codec/scripts/infer_zflowae.py

import argparse
from pathlib import Path

import librosa
import soundfile as sf
import torch
from safetensors import safe_open
from safetensors.torch import save_file
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm

from zcodec.models import ZFlowAutoEncoder


class AudioDataset(Dataset):
    def __init__(self, file_list, target_sr):
        self.paths = file_list
        self.target_sr = target_sr

    def __len__(self):
        return len(self.paths)

    def __getitem__(self, idx):
        path = self.paths[idx]
        wav, sr = sf.read(str(path))
        if sr != self.target_sr:
            wav = librosa.resample(wav, orig_sr=sr, target_sr=self.target_sr)
        wav = torch.tensor(wav).unsqueeze(0).float()  # shape: [1, T]
        return wav, path


class SafeTensorDataset(Dataset):
    """
    On __getitem__, opens the safetensor, uses get_slice() to inspect shape,
    then either drops too-short files (return None) or returns a random subsequence slice.
    """

    def __init__(
        self,
        file_paths: list[str],
        key: str = "audio_z",
    ):
        self.file_paths = file_paths
        self.key = key

    def __len__(self):
        return len(self.file_paths)

    def __getitem__(self, idx: int) -> torch.Tensor | None:
        path = self.file_paths[idx]
        # open file, get a slice wrapper for full tensor
        with safe_open(path, framework="pt") as f:
            tensor = f.get_tensor(self.key)

        return tensor, path


@torch.no_grad()
def encode_batch(model, batch, device, out_dir, save_latent=False):
    wavs, paths = batch
    for wav, path in zip(wavs, paths):
        wav = wav.to(device)
        latent, indices = model.encode(wav)
        if save_latent:
            to_save = latent.cpu()
        else:
            to_save = indices.cpu()
        out_path = out_dir / (path.stem + ".st")
        save_file({"audio_z": to_save}, str(out_path))


def main():
    parser = argparse.ArgumentParser(
        description="Batch encode audio files with ZFlowAutoEncoder."
    )
    parser.add_argument(
        "file_list",
        type=Path,
        help="Text file listing paths to audio files (one per line)",
    )
    parser.add_argument(
        "checkpoint", type=Path, help="Path to ZFlowAutoEncoder checkpoint directory"
    )
    parser.add_argument(
        "output_dir", type=Path, help="Directory to save Safetensors latents"
    )
    parser.add_argument(
        "--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu"
    )

    parser.add_argument("--num_workers", type=int, default=4, help="Num. workers")
    args = parser.parse_args()
    device = torch.device(args.device)

    # Load model
    zflowae = ZFlowAutoEncoder.from_pretrained_local(args.checkpoint)
    zflowae = zflowae.to(device).eval()

    # Prepare dataset and dataloader
    with open(args.file_list, "r") as f:
        file_paths = [Path(line.strip()) for line in f if line.strip()]
    dataset = SafeTensorDataset(file_paths)
    dataloader = DataLoader(
        dataset,
        batch_size=1,
        num_workers=args.num_workers,
        collate_fn=lambda x: list(zip(*x)),
    )

    args.output_dir.mkdir(parents=True, exist_ok=True)

    # Inference loop
    for batch in tqdm(dataloader):
        try:
            encode_batch(zflowae, batch, device, args.output_dir)
        except Exception as e:
            print(f"❌ Batch failed: {e}")


if __name__ == "__main__":
    main()