+ * Felix
58
+ * Jonas
59
+ * Noah
60
+ * Maximilian
61
+
62
+ * Sophie
63
+ * Marie
64
+ * Mia
65
+ * Maria
66
+ * Sophia
67
+ * Lina
68
+ * Lea
69
+
70
+ Unfortunately the dataset had alot more male then female speakers. Also not all speakers could be reconstructed and duplicates can be present. The gender estimation also worked kind of bad.
71
+ ```python
72
+ import torch
73
+ import torchaudio.transforms as T
74
+ import os
75
+ import torch
76
+ from snac import SNAC
77
+
78
+ from peft import PeftModel
79
+ import soundfile as sf
80
+ from transformers import AutoModelForCausalLM, AutoTokenizer
81
+
82
+
83
+ model = AutoModelForCausalLM.from_pretrained(
84
+ "SebastianBodza/Kartoffel_Orpheus-3B_german_synthetic-v0.1",
85
+ device_map="auto",
86
+ )
87
+
88
+ tokenizer = AutoTokenizer.from_pretrained(
89
+ "SebastianBodza/Kartoffel_Orpheus-3B_german_synthetic-v0.1",
90
+ )
91
+
92
+ snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
93
+ snac_model = snac_model.to("cuda")
94
+
95
+ chosen_voice = "Julian"
96
+
97
+ prompts = [
98
+ 'Tief im verwunschenen Wald, wo die Bäume uralte Geheimnisse flüsterten, lebte ein kleiner Gnom namens Fips, der die Sprache der Tiere verstand.',
99
+ ]
100
+
101
+ def process_single_prompt(prompt, chosen_voice):
102
+ if chosen_voice == "in_prompt" or chosen_voice == "":
103
+ full_prompt = prompt
104
+ else:
105
+ full_prompt = f"{chosen_voice}: {prompt}"
106
+ start_token = torch.tensor([[128259]], dtype=torch.int64)
107
+ end_tokens = torch.tensor([[128009, 128260]], dtype=torch.int64)
108
+
109
+ input_ids = tokenizer(full_prompt, return_tensors="pt").input_ids
110
+ modified_input_ids = torch.cat([start_token, input_ids, end_tokens], dim=1)
111
+
112
+ input_ids = modified_input_ids.to("cuda")
113
+ attention_mask = torch.ones_like(input_ids)
114
+
115
+ generated_ids = model.generate(
116
+ input_ids=input_ids,
117
+ attention_mask=attention_mask,
118
+ max_new_tokens=4000,
119
+ do_sample=True,
120
+ temperature=0.6,
121
+ top_p=0.95,
122
+ repetition_penalty=1.1,
123
+ num_return_sequences=1,
124
+ eos_token_id=128258,
125
+ use_cache=True,
126
+ )
127
+
128
+ token_to_find = 128257
129
+ token_to_remove = 128258
130
+
131
+ token_indices = (generated_ids == token_to_find).nonzero(as_tuple=True)
132
+
133
+ if len(token_indices[1]) > 0:
134
+ last_occurrence_idx = token_indices[1][-1].item()
135
+ cropped_tensor = generated_ids[:, last_occurrence_idx + 1 :]
136
+ else:
137
+ cropped_tensor = generated_ids
138
+
139
+ masked_row = cropped_tensor[0][cropped_tensor[0] != token_to_remove]
140
+ row_length = masked_row.size(0)
141
+ new_length = (row_length // 7) * 7
142
+ trimmed_row = masked_row[:new_length]
143
+ code_list = [t - 128266 for t in trimmed_row]
144
+
145
+ return code_list
146
+
147
+
148
+ def redistribute_codes(code_list):
149
+ layer_1 = []
150
+ layer_2 = []
151
+ layer_3 = []
152
+ for i in range((len(code_list) + 1) // 7):
153
+ layer_1.append(code_list[7 * i])
154
+ layer_2.append(code_list[7 * i + 1] - 4096)
155
+ layer_3.append(code_list[7 * i + 2] - (2 * 4096))
156
+ layer_3.append(code_list[7 * i + 3] - (3 * 4096))
157
+ layer_2.append(code_list[7 * i + 4] - (4 * 4096))
158
+ layer_3.append(code_list[7 * i + 5] - (5 * 4096))
159
+ layer_3.append(code_list[7 * i + 6] - (6 * 4096))
160
+
161
+ codes = [
162
+ torch.tensor(layer_1).unsqueeze(0),
163
+ torch.tensor(layer_2).unsqueeze(0),
164
+ torch.tensor(layer_3).unsqueeze(0),
165
+ ]
166
+ codes = [c.to("cuda") for c in codes]
167
+
168
+ audio_hat = snac_model.decode(codes)
169
+ return audio_hat
170
+
171
+
172
+ for i, prompt in enumerate(prompts):
173
+ print(f"Processing prompt {i + 1}/{len(prompts)}")
174
+ with torch.no_grad():
175
+ code_list = process_single_prompt(prompt, chosen_voice)
176
+ samples = redistribute_codes(code_list)
177
+
178
+ audio_numpy = samples.detach().squeeze().to("cpu").numpy()
179
+ sf.write(f"output_{i}.wav", audio_numpy, 24000)
180
+ print(f"Saved output_{i}.wav")
181
+ ```
182
+
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+ "unsloth_version": "2025.3.19",
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+ "use_cache": true,
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+ "vocab_size": 156940
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+ }
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