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neutts-air

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here is a gradio demo that uses the gpu

import gradio as gr
import soundfile as sf
import tempfile
import os
from pathlib import Path
from neuttsair.neutts import NeuTTSAir

# Global variable to store the TTS model
tts_model = None
current_backbone = None

def initialize_model(backbone_choice):
    """Initialize or reinitialize the TTS model with the selected backbone."""
    global tts_model, current_backbone
    
    backbone_map = {
        "Standard (PyTorch)": "neuphonic/neutts-air",
        "Q8 GGUF (Faster)": "neuphonic/neutts-air-q8-gguf",
        "Q4 GGUF (Fastest)": "neuphonic/neutts-air-q4-gguf"
    }
    
    backbone_repo = backbone_map[backbone_choice]
    
    # Only reinitialize if the backbone changed
    if current_backbone != backbone_repo:
        print(f"Initializing model with {backbone_repo}...")
        tts_model = NeuTTSAir(
        backbone_repo=backbone_repo,
        backbone_device="cuda",  # Changed from "cpu"
        codec_repo="neuphonic/neucodec",
        codec_device="cuda"      # Changed from "cpu"
    )
        current_backbone = backbone_repo
        return f"✅ Model loaded: {backbone_choice}"
    return f"✅ Using: {backbone_choice}"

def generate_speech(input_text, ref_audio, ref_text, backbone_choice, progress=gr.Progress()):
    """Generate speech from text using reference audio for voice cloning."""
    try:
        if not input_text or not input_text.strip():
            return None, "❌ Please enter text to synthesize."
        
        if ref_audio is None:
            return None, "❌ Please upload a reference audio file."
        
        if not ref_text or not ref_text.strip():
            return None, "❌ Please enter the reference text."
        
        # Initialize model if needed
        progress(0.1, desc="Loading model...")
        status = initialize_model(backbone_choice)
        
        # Encode reference audio
        progress(0.3, desc="Encoding reference audio...")
        ref_codes = tts_model.encode_reference(ref_audio)
        
        # Generate speech
        progress(0.6, desc="Generating speech...")
        wav = tts_model.infer(input_text, ref_codes, ref_text.strip())
        
        # Save to temporary file
        progress(0.9, desc="Saving audio...")
        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
            sf.write(tmp_file.name, wav, 24000)
            output_path = tmp_file.name
        
        return output_path, f"✅ Speech generated successfully! ({len(wav)/24000:.2f}s)"
    
    except Exception as e:
        return None, f"❌ Error: {str(e)}"

def load_example(example_name):
    """Load example reference audio and text."""
    example_map = {
        "Dave": ("samples/dave.wav", open("samples/dave.txt").read().strip()),
        "Jo": ("samples/jo.wav", open("samples/jo.txt").read().strip())
    }
    
    if example_name in example_map:
        audio_path, text = example_map[example_name]
        if os.path.exists(audio_path):
            return audio_path, text, "✅ Example loaded!"
        else:
            return None, "", "❌ Example file not found."
    return None, "", ""

# Create Gradio interface
with gr.Blocks(title="NeuTTS Air - Voice Cloning Demo", theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # 🎙️ NeuTTS Air - Voice Cloning Demo
    
    Create ultra-realistic voice clones with as little as 3 seconds of audio! This demo uses NeuTTS Air, 
    a state-of-the-art on-device TTS model with instant voice cloning capabilities.
    
    ### 📋 How to use:
    1. **Upload reference audio** (3-15 seconds, clean speech, WAV format recommended)
    2. **Enter what the reference says** (the transcript of your reference audio)
    3. **Type what you want to synthesize** (the text in the cloned voice)
    4. **Choose a model** (Q4 GGUF is fastest for CPU)
    5. **Click Generate!**
    """)
    
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### 🎵 Reference Audio (Voice to Clone)")
            
