README.md CHANGED
@@ -1,3 +1,32 @@
1
  ---
2
  license: cc-by-4.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3
  ---
 
1
  ---
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  license: cc-by-4.0
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+ dataset_info:
4
+ config_name: tele_con_ciencia
5
+ features:
6
+ - name: audio_id
7
+ dtype: string
8
+ - name: audio
9
+ dtype:
10
+ audio:
11
+ sampling_rate: 16000
12
+ - name: speaker_id
13
+ dtype: string
14
+ - name: gender
15
+ dtype: string
16
+ - name: duration
17
+ dtype: float32
18
+ - name: normalized_text
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+ dtype: string
20
+ splits:
21
+ - name: train
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+ num_bytes: 2048626261.057
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+ num_examples: 12073
24
+ download_size: 2041645393
25
+ dataset_size: 2048626261.057
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+ configs:
27
+ - config_name: tele_con_ciencia
28
+ data_files:
29
+ - split: train
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+ path: tele_con_ciencia/train-*
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+ default: true
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  ---
corpus/files/metadata_train.tsv DELETED
The diff for this file is too large to render. See raw diff
 
corpus/files/tars_train.paths DELETED
@@ -1 +0,0 @@
1
- corpus/speech/train.tar.gz
 
 
tele_con_ciencia.py DELETED
@@ -1,123 +0,0 @@
1
- from collections import defaultdict
2
- import os
3
- import json
4
- import csv
5
-
6
- import datasets
7
-
8
- _NAME="tele_con_ciencia"
9
- _VERSION="1.0.0"
10
-
11
- _DESCRIPTION = """
12
- TELEconCIENCIA SPANISH CORPUS. Audio and Transcripts in Spanish in a
13
- CIEMPIESS Corpus style, taken from the radio program Tele con Ciencia
14
- by CONACYT.
15
- """
16
-
17
- _CITATION = """
18
- @misc{menateleconciencia2019,
19
- title={TELEconCIENCIA SPANISH CORPUS: Audio and Transcripts in Spanish taken from broadcast radio programs.},
20
- author={Hernandez Mena, Carlos Daniel},
21
- year={2019},
22
- url={https://huggingface.co/ciempiess/tele_con_ciencia},
23
- }
24
- """
25
-
26
- _HOMEPAGE = "https://huggingface.co/ciempiess/tele_con_ciencia"
27
-
28
- _LICENSE = "CC-BY-SA-4.0, See https://creativecommons.org/licenses/by-sa/4.0/"
29
-
30
- _BASE_DATA_DIR = "corpus/"
31
- _METADATA_TRAIN = os.path.join(_BASE_DATA_DIR,"files", "metadata_train.tsv")
32
-
33
- _TARS_TRAIN = os.path.join(_BASE_DATA_DIR,"files", "tars_train.paths")
34
-
35
- class TeleconCienciaConfig(datasets.BuilderConfig):
36
- """BuilderConfig for TELEconCIENCIA SPANISH CORPUS"""
37
-
38
- def __init__(self, name, **kwargs):
39
- name=_NAME
40
- super().__init__(name=name, **kwargs)
41
-
42
- class TeleconCiencia(datasets.GeneratorBasedBuilder):
43
- """TELEconCIENCIA SPANISH CORPUS"""
44
-
45
- VERSION = datasets.Version(_VERSION)
46
- BUILDER_CONFIGS = [
47
- TeleconCienciaConfig(
48
- name=_NAME,
49
- version=datasets.Version(_VERSION),
50
- )
51
- ]
52
-
53
- def _info(self):
54
- features = datasets.Features(
55
- {
56
- "audio_id": datasets.Value("string"),
57
- "audio": datasets.Audio(sampling_rate=16000),
58
- "speaker_id": datasets.Value("string"),
59
- "gender": datasets.Value("string"),
60
- "duration": datasets.Value("float32"),
61
- "normalized_text": datasets.Value("string"),
62
- }
63
- )
64
- return datasets.DatasetInfo(
65
- description=_DESCRIPTION,
66
- features=features,
67
- homepage=_HOMEPAGE,
68
- license=_LICENSE,
69
- citation=_CITATION,
70
- )
71
-
72
- def _split_generators(self, dl_manager):
73
-
74
- metadata_train=dl_manager.download_and_extract(_METADATA_TRAIN)
75
-
76
- tars_train=dl_manager.download_and_extract(_TARS_TRAIN)
77
-
78
- hash_tar_files=defaultdict(dict)
79
-
80
- with open(tars_train,'r') as f:
81
- hash_tar_files['train']=[path.replace('\n','') for path in f]
82
-
83
- hash_meta_paths={"train":metadata_train}
84
- audio_paths = dl_manager.download(hash_tar_files)
85
-
86
- splits=["train"]
87
- local_extracted_audio_paths = (
88
- dl_manager.extract(audio_paths) if not dl_manager.is_streaming else
89
- {
90
- split:[None] * len(audio_paths[split]) for split in splits
91
- }
92
- )
93
-
94
- return [
95
- datasets.SplitGenerator(
96
- name=datasets.Split.TRAIN,
97
- gen_kwargs={
98
- "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["train"]],
99
- "local_extracted_archives_paths": local_extracted_audio_paths["train"],
100
- "metadata_paths": hash_meta_paths["train"],
101
- }
102
- ),
103
- ]
104
-
105
- def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths):
106
-
107
- features = ["speaker_id","gender","duration","normalized_text"]
108
-
109
- with open(metadata_paths) as f:
110
- metadata = {x["audio_id"]: x for x in csv.DictReader(f, delimiter="\t")}
111
-
112
-
113
-
114
- for audio_archive, local_extracted_archive_path in zip(audio_archives, local_extracted_archives_paths):
115
- for audio_filename, audio_file in audio_archive:
116
- audio_id =os.path.splitext(os.path.basename(audio_filename))[0]
117
- path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename
118
-
119
- yield audio_id, {
120
- "audio_id": audio_id,
121
- **{feature: metadata[audio_id][feature] for feature in features},
122
- "audio": {"path": path, "bytes": audio_file.read()},
123
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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