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README.md

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----
-annotations_creators:
-- expert-generated
-language:
-- pl
-license:
-- mit
-multilinguality:
-- monolingual
-
-dataset_info:
-  - config_name: config
-    features:
-      - name: audio_id
-        dtype: string
-      - name: audio
-        dtype:
-          audio:
-            sampling_rate: 16000
-      - name: text
-        dtype: string
----
-
-
-# Dataset Card for [Dataset Name]
-
-## Table of Contents
-- [Table of Contents](#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:**
-- **Repository:**
-- **Paper:**
-- **Leaderboard:**
-- **Point of Contact:**
-
-### Dataset Summary
-
-[More Information Needed]
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed]
-
-### Languages
-
-[More Information Needed]
-
-## Dataset Structure
-
-### Data Instances
-
-[More Information Needed]
-
-### Data Fields
-
-[More Information Needed]
-
-### Data Splits
-
-[More Information Needed]
-
-## 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
-
-[More Information Needed]
-
-## 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
-
-[More Information Needed]
-
-### Citation Information
-
-[More Information Needed]
-
-### Contributions
-
-Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

clean/on/examples.zip → all/test2-clean.of.parquet

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other/of/examples.zip → all/test2-clean.on.parquet

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other/on/examples.zip → all/test2-other.of.parquet

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clean/of/examples.zip → all/test2-other.on.parquet

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clean/example.tsv

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-audio_id	ngram
-common_voice_pl_20547775.wav	poślemy potest
-common_voice_pl_20547776.wav	poślemy po wastest

clean/keyword.tsv

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-audio_id	ngram
-common_voice_pl_20547774.wav	poślemytrain

clean/test2-of.parquet

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clean/test2-on.parquet

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other/example.tsv

@@ -1,3 +0,0 @@
-audio_id	ngram
-common_voice_pl_20547775.wav	poślemy potest
-common_voice_pl_20547776.wav	poślemy po wastest

