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PaddleSpeech/examples/other/text_frontend
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Hui Zhang e2bcaee4f1
merge deepspeech, parakeet and text_processing into paddlespeech 		3 years ago
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data merge parakeet repo into deepspeech 3 years ago
README.md fix textfrontend readme, fix imgs link 3 years ago
get_g2p_data.py refactor parakeet examples 3 years ago
get_textnorm_data.py refactor parakeet examples 3 years ago
make_sclite.sh merge parakeet repo into deepspeech 3 years ago
run.sh merge parakeet repo into deepspeech 3 years ago
test_g2p.py merge deepspeech, parakeet and text_processing into paddlespeech 3 years ago
test_textnorm.py merge deepspeech, parakeet and text_processing into paddlespeech 3 years ago

README.md

Chinese Text Frontend Example

Here's an example for Chinese text frontend, including g2p and text normalization.

G2P

For g2p, we use BZNSYP's phone label as the ground truth and we delete silence tokens in labels and predicted phones.

You should Download BZNSYP from it's Official Website and extract it. Assume the path to the dataset is ~/datasets/BZNSYP.

We use WER as evaluation criterion.

Text Normalization

For text normalization, the test data is data/textnorm_test_cases.txt, we use | as the separator of raw_data and normed_data.

We use CER as evaluation criterion.

Start

If you want to use sclite to get more detail information of WER, you should run the command below to make sclite first.

./make_sclite.sh

Run the command below to get the results of test.

./run.sh

The avg WER of g2p is: 0.027495061517943988

     ,--------------------------------------------------------------------.
     |        | # Snt    # Wrd  | Corr    Sub    Del    Ins    Err  S.Err |
     |--------+-----------------+-----------------------------------------|
     | Sum/Avg|  9996   299181  | 97.3    2.7    0.0    0.0    2.7   52.5 |
     `--------------------------------------------------------------------'

The avg CER of text normalization is: 0.006388318503308237

      ,-----------------------------------------------------------------.
      |        | # Snt  # Wrd | Corr    Sub    Del    Ins    Err  S.Err |
      |--------+--------------+-----------------------------------------|
      | Sum/Avg|  125    2254 | 99.4    0.1    0.5    0.1    0.7    3.2 |
      `-----------------------------------------------------------------'