replace soundfile with librosa

offline_pipeline.py

@@ -3,7 +3,7 @@ from typing import Any
 
 import gradio as gr
 import numpy as np
-import soundfile as sf
+import librosa 
 
 from constants import APP_TMP_DIR, STREAMER_CLASSES
 from hf_dataset_utils import get_audio, get_transcript
@@ -166,9 +166,7 @@ def run_offline_pipeline(
     if sample is None or len(sample) == 0:
         gr.Warning("No audio to enhance. Please upload a file first.")
         return _empty_pipeline_result(sample_id)
-
-    sample = np.asarray(sample, dtype=np.float32).flatten()
-
+    
     _safe_progress(progress, 0.05, "Initializing enhancement...")
     chunk_size = _init_sdk(sample_rate, enhancement_level)
 
@@ -249,10 +247,11 @@ def load_local_file(
         raise ValueError("Uploaded file exceeds the 5 MB size limit.")
 
     new_sample_stem = os.path.splitext(os.path.basename(sample_path))[0]
-    y, sample_rate = sf.read(sample_path, dtype="float32", always_2d=False)
+    y, sample_rate = librosa.load(sample_path, sr=None, mono=True)
+    sample_rate = int(sample_rate)
+    y = np.asarray(y, dtype=np.float32)
     if normalize:
         y = normalize_lufs(y, sample_rate)
-
     gradio_audio = to_gradio_audio(y, sample_rate)
     return y, new_sample_stem, gradio_audio, sample_rate
 
@@ -268,9 +267,11 @@ def load_file_from_dataset(
 
     try:
         y, sample_rate = get_audio(sample_id, prefix="mix")
-        y_for_gradio = to_gradio_audio(y, sample_rate)
     except Exception as e:
         gr.Warning(str(e))
         raise
-
-    return y_for_gradio, y, new_sample_stem, sample_rate
+    y = np.asarray(y, dtype=np.float32)
+    if y.ndim > 1:
+        y = np.mean(y, axis=0)
+    gradio_audio = to_gradio_audio(y, sample_rate)
+    return gradio_audio, y, new_sample_stem, sample_rate