Update NURC-SP_Corpus_Minimo.py

NURC-SP_Corpus_Minimo.py

@@ -1,80 +1,80 @@
-import csv
-import datasets
-from datasets import BuilderConfig, GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split
-
-
-
-_PROMPTS_URLS = {
-    "all": "segmented_audios.csv",
-}
-
-_ARCHIVES = {
-    "all": "nurc-sp_corpus_minimo.tar.gz", 
-}
-
-
-class NurcSPConfig(BuilderConfig):
-    def __init__(self, prompts_type="original", **kwargs):
-        super().__init__(**kwargs)
-        self.prompts_type = prompts_type
-
-
-class NurcSPDataset(GeneratorBasedBuilder):
-    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"),
-                    "audio": datasets.Audio(sampling_rate=16_000),
-                }
-            )
-        )
-
-    def _split_generators(self, dl_manager):
-        prompts_path = dl_manager.download(_PROMPTS_URLS)
-        archive = dl_manager.download(_ARCHIVES)
-
-        return [
-            {
-                "prompts_path": prompts_path["all"],
-                "audio_files": dl_manager.iter_archive(archive["all"]),
-            }
-        ]
-
-    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:
-                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']
-                examples[path] = {
-                    "name": name,
-                    "speaker": speaker,
-                    "start_time": start_time,
-                    "end_time": end_time,
-                    "normalized_text": normalized_text,
-                    "text": text,
-                }
-        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:
-                    audio = {"path": path, "bytes": f.read()}
-                    yield id_, {**examples[path], "audio": audio}
-                    id_ += 1
-            elif inside_clips_dir:
-                break
+import csv
+import datasets
+from datasets import BuilderConfig, GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split
+
+
+
+_PROMPTS_URLS = {
+    "def": "segmented_audios.csv",
+}
+
+_ARCHIVES = {
+    "def": "nurc-sp_corpus_minimo.tar.gz", 
+}
+
+
+class NurcSPConfig(BuilderConfig):
+    def __init__(self, prompts_type="original", **kwargs):
+        super().__init__(**kwargs)
+        self.prompts_type = prompts_type
+
+
+class NurcSPDataset(GeneratorBasedBuilder):
+    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"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                }
+            )
+        )
+
+    def _split_generators(self, dl_manager):
+        prompts_path = dl_manager.download(_PROMPTS_URLS)
+        archive = dl_manager.download(_ARCHIVES)
+
+        return [
+            {
+                "prompts_path": prompts_path["def"],
+                "audio_files": dl_manager.iter_archive(archive["def"]),
+            }
+        ]
+
+    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:
+                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']
+                examples[path] = {
+                    "name": name,
+                    "speaker": speaker,
+                    "start_time": start_time,
+                    "end_time": end_time,
+                    "normalized_text": normalized_text,
+                    "text": text,
+                }
+        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:
+                    audio = {"path": path, "bytes": f.read()}
+                    yield id_, {**examples[path], "audio": audio}
+                    id_ += 1
+            elif inside_clips_dir:
+                break