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Task: ASR
Release Date: 6/17/2026
Format: MP3
Size: 158.57 MB
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A collection of spontaneous responses to questions in Alsatian (Elsassisch).
Restrictions/Special Constraints
None provided.
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.
gsw)This datasheet is for sps-corpus-4.0-2026-06-12 of the Mozilla Common Voice Spontaneous Speech dataset for Alsatian [Elsassisch - gsw]. The dataset contains 1254 clips representing 8.05 hours of recorded speech (0.55 hours validated) from 79 speakers.
Elsassisch (Alsatian in English, Alsacien in French) is a language spoken in the Alsace region in the East of France. As of 2022, 46 % of the population of the region declares speaking Alsatian. The term Alsatian refers to a linguistic continuum that includes varieties of Alemannic and Franconian. It shares the Alemannic language family with Swiss German and the Franconian language family with Luxembourgish.
Note on the language code : There is currently no language code for specifically Alsatian. GSW is the code of Swiss German. However, the Common Voice community for Swiss German has chosen to be included under the umbrella of German, and thus isn't using the language code. It has been agreed to use GSW for Alsatian in the context of Common Voice. This does not mean that Alsatian is the same as Swiss German (even if some features are shared), and care should be taken to not mix up the two languages.
The dataset clips are categorised by transcription status and training-set assignment. The following tables summarise the distribution.
| Bucket | Clips | % |
|---|---|---|
| Transcribed & Validated | 133 | 10.6% |
| Transcribed & Pending | 1,099 | 87.6% |
| Not transcribed | 22 | 1.8% |
| Bucket | Clips | % |
|---|---|---|
| Train | 0 | 0.0% |
| Dev | 0 | 0.0% |
| Test | 0 | 0.0% |
| Unassigned | 1,254 | 100.0% |
Training split coverage: 0 of 133 transcribed & validated clips (0.0%)
| Bucket | Clips | % |
|---|---|---|
| Validated | 133 | 10.8% |
| Pending | 1,099 | 89.2% |
| Edited | 175 | 14.2% |
There follows a randomly selected sample of questions used in the corpus.
Wàs màche-n’r garn à’me Sùndàà?
Was esch hit fer Wedder?
Wàs redde-n’r àlles fer Sproche? Wie hàn’r se dann gelehrt?
Well ìsch d’letscht Sandùng, wie-n’r àm Radio gheert hàn? Ùm wàs ìsch’s gànge?
Wàs wär fer Éich de perfekt Dàà àn de frìsche Lùft? Wie tääte-n’r ànnegehn ùn wàs tääte-n’r màche?
There follows a randomly selected sample of transcribed responses from the corpus.
s'esch de vierzehnte Janer ùn à sìn twelef Gràd as esch nìt nòrmàl dàss'as à so Wàrm esch
Ich brüch ke àndre Beruef meh màche, ich bin namlig Rantner
wann's geht nam'i de Photoàpàràt mìt
ah einer von de film wie àm lebschte hàb esch gleuwi les bronzés font du ski des kàmmr weiss'i veel mòls sahn ùn esch ìmmer zum làche waije mr làcht jo schlìssli nìt àlle deu ùn esch hàb àm liebschte film wie comédie sìn nìt die polezei dìng do wie ìmmer mort ùn tod schleu esch ùn euh des schrekli nä àm lebschte a film wie mr m'r geut làche kànn sowie les tontons flingeur euj wo eingentli sìns elderi film naja
Minni scheenschte Kleider sìì meischtens schwàrz. Zùm Beispiel, ìch hàb e schiwele, e schwàrzes, wo gànz scheen üsseht ùn [disfluency] e regele àui, e schwàrzes. Ùn [disfluency] ìch àui schwàrzi Hose.
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 5 minutes
non-allowed-script - transcription contains characters from a writing system not associated with the language
mixed-script-words - a single word contains characters from multiple writing systems
mixed-script-transcription - transcription spans multiple writing systems, but each word consistently uses only one
Pascale Erhart
Sam Bigeard <sam.bigeard@inria.fr>
The launch of this language on Common Voice was part of Défi Inria COLaF, which was financed by Plan National de Recherche en Intelligence Artificielle.
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