dictation
FR_Drugs_tiret_dictation_augmented
FR_Drugs_tiret_dictation_augmented
Dictee de listes de medicaments au format "tiret" (patterns du dataset
FR_Drugs_dictation_with_dashes_pattern, molecules completees depuis les sources
autocorrect/medical), synthetisee en TTS Coqui XTTS v2 (600 voix clonees) puis
augmentee acoustiquement.
element
valeur
extraits
141960
moteur
Coqui XTTS v2, 600 voix clonees (16 kHz mono)
base
audio/coqui/<shard>/
parasite seul (65%)
audio_babble_only/
echo seul (25%)… See the full description on the dataset page: https://huggingface.co/datasets/PraxySante/FR_Drugs_tiret_dictation_augmented.FR_VocalCommands_medical_dictation
FR_VocalCommands_medical_dictation
Commandes vocales de dictee medicale en francais (PraxyDictee) : edition,
selection, formatage, majuscules et dictee ponctuee, commentees avec des mots
medicaux (molecules et pathologies des jeux precedents).
element
valeur
textes
22500
fichiers audio
22500
moteurs
coqui XTTS v2 (300 voix clonees), edge-tts, gTTS
augmentations
low_rms (volume -22 dB), low_rms_speedup (atempo 1.2)
format
WAV mono 16 kHz
QC
inference NeMo… See the full description on the dataset page: https://huggingface.co/datasets/PraxySante/FR_VocalCommands_medical_dictation.handy-dictation-editing
Handy dictation-editing corpus
Turns a raw dictated transcript into the text the speaker meant to write.
in : um so the meeting is uh moved to friday no wait thursday at three
out: The meeting is Thursday at three.
Three jobs at once, because they are not separable in speech: drop filler words,
repair punctuation and capitalisation, and — the hard one — when the speaker
changes their mind mid-sentence, delete the wording they abandoned and keep only
what they settled on.
Built… See the full description on the dataset page: https://huggingface.co/datasets/MagicNoThief/handy-dictation-editing.dictation-cleanup-examples
Dictation cleanup examples
A sample of the hand-written cases behind
SpeakoFlow Mini, published so the
conventions the model follows are inspectable rather than described.
Seven cases in each of fifteen categories, spread across short, medium and long transcripts.
Every case was written by hand. None of it is captured speech.
This is not a benchmark
Read that before using it for anything.
These cases are drawn from the training pool, not from the held-out set the… See the full description on the dataset page: https://huggingface.co/datasets/SpeakoFlow/dictation-cleanup-examples.aura-phone-dictation-eval
Aura Phone Dictation Eval
Evaluation set of 365 progressive audio clips from 142 phone-number dictation sequences extracted from Aura Hindi/English call-center recordings.
This dataset is used to evaluate end-of-turn (EOT) detection models on structured phone-number dictation. Each sequence captures a caller dictating a 10-digit Indian mobile number across multiple speech segments. Progressive clips accumulate earlier segments plus trailing silence, ending with a final clip once… See the full description on the dataset page: https://huggingface.co/datasets/ananth-r-gnani/aura-phone-dictation-eval.mac-dictation-privacy-matrix
Mac Dictation Privacy Matrix
An open, source-reviewed dataset comparing the documented privacy boundaries of
18 Mac dictation products.
The matrix separates questions that are often collapsed into one label:
where microphone audio becomes a transcript;
whether an optional cloud, cleanup, assistant, or agent path exists;
whether the documented speech path works offline after setup;
what the publisher says it retains;
which account, subscription, license, or provider boundary… See the full description on the dataset page: https://huggingface.co/datasets/researchaudio/mac-dictation-privacy-matrix.