diodiogod
83bfeffab5
- Add dedicated KugelAudio implementation with relative imports - Update engine/downloader/processor to support KugelAudio with fallback - Regenerate engine comparison tables and update README - Fix: Use relative imports for robustness - Fix: Prevent 'Speaker 1:' prefix regression in processor
128 lines
4.1 KiB
Python
128 lines
4.1 KiB
Python
#!/usr/bin/env python3
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"""Command-line interface for KugelAudio."""
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import argparse
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import sys
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def main():
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parser = argparse.ArgumentParser(
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description="KugelAudio - Open-source text-to-speech",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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# Launch web interface
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kugelaudio ui
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# Launch with public share link
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kugelaudio ui --share
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# Generate speech from command line
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kugelaudio generate "Hello world!" -o output.wav
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# Generate with a specific voice
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kugelaudio generate "Hello world!" --voice default -o output.wav
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# Check watermark in audio file
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kugelaudio verify audio.wav
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""",
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)
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subparsers = parser.add_subparsers(dest="command", help="Available commands")
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# UI command
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ui_parser = subparsers.add_parser("ui", help="Launch Gradio web interface")
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ui_parser.add_argument("--share", action="store_true", help="Create public share link")
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ui_parser.add_argument("--host", default="127.0.0.1", help="Server hostname")
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ui_parser.add_argument("--port", type=int, default=7860, help="Server port")
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# Generate command
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gen_parser = subparsers.add_parser("generate", help="Generate speech from text")
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gen_parser.add_argument("text", help="Text to synthesize")
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gen_parser.add_argument("-o", "--output", default="output.wav", help="Output file path")
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gen_parser.add_argument(
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"-v", "--voice", help="Pre-encoded voice name (from voices.json registry)"
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)
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gen_parser.add_argument("--model", default="kugelaudio/kugelaudio-0-open", help="Model ID")
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gen_parser.add_argument("--cfg-scale", type=float, default=3.0, help="Guidance scale")
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# Verify command
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verify_parser = subparsers.add_parser("verify", help="Check watermark in audio")
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verify_parser.add_argument("audio", help="Audio file to check")
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args = parser.parse_args()
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if args.command == "ui":
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from .ui import launch_app
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launch_app(
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share=args.share,
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server_name=args.host,
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server_port=args.port,
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)
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elif args.command == "generate":
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import torch
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from .models import KugelAudioForConditionalGenerationInference
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from .processors import KugelAudioProcessor
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device = "cuda" if torch.cuda.is_available() else "cpu"
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dtype = torch.bfloat16 if device == "cuda" else torch.float32
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print(f"Loading model {args.model}...")
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model = KugelAudioForConditionalGenerationInference.from_pretrained(
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args.model, torch_dtype=dtype
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).to(device)
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model.eval()
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processor = KugelAudioProcessor.from_pretrained(args.model)
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# Strip encoders to save VRAM (only decoders needed for inference)
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model.model.strip_encoders()
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# Process inputs with optional pre-encoded voice
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inputs = processor(text=args.text, voice=args.voice, return_tensors="pt")
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inputs = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
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print("Generating speech...")
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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cfg_scale=args.cfg_scale,
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max_new_tokens=4096,
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)
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# Audio is already watermarked by the model's generate method
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audio = outputs.speech_outputs[0]
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# Save
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processor.save_audio(audio, args.output)
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print(f"Audio saved to {args.output}")
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elif args.command == "verify":
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import numpy as np
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import soundfile as sf
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from .watermark import AudioWatermark
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audio, sr = sf.read(args.audio)
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watermark = AudioWatermark()
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result = watermark.detect(audio, sample_rate=sr)
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if result.detected:
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print(f"✅ Watermark DETECTED (confidence: {result.confidence:.1%})")
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print("This audio was generated by KugelAudio.")
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else:
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print(f"❌ No watermark detected (confidence: {result.confidence:.1%})")
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print("This audio does not appear to be generated by KugelAudio.")
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else:
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parser.print_help()
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sys.exit(1)
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if __name__ == "__main__":
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main()
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