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README.md

@@ -1,3 +1,71 @@
----
-license: cc-by-nc-4.0
----
+---
+        language:
+        - ru
+
+        license: cc-by-nc-4.0
+        task_categories:
+        - automatic-speech-recognition
+        size_categories:
+        - 10K<n<100K
+        tags:
+        - audio
+- speech
+- Russian
+- ASR
+- voice
+
+        dataset_info:
+          features:
+          -   dtype: string
+    name: id
+  -   dtype: string
+    name: path
+  -   dtype: string
+    name: text
+  -   dtype: float32
+    name: duration
+  -   dtype: audio
+    name: audio
+
+          config_name: asr_calls_v2, buriy_audio_books_2, public_youtube700
+          splits:
+          -   name: train
+  -   name: validate
+
+        pretty_name: open_stt
+        ---
+    
+        # open_stt Dataset
+    
+        ## Dataset Description
+    
+        open_stt is a Russian dataset for speech research.
+    
+        ## Dataset Structure
+    
+        This dataset is organized as follows:
+    
+        
+### asr_calls_v2 subset
+
+| Split | Samples |
+|-------|--------|
+| train | 7770 |
+| validate | 5180 |
+| **Total** | **12950** |
+
+### buriy_audio_books_2 subset
+
+| Split | Samples |
+|-------|--------|
+| train | 4710 |
+| validate | 3140 |
+| **Total** | **7850** |
+
+### public_youtube700 subset
+
+| Split | Samples |
+|-------|--------|
+| train | 4386 |
+| validate | 2925 |
+| **Total** | **7311** |

n_shards.json

@@ -0,0 +1,14 @@
+{
+  "asr_calls_v2": {
+    "train": 1,
+    "validate": 1
+  },
+  "buriy_audio_books_2": {
+    "train": 1,
+    "validate": 1
+  },
+  "public_youtube700": {
+    "train": 1,
+    "validate": 1
+  }
+}

