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merge deepspeech, parakeet and text_processing into paddlespeech
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.mergify.yml

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@@ -41,13 +41,13 @@ pull_request_rules:
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remove: ["conflicts"]
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- name: "auto add label=S2T"
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conditions:
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- files~=^deepspeech/
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- files~=^paddlespeech/s2t/
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actions:
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label:
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add: ["S2T"]
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- name: "auto add label=T2S"
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conditions:
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- files~=^parakeet/
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- files~=^paddlespeech/t2s/
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actions:
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label:
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add: ["T2S"]
@@ -59,7 +59,7 @@ pull_request_rules:
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add: ["Audio"]
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- name: "auto add label=TextProcess"
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conditions:
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- files~=^text_processing/
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- files~=^paddlespeech/text/
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actions:
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label:
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add: ["TextProcess"]

docs/source/asr/models_introduction.md

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@@ -61,15 +61,15 @@ For feature extraction, three methods are implemented, which are linear (FFT wit
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Currently, the released deepspeech2 online model use the linear feature extraction method.
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```
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The code for feature extraction
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vi deepspeech/frontend/featurizer/audio_featurizer.py
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vi paddlespeech/s2t/frontend/featurizer/audio_featurizer.py
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```
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### Encoder
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The encoder is composed of two 2D convolution subsampling layers and a number of stacked single direction rnn layers. The 2D convolution subsampling layers extract feature representation from the raw audio feature and reduce the length of audio feature at the same time. After passing through the convolution subsampling layers, then the feature representation are input into the stacked rnn layers. For the stacked rnn layers, LSTM cell and GRU cell are provided to use. Adding one fully connected (fc) layer after the stacked rnn layers is optional. If the number of stacked rnn layers is less than 5, adding one fc layer after stacked rnn layers is recommand.
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The code of Encoder is in:
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```
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vi deepspeech/models/ds2_online/deepspeech2.py
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vi paddlespeech/s2t/models/ds2_online/deepspeech2.py
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```
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### Decoder
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The code of the decoder is in:
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```
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# The code of constructing the decoder in model
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vi deepspeech/models/ds2_online/deepspeech2.py
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vi paddlespeech/s2t/models/ds2_online/deepspeech2.py
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# The code of CTC Decoder
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vi deepspeech/modules/ctc.py
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vi paddlespeech/s2t/modules/ctc.py
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```
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### Training Process
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The code of encoder and decoder for deepspeech2 offline model is in:
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```
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vi deepspeech/models/ds2/deepspeech2.py
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vi paddlespeech/s2t/models/ds2/deepspeech2.py
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```
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The training process and testing process of deepspeech2 offline model is very similary to deepspeech2 online model.

docs/source/conf.py

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# -- Project information -----------------------------------------------------
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project = 'paddle speech'
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copyright = '2021, Deepspeech-developers'
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author = 'Deepspeech-developers'
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copyright = '2021, paddlespeech-developers'
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author = 'paddlespeech-developers'
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# The full version, including alpha/beta/rc tags
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release = '2.1'

docs/source/index.rst

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Welcome to paddle Deepspeech documentation !
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Welcome to paddle PaddleSpeech documentation !
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==============================================
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**Deepspeech** is a Speech toolkits implemented by paddlepaddle.
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**PaddleSpeech** is a Speech toolkits implemented by paddlepaddle.
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Contents

docs/source/tts/advanced_usage.md

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@@ -67,7 +67,7 @@ There are two common ways to define a model which consists of several modules.
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```
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When a model is a complicated and made up of several components, each of which has a separate functionality, and can be replaced by other components with the same functionality, we prefer to define it in this way.
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In the directory structure of PaddleSpeech TTS, modules with high reusability are placed in `parakeet.modules`, but models for specific tasks are placed in `parakeet.models`. When developing a new model, developers need to consider the feasibility of splitting the modules, and the degree of generality of the modules, and place them in appropriate directories.
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In the directory structure of PaddleSpeech TTS, modules with high reusability are placed in `paddlespeech.t2s.modules`, but models for specific tasks are placed in `paddlespeech.t2s.models`. When developing a new model, developers need to consider the feasibility of splitting the modules, and the degree of generality of the modules, and place them in appropriate directories.
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## PaddleSpeech TTS's Data Components
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Another critical componnet for a deep learning project is data.
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Meanwhile, `cache` is added here, and a multi-process Manager is used to share memory between multiple processes. When `num_workers` is used, it is guaranteed that each sub process will not cache a copy.
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The implementation of `DataTable` can be found in `parakeet/datasets/data_table.py`.
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The implementation of `DataTable` can be found in `paddlespeech/t2s/datasets/data_table.py`.
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```python
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class DataTable(Dataset):
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"""Dataset to load and convert data for general purpose.
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It takes advantage of the globality of Python's module level variables and the effect of context manager.
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There is a module level variable in `parakeet/training/reporter.py` `OBSERVATIONS`,which is a `Dict` to store key-value.
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There is a module level variable in `paddlespeech/t2s/training/reporter.py` `OBSERVATIONS`,which is a `Dict` to store key-value.
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```python
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# parakeet/training/reporter.py
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# paddlespeech/t2s/training/reporter.py
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@contextlib.contextmanager
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def scope(observations):

