fix: write and read audio with soundfile instead of torchaudio

torchaudio 2.11 routes torchaudio.save()/load() through TorchCodec and
ignores the `backend` argument, so every generation died at the save
step with "ImportError: TorchCodec is required for save_with_torchcodec"
after the diffusion had already finished. Reference-audio loading
(audio2audio, repaint, extend) and the training dataset loader hit the
same wall.

soundfile is already a required dependency and covers all four output
formats the UI offers, so use it directly rather than pulling in
TorchCodec and its native FFmpeg stack:

- pipeline_ace_step.save_wav_file(): sf.write(), transposing
  (channels, samples) -> (samples, channels); drops the now-unused
  torchaudio import
- MusicDCAE.load_audio() and text2music_dataset: sf.read(dtype=float32,
  always_2d=True), transposed back to (channels, samples)

torchaudio is still used for Resample/MelScale transforms, which are
unaffected.

Verified end to end: 10s generation on an RTX 3060 in 9.7s, output is
valid non-silent 48kHz stereo; load_audio round-trips it; wav/mp3/ogg/
flac all write and read back.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

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