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License:
CC0-1.0
Steward:
Common VoiceDataset ID:
cmj6wk1bk001tnlms6m8qtngw
Release Date: 12/5/2025
Format: MP3
Size: 21.96 MB
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A collection of spontaneous spoken phrases in German.
Forbidden Usage
It is forbidden to attempt to determine the identity of speakers in the common Voice datasets. It is forbidden to re-host or re-share this dataset
Intended Use
This dataset is intended to be used for training and evaluating automatic speech recognition (ASR) models. It may also be used for applications relating to computer-aided language learning (CALL) and language or heritage revitalisation.
de)This datasheet has been generated automatically, we would love to include more information, if you would like to help out, get in touch!
This datasheet is for version 2.0 of the the Mozilla Common Voice Spontaneous Speech dataset
for German (de). The dataset contains 214 clips representing 2 hours of recorded
speech (1 hours validated) from 17 speakers.
Prompts: 79
Duration: 1:04:12 [h:m:s]
Avg. Transcription Len: 138
Avg. Duration: 18.0[s]
Valid Duration: 260.46[s]
Total hours: 1.07[h]
Valid hours: 0.07[h]
There follows a randomly selected sample of questions used in the corpus.
Welches Tier magst du am meisten und warum?
Fährst du im Urlaub lieber an neue oder an bekannte Orte?
Kochst du gerne und wenn ja, was?
Welche Jahreszeit magst du am liebsten und warum?
Warum denkst du, ist es wichtig, verschiedene historische Persönlichkeiten zu studieren?
There follows a randomly selected sample of transcribed responses from the corpus.
Ich mag am liebsten die Rauchschwalben. Und zwar deswegen, weil sie so wunderbar singen, wenn sie nach dem Winter zurückkommen.
Fährst du im Urlaub lieber an neue oder an bekannte Orte.
Ich koche sehr gerne und sehr unterschiedliche Dinge
Ich mag am liebsten den Herbst, weil sich dann alles ein bisschen zurückfaltet.
Ehrlich gesagt glaube ich nicht, dass es wichtig ist, historische Persönlichkeiten zu studieren. Ich glaube, es ist wichtig, [disfluency]Strukturen zu verstehen, aber die Menschen als Persönlichkeiten sind, glaube ich, nicht so wichtig. Jedenfalls für die Geschichte.
Each row of a tsv file represents a single audio clip, and contains the following information:
client_id - hashed UUID of a given user
audio_id - numeric id for audio file
audio_file - audio file name
duration_ms - duration of audio in milliseconds
prompt_id - numeric id for prompt
prompt - question for user
transcription - transcription of the audio response
votes - number of people that who approved a given transcript
age - age of the speaker1
gender - gender of the speaker1
language - language name
split - for data modelling, which subset of the data does this clip pertain to
char_per_sec - how many characters of transcription per second of audio
quality_tags - some automated assessment of the transcription--audio pair, separated by |
transcription-length - character per second under 3 characters per second
speech-rate - characters per second over 30 characters per second
short-audio - audio length under 2 seconds
long-audio - audio length over 30 seconds
This dataset is released under the Creative Commons Zero (CC-0) licence. By downloading this data you agree to not determine the identity of speakers in the dataset.
For a full list of age, gender, and accent options, see the demographics spec. These will only be reported if the speaker opted in to provide that information. ↩ ↩2