convert/src/voxcpm/cli.py

#!/usr/bin/env python3
"""
VoxCPM Command Line Interface

Unified CLI for voice cloning, direct TTS synthesis, and batch processing.
"""

import argparse
import os
import sys
from pathlib import Path
import soundfile as sf

from voxcpm.core import VoxCPM


# -----------------------------
# Validators
# -----------------------------

def validate_file_exists(file_path: str, file_type: str = "file") -> Path:
    path = Path(file_path)
    if not path.exists():
        raise FileNotFoundError(f"{file_type} '{file_path}' does not exist")
    return path


def validate_output_path(output_path: str) -> Path:
    path = Path(output_path)
    path.parent.mkdir(parents=True, exist_ok=True)
    return path


def validate_ranges(args, parser):
    """Validate numeric argument ranges."""
    if not (0.1 <= args.cfg_value <= 10.0):
        parser.error("--cfg-value must be between 0.1 and 10.0")

    if not (1 <= args.inference_timesteps <= 100):
        parser.error("--inference-timesteps must be between 1 and 100")

    if args.lora_r <= 0:
        parser.error("--lora-r must be a positive integer")

    if args.lora_alpha <= 0:
        parser.error("--lora-alpha must be a positive integer")

    if not (0.0 <= args.lora_dropout <= 1.0):
        parser.error("--lora-dropout must be between 0.0 and 1.0")


# -----------------------------
# Model loading
# -----------------------------

def load_model(args) -> VoxCPM:
    print("Loading VoxCPM model...", file=sys.stderr)

    zipenhancer_path = getattr(args, "zipenhancer_path", None) or os.environ.get(
        "ZIPENHANCER_MODEL_PATH", None
    )

    # Build LoRA config if provided
    lora_config = None
    lora_weights_path = getattr(args, "lora_path", None)
    if lora_weights_path:
        from voxcpm.model.voxcpm import LoRAConfig

        lora_config = LoRAConfig(
            enable_lm=not args.lora_disable_lm,
            enable_dit=not args.lora_disable_dit,
            enable_proj=args.lora_enable_proj,
            r=args.lora_r,
            alpha=args.lora_alpha,
            dropout=args.lora_dropout,
        )

        print(
            f"LoRA config: r={lora_config.r}, alpha={lora_config.alpha}, "
            f"lm={lora_config.enable_lm}, dit={lora_config.enable_dit}, proj={lora_config.enable_proj}",
            file=sys.stderr,
        )

    # Load local model if specified
    if args.model_path:
        try:
            model = VoxCPM(
                voxcpm_model_path=args.model_path,
                zipenhancer_model_path=zipenhancer_path,
                enable_denoiser=not args.no_denoiser,
                lora_config=lora_config,
                lora_weights_path=lora_weights_path,
            )
            print("Model loaded (local).", file=sys.stderr)
            return model
        except Exception as e:
            print(f"Failed to load model (local): {e}", file=sys.stderr)
            sys.exit(1)

    # Load from Hugging Face Hub
    try:
        model = VoxCPM.from_pretrained(
            hf_model_id=args.hf_model_id,
            load_denoiser=not args.no_denoiser,
            zipenhancer_model_id=zipenhancer_path,
            cache_dir=args.cache_dir,
            local_files_only=args.local_files_only,
            lora_config=lora_config,
            lora_weights_path=lora_weights_path,
        )
        print("Model loaded (from_pretrained).", file=sys.stderr)
        return model
    except Exception as e:
        print(f"Failed to load model (from_pretrained): {e}", file=sys.stderr)
        sys.exit(1)


# -----------------------------
# Commands
# -----------------------------

def cmd_clone(args):
    if not args.text:
        sys.exit("Error: Please provide --text for synthesis")

    if not args.prompt_audio or not args.prompt_text:
        sys.exit("Error: Voice cloning requires both --prompt-audio and --prompt-text")

    prompt_audio_path = validate_file_exists(args.prompt_audio, "reference audio file")
    output_path = validate_output_path(args.output)

    model = load_model(args)

    audio_array = model.generate(
        text=args.text,
        prompt_wav_path=str(prompt_audio_path),
        prompt_text=args.prompt_text,
        cfg_value=args.cfg_value,
        inference_timesteps=args.inference_timesteps,
        normalize=args.normalize,
        denoise=args.denoise,
    )

    sf.write(str(output_path), audio_array, model.tts_model.sample_rate)

    duration = len(audio_array) / model.tts_model.sample_rate
    print(f"Saved audio to: {output_path} ({duration:.2f}s)", file=sys.stderr)


def cmd_synthesize(args):
    if not args.text:
        sys.exit("Error: Please provide --text for synthesis")

    output_path = validate_output_path(args.output)
    model = load_model(args)

    audio_array = model.generate(
        text=args.text,
        prompt_wav_path=None,
        prompt_text=None,
        cfg_value=args.cfg_value,
        inference_timesteps=args.inference_timesteps,
        normalize=args.normalize,
        denoise=False,
    )

    sf.write(str(output_path), audio_array, model.tts_model.sample_rate)

    duration = len(audio_array) / model.tts_model.sample_rate
    print(f"Saved audio to: {output_path} ({duration:.2f}s)", file=sys.stderr)


