---
license: mit
base_model: vibevoice/VibeVoice-7B
language:
- "no"
- nb
tags:
- tts
- text-to-speech
- speech-synthesis
- norwegian
- bokmal
- bitsandbytes
- 4bit
- quantized
pipeline_tag: text-to-speech
---

# Prat-9B-NF4 (preview)

Also see [Prat 9B](https://huggingface.co/heiertech/Prat-9B)

## Quantization Details

- **Method**: bitsandbytes NF4 (4-bit NormalFloat)
- **Double quantization**: Enabled
- **Compute dtype**: bfloat16
- **Model size**: ~6.2 GB (vs ~19 GB for bf16)
- **VRAM usage**: ~7 GB

## Usage

```python
import torch
from transformers import BitsAndBytesConfig
from vibevoice.modular.modeling_vibevoice_inference import VibeVoiceForConditionalGenerationInference
from vibevoice.processor.vibevoice_processor import VibeVoiceProcessor

# Load with 4-bit quantization
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
)

model = VibeVoiceForConditionalGenerationInference.from_pretrained(
    "heiertech/Prat-9B-NF4",
    quantization_config=bnb_config,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)
model.eval()
model.set_ddpm_inference_steps(num_steps=10)

processor = VibeVoiceProcessor.from_pretrained("heiertech/Prat-9B-NF4")

# Generate Norwegian speech
text = "Speaker 0: Hei, jeg heter Maria og jeg kommer fra Norge."
inputs = processor(text=[text], padding=True, return_tensors="pt", return_attention_mask=True)
inputs = {k: v.to(model.device) for k, v in inputs.items() if torch.is_tensor(v)}

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        cfg_scale=1.3,
        tokenizer=processor.tokenizer,
        generation_config={"do_sample": False},
    )

audio = outputs.speech_outputs[0]  # 24kHz audio
```