place parakeet into Parakeet/parakeet, and add tests

.gitignore

@@ -0,0 +1,129 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

.gitingore

@@ -1,2 +0,0 @@
-*.pyc
-*.tar.*

parakeet/data/.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "python.pythonPath": "/Users/chenfeiyu/miniconda3/envs/paddle/bin/python"
+}

parakeet/data/batch.py

@@ -3,7 +3,17 @@ functions to make batch for arrays which satisfy some conditions.
 """
 import numpy as np
 
-def text_collate(minibatch):
+class TextIDBatcher(object):
+    """A wrapper class for a function to build a functor, which holds the configs to pass to the function."""
+    def __init__(self, pad_id=0, dtype=np.int64):
+        self.pad_id = pad_id
+        self.dtype = dtype
+    
+    def __call__(self, minibatch):
+        out = batch_text_id(minibatch, pad_id=self.pad_id, dtype=self.dtype)
+        return out
+
+def batch_text_id(minibatch, pad_id=0, dtype=np.int64):
     """
     minibatch: List[Example]
     Example: ndarray, shape(T,), dtype: int64
@@ -17,15 +27,25 @@ def text_collate(minibatch):
     batch = []
     for example in minibatch:
         pad_len = max_len - example.shape[0]
-        batch.append(np.pad(example, [(0, pad_len)], mode='constant', constant_values=0))
+        batch.append(np.pad(example, [(0, pad_len)], mode='constant', constant_values=pad_id))
 
-    return np.array(batch, dtype=np.int64)
+    return np.array(batch, dtype=dtype)
 
-def wav_collate(minibatch):
+class WavBatcher(object):
+    def __init__(self, pad_value=0., dtype=np.float32):
+        self.pad_value = pad_value
+        self.dtype = dtype
+        
+    def __call__(self, minibatch):
+        out = batch_wav(minibatch, pad_value=self.pad_value, dtype=self.dtype)
+        return out
+
+def batch_wav(minibatch, pad_value=0., dtype=np.float32):
     """
     minibatch: List[Example]
     Example: ndarray, shape(C, T) for multi-channel wav, shape(T,) for mono-channel wav, dtype: float32 
     """
+    # detect data format, maybe better to specify it in __init__
     peek_example = minibatch[0]
     if len(peek_example.shape) == 1:
         mono_channel = True
@@ -39,13 +59,23 @@ def wav_collate(minibatch):
     for example in minibatch:
         pad_len = max_len - example.shape[-1]
         if mono_channel:
-            batch.append(np.pad(example, [(0, pad_len)], mode='constant', constant_values=0.))
+            batch.append(np.pad(example, [(0, pad_len)], mode='constant', constant_values=pad_value))
         else:
-            batch.append(np.pad(example, [(0, 0), (0, pad_len)], mode='constant', constant_values=0.)) # what about PCM, no
+            batch.append(np.pad(example, [(0, 0), (0, pad_len)], mode='constant', constant_values=pad_value)) # what about PCM, no
     
-    return np.array(batch, dtype=np.float32)
+    return np.array(batch, dtype=dtype)
 
-def spec_collate(minibatch):
+
+class SpecBatcher(object):
+    def __init__(self, pad_value=0., dtype=np.float32):
+        self.pad_value = pad_value
+        self.dtype = dtype
+
+    def __call__(self, minibatch):
+        out = batch_spec(minibatch, pad_value=self.pad_value, dtype=self.dtype)
+        return out
+
+def batch_spec(minibatch, pad_value=0., dtype=np.float32):
     """
     minibatch: List[Example]
     Example: ndarray, shape(C, F, T) for multi-channel spectrogram, shape(F, T) for mono-channel spectrogram, dtype: float32 
@@ -64,8 +94,8 @@ def spec_collate(minibatch):
     for example in minibatch:
         pad_len = max_len - example.shape[-1]
         if mono_channel:
-            batch.append(np.pad(example, [(0, 0), (0, pad_len)], mode='constant', constant_values=0.))
+            batch.append(np.pad(example, [(0, 0), (0, pad_len)], mode='constant', constant_values=pad_value))
         else:
-            batch.append(np.pad(example, [(0, 0), (0, 0), (0, pad_len)], mode='constant', constant_values=0.)) # what about PCM, no
+            batch.append(np.pad(example, [(0, 0), (0, 0), (0, pad_len)], mode='constant', constant_values=pad_value)) # what about PCM, no
     
