Initial Commit

README.md

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+---
+TODO: Add YAML tags here. Copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging
+---
+
+# 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.

chall.py

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+import json
+import os
+import datasets
+import soundfile as sf
+
+
+_DESCRIPTION = "tbd"
+_CITATION = "tbd"
+
+_META_FILE = "chall_data.jsonl"
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+class ChallConfig(datasets.BuilderConfig):
+
+    split_into_utterances: bool = False
+
+    def __init__(self, split_into_utterances: bool, **kwargs):
+        super(ChallConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.split_into_utterances = split_into_utterances
+
+
+class Chall(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    DEFAULT_CONFIG_NAME = "chall_data"
+
+    BUILDER_CONFIGS = [
+        ChallConfig(
+            name="chall_data",
+            split_into_utterances=False
+        ),
+        ChallConfig(
+            name="asr",
+            split_into_utterances=True
+        )
+    ]
+
+    max_chunk_length: int = int
+
+    def __init__(self, *args, max_chunk_length=12, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.max_chunk_length = max_chunk_length  # max chunk length in seconds
+
+    @property
+    def manual_download_instructions(self):
+        return (
+            "To use the chall dataset you have to download it manually. "
+            "TBD Download Instructions. "  # todo
+            "Please extract all files in one folder and load the dataset with: "
+            "`datasets.load_dataset('chall', data_dir='path/to/folder/folder_name')`"
+        )
+
+    def _info(self):
+
+        if self.config.split_into_utterances:
+            features = datasets.Features({
+                "audio_id": datasets.Value("string"),  # todo maybe shorten to id
+                "intervention": datasets.Value("int32"),
+                "school_grade": datasets.Value("string"),
+                "area_of_school_code": datasets.Value("int32"),
+                "background_noise": datasets.Value("bool"),
+                "speaker": datasets.Value("string"),
+                "words": datasets.features.Sequence(
+                    {
+                        "start": datasets.Value("float"),
+                        "end": datasets.Value("float"),
+                        "duration": datasets.Value("float"),
+                        "text": datasets.Value("string"),
+                    }
+                ),
+                "audio": datasets.Audio(sampling_rate=16_000)
+            })
+        else:
+            features = datasets.Features({
+                "audio_id": datasets.Value("string"),  # todo maybe shorten to id
+                "intervention": datasets.Value("int32"),
+                "school_grade": datasets.Value("string"),
+                "area_of_school_code": datasets.Value("int32"),
+                "participants": datasets.features.Sequence(
+                    {
+                        "pseudonym": datasets.Value("string"),
+                        "gender": datasets.Value("string"),
+                        "year_of_birth": datasets.Value("int32"),
+                        "school_grade": datasets.Value("int32"),
+                        "languages": datasets.Value("string"),
+                        "estimated_l2_proficiency": datasets.Value("string")
+                    }, length=-1
+                ),
+                "background_noise": datasets.Value("bool"),
+                "speakers": datasets.features.Sequence(
+                    {
+                        "spkid": datasets.Value("string"),
+                        "name": datasets.Value("string")
+                    }
+                ),
+                "segments": datasets.features.Sequence(
+                    {
+                        "speaker": datasets.Value("string"),
+                        "words": datasets.features.Sequence(
+                            {
+                                "start": datasets.Value("float"),
+                                "end": datasets.Value("float"),
+                                "duration": datasets.Value("float"),
+                                "text": datasets.Value("string"),
+                            }
+                        ),
+                    }
+                ),
+                "audio": datasets.Audio(sampling_rate=16_000)
+            })
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            # todo No default supervised_keys (as we have to pass both question and context as input).
+            supervised_keys=None,
+            homepage="",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        print("_split_generators")
+
+        # todo define splits?
+
+        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+
+        print(data_dir)
+
+        # todo read ids for splits as we do not separate them by folder
+
+        if not os.path.exists(data_dir):
+            raise FileNotFoundError(
+                f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('chall', data_dir=...)` "
+                f"that includes files unzipped from the chall zip. Manual download instructions: {self.manual_download_instructions}"
+            )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": os.path.join(data_dir, "data"), "metafile": os.path.join(data_dir, _META_FILE)},
+            ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.TEST,
+            #     gen_kwargs={"filepath": os.path.join(data_dir, "data"), "metafile": os.path.join(data_dir, _META_FILE)},
+            # ),
+            # datasets.SplitGenerator(
+            #     name=datasets.Split.VALIDATION,
+            #     gen_kwargs={"filepath": os.path.join(data_dir, "data"), "metafile": os.path.join(data_dir, _META_FILE)},
+            # ),
+        ]
+
+    def _generate_examples(self, filepath, metafile):
+
+        logger.info("generating examples from = %s", filepath)  # todo define logger?
+        print("_generate_examples")
+
+        with open(metafile, 'r') as file:
+            for line in file:
+                data = json.loads(line)
+
+                # load json
+                transcript_file = os.path.join(filepath, data["transcript_file"])
+                with open(transcript_file, 'r') as transcript:
+                    transcript = json.load(transcript)
+
+                audio_id = data['audio_id']
+                audio_file_path = os.path.join(filepath, data["audio_file"])
+
+                if self.config.name == "asr":
+
+                    for segment_i, segment in enumerate(transcript["segments"]):
+
+                        id_ = f"{audio_id}_{str(segment_i).rjust(3, '0')}"
+
+                        data["audio_id"] = id_
+                        data["speaker_id"] = segment["speaker"]
+                        data["words"] = segment["words"]
+
+                        track = sf.SoundFile(audio_file_path)
+
+                        can_seek = track.seekable()
+                        if not can_seek:
+                            raise ValueError("Not compatible with seeking")
+
+                        sr = track.samplerate
+                        start_time = segment["words"][0]["start"]
+                        end_time = segment["words"][-1]["end"]
+
+                        start_frame = int(sr * start_time)
+                        frames_to_read = int(sr * (end_time - start_time))
+
+                        # Seek to the start frame
+                        track.seek(start_frame)
+
+                        # Read the desired frames
+                        audio = track.read(frames_to_read)
+
+                        data["audio"] = {"path": audio_file_path, "array": audio, "sampling_rate": sr}
+
+                        yield id_, data
+                else:
+
+                    id_ = data["audio_id"]
+                    data["speakers"] = transcript["speakers"]
+                    data["segments"] = transcript["segments"]
+
+                    audio, samplerate = sf.read(audio_file_path)
+                    data["audio"] = {"path": audio_file_path, "array": audio, "sampling_rate": samplerate}
+
+                    yield id_, data