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@@ -57,3 +57,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+metadata/tts_russian_addresses_rhvoice_4voices/train.tsv filter=lfs diff=lfs merge=lfs -text
+metadata/tts_russian_addresses_rhvoice_4voices/validate.tsv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,60 @@
----
-license: cc-by-nc-4.0
----
+---
+    language:
+    - ru
+
+    license: cc-by-nc-4.0
+    task_categories:
+    - automatic-speech-recognition
+    size_categories:
+    - n>1M
+    tags:
+    - audio
+- speech
+- Russian
+- ASR
+- voice
+
+    dataset_info:
+      features:
+        - name: id
+          dtype: string
+        - name: path
+          dtype: string
+        - name: text
+          dtype: string
+        - name: duration
+          dtype: float32
+        - name: audio
+          dtype: audio
+      config_name: tts_russian_addresses_rhvoice_4voices
+      splits:
+        - name: train
+    - name: validate
+    pretty_name: silero_open_stt
+    ---
+
+        
+    # silero_open_stt Dataset
+
+    ## Dataset Description
+
+    open_stt is a Russian dataset for speech research.
+
+    ## Dataset Structure
+
+    This dataset is organized in the Common Voice format:
+
+    - `/audio/{subset}/{split}/` - Contains TAR files with audio files
+    - `/metadata/{subset}/` - Contains TSV files with transcriptions
+    - `/n_shards.json` - Contains information about the number of shards for each subset and split
+
+    This dataset is organized as follows:
+
+        
+### tts_russian_addresses_rhvoice_4voices subset
+
+| Split | Samples |
+|-------|--------|
+| train | 1045102 |
+| validate | 696736 |
+| **Total** | **1741838** |

n_shards.json

@@ -0,0 +1,6 @@
+{
+  "tts_russian_addresses_rhvoice_4voices": {
+    "train": 12,
+    "validate": 8
+  }
+}

silero_open_stt.py

@@ -0,0 +1,183 @@
+# 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.
+        """silero_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-4.0"
+
+        _BASE_URL = "https://huggingface.co/datasets/Sh1man/silero_open_stt_opus/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 = {
+        "tts_russian_addresses_rhvoice_4voices": {
+            "description": "Поднабор tts_russian_addresses_rhvoice_4voices датасета silero_open_stt",
+        },
+    }
+
+
+        class silero_open_sttConfig(datasets.BuilderConfig):
+            """BuilderConfig для silero_open_stt."""
+
+            def __init__(self, name, subset, description, **kwargs):
+                """BuilderConfig для silero_open_stt.
+
+                Args:
+                  name: Название набора данных
+                  subset: Поднабор данных
+                  description: Описание поднабора
+                  **kwargs: Дополнительные аргументы для суперкласса
+                """
+                self.subset = subset
+                super(silero_open_sttConfig, self).__init__(
+                    name=name,
+                    version=datasets.Version("1.0.0"),
+                    description=description,
+                    **kwargs,
+                )
+
+
+        class silero_open_stt(datasets.GeneratorBasedBuilder):
+            """Аудио-датасет silero_open_stt."""
+
+            VERSION = datasets.Version("1.0.0")
+            DEFAULT_WRITER_BATCH_SIZE = 1000
+
+            BUILDER_CONFIGS = [
+                silero_open_sttConfig(
+                    name=subset,
+                    subset=subset,
+                    description=f"silero_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 архивы
+                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
+