Orpheus (ko, th, zh)

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ko/Orpheus-3b-Korean-FT-Q8_0.gguf/README.md

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+---
+language:
+- ko
+tags:
+- text-to-speech
+- tts
+- audio
+- speech-synthesis
+- orpheus
+- gguf
+license: apache-2.0
+datasets:
+- internal
+---
+
+# Orpheus-3b-FT-Q8_0
+
+This is a quantised version of [canopylabs/3b-ko-ft-research_release](https://huggingface.co/canopylabs/3b-ko-ft-research_release).
+
+Orpheus is a high-performance Text-to-Speech model fine-tuned for natural, emotional speech synthesis. This repository hosts the 8-bit quantised version of the 3B parameter model, optimised for efficiency while maintaining high-quality output.
+
+## Model Description
+
+**Orpheus-3b-FT-Q8_0** is a 3 billion parameter Text-to-Speech model that converts text inputs into natural-sounding speech with support for multiple voices and emotional expressions. The model has been quantised to 8-bit (Q8_0) format for efficient inference, making it accessible on consumer hardware.
+
+Key features:
+- 2 distinct voice options with different characteristics
+- Support for emotion tags like laughter, sighs, etc.
+- Optimised for CUDA acceleration on RTX GPUs
+- Produces high-quality 24kHz mono audio
+- Fine-tuned for conversational naturalness
+
+## How to Use
+
+This model is designed to be used with an LLM inference server that connects to the [Orpheus-FastAPI](https://github.com/Lex-au/Orpheus-FastAPI) frontend, which provides both a web UI and OpenAI-compatible API endpoints.
+
+### Compatible Inference Servers
+
+This quantised model can be loaded into any of these LLM inference servers:
+
+- [GPUStack](https://github.com/gpustack/gpustack) - GPU optimised LLM inference server (My pick) - supports LAN/WAN tensor split parallelisation
+- [LM Studio](https://lmstudio.ai/) - Load the GGUF model and start the local server
+- [llama.cpp server](https://github.com/ggerganov/llama.cpp) - Run with the appropriate model parameters
+- Any compatible OpenAI API-compatible server
+
+### Quick Start
+
+1. Download this quantised model from [lex-au's Orpheus-FASTAPI collection](https://huggingface.co/collections/lex-au/orpheus-fastapi-67e125ae03fc96dae0517707)
+
+2. Load the model in your preferred inference server and start the server.
+
+3. Clone the Orpheus-FastAPI repository:
+```bash
+git clone https://github.com/Lex-au/Orpheus-FastAPI.git
+cd Orpheus-FastAPI
+```
+
+4. Configure the FastAPI server to connect to your inference server by setting the `ORPHEUS_API_URL` environment variable.
+
+5. Follow the complete installation and setup instructions in the [repository README](https://github.com/Lex-au/Orpheus-FastAPI).
+
+### Available Voices
+
+The model supports 2 different voices:
+- `유나`: Female, Korean, melodic
+- `준서`: Male, Korean, confident
+
+### Emotion Tags
+
+You can add expressiveness to speech by inserting tags:
+- `<laugh>`, `<chuckle>`: For laughter sounds
+- `<sigh>`: For sighing sounds
+- `<cough>`, `<sniffle>`: For subtle interruptions
+- `<groan>`, `<yawn>`, `<gasp>`: For additional emotional expression
+
+## Technical Specifications
+
+- **Architecture**: Specialised token-to-audio sequence model
+- **Parameters**: ~3 billion
+- **Quantisation**: 8-bit (GGUF Q8_0 format)
+- **Audio Sample Rate**: 24kHz
+- **Input**: Text with optional voice selection and emotion tags
+- **Output**: High-quality WAV audio
+- **Language**: Korean
+- **Hardware Requirements**: CUDA-compatible GPU (recommended: RTX series)
+- **Integration Method**: External LLM inference server + Orpheus-FastAPI frontend
+
+## Limitations
+
+- Best performance achieved on CUDA-compatible GPUs
+- Generation speed depends on GPU capability
+
+## License
+
+This model is available under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
+
+## Citation & Attribution
+
+The original Orpheus model was created by Canopy Labs. This repository contains a quantised version optimised for use with the Orpheus-FastAPI server.
+
+If you use this quantised model in your research or applications, please cite:
+
+```
+@misc{orpheus-tts-2025,
+  author = {Canopy Labs},
+  title = {Orpheus-3b-0.1-ft: Text-to-Speech Model},
+  year = {2025},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/canopylabs/orpheus-3b-0.1-ft}}
+}
+
+@misc{orpheus-quantised-2025,
+  author = {Lex-au},
+  title = {Orpheus-3b-FT-Q8_0: Quantised TTS Model with FastAPI Server},
+  note = {GGUF quantisation of canopylabs/orpheus-3b-0.1-ft},
+  year = {2025},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/lex-au/Orpheus-3b-FT-Q8_0.gguf}}
+}
+```

ko/Orpheus-3b-Korean-FT-Q8_0.gguf/source.txt

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th/Orpheus-Porjai-Thai/README.md

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+---
+library_name: transformers
+tags:
+- unsloth
+datasets:
+- CMKL/Porjai-Thai-voice-dataset-central
+language:
+- th
+base_model:
+- canopylabs/orpheus-3b-0.1-pretrained
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- 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. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+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).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
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+
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+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

