Convert dataset to Parquet (#5)

- Convert dataset to Parquet (a166f6884c9921c488997462fd2161b382fe93c6)
- Delete loading script (d579ec82ddb220382658d568e17b7da92d55ff11)

README.md

@@ -9,7 +9,6 @@ license:
 - cc-by-4.0
 multilinguality:
 - monolingual
-pretty_name: VCTK
 size_categories:
 - 10K<n<100K
 source_datasets:
@@ -20,21 +19,9 @@ task_categories:
 - text-to-audio
 task_ids: []
 paperswithcode_id: vctk
-train-eval-index:
-- config: main
-  task: automatic-speech-recognition
-  task_id: speech_recognition
-  splits:
-    train_split: train
-  col_mapping:
-    file: path
-    text: text
-  metrics:
-  - type: wer
-    name: WER
-  - type: cer
-    name: CER
+pretty_name: VCTK
 dataset_info:
+  config_name: main
   features:
   - name: speaker_id
     dtype: string
@@ -58,13 +45,32 @@ dataset_info:
     dtype: string
   - name: comment
     dtype: string
-  config_name: main
   splits:
   - name: train
-    num_bytes: 40103111
+    num_bytes: 13246600829.632
     num_examples: 88156
-  download_size: 11747302977
-  dataset_size: 40103111
+  download_size: 11715680639
+  dataset_size: 13246600829.632
+configs:
+- config_name: main
+  data_files:
+  - split: train
+    path: main/train-*
+  default: true
+train-eval-index:
+- config: main
+  task: automatic-speech-recognition
+  task_id: speech_recognition
+  splits:
+    train_split: train
+  col_mapping:
+    file: path
+    text: text
+  metrics:
+  - type: wer
+    name: WER
+  - type: cer
+    name: CER
 ---
 
 # Dataset Card for VCTK

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vctk.py

@@ -1,133 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Lint as: python3
-"""VCTK dataset."""
-
-
-import os
-import re
-
-import datasets
-from datasets.tasks import AutomaticSpeechRecognition
-
-
-_CITATION = """\
-@inproceedings{Veaux2017CSTRVC,
-    title        = {CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit},
-    author       = {Christophe Veaux and Junichi Yamagishi and Kirsten MacDonald},
-    year         = 2017
-}
-"""
-
-_DESCRIPTION = """\
-The CSTR VCTK Corpus includes speech data uttered by 110 English speakers with various accents.
-"""
-
-_URL = "https://datashare.ed.ac.uk/handle/10283/3443"
-_DL_URL = "https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip"
-
-
-class VCTK(datasets.GeneratorBasedBuilder):
-    """VCTK dataset."""
-
-    VERSION = datasets.Version("0.9.2")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="main", version=VERSION, description="VCTK dataset"),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "speaker_id": datasets.Value("string"),
-                    "audio": datasets.features.Audio(sampling_rate=48_000),
-                    "file": datasets.Value("string"),
-                    "text": datasets.Value("string"),
-                    "text_id": datasets.Value("string"),
-                    "age": datasets.Value("string"),
-                    "gender": datasets.Value("string"),
-                    "accent": datasets.Value("string"),
-                    "region": datasets.Value("string"),
-                    "comment": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=("file", "text"),
-            homepage=_URL,
-            citation=_CITATION,
-            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
-        )
-
-    def _split_generators(self, dl_manager):
-        root_path = dl_manager.download_and_extract(_DL_URL)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"root_path": root_path}),
-        ]
-
-    def _generate_examples(self, root_path):
-        """Generate examples from the VCTK corpus root path."""
-
-        meta_path = os.path.join(root_path, "speaker-info.txt")
-        txt_root = os.path.join(root_path, "txt")
-        wav_root = os.path.join(root_path, "wav48_silence_trimmed")
-        # NOTE: "comment" is handled separately in logic below
-        fields = ["speaker_id", "age", "gender", "accent", "region"]
-
-        key = 0
-        with open(meta_path, encoding="utf-8") as meta_file:
-            _ = next(iter(meta_file))
-            for line in meta_file:
-                data = {}
-                line = line.strip()
-                search = re.search(r"\(.*\)", line)
-                if search is None:
-                    data["comment"] = ""
-                else:
-                    start, _ = search.span()
-                    data["comment"] = line[start:]
-                    line = line[:start]
-                values = line.split()
-                for i, field in enumerate(fields):
-                    if field == "region":
-                        data[field] = " ".join(values[i:])
-                    else:
-                        data[field] = values[i] if i < len(values) else ""
-                speaker_id = data["speaker_id"]
-                speaker_txt_path = os.path.join(txt_root, speaker_id)
-                speaker_wav_path = os.path.join(wav_root, speaker_id)
-                # NOTE: p315 does not have text
-                if not os.path.exists(speaker_txt_path):
-                    continue
-                for txt_file in sorted(os.listdir(speaker_txt_path)):
-                    filename, _ = os.path.splitext(txt_file)
-                    _, text_id = filename.split("_")
-                    for i in [1, 2]:
-                        wav_file = os.path.join(speaker_wav_path, f"{filename}_mic{i}.flac")
-                        # NOTE: p280 does not have mic2 files
-                        if not os.path.exists(wav_file):
-                            continue
-                        with open(os.path.join(speaker_txt_path, txt_file), encoding="utf-8") as text_file:
-                            text = text_file.readline().strip()
-                            more_data = {
-                                "file": wav_file,
-                                "audio": wav_file,
-                                "text": text,
-                                "text_id": text_id,
-                            }
-                            yield key, {**data, **more_data}
-                        key += 1