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Running
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Zero
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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"),
)
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