docs/source/tts/quick_start.md

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import paddle
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import yaml
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from yacs.config import CfgNode
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from parakeet.models.fastspeech2 import FastSpeech2
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from parakeet.models.fastspeech2 import FastSpeech2Inference
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from parakeet.modules.normalizer import ZScore
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from paddlespeech.t2s.models.fastspeech2 import FastSpeech2
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from paddlespeech.t2s.models.fastspeech2 import FastSpeech2Inference
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from paddlespeech.t2s.modules.normalizer import ZScore
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# examples/fastspeech2/baker/frontend.py
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from frontend import Frontend
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import soundfile as sf
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import yaml
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from yacs.config import CfgNode
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from parakeet.models.parallel_wavegan import PWGGenerator
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from parakeet.models.parallel_wavegan import PWGInference
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from parakeet.modules.normalizer import ZScore
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from paddlespeech.t2s.models.parallel_wavegan import PWGGenerator
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from paddlespeech.t2s.models.parallel_wavegan import PWGInference
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from paddlespeech.t2s.modules.normalizer import ZScore
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# load the pretrained model
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checkpoint_dir = Path("parallel_wavegan_baker_ckpt_0.4")

examples/aishell/s0/path.sh

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export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
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MODEL=deepspeech2
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export BIN_DIR=${MAIN_ROOT}/deepspeech/exps/${MODEL}/bin
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/s2t/exps/${MODEL}/bin

examples/aishell/s1/path.sh

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# model exp
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MODEL=u2
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export BIN_DIR=${MAIN_ROOT}/deepspeech/exps/${MODEL}/bin
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/s2t/exps/${MODEL}/bin
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# srilm

examples/aishell3/tts3/path.sh

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export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
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MODEL=fastspeech2
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export BIN_DIR=${MAIN_ROOT}/parakeet/exps/${MODEL}
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL}

examples/aishell3/vc0/README.md

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We use Montreal Force Aligner 1.0. The label in aishell3 include pinyin,so the lexicon we provided to MFA is pinyin rather than Chinese characters. And the prosody marks(`$` and `%`) need to be removed. You shoud preprocess the dataset into the format which MFA needs, the texts have the same name with wavs and have the suffix `.lab`.
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We use [lexicon.txt](https://github.com/PaddlePaddle/DeepSpeech/blob/develop/parakeet/exps/voice_cloning/tacotron2_ge2e/lexicon.txt) as the lexicon.
42+
We use [lexicon.txt](https://github.com/PaddlePaddle/DeepSpeech/blob/develop/paddlespeech/t2s/exps/voice_cloning/tacotron2_ge2e/lexicon.txt) as the lexicon.
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You can download the alignment results from here [alignment_aishell3.tar.gz](https://paddlespeech.bj.bcebos.com/Parakeet/alignment_aishell3.tar.gz), or train your own MFA model reference to [use_mfa example](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/other/use_mfa) (use MFA1.x now) of our repo.
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examples/aishell3/vc0/path.sh

