Dataset Viewer
image
imagewidth (px) 91
344
| annotation
stringclasses 7
values | transcription
stringlengths 0
107
| video_segment
stringlengths 31
34
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🎯 Включение в процесс
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students. Good morning and how are you today?
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segments/target_14.36_18.00.mp4
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🎯 Включение в процесс
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students. Good morning and how are you today?
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segments/target_14.36_18.00.mp4
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🎯 Включение в процесс
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students. Good morning and how are you today?
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segments/target_14.36_18.00.mp4
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🗣️ Чёткое и структурированное изложение с акцентами
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the place theme must be simple. And right now that we use it to talk about completed actions in the task.
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segments/item_201.52_215.72.mp4
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🗣️ Чёткое и структурированное изложение с акцентами
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the place theme must be simple. And right now that we use it to talk about completed actions in the task.
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segments/item_201.52_215.72.mp4
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🗣️ Чёткое и структурированное изложение с акцентами
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the place theme must be simple. And right now that we use it to talk about completed actions in the task.
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segments/item_201.52_215.72.mp4
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🎯 Включение в процесс
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segments/target_250.52_252.72.mp4
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🎯 Включение в процесс
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segments/target_250.52_252.72.mp4
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🎯 Включение в процесс
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segments/target_250.52_252.72.mp4
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🗣️ Чёткое и структурированное изложение с акцентами
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Marking words, how can we know that it is possible that we should use here? It is, let's read down.
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segments/item_373.40_382.20.mp4
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🗣️ Чёткое и структурированное изложение с акцентами
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Marking words, how can we know that it is possible that we should use here? It is, let's read down.
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segments/item_373.40_382.20.mp4
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🗣️ Чёткое и структурированное изложение с акцентами
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Marking words, how can we know that it is possible that we should use here? It is, let's read down.
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segments/item_373.40_382.20.mp4
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💬 Качественная обратная связь
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So why do we use a fast signal here? Because we...
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segments/item_435.68_440.96.mp4
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💬 Качественная обратная связь
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So why do we use a fast signal here? Because we...
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segments/item_435.68_440.96.mp4
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💬 Качественная обратная связь
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So why do we use a fast signal here? Because we...
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segments/item_435.68_440.96.mp4
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💖 Эмоциональная поддержка
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Action action Basically
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segments/heart_495.12_496.68.mp4
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💖 Эмоциональная поддержка
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Action action Basically
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segments/heart_495.12_496.68.mp4
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💖 Эмоциональная поддержка
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Action action Basically
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segments/heart_495.12_496.68.mp4
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🔄 Разнообразие форм работы
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Let's rock the snow. Uh, sorry, the other two.
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segments/refresh_496.76_500.28.mp4
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🔄 Разнообразие форм работы
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Let's rock the snow. Uh, sorry, the other two.
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segments/refresh_496.76_500.28.mp4
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🔄 Разнообразие форм работы
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Let's rock the snow. Uh, sorry, the other two.
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segments/refresh_496.76_500.28.mp4
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🎯 Включение в процесс
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very good industries.
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segments/target_662.48_665.20.mp4
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🎯 Включение в процесс
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very good industries.
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segments/target_662.48_665.20.mp4
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🎯 Включение в процесс
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very good industries.
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segments/target_662.48_665.20.mp4
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💬 Качественная обратная связь
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the pipe we have for constant length of saglassene. We need to change it to the pipe, like whatever for it.
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segments/item_676.68_684.28.mp4
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💬 Качественная обратная связь
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the pipe we have for constant length of saglassene. We need to change it to the pipe, like whatever for it.
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segments/item_676.68_684.28.mp4
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💬 Качественная обратная связь
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the pipe we have for constant length of saglassene. We need to change it to the pipe, like whatever for it.
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segments/item_676.68_684.28.mp4
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💖 Эмоциональная поддержка
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Yeah, thank you.
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segments/heart_813.64_815.20.mp4
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💖 Эмоциональная поддержка
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Yeah, thank you.
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segments/heart_813.64_815.20.mp4
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💖 Эмоциональная поддержка
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Yeah, thank you.
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segments/heart_813.64_815.20.mp4
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🔄 Разнообразие форм работы
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Let's move on please. We're going to stir it up.
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segments/refresh_986.00_990.40.mp4
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🔄 Разнообразие форм работы
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Let's move on please. We're going to stir it up.
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segments/refresh_986.00_990.40.mp4
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🔄 Разнообразие форм работы
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Let's move on please. We're going to stir it up.
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segments/refresh_986.00_990.40.mp4
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🌍 Связь с личным опытом
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What time do you hear here?
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segments/world_1054.72_1057.76.mp4
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🌍 Связь с личным опытом
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What time do you hear here?
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segments/world_1054.72_1057.76.mp4
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🌍 Связь с личным опытом
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What time do you hear here?
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segments/world_1054.72_1057.76.mp4
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🌍 Связь с личным опытом
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That's something. Okay.
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segments/world_1087.28_1090.00.mp4
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🌍 Связь с личным опытом
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That's something. Okay.
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segments/world_1087.28_1090.00.mp4
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🌍 Связь с личным опытом
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That's something. Okay.
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segments/world_1087.28_1090.00.mp4
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💻 Использование ИКТ в обучении
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Here's the falses.
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segments/item_630.40_632.16.mp4
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💻 Использование ИКТ в обучении
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Here's the falses.
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segments/item_630.40_632.16.mp4
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💻 Использование ИКТ в обучении
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Here's the falses.
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segments/item_630.40_632.16.mp4
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YAML Metadata
Warning:
The task_ids "image-classification-other" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, language-identification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-modeling, dialogue-generation, conversational, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, text2text-generation, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, image-inpainting, image-colorization, super-resolution, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering, pose-estimation
Video Dataset - pre-test-colab
Dataset Description
This dataset contains video frames extracted from annotated video segments, along with annotations, transcriptions, and corresponding video clips.
Dataset Structure
frames/— extracted frames grouped by role (start,middle,end)segments/— video clips for each annotation intervalannotations/— original JSON annotationtranscriptions/— transcription files (full_transcription.txt+ per segment)dataset.csv— mapping between frames, annotations, video clips, and segment transcription
Dataset Statistics
- Frames: 42
- Segments: 14
- Unique Labels: 7
Dataset Features
- image: Extracted video frame (JPEG)
- annotation: Label/annotation for the frame segment
- transcription: Text transcription of the audio segment
- video_segment: Path to the corresponding video clip file
Usage
This dataset can be loaded using the Hugging Face datasets library:
from datasets import load_dataset
dataset = load_dataset("your-org/video-dataset-pre-test-colab")
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