Modèle Français 0.6

Jeux de données :

• Lingua Libre (~40h)
• Common Voice FR (v5.1) (~490h, en autorisant jusqu'à 32 duplicatas)
• Training Speech (~180h)
• African Accented French (~15h)
• M-AILABS French (~315h)
• Centre de Conférence Pierre Mendès France (~300h)

Total : ~1340h

Paramètres :

• EPOCHS=32
• LEARNING_RATE=0.0001
• DROPOUT=0.3
• BATCH_SIZE=64
• LM_ALPHA=0.5919543900530122
• LM_BETA=1.6082513974258137

Best params: lm_alpha=0.5919543900530122 and lm_beta=1.6082513974258137 with WER=0.29113864240896115

Language Model : dump wikipedia + dump débats assemblée nationale.

Licence : MPL 2.0 https://github.com/common-voice/commonvoice-fr/blob/5699e59244d14bb14d5b7603b91c934b761c9194/DeepSpeech/LICENSE.txt

Fonctionne avec DeepSpeech v0.7, v0.8, v0.9.

Résultats test set:

Test on /mnt/extracted/data/African_Accented_French/African_Accented_French/African_Accented_French_test.csv - WER: 0.448976, CER: 0.242144, loss: 43.320114
Test on /mnt/extracted/data/M-AILABS/fr_FR/fr_FR_test.csv - WER: 0.097462, CER: 0.027961, loss: 12.057733
Test on /mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR_test.csv - WER: 0.199902, CER: 0.059519, loss: 15.992792
Test on /mnt/extracted/data/cv-fr/clips/test.csv - WER: 0.301279, CER: 0.142777, loss: 37.710129
Test on /mnt/extracted/data/lingualibre/lingua_libre_Q21-fra-French_test.csv - WER: 0.589222, CER: 0.182179, loss: 7.118075
Test on /mnt/extracted/data/ccpmf/transcriptionsXML_audioMP3_MEFR_CCPMF_2012-2020/ccpmf_test.csv - WER: 0.486848, CER: 0.304395, loss: 89.443710

Modèle Français 0.5.2

Jeux de données :

• Lingua Libre (~40h)
• Common Voice FR (v2) (~490h, en autorisant jusqu'à 32 duplicatas)
• Training Speech (~180h)
• African Accented French (~15h)
• M-AILABS French (~315h)

Total : ~1040h

Paramètres :

• EPOCHS=30
• LEARNING_RATE=0.0001
• DROPOUT=0.3
• BATCH_SIZE=64
• LM_ALPHA=0.7203202402564637
• LM_BETA=1.5747698919871918

Language Model : dump wikipedia + dump débats assemblée nationale.

Fonctionne avec DeepSpeech v0.7, v0.8, v0.9.

Correction du packaging de kenlm.scorer
Correction des valeurs par défaut de alpha/beta dans kenlm.scorer

Résultats test set:

Test on /mnt/extracted/data/African_Accented_French/African_Accented_French/African_Accented_French_test.csv - WER: 0.442362, CER: 0.235577, loss: 42.941334
Test on /mnt/extracted/data/M-AILABS/fr_FR/fr_FR_test.csv - WER: 0.092794, CER: 0.026505, loss: 11.276774
Test on /mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR_test.csv - WER: 0.200373, CER: 0.059958, loss: 16.225618
Test on /mnt/extracted/data/cv-fr/clips/test.csv - WER: 0.300508, CER: 0.147202, loss: 39.204407
Test on /mnt/extracted/data/lingualibre/lingua_libre_Q21-fra-French_test.csv - WER: 0.577170, CER: 0.171211, loss: 6.977585

Modèle Français 0.5.1

Jeux de données :

• Lingua Libre (~40h)
• Common Voice FR (v2) (~490h, en autorisant jusqu'à 32 duplicatas)
• Training Speech (~180h)
• African Accented French (~15h)
• M-AILABS French (~315h)

Total : ~1040h

Paramètres :

• EPOCHS=30
• LEARNING_RATE=0.0001
• DROPOUT=0.3
• BATCH_SIZE=64
• LM_ALPHA=0.7203202402564637
• LM_BETA=1.5747698919871918

Language Model : dump wikipedia + dump débats assemblée nationale.

Fonctionne avec DeepSpeech v0.7, v0.8.

