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# xtreme_s_xlsr_300m_fleurs_langid

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - FLEURS.ALL dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.7271
- Accuracy Af Za: 0.3865
- Accuracy Am Et: 0.8818
- Accuracy Ar Eg: 0.9977
- Accuracy As In: 0.9858
- Accuracy Ast Es: 0.8362
- Accuracy Az Az: 0.8386
- Accuracy Be By: 0.4085
- Accuracy Bn In: 0.9989
- Accuracy Bs Ba: 0.2508
- Accuracy Ca Es: 0.6947
- Accuracy Ceb Ph: 0.9852
- Accuracy Cmn Hans Cn: 0.9799
- Accuracy Cs Cz: 0.5353
- Accuracy Cy Gb: 0.9716
- Accuracy Da Dk: 0.6688
- Accuracy De De: 0.7807
- Accuracy El Gr: 0.7692
- Accuracy En Us: 0.9815
- Accuracy Es 419: 0.9846
- Accuracy Et Ee: 0.5230
- Accuracy Fa Ir: 0.8462
- Accuracy Ff Sn: 0.2348
- Accuracy Fi Fi: 0.9978
- Accuracy Fil Ph: 0.9564
- Accuracy Fr Fr: 0.9852
- Accuracy Ga Ie: 0.8468
- Accuracy Gl Es: 0.5016
- Accuracy Gu In: 0.973
- Accuracy Ha Ng: 0.9163
- Accuracy He Il: 0.8043
- Accuracy Hi In: 0.9354
- Accuracy Hr Hr: 0.3654
- Accuracy Hu Hu: 0.8044
- Accuracy Hy Am: 0.9914
- Accuracy Id Id: 0.9869
- Accuracy Ig Ng: 0.9360
- Accuracy Is Is: 0.0217
- Accuracy It It: 0.8
- Accuracy Ja Jp: 0.7385
- Accuracy Jv Id: 0.5824
- Accuracy Ka Ge: 0.8611
- Accuracy Kam Ke: 0.4184
- Accuracy Kea Cv: 0.8692
- Accuracy Kk Kz: 0.8727
- Accuracy Km Kh: 0.7030
- Accuracy Kn In: 0.9630
- Accuracy Ko Kr: 0.9843
- Accuracy Ku Arab Iq: 0.9577
- Accuracy Ky Kg: 0.8936
- Accuracy Lb Lu: 0.8897
- Accuracy Lg Ug: 0.9253
- Accuracy Ln Cd: 0.9644
- Accuracy Lo La: 0.1580
- Accuracy Lt Lt: 0.4686
- Accuracy Luo Ke: 0.9922
- Accuracy Lv Lv: 0.6498
- Accuracy Mi Nz: 0.9613
- Accuracy Mk Mk: 0.7636
- Accuracy Ml In: 0.6962
- Accuracy Mn Mn: 0.8462
- Accuracy Mr In: 0.3911
- Accuracy Ms My: 0.3632
- Accuracy Mt Mt: 0.6188
- Accuracy My Mm: 0.9705
- Accuracy Nb No: 0.6891
- Accuracy Ne Np: 0.8994
- Accuracy Nl Nl: 0.9093
- Accuracy Nso Za: 0.8873
- Accuracy Ny Mw: 0.4691
- Accuracy Oci Fr: 0.1533
- Accuracy Om Et: 0.9512
- Accuracy Or In: 0.5447
- Accuracy Pa In: 0.8153
- Accuracy Pl Pl: 0.7757
- Accuracy Ps Af: 0.8105
- Accuracy Pt Br: 0.7715
- Accuracy Ro Ro: 0.4122
- Accuracy Ru Ru: 0.9794
- Accuracy Rup Bg: 0.9468
- Accuracy Sd Arab In: 0.5245
- Accuracy Sk Sk: 0.8624
- Accuracy Sl Si: 0.0300
- Accuracy Sn Zw: 0.8843
- Accuracy So So: 0.8803
- Accuracy Sr Rs: 0.0257
- Accuracy Sv Se: 0.0145
- Accuracy Sw Ke: 0.9199
- Accuracy Ta In: 0.9526
- Accuracy Te In: 0.9788
- Accuracy Tg Tj: 0.9883
- Accuracy Th Th: 0.9912
