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Datasets:
ivkond
/
synthetic-speech-diarization-ru

Tasks:
Automatic Speech Recognition
Audio Classification
Modalities:
Tabular
Text
Audio
Formats:
parquet
Languages:
Russian
Size:
1K - 10K
Tags:
audio
speech
diarization
Synthetic
Libraries:
Datasets
Dask
License:
Dataset card Data Studio Files
xet
 Community
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ivkond commited on
Commit
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Upload dataset: 2000 tracks using upload_folder

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README.md ADDED
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+ ---
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+ license: mit
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+ task_categories:
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+ - automatic-speech-recognition
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+ - audio-classification
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+ tags:
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+ - audio
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+ - speech
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+ - diarization
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+ - synthetic
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+ ---
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+
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+ # synthetic-speech-diarization-ru
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+
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+ Synthetic speech diarization dataset in Parquet format.
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+
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+ ## Dataset Details
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+
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+ - **Number of tracks**: 2000
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+ - **Sampling rate**: 16000 Hz
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+ - **Audio format**: Embedded in Parquet files (Audio feature compatible)
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+ - **Storage**: Parquet format for efficient loading
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+
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+ ## Dataset Structure
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+
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+ The dataset contains audio tracks with speaker diarization annotations, stored directly in Parquet format.
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+
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+ ### Features
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+
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+ - `audio`: Audio waveform (Audio feature with array and sampling_rate)
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+ - `duration`: Track duration in seconds
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+ - `num_speakers`: Number of speakers in the track
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+ - `speakers`: List of speaker segments with timestamps and text
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+ - `speaker_volumes`: Speaker volume levels
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+ - `conversation_type`: Type of conversation (dialogue, monologue, etc.)
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+ - `difficulty`: Difficulty level (easy, medium, hard)
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+ - `has_overlaps`: Whether track contains overlapping speech
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+ - `has_simultaneous`: Whether track contains simultaneous speech
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+ - `has_noise`: Whether track contains background noise
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+
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+ ## Usage
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ # Load dataset from HuggingFace Hub directly
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+ dataset = load_dataset("ivkond/synthetic-speech-diarization-ru")
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+
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+ # Access a sample
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+ sample = dataset[0]
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+ print(f"Duration: {sample['duration']}s")
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+ print(f"Speakers: {sample['num_speakers']}")
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+ ```
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+
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+ ## Notes
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+
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+ - Audio data is embedded directly in Parquet files for efficient streaming
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+ - No custom loading script required
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+ - Compatible with standard Hugging Face dataset operations
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