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+  "use_cache": true,
+  "vocab_size": 156939
+}

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+---
+language:
+- zh
+tags:
+- text-to-speech
+- tts
+- audio
+- speech-synthesis
+- orpheus
+- gguf
+license: apache-2.0
+datasets:
+- internal
+---
+
+# Orpheus-3b-Chinese-FT-Q8_0
+
+This is a quantised version of [canopylabs/3b-zh-ft-research_release](https://huggingface.co/canopylabs/3b-zh-ft-research_release).
+
+Orpheus is a high-performance Text-to-Speech model fine-tuned for natural, emotional speech synthesis. This repository hosts the 8-bit quantised version of the 3B parameter model, optimised for efficiency while maintaining high-quality output.
+
+## Model Description
+
+**Orpheus-3b-FT-Q8_0** is a 3 billion parameter Text-to-Speech model that converts text inputs into natural-sounding speech with support for multiple voices and emotional expressions. The model has been quantised to 8-bit (Q8_0) format for efficient inference, making it accessible on consumer hardware.
+
+Key features:
+- 2 distinct voice options with different characteristics
+- Support for emotion tags like laughter, sighs, etc.
+- Optimised for CUDA acceleration on RTX GPUs
+- Produces high-quality 24kHz mono audio
+- Fine-tuned for conversational naturalness
+
+## How to Use
+
+This model is designed to be used with an LLM inference server that connects to the [Orpheus-FastAPI](https://github.com/Lex-au/Orpheus-FastAPI) frontend, which provides both a web UI and OpenAI-compatible API endpoints.
+
+### Compatible Inference Servers
+
+This quantised model can be loaded into any of these LLM inference servers:
+
+- [GPUStack](https://github.com/gpustack/gpustack) - GPU optimised LLM inference server (My pick) - supports LAN/WAN tensor split parallelisation
+- [LM Studio](https://lmstudio.ai/) - Load the GGUF model and start the local server
+- [llama.cpp server](https://github.com/ggerganov/llama.cpp) - Run with the appropriate model parameters
+- Any compatible OpenAI API-compatible server
+
+### Quick Start
+
+1. Download this quantised model from [lex-au's Orpheus-FASTAPI collection](https://huggingface.co/collections/lex-au/orpheus-fastapi-67e125ae03fc96dae0517707)
+
+2. Load the model in your preferred inference server and start the server.
+
+3. Clone the Orpheus-FastAPI repository:
+```bash
+git clone https://github.com/Lex-au/Orpheus-FastAPI.git
+cd Orpheus-FastAPI
+```
+
+4. Configure the FastAPI server to connect to your inference server by setting the `ORPHEUS_API_URL` environment variable.
+
+5. Follow the complete installation and setup instructions in the [repository README](https://github.com/Lex-au/Orpheus-FastAPI).
+
+
+### Available Voices
+
+The model supports 2 different voices:
+- `长乐`: Female, Mandarin, gentle
+- `白芷`: Female, Mandarin, clear
+
+### Emotion Tags
+
+You can add expressiveness to speech by inserting tags:
+- `<laugh>`, `<chuckle>`: For laughter sounds
+- `<sigh>`: For sighing sounds
+- `<cough>`, `<sniffle>`: For subtle interruptions
+- `<groan>`, `<yawn>`, `<gasp>`: For additional emotional expression
+
+## Technical Specifications
+
+- **Architecture**: Specialised token-to-audio sequence model
+- **Parameters**: ~3 billion
+- **Quantisation**: 8-bit (GGUF Q8_0 format)
+- **Audio Sample Rate**: 24kHz
+- **Input**: Text with optional voice selection and emotion tags
+- **Output**: High-quality WAV audio
+- **Language**: Mandarin
+- **Hardware Requirements**: CUDA-compatible GPU (recommended: RTX series)
+- **Integration Method**: External LLM inference server + Orpheus-FastAPI frontend
+
+## Limitations
+
+- Best performance achieved on CUDA-compatible GPUs
+- Generation speed depends on GPU capability
+
+## License
+
+This model is available under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
+
+## Citation & Attribution
+
+The original Orpheus model was created by Canopy Labs. This repository contains a quantised version optimised for use with the Orpheus-FastAPI server.
+
+If you use this quantised model in your research or applications, please cite:
+
+```
+@misc{orpheus-tts-2025,
+  author = {Canopy Labs},
+  title = {Orpheus-3b-0.1-ft: Text-to-Speech Model},
+  year = {2025},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/canopylabs/orpheus-3b-0.1-ft}}
+}
+
+@misc{orpheus-quantised-2025,
+  author = {Lex-au},
+  title = {Orpheus-3b-FT-Q8_0: Quantised TTS Model with FastAPI Server},
+  note = {GGUF quantisation of canopylabs/orpheus-3b-0.1-ft},
+  year = {2025},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/lex-au/Orpheus-3b-FT-Q8_0.gguf}}
+}
+```

zh/Orpheus-3b-Chinese-FT-Q8_0.gguf/source.txt

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+https://huggingface.co/lex-au/Orpheus-3b-Chinese-FT-Q8_0.gguf