- Accuracy Tr Tr: 0.7887
- Accuracy Uk Ua: 0.0627
- Accuracy Umb Ao: 0.7863
- Accuracy Ur Pk: 0.0134
- Accuracy Uz Uz: 0.4014
- Accuracy Vi Vn: 0.7246
- Accuracy Wo Sn: 0.4555
- Accuracy Xh Za: 1.0
- Accuracy Yo Ng: 0.7353
- Accuracy Yue Hant Hk: 0.7985
- Accuracy Zu Za: 0.4696
- Loss: 1.3789
- Loss Af Za: 2.6778
- Loss Am Et: 0.4615
- Loss Ar Eg: 0.0149
- Loss As In: 0.0764
- Loss Ast Es: 0.4560
- Loss Az Az: 0.5677
- Loss Be By: 1.9231
- Loss Bn In: 0.0024
- Loss Bs Ba: 2.4954
- Loss Ca Es: 1.2632
- Loss Ceb Ph: 0.0426
- Loss Cmn Hans Cn: 0.0650
- Loss Cs Cz: 1.9334
- Loss Cy Gb: 0.1274
- Loss Da Dk: 1.4990
- Loss De De: 0.8820
- Loss El Gr: 0.9839
- Loss En Us: 0.0827
- Loss Es 419: 0.0516
- Loss Et Ee: 1.9264
- Loss Fa Ir: 0.6520
- Loss Ff Sn: 5.4283
- Loss Fi Fi: 0.0109
- Loss Fil Ph: 0.1706
- Loss Fr Fr: 0.0591
- Loss Ga Ie: 0.5174
- Loss Gl Es: 1.2657
- Loss Gu In: 0.0850
- Loss Ha Ng: 0.3234
- Loss He Il: 0.8299
- Loss Hi In: 0.4190
- Loss Hr Hr: 2.9754
- Loss Hu Hu: 0.8345
- Loss Hy Am: 0.0329
- Loss Id Id: 0.0529
- Loss Ig Ng: 0.2523
- Loss Is Is: 6.5153
- Loss It It: 0.8113
- Loss Ja Jp: 1.3968
- Loss Jv Id: 2.0009
- Loss Ka Ge: 0.6162
- Loss Kam Ke: 2.2192
- Loss Kea Cv: 0.5567
- Loss Kk Kz: 0.5592
- Loss Km Kh: 1.7358
- Loss Kn In: 0.1063
- Loss Ko Kr: 0.1519
- Loss Ku Arab Iq: 0.2075
- Loss Ky Kg: 0.4639
- Loss Lb Lu: 0.4454
- Loss Lg Ug: 0.3764
- Loss Ln Cd: 0.1844
- Loss Lo La: 3.8051
- Loss Lt Lt: 2.5054
- Loss Luo Ke: 0.0479
- Loss Lv Lv: 1.3713
- Loss Mi Nz: 0.1390
- Loss Mk Mk: 0.7952
- Loss Ml In: 1.2999
- Loss Mn Mn: 0.7621
- Loss Mr In: 3.7056
- Loss Ms My: 3.0192
- Loss Mt Mt: 1.5520
- Loss My Mm: 0.1514
- Loss Nb No: 1.1194
- Loss Ne Np: 0.4231
- Loss Nl Nl: 0.3291
- Loss Nso Za: 0.5106
- Loss Ny Mw: 2.7346
- Loss Oci Fr: 5.0983
- Loss Om Et: 0.2297
- Loss Or In: 2.5432
- Loss Pa In: 0.7753
- Loss Pl Pl: 0.7309
- Loss Ps Af: 1.0454
- Loss Pt Br: 0.9782
- Loss Ro Ro: 3.5829
- Loss Ru Ru: 0.0598
- Loss Rup Bg: 0.1695
- Loss Sd Arab In: 2.6198
- Loss Sk Sk: 0.5583
- Loss Sl Si: 6.0923
- Loss Sn Zw: 0.4465
- Loss So So: 0.4492
- Loss Sr Rs: 4.7575
- Loss Sv Se: 6.5858
- Loss Sw Ke: 0.4235
- Loss Ta In: 0.1818
- Loss Te In: 0.0808
- Loss Tg Tj: 0.0912
- Loss Th Th: 0.0462
- Loss Tr Tr: 0.7340
- Loss Uk Ua: 4.6777
- Loss Umb Ao: 1.4021
- Loss Ur Pk: 8.4067
- Loss Uz Uz: 4.3297
- Loss Vi Vn: 1.1304
- Loss Wo Sn: 2.2281
- Loss Xh Za: 0.0009
- Loss Yo Ng: 1.3345
- Loss Yue Hant Hk: 1.0728
- Loss Zu Za: 3.7279
- Predict Samples: 77960

