Delete CORAA-MUPE-ASR.py

CORAA-MUPE-ASR.py

@@ -1,117 +0,0 @@
-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"),
-                    "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']
-                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,
-                    "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