Instructions to use asif00/orpheus-bangla-tts-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use asif00/orpheus-bangla-tts-gguf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-to-speech", model="asif00/orpheus-bangla-tts-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("asif00/orpheus-bangla-tts-gguf", dtype="auto")

llama-cpp-python

How to use asif00/orpheus-bangla-tts-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="asif00/orpheus-bangla-tts-gguf",
	filename="model.gguf",
)

llm.create_chat_completion(
	messages = "\"The answer to the universe is 42\""
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use asif00/orpheus-bangla-tts-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf asif00/orpheus-bangla-tts-gguf
# Run inference directly in the terminal:
llama-cli -hf asif00/orpheus-bangla-tts-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf asif00/orpheus-bangla-tts-gguf
# Run inference directly in the terminal:
llama-cli -hf asif00/orpheus-bangla-tts-gguf

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf asif00/orpheus-bangla-tts-gguf
# Run inference directly in the terminal:
./llama-cli -hf asif00/orpheus-bangla-tts-gguf

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf asif00/orpheus-bangla-tts-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf asif00/orpheus-bangla-tts-gguf

Use Docker

docker model run hf.co/asif00/orpheus-bangla-tts-gguf

LM Studio
Jan
Ollama
How to use asif00/orpheus-bangla-tts-gguf with Ollama:
```
ollama run hf.co/asif00/orpheus-bangla-tts-gguf
```

Unsloth Studio

How to use asif00/orpheus-bangla-tts-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for asif00/orpheus-bangla-tts-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for asif00/orpheus-bangla-tts-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for asif00/orpheus-bangla-tts-gguf to start chatting

How to use asif00/orpheus-bangla-tts-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf asif00/orpheus-bangla-tts-gguf

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "asif00/orpheus-bangla-tts-gguf"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use asif00/orpheus-bangla-tts-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf asif00/orpheus-bangla-tts-gguf

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default asif00/orpheus-bangla-tts-gguf

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use asif00/orpheus-bangla-tts-gguf with Docker Model Runner:
```
docker model run hf.co/asif00/orpheus-bangla-tts-gguf
```

Lemonade

How to use asif00/orpheus-bangla-tts-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull asif00/orpheus-bangla-tts-gguf

Run and chat with the model

lemonade run user.orpheus-bangla-tts-gguf-{{QUANT_TAG}}

List all available models

lemonade list

Orpheus Bangla GGUF (16 bit)

Model Description

This model is a proof-of-concept fine-tuned version of the Orpheus 3B TTS (Text-to-Speech) model for Bengali language support. The model has been trained using the SUST-CSE-Speech/banspeech dataset, which contains 955 audio samples split from audiobooks. This fine-tuning was performed for 10 epochs on a single Google Colab instance equipped with a T4 GPU.

Please note that this model is currently in the proof-of-concept phase and is not recommended for production use.

Intended Use

This model can be used for generating Bengali speech from text. It is ideal for experimenting with TTS systems for Bengali, particularly for audiobooks, conversational AI, or speech synthesis tasks.

Model Training

Dataset: SUST-CSE-Speech/banspeech (955 audiobook audio samples)
Training Epochs: 10 epochs
Hardware: Google Colab (single T4 GPU)
Training Script: A modified Unsloth fine-tuning script was used for the training. The script is available on GitHub: Orpheus TTS Training Script.

Limitations

This model was trained on a small dataset and for a limited number of epochs, which may lead to less natural or less accurate speech synthesis.
Since this is a proof-of-concept model, the synthesis quality may vary based on input text and different conditions. It is not optimized for production environments.