Revert Convert dataset to Parquet

README.md

@@ -9,6 +9,7 @@ license:
 - cc-by-4.0
 multilinguality:
 - monolingual
+pretty_name: VCTK
 size_categories:
 - 10K<n<100K
 source_datasets:
@@ -19,9 +20,21 @@ task_categories:
 - text-to-audio
 task_ids: []
 paperswithcode_id: vctk
-pretty_name: 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
 dataset_info:
-  config_name: main
   features:
   - name: speaker_id
     dtype: string
@@ -45,32 +58,13 @@ dataset_info:
     dtype: string
   - name: comment
     dtype: string
+  config_name: main
   splits:
   - name: train
-    num_bytes: 13246600829.632
+    num_bytes: 40103111
     num_examples: 88156
-  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
+  download_size: 11747302977
+  dataset_size: 40103111
 ---
 
 # Dataset Card for VCTK

main/train-00000-of-00027.parquet

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vctk.py

@@ -0,0 +1,133 @@
+# 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