Update NURC-SP_ENTOA_TTS.py

NURC-SP_ENTOA_TTS.py

@@ -4,75 +4,110 @@ from datasets import BuilderConfig, GeneratorBasedBuilder, DatasetInfo, SplitGen
 from pathlib import Path
 import os
 
-_PROMPTS_URLS = {
+_PROMPTS_PROSODIC_URLS = {
     "dev": "prosodic/validation.csv",
     "train": "prosodic/train.csv",
 }
 
-_PROMPTS_FILTERED_URLS = {
-    "dev": "prosodic/validation.csv",
-    "train": "prosodic/train.csv",
+_PROMPTS_AUTOMATIC_URLS = {
+    "dev": "automatic/validation.csv",
+    "train": "automatic/train.csv",
 }
 
-_ARCHIVES = {
+_ARCHIVES_PROSODIC = {
     "dev": "prosodic/audios.tar.gz",
     "train": "prosodic/audios.tar.gz",
 }
 
+_ARCHIVES_AUTOMATIC = {
+    "dev": "automatic/audios.tar.gz",
+    "train": "automatic/audios.tar.gz",
+}
+
 _PATH_TO_CLIPS = {
     "dev": "",
     "train": "",
 }
 
 class NurcSPConfig(BuilderConfig):
-    def __init__(self, prompts_type="original", **kwargs):
+    def __init__(self, prompts_type, **kwargs):
         super().__init__(**kwargs)
         self.prompts_type = prompts_type
 
 class NurcSPDataset(GeneratorBasedBuilder):
     BUILDER_CONFIGS = [
-        NurcSPConfig(name="original", description="Original audio prompts", prompts_type="original"),
-        NurcSPConfig(name="filtered", description="Filtered audio prompts", prompts_type="filtered"),
+        NurcSPConfig(name="automatic", description="Automatic audio prompts", prompts_type="automatic"),
+        NurcSPConfig(name="prosodic", description="Prosodic audio prompts", prompts_type="prosodic"),
     ]
 
     def _info(self):
-        return DatasetInfo(
-            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),
-                }
+        if self.config.name == "prosodic":
+            return DatasetInfo(
+                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),
+                    }
+                )
+            )
+        elif self.config.name == "automatic":
+            return DatasetInfo(
+                features=datasets.Features(
+                    {
+                        "audio_name": datasets.Value("string"),
+                        "file_path": datasets.Value("string"),
+                        "text": datasets.Value("string"),
+                        "start_time": datasets.Value("string"),
+                        "end_time": datasets.Value("string"),
+                        "duration": datasets.Value("string"),
+                        "quality": datasets.Value("string"),
+                        "speech_genre": datasets.Value("string"),
+                        "speech_style": datasets.Value("string"),
+                        "variety": datasets.Value("string"),
+                        "accent": datasets.Value("string"),
+                        "sex": datasets.Value("string"),
+                        "age_range": datasets.Value("string"),
+                        "num_speakers": datasets.Value("string"),
+                        "speaker_id": datasets.Value("string"),
+                        "audio": datasets.Audio(sampling_rate=16_000),
+                    }
+                )
             )
-        )
 
     def _split_generators(self, dl_manager):
         print("\n=== Configuration ===")
         print(f"Using prompts_type: {self.config.prompts_type}")
         
-        prompts_urls = _PROMPTS_URLS
-        if self.config.prompts_type == "filtered":
-            prompts_urls = _PROMPTS_FILTERED_URLS
+        if self.config.prompts_type == "prosodic":
+            prompts_urls = _PROMPTS_PROSODIC_URLS
+            archive_link = _ARCHIVES_PROSODIC
+        elif self.config.prompts_type == "automatic":
+            prompts_urls = _PROMPTS_AUTOMATIC_URLS
+            archive_link = _ARCHIVES_AUTOMATIC
+        else:
+            print("Invalid config")
+            return
         
         print(f"Downloading prompts from: {prompts_urls}")
         prompts_path = dl_manager.download(prompts_urls)
         print(f"Downloaded prompts to: {prompts_path}")
         
-        print(f"Downloading archives from: {_ARCHIVES}")
-        archive = dl_manager.download(_ARCHIVES)
+        print(f"Downloading archives from: {archive_link}")
+        archive = dl_manager.download(archive_link)
         print(f"Downloaded archives to: {archive}")
 
         return [
@@ -110,27 +145,47 @@ class NurcSPDataset(GeneratorBasedBuilder):
         print("\n=== Reading CSV ===")
         with open(prompts_path, "r") as f:
             csv_reader = csv.DictReader(f)
-            for row in csv_reader:
-                file_path = Path(row['path']).as_posix()
-                examples[file_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'],
-                }
-                csv_paths.append(file_path)
-        
+            if self.config.prompts_type == "prosodic":
+                for row in csv_reader:
+                    file_path = Path(row['path']).as_posix()
+                    examples[file_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'],
+                    }
+                    csv_paths.append(file_path)
+            elif self.config.prompts_type == "automatic":
+                for row in csv_reader:
+                    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'],
+                    }
+            
         print(f"\nFound {len(csv_paths)} entries in CSV")
         print("\nFirst 3 CSV paths:")
         for path in csv_paths[:3]: