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import os
from typing import Dict, Literal, Tuple
import ffmpeg
import numpy as np
import pandas as pd
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms
from bytecover.models.data_model import BatchDict
from bytecover.utils import bcolors
class ByteCoverDataset(Dataset):
def __init__(
self,
data_path: str,
file_ext: str,
dataset_path: str,
data_split: Literal["TRAIN", "VAL", "TEST"],
debug: bool,
target_sr: int,
max_len: int,
) -> None:
super().__init__()
self.data_path = data_path
self.file_ext = file_ext
self.dataset_path = dataset_path
self.data_split = data_split
self.debug = debug
self.target_sr = target_sr
self.max_len = max_len
self._load_data()
self.pipeline = transforms.Compose([self._read_audio, self._pad_or_trim_audio])
def __len__(self) -> int:
return len(self.track_ids)
def __getitem__(self, index: int) -> BatchDict:
track_id = self.track_ids[index]
anchor_audio = self.pipeline(track_id)
clique_id, pos_id, neg_id = self._triplet_sampling(track_id)
if self.data_split == "TRAIN":
positive_audio = self.pipeline(pos_id)
negative_audio = self.pipeline(neg_id)
else:
positive_audio = torch.empty(0)
negative_audio = torch.empty(0)
return dict(
anchor_id=track_id,
anchor=anchor_audio,
anchor_label=torch.tensor(clique_id, dtype=torch.float),
positive_id=pos_id,
positive=positive_audio,
negative_id=neg_id,
negative=negative_audio,
)
def _triplet_sampling(self, track_id: str) -> Tuple[int, str, str]:
clique_id = self.labels.loc[track_id, "clique"]
versions = self.versions.loc[clique_id, "versions"]
np.random.shuffle(versions)
pos_list = np.setdiff1d(versions, track_id)
pos_id = np.random.choice(pos_list, 1)[0]
neg_id = self.labels[~self.labels.index.isin(versions)].sample(1).index[0]
return (clique_id, pos_id, neg_id)
def _load_data(self) -> None:
self.track_ids = np.load(
os.path.join(self.data_path, "splits", f"{self.data_split.lower()}_ids.npy"), allow_pickle=True
)
self.labels = pd.read_csv(os.path.join(self.data_path, "interim", "shs100k.csv"), usecols=["clique", "id"])
self.labels = self.labels[self.labels["id"].isin(self.track_ids)]
self.labels.dropna(inplace=True)
self.labels.set_index("id", inplace=True)
cliques = self.labels["clique"].unique()
mapping = {}
for k, clique in enumerate(cliques):
mapping[clique] = k
self.labels["clique"] = self.labels["clique"].map(lambda x: mapping[x])
self.versions = pd.read_csv(
os.path.join(self.data_path, "interim", "versions.csv"), converters={"versions": eval}
)
self.versions.dropna(inplace=True)
self.versions = self.versions[self.versions["clique"].isin(cliques)]
self.versions["clique"] = self.versions["clique"].map(lambda x: mapping[x])
self.versions.set_index("clique", inplace=True)
def _read_audio(self, track_id: str) -> torch.Tensor:
if self.debug:
seq_len = np.random.randint(10, 200) if self.max_len <= 0 else self.max_len
return torch.rand(seq_len * self.target_sr)
filename = os.path.join(self.dataset_path, f"{track_id}.{self.file_ext}")
try:
# This launches a subprocess to decode audio while down-mixing and resampling as necessary.
# Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
out, _ = (
ffmpeg.input(filename, threads=0)
.output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=self.target_sr)
.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
)
except ffmpeg.Error as e:
raise RuntimeError(
f"{bcolors.WARNING}Failed to load audio:{bcolors.FAIL + filename + bcolors.ENDC}\n{e.stderr.decode()}"
) from e
# int16 ranges between -2^15 and +2^15 (±32768). By convention, floating point audio data is
# normalized to the range of [-1.0, 1.0]
audio = np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
return torch.from_numpy(audio)
def _pad_or_trim_audio(self, audio: torch.Tensor) -> torch.Tensor:
if self.max_len <= 0:
return audio
if (self.data_split == "TRAIN") and (audio.shape[-1] <= self.max_len * self.target_sr):
return F.pad(audio, (0, self.max_len * self.target_sr - audio.shape[-1]))
max_offset = audio.shape[-1] - self.max_len * self.target_sr
offset = np.random.randint(max_offset) if max_offset > 0 else 0
return audio[offset : (offset + self.max_len * self.target_sr)]
def bytecover_dataloader(
data_path: str,
file_ext: str,
dataset_path: str,
data_split: Literal["TRAIN", "VAL", "TEST"],
debug: bool,
max_len: int,
batch_size: int,
target_sr: int,
**config: Dict,
) -> DataLoader:
return DataLoader(
ByteCoverDataset(data_path, file_ext, dataset_path, data_split, debug, target_sr=target_sr, max_len=max_len),
batch_size=batch_size if max_len > 0 else 1,
num_workers=config["num_workers"],
shuffle=config["shuffle"],
drop_last=config["drop_last"],
)
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