add tutorials into sdvanced
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@ -9,16 +9,18 @@ Model
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-------------
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As a common practice with paddlepaddle, models are implemented as subclasses
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of ``paddle.nn.Layer``. More complicated models, it is recommended to split
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the model into different components.
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of ``paddle.nn.Layer``. Models could be simple, like a single layer RNN. For
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complicated models, it is recommended to split the model into different
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components.
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For a encoder-decoder model, it is natural to split it into the encoder and
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the decoder. For a model composed of several similar layers, it is natural to
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extract the sublayer as a seperate layer.
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extract the sublayer as a separate layer.
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There are two common ways to define a model which consists of several modules.
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#. Define a module given the specifications.
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#. Define a module given the specifications. Here is an example with multilayer
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perceptron.
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.. code-block:: python
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@ -32,11 +34,11 @@ There are two common ways to define a model which consists of several modules.
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module = MLP(16, 32, 4) # intialize a module
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When the module is intended to be a generic reusable layer that can be
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When the module is intended to be a generic and reusable layer that can be
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integrated into a larger model, we prefer to define it in this way.
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For considerations of readability and usability, we strongly recommend **NOT** to
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pack specifications into a single object. Here's an example below.
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For considerations of readability and usability, we strongly recommend
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**NOT** to pack specifications into a single object. Here's an example below.
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.. code-block:: python
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@ -48,16 +50,17 @@ There are two common ways to define a model which consists of several modules.
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def forward(self, x):
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return self.linear2(paddle.tanh(self.linear1(x))
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For a module defined in this way, it's harder for the user to initialize a
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instance. The user have to read the code to check what attributes are used.
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For a module defined in this way, it's harder for the user to initialize an
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instance. Users have to read the code to check what attributes are used.
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Code in this style tend to pass a huge config object to initialize every
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module used in an experiment, thought each module may not need the whole
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configuration.
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Also, code in this style tend to be abused by passing a huge config object
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to initialize every module used in an experiment, thought each module may
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not need the whole configuration.
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We prefer to be explicit.
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#. Define a module as a combination given its components.
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#. Define a module as a combination given its components. Here is an example
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for a sequence-to-sequence model.
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.. code-block:: python
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@ -75,15 +78,78 @@ There are two common ways to define a model which consists of several modules.
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decoder = Decoder(...)
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model = Seq2Seq(encoder, decoder) # compose two components
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When a model is a complicated one made up of several components, each of which
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When a model is a complicated and made up of several components, each of which
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has a separate functionality, and can be replaced by other components with the
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same functionality, we prefer to define it in this way.
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Data
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-------------
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Another critical componnet for a deep learning project is data. As a common
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practice, we use the dataset and dataloader abstraction.
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Dataset
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^^^^^^^^^^
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Dataset is the representation of a set of examples used for a projet. In most of
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the cases, dataset is a collection of examples. Dataset is an object which has
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methods below.
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#. ``__len__``, to get the size of the dataset.
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#. ``__getitem__``, to get an example by key or index.
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Examples is a record consisting of several fields. In practice, we usually
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represent it as a namedtuple for convenience, yet dict and user-defined object
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are also supported.
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We define our own dataset by subclassing ``paddle.io.Dataset``.
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DataLoader
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^^^^^^^^^^^
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In deep learning practice, models are trained with minibatches. DataLoader
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meets the need for iterating the dataset in batches. It is done by providing
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a sampler and a batch function in addition to a dataset.
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#. sampler, sample indices or keys used to get examples from the dataset.
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#. batch function, transform a list of examples into a batch.
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An commonly used sampler is ``RandomSampler``, it shuffles all the valid
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indices and then iterate over them sequentially. ``DistributedBatchSampler`` is
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a sampler used for distributed data parallel training, when the sampler handles
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data sharding in a dynamic way.
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Batch function is used to transform selected examples into a batch. For a simple
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case where an example is composed of several fields, each of which is represented
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by an fixed size array, batch function can be simply stacking each field. For
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cases where variable size arrays are included in the example, batching could
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invlove padding and stacking. While in theory, batch function can do more like
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randomly slicing, etc.
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For a custom dataset used for a custom model, it is required to define a batch
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function for it.
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Config
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-------------
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It's common to change the running configuration to compare results. To keep track
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of running configuration, we use ``yaml`` configuration files.
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Also, we want to interact with command line options. Some options that usually
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change according to running environments is provided by command line arguments.
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In addition, we wan to override an option in the config file without editing
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it.