other/keyword.tsv

@@ -1,2 +0,0 @@
-audio_id	ngram
-common_voice_pl_20547774.wav	poślemytrain

other/test2-of.parquet

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other/test2-on.parquet

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test2.py

@@ -1,224 +0,0 @@
-
-# coding=utf-8
-# Lint as: python3
-"""test set"""
-
-
-import csv
-import os
-import json
-
-import datasets
-from datasets.utils.py_utils import size_str
-from tqdm import tqdm
-import os
-
-import datasets
-
-_CITATION = """\
-@inproceedings{panayotov2015librispeech,
-  title={Librispeech: an ASR corpus based on public domain audio books},
-  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
-  booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
-  pages={5206--5210},
-  year={2015},
-  organization={IEEE}
-}
-"""
-
-_DESCRIPTION = """\
-Lorem ipsum
-"""
-_URL = "https://huggingface.co/datasets/j-krzywdziak/test2"
-_AUDIO_URL = "https://huggingface.co/datasets/j-krzywdziak/test2/resolve/main"
-_DATA_URL = "https://huggingface.co/datasets/j-krzywdziak/test2/raw/main"
-_DL_URLS = {
-    "clean": {
-        "of": _AUDIO_URL + "/clean/of/examples.zip",
-        "on": _AUDIO_URL + "/clean/on/examples.zip",
-        "example": _DATA_URL + "/clean/example.tsv",
-        "keyword": _DATA_URL + "/clean/keyword.tsv"
-    },
-    "other": {
-        "of": _AUDIO_URL + "/other/of/examples.zip",
-        "on": _AUDIO_URL + "/other/on/examples.zip",
-        "example": _DATA_URL + "/other/example.tsv",
-        "keyword": _DATA_URL + "/other/keyword.tsv"
-    },
-    "all": {
-        "clean.of": _AUDIO_URL + "/clean/of/examples.zip",
-        "clean.on": _AUDIO_URL + "/clean/on/examples.zip",
-        "other.of": _AUDIO_URL + "/other/of/examples.zip",
-        "other.on": _AUDIO_URL + "/other/on/examples.zip",
-        "clean.example": _DATA_URL + "/clean/example.tsv",
-        "clean.keyword": _DATA_URL + "/clean/keyword.tsv",
-        "other.example": _DATA_URL + "/other/example.tsv",
-        "other.keyword": _DATA_URL + "/other/keyword.tsv"
-
-    },
-}
-
-
-
-class TestASR(datasets.GeneratorBasedBuilder):
-    """Lorem ipsum."""
-    VERSION = "0.0.0"
-    DEFAULT_CONFIG_NAME = "all"
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="clean", description="'Clean' speech."),
-        datasets.BuilderConfig(name="other", description="'Other', more challenging, speech."),
-        datasets.BuilderConfig(name="all", description="Combined clean and other dataset."),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "path": datasets.Value("string"),
-                    "audio": datasets.Audio(sampling_rate=16_000),
-                    "ngram": datasets.Value("string"),
-                    "type": datasets.Value("string")
-                }
-            ),
-            supervised_keys=("file", "text"),
-            homepage=_URL,
-            citation=_CITATION
-        )
-
-    def _split_generators(self, dl_manager):
-        archive_path = dl_manager.download(_DL_URLS[self.config.name])
-        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
-        local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
-        if self.config.name == "clean":
-            of_split = [
-                datasets.SplitGenerator(
-                    name="of",
-                    gen_kwargs={
-                        "local_extracted_archive": local_extracted_archive.get("of"),
-                        "files": dl_manager.iter_archive(archive_path["of"]),
-                        "examples": archive_path["example"],
-                        "keywords": archive_path["keyword"]
-                    },
-                )
-            ]
-            on_split = [
-                datasets.SplitGenerator(
-                    name="on",
-                    gen_kwargs={
-                        "local_extracted_archive": local_extracted_archive.get("on"),
-                        "files": dl_manager.iter_archive(archive_path["on"]),
-                        "examples": archive_path["example"],
-                        "keywords": archive_path["keyword"]
-                    },
-                )
-            ]
-        elif self.config.name == "other":
-            of_split = [
-                datasets.SplitGenerator(
-                    name="of",
-                    gen_kwargs={
-                        "local_extracted_archive": local_extracted_archive.get("of"),
-                        "files": dl_manager.iter_archive(archive_path["of"]),
-                        "examples": archive_path["example"],
-                        "keywords": archive_path["keyword"]
-                    },
-                )
-            ]
-            on_split = [
-                datasets.SplitGenerator(
-                    name="on",
-                    gen_kwargs={
-                        "local_extracted_archive": local_extracted_archive.get("on"),
-                        "files": dl_manager.iter_archive(archive_path["on"]),
-                        "examples": archive_path["example"],
-                        "keywords": archive_path["keyword"]
-                    },
-                )
-            ]
-        elif self.config.name == "all":
-            of_split = [
-                datasets.SplitGenerator(
-                    name="clean.of",
-                    gen_kwargs={
-                        "local_extracted_archive": local_extracted_archive.get("clean.of"),
-                        "files": dl_manager.iter_archive(archive_path["clean.of"]),
-                        "examples": archive_path["clean.example"],
-                        "keywords": archive_path["clean.keyword"]
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name="other.of",
-                    gen_kwargs={
-                        "local_extracted_archive": local_extracted_archive.get("other.of"),
-                        "files": dl_manager.iter_archive(archive_path["other.of"]),
-                        "examples": archive_path["other.example"],
-                        "keywords": archive_path["other.keyword"]
-                    }
-                )
-            ]
-            on_split = [
-                datasets.SplitGenerator(
-                    name="clean.on",
-                    gen_kwargs={
-                        "local_extracted_archive": local_extracted_archive.get("clean.on"),
-                        "files": dl_manager.iter_archive(archive_path["clean.on"]),
-                        "examples": archive_path["clean.example"],
-                        "keywords": archive_path["clean.keyword"]
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name="other.on",
-                    gen_kwargs={
-                        "local_extracted_archive": local_extracted_archive.get("other.on"),
-                        "files": dl_manager.iter_archive(archive_path["other.on"]),
-                        "examples": archive_path["other.example"],
-                        "keywords": archive_path["other.keyword"]
-                    }
-                )
-            ]
-        return on_split + of_split
-
-    def _generate_examples(self, files, local_extracted_archive, examples, keywords):
-        """Lorem ipsum."""
-        audio_data = {}
-        transcripts = []
-        key = 0
-        print(examples, keywords)
-        print(local_extracted_archive)
-        for path, f in files:
-            audio_data[path] = f.read()
-        with open(keywords, encoding="utf-8") as f:
-            next(f)
-            for row in f:
-                r = row.split("\t")
-                print(r)
-                path = 'examples/'+r[0]
-                ngram = r[1]
-                transcripts.append({
-                    "path": path,
-                    "ngram": ngram,
-                    "type": "keyword"
-                })
-        with open(examples, encoding="utf-8") as f2:
-            next(f2)
-            for row in f2:
-                r = row.split("\t")
-                print(r)
-                path = 'examples/'+r[0]
-                ngram = r[1]
-                transcripts.append({
-                    "path": path,
-                    "ngram": ngram,
-                    "type": "example"
-                })
-        print("AUDIO DATA: ", audio_data)
-        print("TRANSCRIPT: ", transcripts)
-        if audio_data and len(audio_data) == len(transcripts):
-            for transcript in transcripts:
-                audio = {"path": transcript["path"], "bytes": audio_data[transcript["path"]]}
-                yield key, {"audio": audio, **transcript}
-                key += 1
-            audio_data = {}
-            transcripts = []