Résultats test set:

Test on /mnt/extracted/data/African_Accented_French/African_Accented_French/African_Accented_French_test.csv - WER: 0.442362, CER: 0.235577, loss: 42.941334
Test on /mnt/extracted/data/M-AILABS/fr_FR/fr_FR_test.csv - WER: 0.092794, CER: 0.026505, loss: 11.276774
Test on /mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR_test.csv - WER: 0.200373, CER: 0.059958, loss: 16.225618
Test on /mnt/extracted/data/cv-fr/clips/test.csv - WER: 0.300508, CER: 0.147202, loss: 39.204407
Test on /mnt/extracted/data/lingualibre/lingua_libre_Q21-fra-French_test.csv - WER: 0.577170, CER: 0.171211, loss: 6.977585

Modèle Français 0.4

Jeux de données :

• Lingua Libre (~20h)
• Common Voice FR (v2) (~290h, en autorisant jusqu'à 8 duplicatas)
• Training Speech (~180h)
• African Accented French (~15h)
• M-AILABS French (~315h)

Total : ~820h

Paramètres :

• LEARNING_RATE=0.0001
• DROPOUT=0.3
• BATCH_SIZE=64
• LM_ALPHA=0.65
• LM_BETA=1.45

Language Model : dump wikipedia + dump débats assemblée nationale.

Fonctionne avec DeepSpeech v0.6.1.

Résultats test set:

Test on /mnt/extracted/data/lingualibre/lingua_libre_Q21-fra-French_test.csv - WER: 0.541340, CER: 0.150946, loss: 5.962852
--------------------------------------------------------------------------------
WER: 5.000000, CER: 0.241379, loss: 3.496368
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/Lyokoï/électroencéphalographiquement.wav
 - src: "électroencéphalographiquement"
 - res: "électro en céphale orphique ment"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 0.333333, loss: 3.654961
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/Lyokoï/aposématisme.wav
 - src: "aposématisme"
 - res: "a posé ma time"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 0.400000, loss: 4.680493
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/WikiLucas00/oligoasthénotératospermie.wav
 - src: "oligoasthénotératospermie"
 - res: "aligoté notera to sperm"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 0.285714, loss: 7.043005
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/WikiLucas00/octingentesimo.wav
 - src: "octingentesimo"
 - res: "acting en tesi mo"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 0.500000, loss: 12.178319
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/Lyokoï/limousinerie.wav
 - src: "limousinerie"
 - res: "il vous i neri"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 0.263158, loss: 17.644501
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/WikiLucas00/paléontologiquement.wav
 - src: "paléontologiquement"
 - res: "pale on a logiquement"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 0.538462, loss: 20.121408
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/WikiLucas00/mielleusement.wav
 - src: "mielleusement"
 - res: "in a le cement"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 0.454545, loss: 23.273678
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/Poslovitch/ennuagement.wav
 - src: "ennuagement"
 - res: "en eut age ment"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 0.692308, loss: 36.408180
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/Xenophôn/Hondevilliers.wav
 - src: "hondevilliers"
 - res: "on ne vit le"
--------------------------------------------------------------------------------
WER: 4.000000, CER: 0.687500, loss: 38.046669
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/WikiLucas00/téléconsultation.wav
 - src: "téléconsultation"
 - res: "tel que les consultations"
--------------------------------------------------------------------------------
Test on /mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR_test.csv - WER: 0.197745, CER: 0.059797, loss: 17.292450
--------------------------------------------------------------------------------
WER: 4.000000, CER: 1.333333, loss: 38.737186
 - wav: file:///mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR/LeComteDeMonteCristoT1Chap5_0237.converted.wav
 - src: "espoir"
 - res: "n est ce soir"
--------------------------------------------------------------------------------
WER: 3.000000, CER: 1.000000, loss: 47.523190
 - wav: file:///mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR/MonsieurLecoqP1C16_0188.converted.wav
 - src: "continuez"
 - res: "quand il est"
--------------------------------------------------------------------------------
WER: 2.000000, CER: 0.250000, loss: 0.010373
 - wav: file:///mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR/MonsieurLecoqT2P16_0185.converted.wav
 - src: "chanlouineau"
 - res: "chan luneau"
--------------------------------------------------------------------------------
WER: 2.000000, CER: 0.142857, loss: 0.052286
 - wav: file:///mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR/MonsieurLecoqP1C42_0070.converted.wav
 - src: "parbleu"
 - res: "par bleu"
--------------------------------------------------------------------------------
WER: 2.000000, CER: 0.142857, loss: 0.219133
 - wav: file:///mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR/LeComteDeMonteCristoT1Chap3_0284.converted.wav
 - src: "pardieu"
 - res: "par dieu"
--------------------------------------------------------------------------------
WER: 2.000000, CER: 0.333333, loss: 1.239774
 - wav: file:///mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR/LesMysteresDeParisT3P5C14_0002.converted.wav
 - src: "amitie"
 - res: "a miti"
--------------------------------------------------------------------------------
WER: 2.000000, CER: 0.384615, loss: 1.923999
 - wav: file:///mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR/LeComteDeMonteCristoT1Chap24_0002.converted.wav
 - src: "eblouissement"
 - res: "et boisement"
--------------------------------------------------------------------------------
WER: 2.000000, CER: 0.250000, loss: 2.610425
 - wav: file:///mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR/MonsieurLecoqT2P33_0032.converted.wav
 - src: "chimeres"
 - res: "chi mere"
--------------------------------------------------------------------------------
WER: 2.000000, CER: 0.500000, loss: 3.350882
 - wav: file:///mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR/MonsieurLecoqT2P04_0012.converted.wav
 - src: "hola"
 - res: "a la"
--------------------------------------------------------------------------------
WER: 2.000000, CER: 0.400000, loss: 7.205533
 - wav: file:///mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR/LeDernierJourDunCondamne_0712.converted.wav
 - src: "lirlonfa malure"
 - res: "le lan fan maure"
--------------------------------------------------------------------------------
Test on /mnt/extracted/data/M-AILABS/fr_FR/fr_FR_test.csv - WER: 0.090398, CER: 0.025351, loss: 11.177062
--------------------------------------------------------------------------------
WER: 2.000000, CER: 0.166667, loss: 3.342017
 - wav: file:///mnt/extracted/data/M-AILABS/fr_FR/female/ezwa/monsieur_lecoq/wavs/monsieur_lecoq_2_36_f000179.wav
 - src: "dubois"
 - res: "du bois"
--------------------------------------------------------------------------------
WER: 2.000000, CER: 0.857143, loss: 8.253085
 - wav: file:///mnt/extracted/data/M-AILABS/fr_FR/female/nadine_eckert_boulet/les_tribulations_dun_chinoise/wavs/les_tribulations_dun_chinoise_10_f000043.wav
 - src: "bidulph"
 - res: "le bip"
--------------------------------------------------------------------------------
WER: 2.000000, CER: 0.375000, loss: 10.294103
 - wav: file:///mnt/extracted/data/M-AILABS/fr_FR/male/gilles_g_le_blanc/lupin_contre_holmes/wavs/lupin_contre_holmes_13_f000184.wav
 - src: "personne"
 - res: "le songe"
--------------------------------------------------------------------------------
WER: 2.000000, CER: 6.000000, loss: 20.541677
 - wav: file:///mnt/extracted/data/M-AILABS/fr_FR/female/nadine_eckert_boulet/les_mysteres_de_paris/wavs/les_mysteres_de_paris_4_13_f000027.wav
 - src: "m"
 - res: "on ne "
--------------------------------------------------------------------------------
WER: 1.500000, CER: 0.400000, loss: 4.110573
 - wav: file:///mnt/extracted/data/M-AILABS/fr_FR/male/gilles_g_le_blanc/lupin_contre_holmes/wavs/lupin_contre_holmes_07_f000165.wav
 - src: "m destange"
 - res: "mais des tange"
--------------------------------------------------------------------------------
WER: 1.500000, CER: 0.266667, loss: 4.140529
 - wav: file:///mnt/extracted/data/M-AILABS/fr_FR/male/gilles_g_le_blanc/lupin_contre_holmes/wavs/lupin_contre_holmes_14_f000218.wav
 - src: "langlais ricana"
 - res: "l'anglais et cana"
--------------------------------------------------------------------------------
WER: 1.200000, CER: 0.279070, loss: 58.677330
 - wav: file:///mnt/extracted/data/M-AILABS/fr_FR/female/ezwa/monsieur_lecoq/wavs/monsieur_lecoq_2_40_f000027.wav
 - src: "incompréhensible balbutia t il inimaginable"
 - res: "un coupé aussi ble balbutiant il imaginable"
--------------------------------------------------------------------------------
WER: 1.000000, CER: 0.125000, loss: 0.046964
 - wav: file:///mnt/extracted/data/M-AILABS/fr_FR/male/gilles_g_le_blanc/lupin_contre_holmes/wavs/lupin_contre_holmes_11_f000012.wav
 - src: "ganimard"
 - res: "gaimard"
--------------------------------------------------------------------------------
WER: 1.000000, CER: 0.142857, loss: 0.094500
 - wav: file:///mnt/extracted/data/M-AILABS/fr_FR/male/gilles_g_le_blanc/lupin_contre_holmes/wavs/lupin_contre_holmes_01_f000115.wav
 - src: "gerbois"
 - res: "gerboise"
--------------------------------------------------------------------------------
WER: 1.000000, CER: 0.150000, loss: 0.097039
 - wav: file:///mnt/extracted/data/M-AILABS/fr_FR/female/ezwa/monsieur_lecoq/wavs/monsieur_lecoq_2_49_f000013.wav
 - src: "chanlouineau fusillé"
 - res: "chanoine au fusillé"
--------------------------------------------------------------------------------
Test on /mnt/extracted/data/African_A...