            # Example selector
            with gr.Row():
                example_selector = gr.Radio(
                    choices=["Dave", "Jo", "Custom"],
                    value="Custom",
                    label="Quick Examples or Custom",
                    info="Try built-in examples or upload your own"
                )
            
            ref_audio_input = gr.Audio(
                label="Reference Audio (3-15s of clean speech)",
                type="filepath"
            )
            
            ref_text_input = gr.Textbox(
                label="Reference Text (Transcript of the reference audio)",
                placeholder="What does the reference audio say?",
                lines=3
            )
            
            example_status = gr.Textbox(label="Status", interactive=False, visible=False)
        
        with gr.Column(scale=1):
            gr.Markdown("### ✍️ Text to Synthesize")
            
            input_text = gr.Textbox(
                label="Input Text (Text to synthesize in the cloned voice)",
                placeholder="Enter the text you want to synthesize in the cloned voice...",
                lines=5
            )
            
            backbone_choice = gr.Radio(
                choices=["Standard (PyTorch)", "Q8 GGUF (Faster)", "Q4 GGUF (Fastest)"],
                value="Q4 GGUF (Fastest)",
                label="Model Selection (Q4 GGUF recommended for CPU)"
            )
            
            generate_btn = gr.Button("🎵 Generate Speech", variant="primary", size="lg")
    
    with gr.Row():
        with gr.Column():
            output_audio = gr.Audio(
                label="Generated Speech",
                type="filepath"
            )
            status_output = gr.Textbox(label="Generation Status", interactive=False)
    
    gr.Markdown("""
    ### 📌 Tips for Best Results:
    - **Reference audio should be**: mono, 16-44kHz, 3-15 seconds, clean with minimal background noise
    - **Natural speech works best**: conversational tone with few pauses
    - **GGUF models**: Much faster on CPU, slight quality trade-off
    - **Longer texts**: May take more time but produce great results
    
    ### ⚠️ Responsible Use:
    - Only clone voices you have permission to use
    - Generated audio is watermarked for detection
    - Built by [Neuphonic](https://neuphonic.com)
    """)
    
    # Event handlers
    def handle_example_change(example_name):
        if example_name in ["Dave", "Jo"]:
            return load_example(example_name)
        return None, "", ""
    
    example_selector.change(
        fn=handle_example_change,
        inputs=[example_selector],
        outputs=[ref_audio_input, ref_text_input, example_status]
    )
    
    generate_btn.click(
        fn=generate_speech,
        inputs=[input_text, ref_audio_input, ref_text_input, backbone_choice],
        outputs=[output_audio, status_output]
    )
    
    # Example inputs for quick testing
    gr.Examples(
        examples=[
            ["Hey there, I'm exploring the world of voice AI and it's absolutely fascinating!"],
            ["The quick brown fox jumps over the lazy dog."],
            ["My name is Alex, and I'm 28 years old. I work in technology and love building innovative products."],
        ],
        inputs=[input_text],
        label="Example Texts to Try"
    )

if __name__ == "__main__":
    demo.launch(
        share=False,
        server_name="0.0.0.0",
        server_port=7868
    )

Appears you've just changed CPU to CUDA -- doesn't seem to work,
"AssertionError: Torch not compiled with CUDA enabled"

@jattoedaltni
it's because in the requirements.txt you have "torch" compiled for CPU only. To change it to a CUDA version follow these steps:

1. check https://pytorch.org/get-started/locally/ to get the necessary pip command for your version of CUDA. Let's presume you have CUDA v12.6, then you will get pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu126. You need only the link part.

2. open requirements.txt. Remove torch and add this to the end of the file:
   --extra-index-url https://download.pytorch.org/whl/cu126
   torch
   torchvision

3. execute pip uninstall torch to remove the previous version of torch

4. execute pip install -r requirements.txt. A different type of torch must be downloading (weights about 2 GB)

5. Use the example above with gradio from the previous commenter, and run the app. It must use GPU now

Note that actual layers of the model will only be offloaded to the GPU (when using GGUF) if the device is set to gpu, not if it's set to cuda.

If it's set to cuda you'll still be using the GPU (assuming llama_cpp_python was compiled with CUDA support) but the actual model will reside on the CPU.

From the code in the GitHub repo:

self.backbone = Llama.from_pretrained(
    repo_id=backbone_repo,
    filename="*.gguf",
    verbose=False,
    n_gpu_layers=-1 if backbone_device == "gpu" else 0,
    n_ctx=self.max_context,
    mlock=True,
    flash_attn=True if backbone_device == "gpu" else False,
 )