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 5.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 0.5296        | 0.26  | 1000  | 0.4016   | 2.6633          |
| 0.4252        | 0.52  | 2000  | 0.5751   | 1.8582          |
| 0.2989        | 0.78  | 3000  | 0.6332   | 1.6780          |
| 0.3563        | 1.04  | 4000  | 0.6799   | 1.4479          |
| 0.1617        | 1.3   | 5000  | 0.6679   | 1.5066          |
| 0.1409        | 1.56  | 6000  | 0.6992   | 1.4082          |
| 0.01          | 1.82  | 7000  | 0.7071   | 1.2448          |
| 0.0018        | 2.08  | 8000  | 0.7148   | 1.1996          |
| 0.0014        | 2.34  | 9000  | 0.6410   | 1.6505          |
| 0.0188        | 2.6   | 10000 | 0.6840   | 1.4050          |
| 0.0007        | 2.86  | 11000 | 0.6621   | 1.5831          |
| 0.1038        | 3.12  | 12000 | 0.6829   | 1.5441          |
| 0.0003        | 3.38  | 13000 | 0.6900   | 1.3483          |
| 0.0004        | 3.64  | 14000 | 0.6414   | 1.7070          |
| 0.0003        | 3.9   | 15000 | 0.7075   | 1.3198          |
| 0.0002        | 4.16  | 16000 | 0.7105   | 1.3118          |
| 0.0001        | 4.42  | 17000 | 0.7029   | 1.4099          |
| 0.0           | 4.68  | 18000 | 0.7180   | 1.3658          |
| 0.0001        | 4.93  | 19000 | 0.7236   | 1.3514          |


### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.1+cu111
- Datasets 1.18.4.dev0
- Tokenizers 0.11.6

{"language": ["all"], "license": "apache-2.0", "tags": ["fleurs-lang_id", "google/xtreme_s", "generated_from_trainer"], "datasets": ["google/xtreme_s"], "metrics": ["accuracy"], "model-index": [{"name": "xtreme_s_xlsr_300m_fleurs_langid", "results": []}]}
transformers pytorch tensorboard wav2vec2 audio-classification fleurs-lang_id google/xtreme_s generated_from_trainer all dataset:google/xtreme_s license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #fleurs-lang_id #google/xtreme_s #generated_from_trainer #all #dataset-google/xtreme_s #license-apache-2.0 #endpoints_compatible #region-us 

xtreme\_s\_xlsr\_300m\_fleurs\_langid
=====================================


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME\_S - FLEURS.ALL dataset.
It achieves the following results on the evaluation set:


