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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
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