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---
base_model: facebook/w2v-bert-2.0
datasets:
- common_voice_10_0
metrics:
- wer
model-index:
- name: w2v-bert-2.0-uk
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_10_0
type: common_voice_10_0
config: uk
split: test
args: uk
metrics:
- name: Wer
type: wer
value: 0.0655
---
# wav2vec2-bert-uk
- Join our Speech Recognition Group in Telegram: https://t.me/speech_recognition_uk
- Join our **Discord server** - https://discord.gg/nmUCXz55 - where we're talking about AI
Quality:
- AM:
- WER: 0.0727
- CER: 0.0151
- Accuracy: 92.73%
- AM + LM:
- WER: 0.0655
- CER: 0.0139
- Accuracy: 93.45%
This model was trained with the following hparams with 2 RTX A4000:
```
torchrun --standalone --nnodes=1 --nproc-per-node=2 ../train_w2v2_bert.py \
--custom_set ~/cv10/train.csv \
--custom_set_eval ~/cv10/test.csv \
--num_train_epochs 15 \
--tokenize_config . \
--w2v2_bert_model facebook/w2v-bert-2.0 \
--batch 4 \
--num_proc 5 \
--grad_accum 1 \
--learning_rate 3e-5 \
--logging_steps 20 \
--eval_step 500 \
--group_by_length \
--attention_dropout 0.0 \
--activation_dropout 0.05 \
--feat_proj_dropout 0.05 \
--feat_quantizer_dropout 0.0 \
--hidden_dropout 0.05 \
--layerdrop 0.0 \
--final_dropout 0.0 \
--mask_time_prob 0.0 \
--mask_time_length 10 \
--mask_feature_prob 0.0 \
--mask_feature_length 10
```
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