Datasets:
File size: 3,760 Bytes
f0d6662 3f72b81 |
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 |
import os
import torch
import pandas as pd
import torchaudio
from torch.utils.data import Dataset
from typing import List, Optional
class Libris2sDataset(torch.utils.data.Dataset):
def __init__(self, data_dir: str, split: str, transform=None, book_ids: Optional[List[str]]=None):
"""
Initialize the LibriS2S dataset.
Args:
data_dir (str): Root directory containing the dataset
split (str): Path to the CSV file containing alignments
transform (callable, optional): Optional transform to be applied on the audio
book_ids (List[str], optional): List of book IDs to include. If None, includes all books.
Example: ['9', '10', '11'] will only load these books.
"""
self.data_dir = data_dir
self.transform = transform
self.book_ids = set(book_ids) if book_ids is not None else None
# Load alignment CSV file
self.alignments = pd.read_csv(split)
# Create lists to store paths and metadata
self.de_audio_paths = []
self.en_audio_paths = []
self.de_transcripts = []
self.en_transcripts = []
self.alignment_scores = []
# Process each entry in the alignments
for _, row in self.alignments.iterrows():
# Get book ID from the path
book_id = str(row['book_id'])
# Skip if book_id is not in the filtered set
if self.book_ids is not None and book_id not in self.book_ids:
continue
# Get full paths from CSV
de_audio = os.path.join(data_dir, row['DE_audio'])
en_audio = os.path.join(data_dir, row['EN_audio'])
# Only add if both audio files exist
if os.path.exists(de_audio) and os.path.exists(en_audio):
self.de_audio_paths.append(de_audio)
self.en_audio_paths.append(en_audio)
self.de_transcripts.append(row['DE_transcript'])
self.en_transcripts.append(row['EN_transcript'])
self.alignment_scores.append(float(row['score']))
else:
print(f"Skipping {de_audio} or {en_audio} because they don't exist")
def __len__(self):
"""Return the number of items in the dataset."""
return len(self.de_audio_paths)
def __getitem__(self, idx):
"""
Get a single item from the dataset.
Args:
idx (int): Index of the item to get
Returns:
dict: A dictionary containing:
- de_audio: German audio waveform
- de_sample_rate: German audio sample rate
- en_audio: English audio waveform
- en_sample_rate: English audio sample rate
- de_transcript: German transcript
- en_transcript: English transcript
- alignment_score: Alignment score between the pair
"""
# Load audio files
de_audio, de_sr = torchaudio.load(self.de_audio_paths[idx])
en_audio, en_sr = torchaudio.load(self.en_audio_paths[idx])
# Apply transforms if specified
if self.transform:
de_audio = self.transform(de_audio)
en_audio = self.transform(en_audio)
return {
'de_audio': de_audio,
'de_sample_rate': de_sr,
'en_audio': en_audio,
'en_sample_rate': en_sr,
'de_transcript': self.de_transcripts[idx],
'en_transcript': self.en_transcripts[idx],
'alignment_score': self.alignment_scores[idx]
} |