Update NURC-SP_ENTOA_TTS.py

NURC-SP_ENTOA_TTS.py

@@ -2,6 +2,8 @@ import csv
 import datasets
 from datasets import BuilderConfig, GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split
 
+
+
 _PROMPTS_URLS = {
     "dev": "automatic/validation.csv",
     "train": "automatic/train.csv",
@@ -22,11 +24,13 @@ _PATH_TO_CLIPS = {
     "train": "train",
 }
 
+
 class NurcSPConfig(BuilderConfig):
     def __init__(self, prompts_type="original", **kwargs):
         super().__init__(**kwargs)
         self.prompts_type = prompts_type
 
+
 class NurcSPDataset(GeneratorBasedBuilder):
     BUILDER_CONFIGS = [
         NurcSPConfig(name="original", description="Original audio prompts", prompts_type="original"),
@@ -58,52 +62,78 @@ class NurcSPDataset(GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        prompts_urls = _PROMPTS_URLS if self.config.prompts_type == "original" else _PROMPTS_FILTERED_URLS
-        
-        # Download the prompts CSV files and audio archive
-        prompts_path = dl_manager.download_and_extract(prompts_urls)
-        archive = dl_manager.download_and_extract(_ARCHIVES)
+        prompts_urls = _PROMPTS_URLS  # Default to original prompts URLs
+
+        if self.config.prompts_type == "filtered":
+            prompts_urls = _PROMPTS_FILTERED_URLS
+
+        prompts_path = dl_manager.download(prompts_urls)
+        archive = dl_manager.download(_ARCHIVES)
 
-        # Return split generators
         return [
             SplitGenerator(
-                name=Split.TRAIN,
+                name=Split.VALIDATION,
                 gen_kwargs={
-                    "prompts_path": prompts_path["train"],
-                    "path_to_clips": _PATH_TO_CLIPS["train"],
-                    "audio_files": archive["train"],
+                    "prompts_path": prompts_path["dev"],
+                    "path_to_clips": _PATH_TO_CLIPS["dev"],
+                    "audio_files": dl_manager.iter_archive(archive["dev"]),
                 }
             ),
             SplitGenerator(
-                name=Split.VALIDATION,  # Changed from Split.VALIDATION to match error message
+                name=Split.TRAIN,
                 gen_kwargs={
-                    "prompts_path": prompts_path["dev"],
-                    "path_to_clips": _PATH_TO_CLIPS["dev"],
-                    "audio_files": archive["dev"],
+                    "prompts_path": prompts_path["train"],
+                    "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):
-        # Load CSV data
         examples = {}
-        with open(prompts_path, "r", encoding='utf-8') as f:
+        with open(prompts_path, "r") as f:
             csv_reader = csv.DictReader(f)
             for row in csv_reader:
-                examples[row['file_path']] = {
-                    key: row[key] 
-                    for key in row.keys()
+                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,
                 }
-
-        # Process audio files
+        inside_clips_dir = False
         id_ = 0
-        for root, _, files in datasets.utils.py_utils.walk(audio_files):
-            if path_to_clips in root:
-                for fname in files:
-                    file_path = f"{path_to_clips}/{fname}"
-                    if file_path in examples:
-                        full_path = f"{root}/{fname}"
-                        with open(full_path, "rb") as audio_file:
-                            audio = {"path": file_path, "bytes": audio_file.read()}
-                            yield id_, {**examples[file_path], "audio": audio}
-                            id_ += 1
+        for path, f in audio_files:
+            if path.startswith(path_to_clips):
+                inside_clips_dir = True
+                if path in examples:
+                    audio = {"path": path, "bytes": f.read()}
+                    yield id_, {**examples[path], "audio": audio}
+                    id_ += 1
+            elif inside_clips_dir:
+                break