Initial commit

zipvoice/utils/feature.py

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+#!/usr/bin/env python3
+# Copyright         2024  Xiaomi Corp.        (authors: Han Zhu)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+from typing import Union
+
+import numpy as np
+import torch
+import torchaudio
+from lhotse.features.base import FeatureExtractor, register_extractor
+from lhotse.utils import Seconds, compute_num_frames
+
+
+@dataclass
+class VocosFbankConfig:
+    sampling_rate: int = 24000
+    n_mels: int = 100
+    n_fft: int = 1024
+    hop_length: int = 256
+
+
+@register_extractor
+class VocosFbank(FeatureExtractor):
+
+    name = "VocosFbank"
+    config_type = VocosFbankConfig
+
+    def __init__(self, num_channels: int = 1):
+        config = VocosFbankConfig
+        super().__init__(config=config)
+        assert num_channels in (1, 2)
+        self.num_channels = num_channels
+        self.fbank = torchaudio.transforms.MelSpectrogram(
+            sample_rate=self.config.sampling_rate,
+            n_fft=self.config.n_fft,
+            hop_length=self.config.hop_length,
+            n_mels=self.config.n_mels,
+            center=True,
+            power=1,
+        )
+
+    def _feature_fn(self, sample):
+        mel = self.fbank(sample)
+        logmel = mel.clamp(min=1e-7).log()
+
+        return logmel
+
+    @property
+    def device(self) -> Union[str, torch.device]:
+        return self.config.device
+
+    def feature_dim(self, sampling_rate: int) -> int:
+        return self.config.n_mels
+
+    def extract(
+        self,
+        samples: Union[np.ndarray, torch.Tensor],
+        sampling_rate: int,
+    ) -> Union[np.ndarray, torch.Tensor]:
+        # Check for sampling rate compatibility.
+        expected_sr = self.config.sampling_rate
+        assert sampling_rate == expected_sr, (
+            f"Mismatched sampling rate: extractor expects {expected_sr}, "
+            f"got {sampling_rate}"
+        )
+        is_numpy = False
+        if not isinstance(samples, torch.Tensor):
+            samples = torch.from_numpy(samples)
+            is_numpy = True
+
+        if len(samples.shape) == 1:
+            samples = samples.unsqueeze(0)
+        else:
+            assert samples.ndim == 2, samples.shape
+
+        if self.num_channels == 1:
+            if samples.shape[0] == 2:
+                samples = samples.mean(dim=0, keepdims=True)
+        else:
+            assert samples.shape[0] == 2, samples.shape
+
+        mel = self._feature_fn(samples)
+        # (1, n_mels, time) or (2, n_mels, time)
+        mel = mel.reshape(-1, mel.shape[-1]).t()
+        # (time, n_mels) or (time, 2 * n_mels)
+
+        num_frames = compute_num_frames(
+            samples.shape[1] / sampling_rate, self.frame_shift, sampling_rate
+        )
+
+        if mel.shape[0] > num_frames:
+            mel = mel[:num_frames]
+        elif mel.shape[0] < num_frames:
+            mel = mel.unsqueeze(0)
+            mel = torch.nn.functional.pad(
+                mel, (0, 0, 0, num_frames - mel.shape[1]), mode="replicate"
+            ).squeeze(0)
+
+        if is_numpy:
+            return mel.cpu().numpy()
+        else:
+            return mel
+
+    @property
+    def frame_shift(self) -> Seconds:
+        return self.config.hop_length / self.config.sampling_rate