test

CORAA-MUPE-ASR-GZ.py

@@ -0,0 +1,132 @@
+import csv
+import datasets
+
+_PROMPTS_URLS = {
+    "train": "train.csv",
+    "validation": "validation.csv",
+    "test": "test.csv",
+}
+
+_ARCHIVES = {
+    "train": "train.tar.gz",
+    "validation": "validation.tar.gz",
+    "test": "test.tar.gz",
+}   
+
+_PATH_TO_CLIPS = {
+    "train": "train",
+    "validation": "validation",
+    "test": "test",
+}
+
+class MUPEASRDataset(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "audio_id": datasets.Value("int64"),
+                    "audio_name": datasets.Value("string"),
+                    "file_path": datasets.Value("string"),
+                    "speaker_type": datasets.Value("string"),
+                    "speaker_code": datasets.Value("string"),
+                    "speaker_gender": datasets.Value("string"),
+                    "education": datasets.Value("string"),
+                    "birth_state": datasets.Value("string"),
+                    "birth_country": datasets.Value("string"),
+                    "age": datasets.Value("int64"),
+                    "birth_year": datasets.Value("int64"),
+                    "audio_quality": datasets.Value("string"),
+                    "start_time": datasets.Value("float32"),
+                    "end_time": datasets.Value("float32"),
+                    "duration": datasets.Value("float32"),
+                    "normalized_text": datasets.Value("string"),
+                    "original_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 [
+            datasets.SplitGenerator(
+                name = datasets.Split.VALIDATION,
+                gen_kwargs = {
+                    "prompts_path": prompts_path["validation"],
+                    "path_to_clips": _PATH_TO_CLIPS["validation"],
+                    "audio_files": dl_manager.iter_archive(archive["validation"]),
+                }
+            ),
+            datasets.SplitGenerator(
+                name = datasets.Split.TEST,
+                gen_kwargs = {
+                    "prompts_path": prompts_path["test"],
+                    "path_to_clips": _PATH_TO_CLIPS["test"],
+                    "audio_files": dl_manager.iter_archive(archive["test"]),
+                }
+            ),
+            datasets.SplitGenerator(
+                name = datasets.Split.TRAIN,
+                gen_kwargs = {
+                    "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):
+        examples = {}
+        with open(prompts_path, "r") as f:
+            csv_reader = csv.DictReader(f)
+            for row in csv_reader:
+                audio_id = row['audio_id']
+                audio_name = row['audio_name']
+                file_path = row['file_path']
+                speaker_type = row['speaker_type']
+                speaker_code = row['speaker_code']
+                speaker_gender = row['speaker_gender']
+                education = row['education']
+                birth_state = row['birth_state']
+                birth_country = row['birth_country']
+                age = row['age']
+                birth_year = row['birth_year']
+                audio_quality = row['audio_quality']
+                start_time = row['start_time']
+                end_time = row['end_time']
+                duration = row['duration']
+                normalized_text = row['normalized_text']
+                original_text = row['original_text']
+
+                examples[file_path] = {
+                    "audio_id" : audio_id,
+                    "audio_name" : audio_name,
+                    "file_path" : file_path,
+                    "speaker_type" : speaker_type,
+                    "speaker_code" : speaker_code,
+                    "speaker_gender" : speaker_gender,
+                    "education" : education,
+                    "birth_state" : birth_state,
+                    "birth_country" : birth_country,
+                    "age" : age,
+                    "birth_year" : birth_year,
+                    "audio_quality" : audio_quality,
+                    "start_time" : start_time,
+                    "end_time" : end_time,
+                    "duration" : duration,
+                    "normalized_text" : normalized_text,
+                    "original_text" : original_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