diff --git a/acestep/music_dcae/music_dcae_pipeline.py b/acestep/music_dcae/music_dcae_pipeline.py
index 8112638..af23ed8 100644
--- a/acestep/music_dcae/music_dcae_pipeline.py
+++ b/acestep/music_dcae/music_dcae_pipeline.py
@@ -10,6 +10,7 @@ import os
 import torch
 from diffusers import AutoencoderDC
 import torchaudio
+import soundfile as sf
 import torchvision.transforms as transforms
 from diffusers.models.modeling_utils import ModelMixin
 from diffusers.loaders import FromOriginalModelMixin
@@ -60,7 +61,11 @@ class MusicDCAE(ModelMixin, ConfigMixin, FromOriginalModelMixin):
         self.shift_factor = -1.9091
 
     def load_audio(self, audio_path):
-        audio, sr = torchaudio.load(audio_path)
+        # Read with soundfile rather than torchaudio.load(): since torchaudio
+        # 2.11 the latter routes I/O through TorchCodec, an extra native
+        # dependency we do not require.
+        data, sr = sf.read(audio_path, dtype="float32", always_2d=True)
+        audio = torch.from_numpy(data.T)
         if audio.shape[0] == 1:
             audio = audio.repeat(2, 1)
         return audio, sr
@@ -362,7 +367,8 @@ class MusicDCAE(ModelMixin, ConfigMixin, FromOriginalModelMixin):
 
 if __name__ == "__main__":
 
-    audio, sr = torchaudio.load("test.wav")
+    _data, sr = sf.read("test.wav", dtype="float32", always_2d=True)
+    audio = torch.from_numpy(_data.T)
     audio_lengths = torch.tensor([audio.shape[1]])
     audios = audio.unsqueeze(0)
 
@@ -378,5 +384,5 @@ if __name__ == "__main__":
     print("latents shape: ", latents.shape)
     print("latent_lengths: ", latent_lengths)
     print("sr: ", sr)
-    torchaudio.save("test_reconstructed.wav", pred_wavs[0], sr)
+    sf.write("test_reconstructed.wav", pred_wavs[0].float().cpu().transpose(0, 1).numpy(), sr)
     print("test_reconstructed.wav")
diff --git a/acestep/pipeline_ace_step.py b/acestep/pipeline_ace_step.py
index a734d1a..552a2a1 100644
--- a/acestep/pipeline_ace_step.py
+++ b/acestep/pipeline_ace_step.py
@@ -12,6 +12,7 @@ import os
 import re
 
 import torch
+import soundfile as sf
 from loguru import logger
 from tqdm import tqdm
 import json
@@ -46,7 +47,6 @@ from acestep.apg_guidance import (
     cfg_zero_star,
     cfg_double_condition_forward,
 )
-import torchaudio
 from .cpu_offload import cpu_offload
 
 
@@ -1405,13 +1405,17 @@ class ACEStepPipeline:
             else:
                 output_path_wav = save_path
 
-        target_wav = target_wav.float()
-        backend = "soundfile"
-        if format == "ogg":
-            backend = "sox"
-        logger.info(f"Saving audio to {output_path_wav} using backend {backend}")
-        torchaudio.save(
-            output_path_wav, target_wav, sample_rate=sample_rate, format=format, backend=backend
+        target_wav = target_wav.float().cpu()
+        logger.info(f"Saving audio to {output_path_wav}")
+        # Write with soundfile rather than torchaudio.save(): since torchaudio
+        # 2.11 the latter ignores the `backend` argument and routes everything
+        # through TorchCodec, an extra native dependency we do not require.
+        # soundfile expects (samples, channels), torch tensors are (channels, samples).
+        sf.write(
+            output_path_wav,
+            target_wav.transpose(0, 1).numpy(),
+            sample_rate,
+            format=format.upper(),
         )
         return output_path_wav
 
diff --git a/acestep/text2music_dataset.py b/acestep/text2music_dataset.py
index 0a5b307..0a964c4 100644
--- a/acestep/text2music_dataset.py
+++ b/acestep/text2music_dataset.py
@@ -7,6 +7,7 @@ from loguru import logger
 import time
 import traceback
 import torchaudio
+import soundfile as sf
 from pathlib import Path
 import re
 from acestep.language_segmentation import LangSegment
@@ -398,7 +399,10 @@ class Text2MusicDataset(Dataset):
         filename = item["filename"]
         sr = 48000
         try:
-            audio, sr = torchaudio.load(filename)
+            # soundfile instead of torchaudio.load(): torchaudio 2.11 routes
+            # I/O through TorchCodec, an extra native dependency.
+            _data, sr = sf.read(filename, dtype="float32", always_2d=True)
+            audio = torch.from_numpy(_data.T)
         except Exception as e:
             logger.error(f"Failed to load audio {item}: {e}")
             return None