refactor: rename canto-backend → backend, canto-frontend → frontend

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/indextts/utils/common.py

@@ -0,0 +1,121 @@
+import os
+import random
+import re
+
+import torch
+import torchaudio
+
+MATPLOTLIB_FLAG = False
+
+
+def load_audio(audiopath, sampling_rate):
+    audio, sr = torchaudio.load(audiopath)
+    # print(f"wave shape: {audio.shape}, sample_rate: {sr}")
+
+    if audio.size(0) > 1:  # mix to mono
+        audio = audio[0].unsqueeze(0)
+
+    if sr != sampling_rate:
+        try:
+            audio = torchaudio.functional.resample(audio, sr, sampling_rate)
+        except Exception as e:
+            print(f"Warning: {audiopath}, wave shape: {audio.shape}, sample_rate: {sr}")
+            return None
+    # clip audio invalid values
+    audio.clip_(-1, 1)
+    return audio
+
+
+def tokenize_by_CJK_char(line: str, do_upper_case=True) -> str:
+    """
+    Tokenize a line of text with CJK char.
+
+    Note: All return charaters will be upper case.
+
+    Example:
+      input = "你好世界是 hello world 的中文"
+      output = "你 好 世 界 是 HELLO WORLD 的 中 文"
+
+    Args:
+      line:
+        The input text.
+
+    Return:
+      A new string tokenize by CJK char.
+    """
+    # The CJK ranges is from https://github.com/alvations/nltk/blob/79eed6ddea0d0a2c212c1060b477fc268fec4d4b/nltk/tokenize/util.py
+    CJK_RANGE_PATTERN = (
+        r"([\u1100-\u11ff\u2e80-\ua4cf\ua840-\uD7AF\uF900-\uFAFF\uFE30-\uFE4F\uFF65-\uFFDC\U00020000-\U0002FFFF])"
+    )
+    chars = re.split(CJK_RANGE_PATTERN, line.strip())
+    return " ".join([w.strip().upper() if do_upper_case else w.strip() for w in chars if w.strip()])
+
+
+def de_tokenized_by_CJK_char(line: str, do_lower_case=False) -> str:
+    """
+    Example:
+      input = "你 好 世 界 是 HELLO WORLD 的 中 文"
+      output = "你好世界是 hello world 的中文"
+
+    do_lower_case:
+      input = "SEE YOU!"
+      output = "see you!"
+    """
+    # replace english words in the line with placeholders
+    english_word_pattern = re.compile(r"([A-Z]+(?:[\s-][A-Z-]+)*)", re.IGNORECASE)
+    english_sents = english_word_pattern.findall(line)
+    for i, sent in enumerate(english_sents):
+        line = line.replace(sent, f"<sent_{i}>")
+
+    words = line.split()
+    # restore english sentences
+    sent_placeholder_pattern = re.compile(r"^.*?(<sent_(\d+)>)")
+    for i in range(len(words)):
+        m = sent_placeholder_pattern.match(words[i])
+        if m:
+            # restore the english word
+            placeholder_index = int(m.group(2))
+            words[i] = words[i].replace(m.group(1), english_sents[placeholder_index])
+            if do_lower_case:
+                words[i] = words[i].lower()
+    return "".join(words)
+
+
+def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
+    """Make mask tensor containing indices of padded part.
+
+    See description of make_non_pad_mask.
+
+    Args:
+        lengths (torch.Tensor): Batch of lengths (B,).
+    Returns:
+        torch.Tensor: Mask tensor containing indices of padded part.
+
+    Examples:
+        >>> lengths = [5, 3, 2]
+        >>> make_pad_mask(lengths)
+        masks = [[0, 0, 0, 0 ,0],
+                 [0, 0, 0, 1, 1],
+                 [0, 0, 1, 1, 1]]
+    """
+    batch_size = lengths.size(0)
+    max_len = max_len if max_len > 0 else lengths.max().item()
+    seq_range = torch.arange(0, max_len, dtype=torch.int64, device=lengths.device)
+    seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
+    seq_length_expand = lengths.unsqueeze(-1)
+    mask = seq_range_expand >= seq_length_expand
+    return mask
+
+
+def safe_log(x: torch.Tensor, clip_val: float = 1e-7) -> torch.Tensor:
+    """
+    Computes the element-wise logarithm of the input tensor with clipping to avoid near-zero values.
+
+    Args:
+        x (Tensor): Input tensor.
+        clip_val (float, optional): Minimum value to clip the input tensor. Defaults to 1e-7.
+
+    Returns:
+        Tensor: Element-wise logarithm of the input tensor with clipping applied.
+    """
+    return torch.log(torch.clip(x, min=clip_val))