File size: 2,860 Bytes
11801b3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
#!/bash/bin/python3

import datasets

data_name = "asd_syl_240115"

_DATA_URL = f"data/{data_name}.tar.gz"

_PROMPTS_URLS = {
    "train": "data/prompts-train.txt.gz",
    "valid": "data/prompts-valid.txt.gz",
    "test": "data/prompts-test.txt.gz",
}

class KscgSmall25(datasets.GeneratorBasedBuilder):
    """KSCG Small 20v50"""

    def _info(self):
        features = datasets.Features(
            {
                'path': datasets.Value('string'),
                'audio': datasets.Audio(sampling_rate=16_000),
                'sentence': datasets.Value('string'),
            }
        )

        return datasets.DatasetInfo(
            features=features,
            supervised_keys=None,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""

        prompts_paths = dl_manager.download_and_extract(_PROMPTS_URLS)
        archive = dl_manager.download(_DATA_URL)
        train_dir = f"{data_name}/train"
        valid_dir = f"{data_name}/valid"
        test_dir = f"{data_name}/test"

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "prompts_path": prompts_paths["train"],
                    "path_to_clips": train_dir,
                    "audio_files": dl_manager.iter_archive(archive)
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "prompts_path": prompts_paths["valid"],
                    "path_to_clips": valid_dir,
                    "audio_files": dl_manager.iter_archive(archive)
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "prompts_path": prompts_paths["test"],
                    "path_to_clips": test_dir,
                    "audio_files": dl_manager.iter_archive(archive)
                },
            ),
        ]

    def _generate_examples(self, prompts_path, path_to_clips, audio_files):
        examples = {}
        with open(prompts_path, encoding='utf-8') as f:
            for row in f:
                data = row.strip().split(",")
                audio_path = data[0] + ".wav"
                examples[audio_path] = {
                    '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