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export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
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MODEL=voice_cloning/tacotron2_ge2e
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export BIN_DIR=${MAIN_ROOT}/parakeet/exps/${MODEL}
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL}

examples/callcenter/s1/path.sh

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MODEL=u2
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export BIN_DIR=${MAIN_ROOT}/deepspeech/exps/${MODEL}/bin
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/s2t/exps/${MODEL}/bin

examples/csmsc/tts2/path.sh

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export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
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MODEL=speedyspeech
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export BIN_DIR=${MAIN_ROOT}/parakeet/exps/${MODEL}
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL}

examples/csmsc/tts3/path.sh

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export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
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MODEL=fastspeech2
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export BIN_DIR=${MAIN_ROOT}/parakeet/exps/${MODEL}
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL}

examples/csmsc/voc1/path.sh

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export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
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MODEL=parallelwave_gan
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export BIN_DIR=${MAIN_ROOT}/parakeet/exps/gan_vocoder/${MODEL}
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/gan_vocoder/${MODEL}

examples/csmsc/voc3/path.sh

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export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
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MODEL=multi_band_melgan
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export BIN_DIR=${MAIN_ROOT}/parakeet/exps/gan_vocoder/${MODEL}
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/gan_vocoder/${MODEL}

examples/librispeech/s0/path.sh

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MODEL=deepspeech2
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export BIN_DIR=${MAIN_ROOT}/deepspeech/exps/${MODEL}/bin
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/s2t/exps/${MODEL}/bin

examples/librispeech/s1/path.sh

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MODEL=u2
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export BIN_DIR=${MAIN_ROOT}/deepspeech/exps/${MODEL}/bin
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/s2t/exps/${MODEL}/bin

examples/librispeech/s2/path.sh

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MODEL=u2_kaldi
15-
export BIN_DIR=${MAIN_ROOT}/deepspeech/exps/${MODEL}/bin
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/s2t/exps/${MODEL}/bin
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# srilm
1818
export LIBLBFGS=${MAIN_ROOT}/tools/liblbfgs-1.10

examples/ljspeech/tts0/path.sh

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export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
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MODEL=tacotron2
13-
export BIN_DIR=${MAIN_ROOT}/parakeet/exps/${MODEL}
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL}

examples/ljspeech/tts1/path.sh

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export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
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MODEL=transformer_tts
13-
export BIN_DIR=${MAIN_ROOT}/parakeet/exps/${MODEL}
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL}

examples/ljspeech/tts3/path.sh

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export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
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MODEL=fastspeech2
13-
export BIN_DIR=${MAIN_ROOT}/parakeet/exps/${MODEL}
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL}

examples/ljspeech/voc0/path.sh

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export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
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MODEL=waveflow
13-
export BIN_DIR=${MAIN_ROOT}/parakeet/exps/${MODEL}
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL}

examples/ljspeech/voc1/path.sh

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export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
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MODEL=parallelwave_gan
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export BIN_DIR=${MAIN_ROOT}/parakeet/exps/gan_vocoder/${MODEL}
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/gan_vocoder/${MODEL}

examples/other/1xt2x/src_deepspeech2x/__init__.py

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from paddle.fluid import core
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from paddle.nn import functional as F
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24-
from deepspeech.utils.log import Log
24+
from paddlespeech.s2t.utils.log import Log
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2626
#TODO(Hui Zhang): remove fluid import
2727
logger = Log(__name__).getlog()

examples/other/1xt2x/src_deepspeech2x/bin/test.py

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"""Evaluation for DeepSpeech2 model."""
1515
from src_deepspeech2x.test_model import DeepSpeech2Tester as Tester
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17-
from deepspeech.exps.deepspeech2.config import get_cfg_defaults
18-
from deepspeech.training.cli import default_argument_parser
19-
from deepspeech.utils.utility import print_arguments
17+
from paddlespeech.s2t.exps.deepspeech2.config import get_cfg_defaults
18+
from paddlespeech.s2t.training.cli import default_argument_parser
19+
from paddlespeech.s2t.utils.utility import print_arguments
2020

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def main_sp(config, args):

examples/other/1xt2x/src_deepspeech2x/models/ds2/deepspeech2.py

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1919
from src_deepspeech2x.models.ds2.rnn import RNNStack
2020
from yacs.config import CfgNode
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22-
from deepspeech.models.ds2.conv import ConvStack
23-
from deepspeech.modules.ctc import CTCDecoder
24-
from deepspeech.utils import layer_tools
25-
from deepspeech.utils.checkpoint import Checkpoint
26-
from deepspeech.utils.log import Log
22+
from paddlespeech.s2t.models.ds2.conv import ConvStack
23+
from paddlespeech.s2t.modules.ctc import CTCDecoder
24+
from paddlespeech.s2t.utils import layer_tools
25+
from paddlespeech.s2t.utils.checkpoint import Checkpoint
26+
from paddlespeech.s2t.utils.log import Log
2727
logger = Log(__name__).getlog()
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__all__ = ['DeepSpeech2Model', 'DeepSpeech2InferModel']