* Accuracy: 0.7271
* Accuracy Af Za: 0.3865
* Accuracy Am Et: 0.8818
* Accuracy Ar Eg: 0.9977
* Accuracy As In: 0.9858
* Accuracy Ast Es: 0.8362
* Accuracy Az Az: 0.8386
* Accuracy Be By: 0.4085
* Accuracy Bn In: 0.9989
* Accuracy Bs Ba: 0.2508
* Accuracy Ca Es: 0.6947
* Accuracy Ceb Ph: 0.9852
* Accuracy Cmn Hans Cn: 0.9799
* Accuracy Cs Cz: 0.5353
* Accuracy Cy Gb: 0.9716
* Accuracy Da Dk: 0.6688
* Accuracy De De: 0.7807
* Accuracy El Gr: 0.7692
* Accuracy En Us: 0.9815
* Accuracy Es 419: 0.9846
* Accuracy Et Ee: 0.5230
* Accuracy Fa Ir: 0.8462
* Accuracy Ff Sn: 0.2348
* Accuracy Fi Fi: 0.9978
* Accuracy Fil Ph: 0.9564
* Accuracy Fr Fr: 0.9852
* Accuracy Ga Ie: 0.8468
* Accuracy Gl Es: 0.5016
* Accuracy Gu In: 0.973
* Accuracy Ha Ng: 0.9163
* Accuracy He Il: 0.8043
* Accuracy Hi In: 0.9354
* Accuracy Hr Hr: 0.3654
* Accuracy Hu Hu: 0.8044
* Accuracy Hy Am: 0.9914
* Accuracy Id Id: 0.9869
* Accuracy Ig Ng: 0.9360
* Accuracy Is Is: 0.0217
* Accuracy It It: 0.8
* Accuracy Ja Jp: 0.7385
* Accuracy Jv Id: 0.5824
* Accuracy Ka Ge: 0.8611
* Accuracy Kam Ke: 0.4184
* Accuracy Kea Cv: 0.8692
* Accuracy Kk Kz: 0.8727
* Accuracy Km Kh: 0.7030
* Accuracy Kn In: 0.9630
* Accuracy Ko Kr: 0.9843
* Accuracy Ku Arab Iq: 0.9577
* Accuracy Ky Kg: 0.8936
* Accuracy Lb Lu: 0.8897
* Accuracy Lg Ug: 0.9253
* Accuracy Ln Cd: 0.9644
* Accuracy Lo La: 0.1580
* Accuracy Lt Lt: 0.4686
* Accuracy Luo Ke: 0.9922
* Accuracy Lv Lv: 0.6498
* Accuracy Mi Nz: 0.9613
* Accuracy Mk Mk: 0.7636
* Accuracy Ml In: 0.6962
* Accuracy Mn Mn: 0.8462
* Accuracy Mr In: 0.3911
* Accuracy Ms My: 0.3632
* Accuracy Mt Mt: 0.6188
* Accuracy My Mm: 0.9705
* Accuracy Nb No: 0.6891
* Accuracy Ne Np: 0.8994
* Accuracy Nl Nl: 0.9093
* Accuracy Nso Za: 0.8873
* Accuracy Ny Mw: 0.4691
* Accuracy Oci Fr: 0.1533
* Accuracy Om Et: 0.9512
* Accuracy Or In: 0.5447
* Accuracy Pa In: 0.8153
* Accuracy Pl Pl: 0.7757
* Accuracy Ps Af: 0.8105
* Accuracy Pt Br: 0.7715
* Accuracy Ro Ro: 0.4122
* Accuracy Ru Ru: 0.9794
* Accuracy Rup Bg: 0.9468
* Accuracy Sd Arab In: 0.5245
* Accuracy Sk Sk: 0.8624
* Accuracy Sl Si: 0.0300
* Accuracy Sn Zw: 0.8843
* Accuracy So So: 0.8803
* Accuracy Sr Rs: 0.0257
* Accuracy Sv Se: 0.0145
* Accuracy Sw Ke: 0.9199
* Accuracy Ta In: 0.9526
* Accuracy Te In: 0.9788
* Accuracy Tg Tj: 0.9883
* Accuracy Th Th: 0.9912
* Accuracy Tr Tr: 0.7887
* Accuracy Uk Ua: 0.0627
* Accuracy Umb Ao: 0.7863
* Accuracy Ur Pk: 0.0134
* Accuracy Uz Uz: 0.4014
* Accuracy Vi Vn: 0.7246
* Accuracy Wo Sn: 0.4555
* Accuracy Xh Za: 1.0
* Accuracy Yo Ng: 0.7353
* Accuracy Yue Hant Hk: 0.7985
* Accuracy Zu Za: 0.4696
* Loss: 1.3789
* Loss Af Za: 2.6778
* Loss Am Et: 0.4615
* Loss Ar Eg: 0.0149
* Loss As In: 0.0764
* Loss Ast Es: 0.4560
* Loss Az Az: 0.5677
* Loss Be By: 1.9231
* Loss Bn In: 0.0024
* Loss Bs Ba: 2.4954
* Loss Ca Es: 1.2632
* Loss Ceb Ph: 0.0426
* Loss Cmn Hans Cn: 0.0650
* Loss Cs Cz: 1.9334
* Loss Cy Gb: 0.1274
