Update NURC-SP_ENTOA_TTS.py

NURC-SP_ENTOA_TTS.py

@@ -2,112 +2,44 @@ import csv
 import datasets
 from datasets import BuilderConfig, GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split
 
-_PROSODIC_PROMPTS_URLS = {
-    "validation": "prosodic/validation.csv",
-    "train": "prosodic/train.csv",
+
+
+_PROMPTS_URLS = {
+    "dev": "automatic/validation.csv",
+    "train": "automatic/train.csv",
 }
 
-_AUTOMATIC_PROMPTS_URLS = {
-    "validation": "automatic/validation.csv",
+_PROMPTS_FILTERED_URLS = {
+    "dev": "automatic/validation.csv",
     "train": "automatic/train.csv",
 }
 
 _ARCHIVES = {
-    "prosodic": "prosodic.tar.gz",
-    "automatic": "automatic.tar.gz",
+    "dev": "automatic.tar.gz",
+    "train": "automatic.tar.gz",
 }
 
 _PATH_TO_CLIPS = {
-    "validation_prosodic": "",
-    "train_prosodic": "",
-    "validation_automatic": "automatic/validation",
-    "train_automatic": "automatic/train",
+    "dev": "validation",
+    "train": "train",
 }
 
-def debug_path_matching(csv_path, archive_files):
-    """
-    Debug utility to compare paths between CSV and archive files
-    """
-    import csv
-    from collections import defaultdict
-    
-    # Store CSV paths
-    csv_paths = set()
-    
-    with open(csv_path, "r") as f:
-        reader = csv.DictReader(f)
-        for row in reader:
-            # Store both the full path and filename
-            path = row.get("path") or row.get("file_path")
-            csv_paths.add(path)
-            csv_paths.add(path.split("/")[-1])
-    
-    # Compare with archive paths
-    archive_paths = set()
-    matches = defaultdict(list)
-    
-    for path, _ in archive_files:
-        archive_paths.add(path)
-        archive_paths.add(path.split("/")[-1])
-        
-        # Check for matches
-        for csv_path in csv_paths:
-            if path.endswith(csv_path) or csv_path.endswith(path):
-                matches[path].append(csv_path)
-    
-    print("=== Debug Report ===")
-    print(f"CSV Paths: {len(csv_paths)}")
-    print(f"Archive Paths: {len(archive_paths)}")
-    print(f"Matched Paths: {len(matches)}")
-    print("\nSample CSV paths:")
-    for path in list(csv_paths)[:5]:
-        print(f"  {path}")
-    print("\nSample Archive paths:")
-    for path in list(archive_paths)[:5]:
-        print(f"  {path}")
-    print("\nSample Matches:")
-    for archive_path, csv_paths in list(matches.items())[:5]:
-        print(f"  Archive: {archive_path}")
-        print(f"  CSV: {csv_paths}")
-        print()
-    
-    return csv_paths, archive_paths, matches
-
-class EntoaConfig(BuilderConfig):
-    def __init__(self, prompts_type="prosodic", **kwargs):
+
+class NurcSPConfig(BuilderConfig):
+    def __init__(self, prompts_type="original", **kwargs):
         super().__init__(**kwargs)
         self.prompts_type = prompts_type
 
-class EntoaDataset(GeneratorBasedBuilder):
+
+class NurcSPDataset(GeneratorBasedBuilder):
     BUILDER_CONFIGS = [
-        EntoaConfig(name="prosodic", description="Prosodic audio prompts", prompts_type="prosodic"),
-        EntoaConfig(name="automatic", description="Automatic audio prompts", prompts_type="automatic"),
+        NurcSPConfig(name="original", description="Original audio prompts", prompts_type="original"),
+        NurcSPConfig(name="filtered", description="Filtered audio prompts", prompts_type="filtered"),
     ]
 
     def _info(self):
-        if self.config.name == "prosodic":
-            features = datasets.Features(
-                {
-                    "path": datasets.Value("string"),
-                    "name": datasets.Value("string"),
-                    "speaker": datasets.Value("string"),
-                    "start_time": datasets.Value("string"),
-                    "end_time": datasets.Value("string"),
-                    "normalized_text": datasets.Value("string"),
-                    "text": datasets.Value("string"),
-                    "duration": datasets.Value("string"),
-                    "type": datasets.Value("string"),
-                    "year": datasets.Value("string"),
-                    "gender": datasets.Value("string"),
-                    "age_range": datasets.Value("string"),
-                    "total_duration": datasets.Value("string"),
-                    "quality": datasets.Value("string"),
-                    "theme": datasets.Value("string"),
-                    "audio": datasets.Audio(sampling_rate=16_000),
-                }
-            )
-        else:  # automatic
-            features = datasets.Features(
+        return DatasetInfo(
+            features=datasets.Features(
                 {
                     "audio_name": datasets.Value("string"),
                     "file_path": datasets.Value("string"),
@@ -127,102 +59,81 @@ class EntoaDataset(GeneratorBasedBuilder):
                     "audio": datasets.Audio(sampling_rate=16_000),
                 }
             )
-        return DatasetInfo(features=features)
+        )
 
     def _split_generators(self, dl_manager):
-        prompts_urls = _PROSODIC_PROMPTS_URLS if self.config.name == "prosodic" else _AUTOMATIC_PROMPTS_URLS
-        archive = dl_manager.download(_ARCHIVES[self.config.name])
-        prompts_path = dl_manager.download(prompts_urls)
+        prompts_urls = _PROMPTS_URLS  # Default to original prompts URLs
 
