upload hf dataset and rename py dataset

dataset.py

@@ -0,0 +1,36 @@
+import csv
+from datasets import DatasetInfo, GeneratorBasedBuilder, SplitGenerator, Split, Features, Value, Audio
+
+class Libris2s(GeneratorBasedBuilder):
+    def _info(self):
+        return DatasetInfo(
+            features=Features({
+                "book_id": Value("int64"),
+                "DE_audio": Audio(),
+                "EN_audio": Audio(),
+                "score": Value("float32"),
+                "DE_transcript": Value("string"),
+                "EN_transcript": Value("string"),
+            }),
+        )
+    
+    def _split_generators(self, dl_manager):
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={"filepath": "alignments/all_de_en_alligned_cleaned.csv"}
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            for idx, row in enumerate(reader):
+                yield idx, {
+                    "book_id": int(row["book_id"]),
+                    "DE_audio": row["DE_audio"],
+                    "EN_audio": row["EN_audio"],
+                    "score": float(row["score"]),
+                    "DE_transcript": row["DE_transcript"],
+                    "EN_transcript": row["EN_transcript"],
+                }

libris2s_dataset.py → libris2s_dataset_pt.py

@@ -1,95 +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]
+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]
         }