Upload vivos_noisy.py

vivos_noisy.py

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+import datasets
+
+_DATA_URL = "data/vivos_noisy.tar.gz"
+
+_PROMPTS_URLS = {
+    "train": "data/train_prompts.txt.gz",
+    "test": "data/test_prompts.txt.gz",
+}
+
+
+class VivosNoisyDataset(datasets.GeneratorBasedBuilder):
+    """VIVOS NOISY is a Vietnamese speech corpus with added noise, based on the original VIVOS dataset.
+    This corpus is prepared for Vietnamese Automatic Speech Recognition task under noisy environments."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "speaker_id": datasets.Value("string"),
+                    "path": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "sentence": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        prompts_paths = dl_manager.download_and_extract(_PROMPTS_URLS)
+        archive = dl_manager.download(_DATA_URL)
+        train_dir = "vivos_noisy/train"
+        test_dir = "vivos_noisy/test"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "prompts_path": prompts_paths["train"],
+                    "path_to_clips": train_dir + "/waves",
+                    "audio_files": dl_manager.iter_archive(archive),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "prompts_path": prompts_paths["test"],
+                    "path_to_clips": test_dir + "/waves",
+                    "audio_files": dl_manager.iter_archive(archive),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, prompts_path, path_to_clips, audio_files):
+        """Yields examples as (key, example) tuples."""
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+        examples = {}
+        with open(prompts_path, encoding="utf-8") as f:
+            for row in f:
+                data = row.strip().split(" ", 1)
+                # Extract speaker_id from the full filename
+                # For example: VIVOS_NOISY_VIVOSDEV01_R002_001 -> VIVOS_NOISY_VIVOSDEV01
+                filename_parts = data[0].split("_")
+                if len(filename_parts) >= 3:
+                    # Join the first 3 parts to get the speaker_id (VIVOS_NOISY_VIVOSDEV01)
+                    speaker_id = "_".join(filename_parts[:3])
+                else:
+                    # Fallback if the naming convention is different
+                    speaker_id = filename_parts[0]
+                
+                audio_path = "/".join([path_to_clips, speaker_id, data[0] + ".wav"])
+                examples[audio_path] = {
+                    "speaker_id": speaker_id,
+                    "path": audio_path,
+                    "sentence": data[1],
+                }
+        
+        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