examples/other/1xt2x/src_deepspeech2x/models/ds2/rnn.py

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from paddle.nn import functional as F
1919
from paddle.nn import initializer as I
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21-
from deepspeech.modules.activation import brelu
22-
from deepspeech.modules.mask import make_non_pad_mask
23-
from deepspeech.utils.log import Log
21+
from paddlespeech.s2t.modules.activation import brelu
22+
from paddlespeech.s2t.modules.mask import make_non_pad_mask
23+
from paddlespeech.s2t.utils.log import Log
2424
logger = Log(__name__).getlog()
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2626
__all__ = ['RNNStack']

examples/other/1xt2x/src_deepspeech2x/test_model.py

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from src_deepspeech2x.models.ds2 import DeepSpeech2Model
2727
from yacs.config import CfgNode
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29-
from deepspeech.frontend.featurizer.text_featurizer import TextFeaturizer
30-
from deepspeech.io.collator import SpeechCollator
31-
from deepspeech.io.dataset import ManifestDataset
32-
from deepspeech.io.sampler import SortagradBatchSampler
33-
from deepspeech.io.sampler import SortagradDistributedBatchSampler
34-
from deepspeech.models.ds2_online import DeepSpeech2InferModelOnline
35-
from deepspeech.models.ds2_online import DeepSpeech2ModelOnline
36-
from deepspeech.training.gradclip import ClipGradByGlobalNormWithLog
37-
from deepspeech.training.trainer import Trainer
38-
from deepspeech.utils import error_rate
39-
from deepspeech.utils import layer_tools
40-
from deepspeech.utils import mp_tools
41-
from deepspeech.utils.log import Log
29+
from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
30+
from paddlespeech.s2t.io.collator import SpeechCollator
31+
from paddlespeech.s2t.io.dataset import ManifestDataset
32+
from paddlespeech.s2t.io.sampler import SortagradBatchSampler
33+
from paddlespeech.s2t.io.sampler import SortagradDistributedBatchSampler
34+
from paddlespeech.s2t.models.ds2_online import DeepSpeech2InferModelOnline
35+
from paddlespeech.s2t.models.ds2_online import DeepSpeech2ModelOnline
36+
from paddlespeech.s2t.training.gradclip import ClipGradByGlobalNormWithLog
37+
from paddlespeech.s2t.training.trainer import Trainer
38+
from paddlespeech.s2t.utils import error_rate
39+
from paddlespeech.s2t.utils import layer_tools
40+
from paddlespeech.s2t.utils import mp_tools
41+
from paddlespeech.s2t.utils.log import Log
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logger = Log(__name__).getlog()
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examples/other/ge2e/path.sh

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export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
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MODEL=ge2e
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export BIN_DIR=${MAIN_ROOT}/parakeet/exps/${MODEL}
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL}

examples/other/text_frontend/test_g2p.py

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1515
import re
1616
from pathlib import Path
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18-
from parakeet.frontend.zh_frontend import Frontend as zhFrontend
19-
from parakeet.utils.error_rate import word_errors
18+
from paddlespeech.t2s.frontend.zh_frontend import Frontend as zhFrontend
19+
from paddlespeech.t2s.utils.error_rate import word_errors
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SILENCE_TOKENS = {"sp", "sil", "sp1", "spl"}
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examples/other/text_frontend/test_textnorm.py

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import re
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from pathlib import Path
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from parakeet.frontend.zh_normalization.text_normlization import TextNormalizer
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from parakeet.utils.error_rate import char_errors
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from paddlespeech.t2s.frontend.zh_normalization.text_normlization import TextNormalizer
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from paddlespeech.t2s.utils.error_rate import char_errors
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# delete english characters

examples/ted_en_zh/t0/path.sh

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MODEL=u2_st
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export BIN_DIR=${MAIN_ROOT}/deepspeech/exps/${MODEL}/bin
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/s2t/exps/${MODEL}/bin

examples/timit/s1/path.sh

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MODEL=u2
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export BIN_DIR=${MAIN_ROOT}/deepspeech/exps/${MODEL}/bin
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/s2t/exps/${MODEL}/bin

examples/tiny/s0/path.sh

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MODEL=deepspeech2
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export BIN_DIR=${MAIN_ROOT}/deepspeech/exps/${MODEL}/bin
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/s2t/exps/${MODEL}/bin

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