-        # Debug prints for downloaded paths
-        print(f"Downloaded prompts: {prompts_path}")
-        print(f"Downloaded archive: {archive}")
+        if self.config.prompts_type == "filtered":
+            prompts_urls = _PROMPTS_FILTERED_URLS
+
+        prompts_path = dl_manager.download(prompts_urls)
+        archive = dl_manager.download(_ARCHIVES)
 
         return [
             SplitGenerator(
                 name=Split.VALIDATION,
                 gen_kwargs={
-                    "prompts_path": prompts_path["validation"],
-                    "path_to_clips": _PATH_TO_CLIPS[f"validation_{self.config.name}"],
-                    "audio_files": dl_manager.iter_archive(archive),
-                },
+                    "prompts_path": prompts_path["dev"],
+                    "path_to_clips": _PATH_TO_CLIPS["dev"],
+                    "audio_files": dl_manager.iter_archive(archive["dev"]),
+                }
             ),
             SplitGenerator(
                 name=Split.TRAIN,
                 gen_kwargs={
                     "prompts_path": prompts_path["train"],
-                    "path_to_clips": _PATH_TO_CLIPS[f"train_{self.config.name}"],
-                    "audio_files": dl_manager.iter_archive(archive),
-                },
+                    "path_to_clips": _PATH_TO_CLIPS["train"],
+                    "audio_files": dl_manager.iter_archive(archive["train"]),
+                }
             ),
         ]
 
-
-        
-
     def _generate_examples(self, prompts_path, path_to_clips, audio_files):
-        csv_paths, archive_paths, matches = debug_path_matching(prompts_path, audio_files)
         examples = {}
         with open(prompts_path, "r") as f:
             csv_reader = csv.DictReader(f)
             for row in csv_reader:
-    
-                if self.config.name == "prosodic":
-                    examples[row["path"]] = {
-                        "path": row["path"],
-                        "name": row["name"],
-                        "speaker": row["speaker"],
-                        "start_time": row["start_time"],
-                        "end_time": row["end_time"],
-                        "normalized_text": row["normalized_text"],
-                        "text": row["text"],
-                        "duration": row["duration"],
-                        "type": row["type"],
-                        "year": row["year"],
-                        "gender": row["gender"],
-                        "age_range": row["age_range"],
-                        "total_duration": row["total_duration"],
-                        "quality": row["quality"],
-                        "theme": row["theme"],
-                    }
-                else:  # automatic
-                    examples[row["file_path"]] = {
-                        "audio_name": row["audio_name"],
-                        "file_path": row["file_path"],
-                        "text": row["text"],
-                        "start_time": row["start_time"],
-                        "end_time": row["end_time"],
-                        "duration": row["duration"],
-                        "quality": row["quality"],
-                        "speech_genre": row["speech_genre"],
-                        "speech_style": row["speech_style"],
-                        "variety": row["variety"],
-                        "accent": row["accent"],
-                        "sex": row["sex"],
-                        "age_range": row["age_range"],
-                        "num_speakers": row["num_speakers"],
-                        "speaker_id": row["speaker_id"],
-                    }
-    
-        id_ = 0
+                audio_name = row['audio_name']
+                file_path = row['file_path']
+                text = row['text']
+                start_time = row['start_time']
+                end_time = row['end_time']
+                duration = row['duration']
+                quality = row['quality']
+                speech_genre = row['speech_genre']
+                speech_style = row['speech_style']
+                variety = row['variety']
+                accent = row['accent']
+                sex = row['sex']
+                age_range = row['age_range']
+                num_speakers = row['num_speakers']
+                speaker_id = row['speaker_id']
+                examples[file_path] = {
+                    "audio_name": audio_name,
+                    "file_path": file_path,
+                    "text": text,
+                    "start_time": start_time,
+                    "end_time": end_time,
+                    "duration": duration,
+                    "quality": quality,
+                    "speech_genre": speech_genre,
+                    "speech_style": speech_style,
+                    "variety": variety,
+                    "accent": accent,
+                    "sex": sex,
+                    "age_range": age_range,
+                    "num_speakers": num_speakers,
+                    "speaker_id": speaker_id,
+                }
         inside_clips_dir = False
-    
+        id_ = 0
         for path, f in audio_files:
-    
             if path.startswith(path_to_clips):
                 inside_clips_dir = True
                 if path in examples:
-                    # Debug: Match found
-                    print(f"Match found for: {path}")
                     audio = {"path": path, "bytes": f.read()}
                     yield id_, {**examples[path], "audio": audio}
                     id_ += 1
-                else:
-                    # Debug: No match for this file
-                    print(f"No match for: {path}")
             elif inside_clips_dir:
                 break
-    
-        # Debug: Print total examples generated
-        print(f"Completed generating examples. Total examples: {id_}")
-