Upload 4 files

uploaded some new files:
- clean_up_csv.py to clean up the original allignment csv for easier use
- libris2s_dataset.py: a pytorch dataset class as a starting point for easy loading
- a notebook to test the dataset class

clean_up_csv.py

@@ -0,0 +1,42 @@
+import pandas as pd
+import os
+
+# Read the original CSV
+alignment = pd.read_csv("alignments/all_de_en_alligned.csv", index_col=0)
+
+# Create mapping of book numbers to English folder names
+en_folder_map = {}
+for folder in os.listdir("EN"):
+    book_id = folder.split('.')[0]
+    en_folder_map[book_id] = folder
+
+# Function to construct full German audio path
+def get_de_path(row):
+    if "67" in row['book']:
+        return os.path.join("DE","67.frankenstein_de_1211_librivox_newly_alligned", "sentence_level_audio", row['DE_audio'])
+    return os.path.join("DE", row['book'], "sentence_level_audio", row['DE_audio'])
+
+# Function to construct full English audio path
+def get_en_path(row):
+    book_id = str(row['book_id'])
+    if book_id in en_folder_map:
+        return os.path.join("EN", en_folder_map[book_id], "sentence_level_audio", row['EN_audio'] + ".wav")
+    return None
+
+# Update paths in the DataFrame
+alignment['DE_audio'] = alignment.apply(get_de_path, axis=1)
+alignment['EN_audio'] = alignment.apply(get_en_path, axis=1)
+
+# Drop the 'book' column since paths are now complete
+alignment = alignment.drop('book', axis=1)
+
+# Drop rows where EN_audio path couldn't be constructed (book_id not found)
+alignment = alignment.dropna(subset=['EN_audio'])
+
+# Save the cleaned up csv
+alignment.to_csv("alignments/all_de_en_alligned_cleaned.csv", index=False)
+
+print(f"Saved cleaned CSV with {len(alignment)} rows")
+print("\nFirst few rows of cleaned CSV:")
+print(alignment.head())
+

libris2s_dataset.py

@@ -0,0 +1,95 @@
+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]
+        }

requirements.txt

@@ -1,5 +1,8 @@
-aeneas=1.7.3.0
-pandas>=1.1.4
-pydub=0.24.1
-beautifulsoup4=4.9.3
-requests=2.25.1
+aeneas=1.7.3.0
+pandas>=1.1.4
+pydub=0.24.1
+beautifulsoup4=4.9.3
+requests=2.25.1
+torch>=2.0.0
+torchaudio>=2.0.0
+soundfile