Modèle Français 0.3.4

Jeux de données :

• Lingua Libre (~20h)
• Common Voice FR (v2) (~120h, en autorisant des duplicatas)
• Training Speech (~180h)
• African Accented French (~15h)
• M-AILABS French (~315h)

Total : ~650h

Paramètres :

• LEARNING_RATE=0.0001
• DROPOUT=0.3
• BATCH_SIZE=96
• LM_ALPHA=0.65
• LM_BETA=1.45

Language Model : dump wikipedia + dump débats assemblée nationale.

Fonctionne avec DeepSpeech v0.6.0. Ré-export de 0.3.3 pour corriger un bug dans TFLite

Résultats test set:

Testing model on /mnt/extracted/data/lingualibre/lingua_libre_Q21-fra-French_test.csv                                                                                                                                                                                                                                                                                                         
Test epoch | Steps: 75 | Elapsed Time: 0:01:44                                                                                                                                                                                                                                                                                                                                                
Test on /mnt/extracted/data/lingualibre/lingua_libre_Q21-fra-French_test.csv - WER: 0.467659, CER: 0.138508, loss: 6.800947                                                                    
--------------------------------------------------------------------------------                                                                                                                                                                                                                                                                                                              
WER: 4.000000, CER: 2.200000, loss: 39.604939                                                                                                                                                  
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/Vahidmasrour/abhal.wav                                                                                                                                                                                                                                                                                             
 - src: "abhal"                                                                                                                                                                                
 - res: "le panel a bal"                                                                                                                                                                                                                                                                                                                                                                      
--------------------------------------------------------------------------------                                                                                                               
WER: 3.000000, CER: 0.600000, loss: 2.462182                                                                                                                                                                                                                                                                                                                                                  
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/WikiLucas00/irato.wav                                                                                               
 - src: "irato"                                                                                                                                                                                                                                                                                                                                                                               
 - res: "il a to"                                                                                                                                                                              
--------------------------------------------------------------------------------                                                                                                                                                                                                                                                                                                              
WER: 3.000000, CER: 0.111111, loss: 3.428576                                                                                                                                                   
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/WikiLucas00/ultratrifoliophile.wav                                                                                                                                                                                                                                                                                 
 - src: "ultratrifoliophile"                                                                                                                                                                   
 - res: "ultra trifolio phile"                                                                                                                                                                                                                                                                                                                                                                
--------------------------------------------------------------------------------
WER: 3.000000, CER: 0.333333, loss: 5.036440
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/WikiLucas00/cuthomiurophile.wav
 - src: "cuthomiurophile"
 - res: "culto miro phile"
--------------------------------------------------------------------------------
WER: 3.000000, CER: 0.333333, loss: 5.090287
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/WikiLucas00/remiauler.wav
 - src: "remiauler"
 - res: "remi au le"
--------------------------------------------------------------------------------
WER: 3.000000, CER: 0.285714, loss: 6.972348
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/Lyokoï/indoeuropéiste.wav
 - src: "indoeuropéiste"
 - res: "in doro péiste"
--------------------------------------------------------------------------------
WER: 3.000000, CER: 0.454545, loss: 7.742430
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/Jules78120/Antarctique.wav
 - src: "antarctique"
 - res: "en parti que"
--------------------------------------------------------------------------------
WER: 3.000000, CER: 0.833333, loss: 8.499911
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/Guilhelma (Ives)/padena.wav
 - src: "padena"
 - res: "pas de nom"
--------------------------------------------------------------------------------
WER: 3.000000, CER: 0.307692, loss: 8.974085
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/Lyokoï/pleurogynique.wav
 - src: "pleurogynique"
 - res: "pleu rogi mique"
--------------------------------------------------------------------------------
WER: 3.000000, CER: 0.230769, loss: 9.156916
 - wav: file:///mnt/extracted/data/lingualibre/lingua_libre/Q21-fra-French/WikiLucas00/bonhomisation.wav
 - src: "bonhomisation"
 - res: "bon ami sation"
--------------------------------------------------------------------------------
Testing model on /mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR_test.csv
Test epoch | Steps: 129 | Elapsed Time: 0:10:36                                                                                                                                                                                                                                                                                                                                               
Test on /mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR_test.csv - WER: 0.185852, CER: 0.061034, loss: 21.406639
--------------------------------------------------------------------------------
WER: 4.000000, CER: 1.222222, loss: 28.107866
 - wav: file:///mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR/MonsieurLecoqP1C16_0188.converted.wav
 - src: "continuez"
 - res: "quand il ne est"
--------------------------------------------------------------------------------
WER: 2.333333, CER: 0.818182, loss: 123.491005
 - wav: file:///mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR/LesMysteresDeParisT1P1C5_0129.converted.wav
 - src: "diminution de fourloir"
 - res: "des minutions de de fournoue a sa fin"
--------------------------------------------------------------------------------
WER: 2.000000, CER: 0.142857, loss: 0.981466
 - wav: file:///mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR/LaGloireDuComacchio_0097.converted.wav
 - src: "pardieu"
 - res: "par dieu"
--------------------------------------------------------------------------------
WER: 2.000000, CER: 0.500000, loss: 5.709780
 - wav: file:///mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR/LeComteDeMonteCristoT1Chap3_0240.converted.wav
 - src: "hola"
 - res: "a la"
--------------------------------------------------------------------------------
WER: 2.000000, CER: 0.750000, loss: 6.360806
 - wav: file:///mnt/extracted/data/trainingspeech/ts_2019-04-11_fr_FR/madamebovaryC24_0123.converted.wav
 - src: "leon"
 - res: "et on"
---------------------------...

