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

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

backend/indextts/utils/feature_extractors.py

@@ -0,0 +1,50 @@
+import torch
+import torchaudio
+from torch import nn
+from indextts.utils.common import safe_log
+
+
+class FeatureExtractor(nn.Module):
+    """Base class for feature extractors."""
+
+    def forward(self, audio: torch.Tensor, **kwargs) -> torch.Tensor:
+        """
+        Extract features from the given audio.
+
+        Args:
+            audio (Tensor): Input audio waveform.
+
+        Returns:
+            Tensor: Extracted features of shape (B, C, L), where B is the batch size,
+                    C denotes output features, and L is the sequence length.
+        """
+        raise NotImplementedError("Subclasses must implement the forward method.")
+
+
+class MelSpectrogramFeatures(FeatureExtractor):
+    def __init__(self, sample_rate=24000, n_fft=1024, hop_length=256, win_length=None,
+                 n_mels=100, mel_fmin=0, mel_fmax=None, normalize=False, padding="center"):
+        super().__init__()
+        if padding not in ["center", "same"]:
+            raise ValueError("Padding must be 'center' or 'same'.")
+        self.padding = padding
+        self.mel_spec = torchaudio.transforms.MelSpectrogram(
+            sample_rate=sample_rate,
+            n_fft=n_fft,
+            hop_length=hop_length,
+            win_length=win_length,
+            power=1,
+            normalized=normalize,
+            f_min=mel_fmin,
+            f_max=mel_fmax,
+            n_mels=n_mels,
+            center=padding == "center",
+        )
+
+    def forward(self, audio, **kwargs):
+        if self.padding == "same":
+            pad = self.mel_spec.win_length - self.mel_spec.hop_length
+            audio = torch.nn.functional.pad(audio, (pad // 2, pad // 2), mode="reflect")
+        mel = self.mel_spec(audio)
+        mel = safe_log(mel)
+        return mel