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Taking these requirements in to consideration, we use `yacs <https://github.com/rbgirshick/yacs>`_
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as a confi management tool. Other tools like `omegaconf <https://github.com/omry/omegaconf>`_
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are also powerful and have similar functions.
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In each example provided, there is a ``config.py``, where the default config is
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defined. If you want to get the default config, import ``config.py`` and call
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``get_cfg_defaults()`` to get the default config. Then it can be updated with
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yaml config file or command line arguments if needed.
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For details about how to use yacs in experiments, see `yacs <https://github.com/rbgirshick/yacs>`_.
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Experiment
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--------------
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--------------
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@ -1,24 +1,30 @@
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===========
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Tutorials
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===========
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Basic Usage
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-------------------
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===========
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Pretrained models are provided in a archive. Extract it to get a folder like this::
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This section shows how to use pretrained models provided by parakeet and make
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inference with them.
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Pretrained models are provided in a archive. Extract it to get a folder like
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this::
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checkpoint_name/
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├──config.yaml
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└──step-310000.pdparams
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The ``config.yaml`` stores the config used to train the model, the ``step-N.pdparams`` is the parameter file, where N is the steps it has been trained.
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The ``config.yaml`` stores the config used to train the model, the
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``step-N.pdparams`` is the parameter file, where N is the steps it has been
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trained.
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The example code below shows how to use the models for prediction.
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text to spectrogram
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^^^^^^^^^^^^^^^^^^^^^^
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The code below show how to use a transformer_tts model. After loading the pretrained model, use ``model.predict(sentence)`` to generate spectrogram (in numpy.ndarray format), which can be further used to synthesize waveflow.
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The code below show how to use a transformer_tts model. After loading the
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pretrained model, use ``model.predict(sentence)`` to generate spectrograms
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(in numpy.ndarray format), which can be further used to synthesize raw audio
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with a vocoder.
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>>> import parakeet
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>>> from parakeet.frontend import English
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@ -43,7 +49,8 @@ The code below show how to use a transformer_tts model. After loading the pretra
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vocoder
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^^^^^^^^^^
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Like the example above, after loading the pretrained ConditionalWaveFlow model, call ``model.predict(mel)`` to synthesize waveflow (in numpy.ndarray format).
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Like the example above, after loading the pretrained ``ConditionalWaveFlow``
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model, call ``model.predict(mel)`` to synthesize raw audio (in wav format).
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>>> import soundfile as df
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>>> from parakeet.models import ConditionalWaveFlow
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@ -60,8 +67,3 @@ Like the example above, after loading the pretrained ConditionalWaveFlow model,
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>>> sf.write(audio_path, audio, config.data.sample_rate)
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For more details on how to use the model, please refer the documentation.
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@ -19,7 +19,7 @@ Parakeet
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:maxdepth: 1
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install
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tutorials
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basic
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advanced
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.. toctree::
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@ -4,8 +4,8 @@ Installation
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Install PaddlePaddle
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-------------------
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Parakeet requires PaddlePaddle as its backend. Not that 2.0rc or newer versions
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------------------------
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Parakeet requires PaddlePaddle as its backend. Not that 2.0.0rc1 or newer versions
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of paddle is required.
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Since paddlepaddle has multiple packages depending on the device (cpu or gpu)
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@ -50,7 +50,7 @@ are listed below.
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# ubuntu, debian
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sudo apt-get install libsndfile1
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# centos, fedora,
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# centos, fedora
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sudo yum install libsndfile
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# openSUSE
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@ -64,22 +64,20 @@ Install Parakeet
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There are two ways to install parakeet according to the purpose of using it.
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1. If you want to run experiments provided by parakeet or add new models and
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experiments, it is recommended to clone the project from github
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(`Parakeet <https://github.com/PaddlePaddle/Parakeet>`_), and install it in
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editable mode.
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#. If you want to run experiments provided by parakeet or add new models and
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experiments, it is recommended to clone the project from github
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(`Parakeet <https://github.com/PaddlePaddle/Parakeet>`_), and install it in
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editable mode.
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.. code-block:: bash
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git clone https://github.com/PaddlePaddle/Parakeet
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cd Parakeet
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pip install -e .
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git clone https://github.com/PaddlePaddle/Parakeet
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cd Parakeet
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pip install -e .
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#. If you only need to use the models for inference by parakeet, install from
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pypi is recommended。
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pypi is recommended.
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.. code-block:: bash
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pip install paddle-parakeet
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pip install paddle-parakeet
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