Modèle Français 0.3.2

Jeux de données :

• Lingua Libre (~20h)
• Common Voice FR (v2) (~120h, en autorisant des duplicatas)
• Training Speech (~180h)
• African Accented French (~15h)
• M-AILABS French (~315h)

Total : ~650h

Paramètres :

• LEARNING_RATE=0.0001
• DROPOUT=0.3
• BATCH_SIZE=64
• LM_ALPHA=0.65
• LM_BETA=1.4

Language Model : dump wikipedia + dump débats assemblée nationale.

Fonctionne avec DeepSpeech v0.6.0-alpha.15.

Modèle Français 0.3.1

Jeux de données :

• Lingua Libre (~20h)
• Common Voice FR (v2) (~120h, en autorisant des duplicatas)
• Training Speech (~180h)
• African Accented French (~15h)
• M-AILABS French (~315h)

Total : ~650h

Paramètres :

• LEARNING_RATE=0.0001
• DROPOUT=0.3
• BATCH_SIZE=64
• LM_ALPHA=0.65
• LM_BETA=1.4

Language Model : dump wikipedia + dump débats assemblée nationale.

Fonctionne avec DeepSpeech v0.6.0-alpha.14.

Modèle Français 0.3

Jeux de données :

• Lingua Libre (~20h)
• Common Voice FR (v2) (~120h, en autorisant des duplicatas)
• Training Speech (~180h)
• African Accented French (~15h)
• M-AILABS French (~315h)

Total : ~650h

Paramètres :

• LEARNING_RATE=0.0001
• DROPOUT=0.3
• BATCH_SIZE=64
• LM_ALPHA=0.65
• LM_BETA=1.4

Language Model : dump wikipedia + dump débats assemblée nationale.

Fonctionne avec DeepSpeech v0.6.0-alpha.10.

Modèle Français 0.2

Seconde version alpha d'un modèle français.

• LEARNING_RATE=0.0001
• DROPOUT=0.2
• LM_ALPHA=0.85
• LM_BETA=1.45

Discussions et retours sur https://discourse.mozilla.org/t/modele-francais-0-2-pour-deepspeech-v0-6/45808

Modèle Français 0.1

Première version alpha d'un modèle français.

Merci de bien lire le message descriptif sur Discourse: https://discourse.mozilla.org/t/un-premier-modele-francais/41100

Les retours sont les bienvenus.