Duplicate from CSTR-Edinburgh/vctk

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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- crowdsourced
+language:
+- en
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: VCTK
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- automatic-speech-recognition
+- text-to-speech
+- 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
+dataset_info:
+  features:
+  - name: speaker_id
+    dtype: string
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 48000
+  - name: file
+    dtype: string
+  - name: text
+    dtype: string
+  - name: text_id
+    dtype: string
+  - name: age
+    dtype: string
+  - name: gender
+    dtype: string
+  - name: accent
+    dtype: string
+  - name: region
+    dtype: string
+  - name: comment
+    dtype: string
+  config_name: main
+  splits:
+  - name: train
+    num_bytes: 40103111
+    num_examples: 88156
+  download_size: 11747302977
+  dataset_size: 40103111
+---
+
+# Dataset Card for VCTK
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [Edinburg DataShare](https://doi.org/10.7488/ds/2645)
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+This CSTR VCTK Corpus includes around 44-hours of speech data uttered by 110 English speakers with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive.
+
+### Supported Tasks
+
+- `automatic-speech-recognition`, `speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
+- `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS).
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+A data point comprises the path to the audio file, called `file` and its transcription, called `text`.
+
+```
+{
+  'speaker_id': 'p225',
+  'text_id': '001',
+  'text': 'Please call Stella.',
+  'age': '23',
+  'gender': 'F',
+  'accent': 'English',
+  'region': 'Southern England',
+  'file': '/datasets/downloads/extracted/8ed7dad05dfffdb552a3699777442af8e8ed11e656feb277f35bf9aea448f49e/wav48_silence_trimmed/p225/p225_001_mic1.flac',
+  'audio':
+    {
+      'path': '/datasets/downloads/extracted/8ed7dad05dfffdb552a3699777442af8e8ed11e656feb277f35bf9aea448f49e/wav48_silence_trimmed/p225/p225_001_mic1.flac',
+      'array': array([0.00485229, 0.00689697, 0.00619507, ..., 0.00811768, 0.00836182, 0.00854492], dtype=float32),
+      'sampling_rate': 48000
+    },
+  'comment': ''
+}
+```
+
+Each audio file is a single-channel FLAC with a sample rate of 48000 Hz.
+
+### Data Fields
+
+Each row consists of the following fields:
+
+- `speaker_id`: Speaker ID
+- `audio`: Audio recording
+- `file`: Path to audio file
+- `text`: Text transcription of corresponding audio
+- `text_id`: Text ID
+- `age`: Speaker's age
+- `gender`: Speaker's gender
+- `accent`: Speaker's accent
+- `region`: Speaker's region, if annotation exists
+- `comment`: Miscellaneous comments, if any
+
+### Data Splits
+
+The dataset has no predefined splits.
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+The dataset consists of people who have donated their voice online.  You agree to not attempt to determine the identity of speakers in this dataset.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode))
+
+### Citation Information
+
+```bibtex
+@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
+}
+```
+
+### Contributions
+
+Thanks to [@jaketae](https://github.com/jaketae) for adding this dataset.

vctk.py

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+# 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
+
+
+_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,
+        )
+
+    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