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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
1
Dataset Viewer
Auto-converted to Parquet Duplicate
audio
dict
duration
float64
30.7
75.8
num_speakers
int64
1
4
sampling_rate
int64
16k
16k
conversation_type
stringclasses
4 values
difficulty
stringclasses
3 values
has_overlaps
bool
2 classes
has_simultaneous
bool
2 classes
has_noise
bool
2 classes
speakers
listlengths
2
19
noise_type
stringclasses
7 values
snr
float64
10
30
⌀
speaker_volumes
listlengths
1
4
simultaneous_segments
listlengths
0
19
{"array":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.(...TRUNCATED)
46.13
2
16,000
dialogue
medium
true
false
false
[{"speaker_id":1,"start":0.0,"end":4.12,"duration":4.12,"text":"расскажи мне о сего(...TRUNCATED)
null
null
[ { "speaker_id": 1, "volume": 1.06 }, { "speaker_id": 2, "volume": 1.12 } ]
[]
{"array":[0.0,-0.000030517578125,0.0,-0.000030517578125,0.0,-0.000030517578125,-0.004669189453125,-0(...TRUNCATED)
38.96
2
16,000
dialogue
medium
true
false
true
[{"speaker_id":1,"start":0.0,"end":4.86,"duration":4.86,"text":"создай электронное (...TRUNCATED)
pink
16.809999
[ { "speaker_id": 1, "volume": 0.84 }, { "speaker_id": 2, "volume": 0.88 } ]
[]
{"array":[0.0,-0.000030517578125,0.0,-0.000030517578125,0.0,-0.000030517578125,-0.004669189453125,-0(...TRUNCATED)
38.96
2
16,000
dialogue
medium
true
false
true
[{"speaker_id":1,"start":0.0,"end":4.86,"duration":4.86,"text":"создай электронное (...TRUNCATED)
pink
16.809999
[ { "speaker_id": 1, "volume": 0.84 }, { "speaker_id": 2, "volume": 0.88 } ]
[]
{"array":[0.0,-0.000030517578125,0.0,-0.000030517578125,0.0,-0.000030517578125,0.0,-0.00003051757812(...TRUNCATED)
41.05
2
16,000
interview
medium
true
false
false
[{"speaker_id":1,"start":0.0,"end":2.64,"duration":2.64,"text":"какой будет погода (...TRUNCATED)
null
null
[ { "speaker_id": 1, "volume": 1.27 }, { "speaker_id": 2, "volume": 0.74 } ]
[]
{"array":[-0.000030517578125,0.0,-0.000030517578125,0.0,0.0,-0.000030517578125,0.0,-0.00003051757812(...TRUNCATED)
55.69
2
16,000
interview
medium
true
false
false
[{"speaker_id":1,"start":0.0,"end":6.9,"duration":6.9,"text":"напиши иимейл боссу (...TRUNCATED)
null
null
[ { "speaker_id": 1, "volume": 0.83 }, { "speaker_id": 2, "volume": 0.76 } ]
[]
{"array":[0.0,-0.000030517578125,0.0,-0.000030517578125,0.0,-0.000030517578125,-0.004669189453125,-0(...TRUNCATED)
38.96
2
16,000
dialogue
medium
true
false
true
[{"speaker_id":1,"start":0.0,"end":4.86,"duration":4.86,"text":"создай электронное (...TRUNCATED)
pink
16.809999
[ { "speaker_id": 1, "volume": 0.84 }, { "speaker_id": 2, "volume": 0.88 } ]
[]
{"array":[-0.000030517578125,0.0,-0.000030517578125,0.0,0.0,-0.000030517578125,0.0,-0.00003051757812(...TRUNCATED)
55.69
2
16,000
interview
medium
true
false
false
[{"speaker_id":1,"start":0.0,"end":6.9,"duration":6.9,"text":"напиши иимейл боссу (...TRUNCATED)
null
null
[ { "speaker_id": 1, "volume": 0.83 }, { "speaker_id": 2, "volume": 0.76 } ]
[]
{"array":[0.0,-0.000030517578125,0.0,-0.000030517578125,0.0,-0.000030517578125,-0.004669189453125,-0(...TRUNCATED)
38.96
2
16,000
dialogue
medium
true
false
true
[{"speaker_id":1,"start":0.0,"end":4.86,"duration":4.86,"text":"создай электронное (...TRUNCATED)
pink
16.809999
[ { "speaker_id": 1, "volume": 0.84 }, { "speaker_id": 2, "volume": 0.88 } ]
[]
{"array":[0.0,-0.000030517578125,0.0,0.0,-0.000030517578125,0.0,-0.000030517578125,0.0,-0.0000305175(...TRUNCATED)
55.08
2
16,000
interview
medium
false
true
false
[{"speaker_id":1,"start":0.0,"end":3.0,"duration":3.0,"text":"эта песня димы билан(...TRUNCATED)
null
null
[ { "speaker_id": 1, "volume": 1.06 }, { "speaker_id": 2, "volume": 0.75 } ]
[{"start":3.0,"end":5.19,"speaker1_id":2,"speaker2_id":1,"duration":2.19},{"start":7.32,"end":8.91,"(...TRUNCATED)
{"array":[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.(...TRUNCATED)
41.01
2
16,000
dialogue
medium
true
false
false
[{"speaker_id":1,"start":0.0,"end":3.64,"duration":3.64,"text":"какая сейчас погода(...TRUNCATED)
null
null
[ { "speaker_id": 1, "volume": 0.91 }, { "speaker_id": 2, "volume": 1.24 } ]
[]
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synthetic-speech-diarization-ru

Synthetic speech diarization dataset in Parquet format.

Dataset Details

  • Number of tracks: 2000
  • Sampling rate: 16000 Hz
  • Audio format: Embedded in Parquet files (Audio feature compatible)
  • Storage: Parquet format for efficient loading

Dataset Structure

The dataset contains audio tracks with speaker diarization annotations, stored directly in Parquet format.

Features

  • audio: Audio waveform (Audio feature with array and sampling_rate)
  • duration: Track duration in seconds
  • num_speakers: Number of speakers in the track
  • speakers: List of speaker segments with timestamps and text
  • speaker_volumes: Speaker volume levels
  • conversation_type: Type of conversation (dialogue, monologue, etc.)
  • difficulty: Difficulty level (easy, medium, hard)
  • has_overlaps: Whether track contains overlapping speech
  • has_simultaneous: Whether track contains simultaneous speech
  • has_noise: Whether track contains background noise

Usage 

from datasets import load_dataset

# Load dataset from HuggingFace Hub directly
dataset = load_dataset("ivkond/synthetic-speech-diarization-ru")

# Access a sample
sample = dataset[0]
print(f"Duration: {sample['duration']}s")
print(f"Speakers: {sample['num_speakers']}")

Notes

  • Audio data is embedded directly in Parquet files for efficient streaming
  • No custom loading script required
  • Compatible with standard Hugging Face dataset operations
Downloads last month
47
Size of downloaded dataset files:
2.28 GB
Size of the auto-converted Parquet files:
2.28 GB
Number of rows:
2,000