Upload NURC-SP_ENTOA_TTS.py

NURC-SP_ENTOA_TTS.py

@@ -0,0 +1,162 @@
+import csv
+import datasets
+from datasets import BuilderConfig, GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split
+
+_PROSODIC_PROMPTS_URLS = {
+    "validation": "prosodic/audios_dev_metadata.csv",
+    "train": "prosodic/audios_train_metadata.csv",
+}
+
+_AUTOMATIC_PROMPTS_URLS = {
+    "validation": "automatic/audios_dev_metadata.csv",
+    "train": "automatic/audios_train_metadata.csv",
+}
+
+_ARCHIVES = {
+    "validation_prosodic": "prosodic/audios_dev.zip",
+    "train_prosodic": "prosodic/audios_train.zip",
+    "validation_automatic": "automatic/audios_dev.zip",
+    "train_automatic": "automatic/audios_train.zip",
+}
+
+_PATH_TO_CLIPS = {
+    "validation_prosodic": "prosodic/audios_dev",
+    "train_prosodic": "prosodic/audios_train",
+    "validation_automatic": "automatic/audios_dev",
+    "train_automatic": "automatic/audios_train",
+}
+
+class EntoaConfig(BuilderConfig):
+    def __init__(self, prompts_type="prosodic", **kwargs):
+        super().__init__(**kwargs)
+        self.prompts_type = prompts_type
+
+class EntoaDataset(GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        EntoaConfig(name="prosodic", description="Prosodic audio prompts", prompts_type="prosodic"),
+        EntoaConfig(name="automatic", description="Automatic audio prompts", prompts_type="automatic"),
+    ]
+
+    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(
+                {
+                    "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),
+                }
+            )
+        return DatasetInfo(features=features)
+
+    def _split_generators(self, dl_manager):
+        prompts_urls = _PROSODIC_PROMPTS_URLS if self.config.name == "prosodic" else _AUTOMATIC_PROMPTS_URLS
+        path_to_clips = _PATH_TO_CLIPS
+        archive = dl_manager.download(_ARCHIVES)
+        prompts_path = dl_manager.download(prompts_urls)
+
+        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[f"validation_{self.config.name}"]),
+                },
+            ),
+            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[f"train_{self.config.name}"]),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, prompts_path, path_to_clips, 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
+        inside_clips_dir = False
+        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