* Loss Da Dk: 1.4990
* Loss De De: 0.8820
* Loss El Gr: 0.9839
* Loss En Us: 0.0827
* Loss Es 419: 0.0516
* Loss Et Ee: 1.9264
* Loss Fa Ir: 0.6520
* Loss Ff Sn: 5.4283
* Loss Fi Fi: 0.0109
* Loss Fil Ph: 0.1706
* Loss Fr Fr: 0.0591
* Loss Ga Ie: 0.5174
* Loss Gl Es: 1.2657
* Loss Gu In: 0.0850
* Loss Ha Ng: 0.3234
* Loss He Il: 0.8299
* Loss Hi In: 0.4190
* Loss Hr Hr: 2.9754
* Loss Hu Hu: 0.8345
* Loss Hy Am: 0.0329
* Loss Id Id: 0.0529
* Loss Ig Ng: 0.2523
* Loss Is Is: 6.5153
* Loss It It: 0.8113
* Loss Ja Jp: 1.3968
* Loss Jv Id: 2.0009
* Loss Ka Ge: 0.6162
* Loss Kam Ke: 2.2192
* Loss Kea Cv: 0.5567
* Loss Kk Kz: 0.5592
* Loss Km Kh: 1.7358
* Loss Kn In: 0.1063
* Loss Ko Kr: 0.1519
* Loss Ku Arab Iq: 0.2075
* Loss Ky Kg: 0.4639
* Loss Lb Lu: 0.4454
* Loss Lg Ug: 0.3764
* Loss Ln Cd: 0.1844
* Loss Lo La: 3.8051
* Loss Lt Lt: 2.5054
* Loss Luo Ke: 0.0479
* Loss Lv Lv: 1.3713
* Loss Mi Nz: 0.1390
* Loss Mk Mk: 0.7952
* Loss Ml In: 1.2999
* Loss Mn Mn: 0.7621
* Loss Mr In: 3.7056
* Loss Ms My: 3.0192
* Loss Mt Mt: 1.5520
* Loss My Mm: 0.1514
* Loss Nb No: 1.1194
* Loss Ne Np: 0.4231
* Loss Nl Nl: 0.3291
* Loss Nso Za: 0.5106
* Loss Ny Mw: 2.7346
* Loss Oci Fr: 5.0983
* Loss Om Et: 0.2297
* Loss Or In: 2.5432
* Loss Pa In: 0.7753
* Loss Pl Pl: 0.7309
* Loss Ps Af: 1.0454
* Loss Pt Br: 0.9782
* Loss Ro Ro: 3.5829
* Loss Ru Ru: 0.0598
* Loss Rup Bg: 0.1695
* Loss Sd Arab In: 2.6198
* Loss Sk Sk: 0.5583
* Loss Sl Si: 6.0923
* Loss Sn Zw: 0.4465
* Loss So So: 0.4492
* Loss Sr Rs: 4.7575
* Loss Sv Se: 6.5858
* Loss Sw Ke: 0.4235
* Loss Ta In: 0.1818
* Loss Te In: 0.0808
* Loss Tg Tj: 0.0912
* Loss Th Th: 0.0462
* Loss Tr Tr: 0.7340
* Loss Uk Ua: 4.6777
* Loss Umb Ao: 1.4021
* Loss Ur Pk: 8.4067
* Loss Uz Uz: 4.3297
* Loss Vi Vn: 1.1304
* Loss Wo Sn: 2.2281
* Loss Xh Za: 0.0009
* Loss Yo Ng: 1.3345
* Loss Yue Hant Hk: 1.0728
* Loss Zu Za: 3.7279
* Predict Samples: 77960


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


More information needed


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 1
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.10.1+cu111
* Datasets 1.18.4.dev0
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 1
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.10.1+cu111
* Datasets 1.18.4.dev0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #fleurs-lang_id #google/xtreme_s #generated_from_trainer #all #dataset-google/xtreme_s #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 1
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.10.1+cu111
* Datasets 1.18.4.dev0
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #fleurs-lang_id #google/xtreme_s #generated_from_trainer #all #dataset-google/xtreme_s #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 1
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 5.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.10.1+cu111
* Datasets 1.18.4.dev0
* Tokenizers 0.11.6