open_stt.py

@@ -0,0 +1,189 @@
+# coding=utf-8
+    # Copyright 2023 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.
+    """Open STT Dataset"""
+
+    import csv
+    import os
+    import json
+
+    import datasets
+    from tqdm import tqdm
+
+
+    _DESCRIPTION = """open_stt is a Russian dataset for speech research."""
+
+    _CITATION = """None"""
+
+    _HOMEPAGE = "https://github.com/snakers4/open_stt"
+    _LICENSE = "CC-BY-NC. Commercial usage available after agreement with dataset authors"
+
+    _BASE_URL = "https://huggingface.co/datasets/Sh1man/silero_open_stt/resolve/main/"
+    _AUDIO_URL = _BASE_URL + "data/{subset}/{split}/{subset}_{split}_{shard_idx}.tar"
+    _METADATA_URL = _BASE_URL + "metadata/{subset}/{split}.tsv"
+    _N_SHARDS_URL = _BASE_URL + "n_shards.json"
+
+    # Информация о поднаборах
+    SUBSETS = {
+        "asr_calls_v2": {
+            "description": "Телефонные звонки на русском языке",
+        },
+        "buriy_audio_books_2": {
+            "description": "Аудиокниги на русском языке",
+        },
+        "public_youtube700": {
+            "description": "YouTube-видео с русской речью",
+        },
+    }
+
+
+    class OpenSttConfig(datasets.BuilderConfig):
+        """BuilderConfig для OpenStt."""
+
+        def __init__(self, name, subset, description, **kwargs):
+            """BuilderConfig для OpenStt.
+
+            Args:
+              name: Название набора данных
+              subset: Поднабор данных
+              description: Описание поднабора
+              **kwargs: Дополнительные аргументы для суперкласса
+            """
+            self.subset = subset
+            super(OpenSttConfig, self).__init__(
+                name=name,
+                version=datasets.Version("1.0.0"),
+                description=description,
+                **kwargs,
+            )
+
+
+    class OpenStt(datasets.GeneratorBasedBuilder):
+        """Аудио-датасет Open STT."""
+
+        VERSION = datasets.Version("1.0.0")
+        DEFAULT_WRITER_BATCH_SIZE = 1000
+
+        BUILDER_CONFIGS = [
+            OpenSttConfig(
+                name=subset,
+                subset=subset,
+                description=f"Open STT - {info['description']}",
+            )
+            for subset, info in SUBSETS.items()
+        ]
+
+        def _info(self):
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "path": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "duration": datasets.Value("float32"),
+                    "audio": datasets.Audio(sampling_rate=16000),
+                }
+            )
+
+            return datasets.DatasetInfo(
+                description=_DESCRIPTION,
+                features=features,
+                supervised_keys=None,
+                homepage=_HOMEPAGE,
+                license=_LICENSE,
+                citation=_CITATION
+            )
+
+        def _split_generators(self, dl_manager):
+            """Returns SplitGenerators."""
+            subset = self.config.subset
+
+            # Загружаем информацию о количестве шардов
+            n_shards_path = dl_manager.download_and_extract(_N_SHARDS_URL)
+            with open(n_shards_path, encoding="utf-8") as f:
+                n_shards = json.load(f)
+
+            # Проверяем наличие данных для выбранного поднабора
+            if subset not in n_shards:
+                raise ValueError(f"Subset {subset} not found in n_shards.json")
+
+            # Определяем доступные сплиты для этого поднабора
+            splits = list(n_shards[subset].keys())
+            if not splits:
+                raise ValueError(f"No splits found for subset {subset}")
+
+            # Создаем URLs для аудио файлов
+            audio_urls = {}
+            for split in splits:
+                if n_shards[subset][split] > 0:
+                    audio_urls[split] = [
+                        _AUDIO_URL.format(subset=subset, split=split, shard_idx=i) 
+                        for i in range(n_shards[subset][split])
+                    ]
+
+            # Скачиваем ар��ивы
+            archive_paths = dl_manager.download(audio_urls)
+            local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
+
+            # Скачиваем метаданные
+            meta_urls = {split: _METADATA_URL.format(subset=subset, split=split) for split in splits}
+            meta_paths = dl_manager.download_and_extract(meta_urls)
+
+            # Определяем генераторы сплитов
+            split_generators = []
+            split_names = {
+                "train": datasets.Split.TRAIN,
+                "validate": datasets.Split.VALIDATION,
+                "test": datasets.Split.TEST,
+            }
+
+            for split in splits:
+                split_generators.append(
+                    datasets.SplitGenerator(
+                        name=split_names.get(split, split),
+                        gen_kwargs={
+                            "local_extracted_archive_paths": local_extracted_archive_paths.get(split),
+                            "archives": [dl_manager.iter_archive(path) for path in archive_paths[split]],
+                            "meta_path": meta_paths[split],
+                        },
+                    ),
+                )
+
+            return split_generators
+
+        def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
+            """Yields examples."""
+            data_fields = list(self._info().features.keys())
+            metadata = {}
+
+            with open(meta_path, encoding="utf-8") as f:
+                reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+                for row in tqdm(reader, desc="Reading metadata..."):
+                    # Проверяем наличие всех полей
+                    for field in data_fields:
+                        if field not in row and field != "audio":
+                            row[field] = ""
+
+                    metadata[row["path"]] = row
+
+            for i, audio_archive in enumerate(archives):
+                for path, file in audio_archive:
+                    _, filename = os.path.split(path)
+                    if filename in metadata:
+                        result = dict(metadata[filename])
+                        # set the audio feature and the path to the extracted file
+                        path = os.path.join(local_extracted_archive_paths[i], path) if local_extracted_archive_paths else path
+                        result["audio"] = {"path": path, "bytes": file.read()}
+                        result["path"] = path
+                        yield path, result
+