slplab's picture
Update README.md
bcc264e
|
raw
history blame
2.04 kB
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-xls-r-phone-mfa_korean
results: []
language:
- ko
metrics:
- wer
pipeline_tag: automatic-speech-recognition
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m_phoneme-mfa_korean
Creator & Uploader: Jooyoung Lee ([email protected])
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on a phonetically balanced native Korean read-speech corpus.
# Training and Evaluation Data
Training Data
- Data Name: Phonetically Balanced Native Korean Read-speech Corpus
- Num. of Samples: 54,000
- Audio Length: 108 Hours
Evaluation Data
- Data Name: Phonetically Balanced Native Korean Read-speech Corpus
- Num. of Samples: 6,000
- Audio Length: 12 Hours
# Training Hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 20 (EarlyStopping: patience: 5 epochs max)
- mixed_precision_training: Native AMP
# Evaluation Result
Phone Error Rate 3.88%
# Output Examples
![output_examples](./output_examples.png)
# MFA-IPA Phoneset Tables
## Vowels
![mfa_ipa_chart_vowels](./mfa_ipa_chart_vowels.png)
## Consonants
![mfa_ipa_chart_consonants](./mfa_ipa_chart_consonants.png)
## Experimental Results
Official implementation of the paper (in review)
Major error patterns of L2 Korean speech from five different L1s: Chinese (ZH), Vietnamese (VI), Japanese (JP), Thai (TH), English (EN)
![Experimental Results](./ICPHS2023_tabl2.png)
# Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1