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utils.py

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+import re
+import librosa
+import torch
+
+def load_audio(wavpath, sr):
+    audio, _ = librosa.load(wavpath, sr=sr, mono=True)
+    return torch.from_numpy(audio).unsqueeze(0)
+
+def normalize_text(text):
+    text = text.lower()
+    text = re.sub(r'["“”‘’]', ' ', text)
+    text = re.sub(r'\s+', ' ', text)
+    return text
+
+def approx_duration_from_text(text, max_duration=30.0):
+    EN_DUR_PER_CHAR = 0.082
+    ZH_DUR_PER_CHAR = 0.21
+    text = re.sub(r"\s+", "", text)
+    num_zh = num_en = num_other = 0
+    for c in text:
+        if "\u4e00" <= c <= "\u9fff":
+            num_zh += 1
+        elif c.isalpha():
+            num_en += 1
+        else:
+            num_other += 1
+    if num_zh > num_en:
+        num_zh += num_other
+    else:
+        num_en += num_other
+    return min(max_duration, num_zh * ZH_DUR_PER_CHAR + num_en * EN_DUR_PER_CHAR)