Spaces:
Sleeping
Sleeping
File size: 5,592 Bytes
32b2aaa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 |
import logging
import random
from pathlib import Path
import numpy as np
import torch
import torchaudio
import torchaudio.functional as AF
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import Dataset as DatasetBase
from ..hparams import HParams
from .distorter import Distorter
from .utils import rglob_audio_files
logger = logging.getLogger(__name__)
def _normalize(x):
return x / (np.abs(x).max() + 1e-7)
def _collate(batch, key, tensor=True, pad=True):
l = [d[key] for d in batch]
if l[0] is None:
return None
if tensor:
l = [torch.from_numpy(x) for x in l]
if pad:
assert tensor, "Can't pad non-tensor"
l = pad_sequence(l, batch_first=True)
return l
def praat_augment(wav, sr):
try:
import parselmouth
except ImportError:
raise ImportError("Please install parselmouth>=0.5.0 to use Praat augmentation")
# "praat-parselmouth @ git+https://github.com/YannickJadoul/Parselmouth@0bbcca69705ed73322f3712b19d71bb3694b2540",
# https://github.com/YannickJadoul/Parselmouth/issues/68
# note that this function may hang if the praat version is 0.4.3
assert wav.ndim == 1, f"wav.ndim must be 1 but got {wav.ndim}"
sound = parselmouth.Sound(wav, sr)
formant_shift_ratio = random.uniform(1.1, 1.5)
pitch_range_factor = random.uniform(0.5, 2.0)
sound = parselmouth.praat.call(sound, "Change gender", 75, 600, formant_shift_ratio, 0, pitch_range_factor, 1.0)
wav = np.array(sound.values)[0].astype(np.float32)
return wav
class Dataset(DatasetBase):
def __init__(
self,
fg_paths: list[Path],
hp: HParams,
training=True,
max_retries=100,
silent_fg_prob=0.01,
mode=False,
):
super().__init__()
assert mode in ("enhancer", "denoiser"), f"Invalid mode: {mode}"
self.hp = hp
self.fg_paths = fg_paths
self.bg_paths = rglob_audio_files(hp.bg_dir)
if len(self.fg_paths) == 0:
raise ValueError(f"No foreground audio files found in {hp.fg_dir}")
if len(self.bg_paths) == 0:
raise ValueError(f"No background audio files found in {hp.bg_dir}")
logger.info(f"Found {len(self.fg_paths)} foreground files and {len(self.bg_paths)} background files")
self.training = training
self.max_retries = max_retries
self.silent_fg_prob = silent_fg_prob
self.mode = mode
self.distorter = Distorter(hp, training=training, mode=mode)
def _load_wav(self, path, length=None, random_crop=True):
wav, sr = torchaudio.load(path)
wav = AF.resample(
waveform=wav,
orig_freq=sr,
new_freq=self.hp.wav_rate,
lowpass_filter_width=64,
rolloff=0.9475937167399596,
resampling_method="sinc_interp_kaiser",
beta=14.769656459379492,
)
wav = wav.float().numpy()
if wav.ndim == 2:
wav = np.mean(wav, axis=0)
if length is None and self.training:
length = int(self.hp.training_seconds * self.hp.wav_rate)
if length is not None:
if random_crop:
start = random.randint(0, max(0, len(wav) - length))
wav = wav[start : start + length]
else:
wav = wav[:length]
if length is not None and len(wav) < length:
wav = np.pad(wav, (0, length - len(wav)))
wav = _normalize(wav)
return wav
def _getitem_unsafe(self, index: int):
fg_path = self.fg_paths[index]
if self.training and random.random() < self.silent_fg_prob:
fg_wav = np.zeros(int(self.hp.training_seconds * self.hp.wav_rate), dtype=np.float32)
else:
fg_wav = self._load_wav(fg_path)
if random.random() < self.hp.praat_augment_prob and self.training:
fg_wav = praat_augment(fg_wav, self.hp.wav_rate)
if self.hp.load_fg_only:
bg_wav = None
fg_dwav = None
bg_dwav = None
else:
fg_dwav = _normalize(self.distorter(fg_wav, self.hp.wav_rate)).astype(np.float32)
if self.training:
bg_path = random.choice(self.bg_paths)
else:
# Deterministic for validation
bg_path = self.bg_paths[index % len(self.bg_paths)]
bg_wav = self._load_wav(bg_path, length=len(fg_wav), random_crop=self.training)
bg_dwav = _normalize(self.distorter(bg_wav, self.hp.wav_rate)).astype(np.float32)
return dict(
fg_wav=fg_wav,
bg_wav=bg_wav,
fg_dwav=fg_dwav,
bg_dwav=bg_dwav,
)
def __getitem__(self, index: int):
for i in range(self.max_retries):
try:
return self._getitem_unsafe(index)
except Exception as e:
if i == self.max_retries - 1:
raise RuntimeError(f"Failed to load {self.fg_paths[index]} after {self.max_retries} retries") from e
logger.debug(f"Error loading {self.fg_paths[index]}: {e}, skipping")
index = np.random.randint(0, len(self))
def __len__(self):
return len(self.fg_paths)
@staticmethod
def collate_fn(batch):
return dict(
fg_wavs=_collate(batch, "fg_wav"),
bg_wavs=_collate(batch, "bg_wav"),
fg_dwavs=_collate(batch, "fg_dwav"),
bg_dwavs=_collate(batch, "bg_dwav"),
)
|