laion/BUD-E-Whisper_V1.21
Automatic Speech Recognition • 0.2B • Updated • 11
Datasets:
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 90, in _split_generators
inferred_arrow_schema = pa.concat_tables(pa_tables, promote_options="default").schema
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 6319, in pyarrow.lib.concat_tables
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowTypeError: Unable to merge: Field json has incompatible types: struct<caption: string, caption_type: string, duration: double, emotion_whisper: string, emotion_whisper_half_1: string, emotion_whisper_half_2: string, score_background_quality: double, score_content_enjoyment: double, score_overall_quality: double, score_speech_quality: double, characters_per_second: double, transcription: string> vs struct<caption: string, caption_type: string, characters_per_second: int64, duration: double, emotion_whisper: string, emotion_whisper_half_1: string, emotion_whisper_half_2: string, score_background_quality: double, score_content_enjoyment: double, score_overall_quality: double, score_speech_quality: double, transcription: string>: Unable to merge: Field characters_per_second has incompatible types: double vs int64
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Balanced Emotion Dataset — Majestrino with Temporal Detailed Captions
An emotion-balanced subset of TTS-AGI/majestrino-unified-detailed-captions-temporal.
Overview
- Total samples: 482,594
- Samples per emotion category: 12,997
- Number of emotion categories: 40
- Format: WebDataset (tar files with FLAC audio + JSON metadata)
- Number of tar files: 483
- Samples per tar: ~1000
Balancing Strategy
Samples were selected from the source dataset using keyword matching on captions. Each of the 40 emotion categories has exactly 12,997 samples, balanced by the rarest category (Intoxication/Altered States). Samples are spread across diverse source shards for maximum variety. Some samples (~7.3%) appear in multiple categories due to multi-emotion captions.
Emotion Categories
| Category | Samples | Example Keywords |
|---|---|---|
| Amusement | 12997 | lighthearted fun, amusement, mirth, joviality, laughter |
| Elation | 12997 | happiness, excitement, joy, exhilaration, delight |
| Pleasure/Ecstasy | 12997 | ecstasy, pleasure, bliss, rapture, beatitude |
| Contentment | 12997 | contentment, relaxation, peacefulness, calmness, satisfaction |
| Thankfulness/Gratitude | 12997 | thankfulness, gratitude, appreciation, gratefulness |
| Affection | 12997 | sympathy, compassion, warmth, trust, caring |
| Infatuation | 12997 | infatuation, having a crush, romantic desire, fondness, butterflies in the stomach |
| Hope/Optimism | 12997 | hope, enthusiasm, optimism, anticipation, courage |
| Triumph | 12997 | triumph, superiority |
| Pride | 12997 | pride, dignity, self-confidently, honor, self-consciousness |
| Interest | 12997 | interest, fascination, curiosity, intrigue |
| Awe | 12997 | awe, awestruck, wonder |
| Astonishment/Surprise | 12997 | astonishment, surprise, amazement, shock, startlement |
| Concentration | 12997 | concentration, deep focus, engrossment, absorption, attention |
| Contemplation | 12997 | contemplation, thoughtfulness, pondering, reflection, meditation |
| Relief | 12997 | relief, respite, alleviation, solace, comfort |
| Longing | 12997 | yearning, longing, pining, wistfulness, nostalgia |
| Teasing | 12997 | teasing, bantering, mocking playfully, ribbing, provoking lightly |
| Impatience and Irritability | 12997 | impatience, irritability, irritation, restlessness, short-temperedness |
| Sexual Lust | 12997 | sexual lust, carnal desire, lust, feeling horny, feeling turned on |
| Doubt | 12997 | doubt, distrust, suspicion, skepticism, uncertainty |
| Fear | 12997 | fear, terror, dread, apprehension, alarm |
| Distress | 12997 | worry, anxiety, unease, anguish, trepidation |
| Confusion | 12997 | confusion, bewilderment, flabbergasted, disorientation, perplexity |
| Embarrassment | 12997 | embarrassment, shyness, mortification, discomfiture, awkwardness |
| Shame | 12997 | shame, guilt, remorse, humiliation, contrition |
| Disappointment | 12997 | disappointment, regret, dismay, letdown, chagrin |
| Sadness | 12997 | sadness, sorrow, grief, melancholy, dejection |
| Bitterness | 12997 | resentment, acrimony, bitterness, cynicism, rancor |
| Contempt | 12997 | contempt, disapproval, scorn, disdain, loathing |
| Disgust | 12997 | disgust, revulsion, repulsion, abhorrence, loathing |
| Anger | 12997 | anger, rage, fury, hate, irascibility |
| Malevolence/Malice | 12997 | spite, sadism, malevolence, malice, desire to harm |
| Sourness | 12997 | sourness, tartness, acidity, acerbity, sharpness |
| Pain | 12997 | physical pain, suffering, torment, ache, agony |
| Helplessness | 12997 | helplessness, powerlessness, desperation, submission |
| Fatigue/Exhaustion | 12997 | fatigue, exhaustion, weariness, lethargy, burnout |
| Emotional Numbness | 12997 | numbness, detachment, insensitivity, emotional blunting, apathy |
| Intoxication/Altered States | 12997 | being drunk, stupor, intoxication, disorientation, altered perception |
| Jealousy & Envy | 12997 | jealousy, envy, covetousness |
Data Format
Each tar file contains paired .flac and .json files:
- FLAC: Audio recording
- JSON: Metadata including
caption(unified detailed caption with temporal aspects),transcription,duration,characters_per_second, quality scores, and emotion scores
Usage
import webdataset as wds
dataset = wds.WebDataset("data/{00000..00482}.tar")
for sample in dataset:
audio = sample["flac"] # FLAC bytes
meta = json.loads(sample["json"])
caption = meta["caption"]
Source
Built from TTS-AGI/majestrino-unified-detailed-captions-temporal using emotion keyword matching across all 821 training shards.
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