def cmd_batch(args):
    input_file = validate_file_exists(args.input, "input file")
    output_dir = Path(args.output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)

    with open(input_file, "r", encoding="utf-8") as f:
        texts = [line.strip() for line in f if line.strip()]

    if not texts:
        sys.exit("Error: Input file is empty")

    model = load_model(args)

    prompt_audio_path = None
    if args.prompt_audio:
        prompt_audio_path = str(validate_file_exists(args.prompt_audio, "reference audio file"))

    success_count = 0

    for i, text in enumerate(texts, 1):
        try:
            audio_array = model.generate(
                text=text,
                prompt_wav_path=prompt_audio_path,
                prompt_text=args.prompt_text,
                cfg_value=args.cfg_value,
                inference_timesteps=args.inference_timesteps,
                normalize=args.normalize,
                denoise=args.denoise and prompt_audio_path is not None,
            )

            output_file = output_dir / f"output_{i:03d}.wav"
            sf.write(str(output_file), audio_array, model.tts_model.sample_rate)

            duration = len(audio_array) / model.tts_model.sample_rate
            print(f"Saved: {output_file} ({duration:.2f}s)", file=sys.stderr)
            success_count += 1

        except Exception as e:
            print(f"Failed on line {i}: {e}", file=sys.stderr)

    print(f"\nBatch finished: {success_count}/{len(texts)} succeeded", file=sys.stderr)


# -----------------------------
# Parser
# -----------------------------

def _build_unified_parser():
    parser = argparse.ArgumentParser(
        description="VoxCPM CLI - voice cloning, direct TTS, and batch processing",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
Examples:
  voxcpm --text "Hello world" --output out.wav
  voxcpm --text "Hello" --prompt-audio ref.wav --prompt-text "hi" --output out.wav --denoise
  voxcpm --input texts.txt --output-dir ./outs
        """,
    )

    # Mode selection
    parser.add_argument("--input", "-i", help="Input text file (batch mode only)")
    parser.add_argument("--output-dir", "-od", help="Output directory (batch mode only)")
    parser.add_argument("--text", "-t", help="Text to synthesize (single or clone mode)")
    parser.add_argument("--output", "-o", help="Output audio file path (single or clone mode)")

    # Prompt
    parser.add_argument("--prompt-audio", "-pa", help="Reference audio file path (clone mode)")
    parser.add_argument("--prompt-text", "-pt", help="Reference text corresponding to the audio")
    parser.add_argument("--denoise", action="store_true", help="Enable prompt speech enhancement")

    # Generation parameters
    parser.add_argument("--cfg-value", type=float, default=2.0,
                        help="CFG guidance scale (float, recommended 0.5–5.0, default: 2.0)")
    parser.add_argument("--inference-timesteps", type=int, default=10,
                        help="Inference steps (int, 1–100, default: 10)")
    parser.add_argument("--normalize", action="store_true", help="Enable text normalization")

    # Model loading
    parser.add_argument("--model-path", type=str, help="Local VoxCPM model path")
    parser.add_argument("--hf-model-id", type=str, default="openbmb/VoxCPM1.5",
                        help="Hugging Face repo id (default: openbmb/VoxCPM1.5)")
    parser.add_argument("--cache-dir", type=str, help="Cache directory for Hub downloads")
    parser.add_argument("--local-files-only", action="store_true", help="Disable network access")
    parser.add_argument("--no-denoiser", action="store_true", help="Disable denoiser model loading")
    parser.add_argument("--zipenhancer-path", type=str,
                        help="ZipEnhancer model id or local path (or env ZIPENHANCER_MODEL_PATH)")

    # LoRA
    parser.add_argument("--lora-path", type=str, help="Path to LoRA weights")
    parser.add_argument("--lora-r", type=int, default=32, help="LoRA rank (positive int, default: 32)")
    parser.add_argument("--lora-alpha", type=int, default=16, help="LoRA alpha (positive int, default: 16)")
    parser.add_argument("--lora-dropout", type=float, default=0.0,
                        help="LoRA dropout rate (0.0–1.0, default: 0.0)")
    parser.add_argument("--lora-disable-lm", action="store_true", help="Disable LoRA on LM layers")
    parser.add_argument("--lora-disable-dit", action="store_true", help="Disable LoRA on DiT layers")
    parser.add_argument("--lora-enable-proj", action="store_true", help="Enable LoRA on projection layers")

    return parser


# -----------------------------
# Entrypoint
# -----------------------------

def main():
    parser = _build_unified_parser()
    args = parser.parse_args()

    # Validate ranges
    validate_ranges(args, parser)

    # Mode conflict checks
    if args.input and args.text:
        parser.error("Use either batch mode (--input) or single mode (--text), not both.")

    # Batch mode
    if args.input:
        if not args.output_dir:
            parser.error("Batch mode requires --output-dir")
        return cmd_batch(args)

    # Single mode
    if not args.text or not args.output:
        parser.error("Single-sample mode requires --text and --output")

    # Clone mode
    if args.prompt_audio or args.prompt_text:
        return cmd_clone(args)

    # Direct synthesis
    return cmd_synthesize(args)


if __name__ == "__main__":
    main()