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Usin2705/
CaptainA_v0

Name: CaptainA_v0
Availability: InStock

automatic-speech-recognition

Finnish parliament data slow samples 300h

cc-by-nc-sa-4.0

endpoints_compatible

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# CaptainA_XLS-R_entropy-10_v0

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Finnish Parliament Corpus with 210h of slow and clean samples. It achieves the following results on the evaluation set (70h of the slow and clean samples from the same corpus):

Loss: 196.9006
Cer: 0.0178
Wer: 0.0592

Model description

This model is used in the CaptainA app.

Intended uses & limitations

The model was fine-tuned with entropy regularization to improve generalization for the Finnish L2 MDD task. Even though the model was trained for the purpose of MDD for L2 Finnish speakers, it was not fine-tuned with any L2 data due to the lack of proper corpus for Finnish L2 MDD.

Therefore, it is important to note that this model is NOT intended for Finnish L2 ASR and will need to be improved further even for the MDD task (hence the model version is 0).

Because the model was fine-tuned with the Finnish Parliament Corpus, it has the same biases from that corpus. The biases are more notable since the model's intended uses are for L2 Finnish speakers. More detail can be read in the Master's thesis: A Mobile App For Practicing Finnish Pronunciation Using Wav2vec 2.0

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 128
eval_batch_size: 128
seed: 1011
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 10.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Cer	Validation Loss	Wer
409.2657	0.47	500	1.0	409.9399	1.0
242.0796	0.94	1000	0.0762	222.4043	0.4204
214.4323	1.4	1500	0.0342	205.3006	0.1620
208.1767	1.87	2000	0.0261	201.7304	0.1095
205.3693	2.34	2500	0.0248	200.3012	0.1037
204.3477	2.81	3000	0.0219	199.3383	0.0830
202.9748	3.28	3500	0.0207	199.1589	0.0782
201.9818	3.75	4000	0.0207	198.5560	0.0769
201.8992	4.21	4500	0.0201	198.0990	0.0724
201.6079	4.68	5000	0.0209	197.8516	0.0712
200.6187	5.15	5500	0.0191	197.6185	0.0667
200.5608	5.62	6000	0.0189	197.5194	0.0658
200.1649	6.09	6500	0.0191	197.3655	0.0641
200.1713	6.55	7000	0.0186	197.2977	0.0629
200.1245	7.02	7500	0.0193	197.0914	0.0638
199.5289	7.49	8000	0.0181	197.0704	0.0608
199.4458	7.96	8500	0.0183	196.9986	0.0606
199.1502	8.43	9000	0.0178	197.0260	0.0590
199.4437	8.9	9500	0.0180	196.9412	0.0595
198.8669	9.36	10000	0.0180	196.8834	0.0600
199.1329	9.83	10500	0.0178	196.9176	0.0591

Framework versions

Transformers 4.18.0
Pytorch 1.12.0.dev20220305
Datasets 1.18.4.dev0
Tokenizers 0.11.6

Usin2705/CaptainA_v0