-    return np.array(batch, dtype=np.float32)   
+    return np.array(batch, dtype=dtype) 

parakeet/data/datacargo.py

@@ -1,10 +1,9 @@
-from sampler import SequentialSampler, RandomSampler, BatchSampler
+from .sampler import SequentialSampler, RandomSampler, BatchSampler
 
-class DataLoader(object):
-    def __init__(self, dataset, batch_size=1, collate_fn = lambda x: x, 
-                 sampler=None, shuffle=False, batch_sampler=None, drop_last=False):
+class DataCargo(object):
+    def __init__(self, dataset, batch_size=1, sampler=None, 
+                 shuffle=False, batch_sampler=None, drop_last=False):
         self.dataset = dataset
-        self.collate_fn = collate_fn
         
         if batch_sampler is not None:
             # auto_collation with custom batch_sampler
@@ -14,20 +13,14 @@ class DataLoader(object):
                                  'drop_last')
             batch_size = None
             drop_last = False
+            shuffle = False
         elif batch_size is None:
-            # no auto_collation
-            if shuffle or drop_last:
-                raise ValueError('batch_size=None option disables auto-batching '
-                                 'and is mutually exclusive with '
-                                 'shuffle, and drop_last')
-        
-        if sampler is None:  # give default samplers
+            raise ValueError('batch sampler is none. then batch size must not be none.')
+        elif sampler is None:
             if shuffle:
                 sampler = RandomSampler(dataset)
             else:
                 sampler = SequentialSampler(dataset)
-
-        if batch_size is not None and batch_sampler is None:
             # auto_collation without custom batch_sampler
             batch_sampler = BatchSampler(sampler, batch_size, drop_last)
 
@@ -73,7 +66,7 @@ class DataIterator(object):
     def __next__(self):
         index = self._next_index()  # may raise StopIteration, TODO(chenfeiyu): use dynamic batch size
         minibatch = [self._dataset[i] for i in index] # we can abstract it, too to use dynamic batch size
-        minibatch = self.loader.collate_fn(minibatch) # list[Example] -> Batch
+        minibatch = self._dataset._batch_examples(minibatch) # list[Example] -> Batch
         return minibatch
     
     def _next_index(self):

parakeet/data/dataset.py

@@ -1,16 +1,16 @@
 class Dataset(object):
-    def __init__(self, lazy=True, stream=False):
-        # note that lazy and stream means two different things in our glossary
-        # lazy means to place preprocessing in __getitem__
-        # stram means the data source is itself a stream
-        self.lazy = lazy
-        self.stream = stream
+    def __init__(self):
+        pass
     
     def _load_metadata(self):
         raise NotImplementedError
     
     def _get_example(self):
-        """return a Record"""
+        """return a Record (or Example, Instance according to your glossary)"""
+        raise NotImplementedError
+    
+    def _batch_examples(self, minibatch):
+        """get a list of examples, return a batch, whose structure is the same as an example"""
         raise NotImplementedError
     
     def _prepare_metadata(self):

parakeet/data/ljspeech.py

@@ -2,23 +2,19 @@ from pathlib import Path
 import numpy as np
 import pandas as pd
 import librosa
-import g2p
+from .. import g2p
 
-from sampler import SequentialSampler, RandomSampler, BatchSampler
-from dataset import Dataset
-from dataloader import DataLoader
+from .sampler import SequentialSampler, RandomSampler, BatchSampler
+from .dataset import Dataset
+from .datacargo import DataCargo
+from .batch import TextIDBatcher, SpecBatcher
 
-from collate import text_collate, spec_collate
 
-LJSPEECH_ROOT = Path("/Users/chenfeiyu/projects/LJSpeech-1.1")
 class LJSpeech(Dataset):
-    def __init__(self, root=LJSPEECH_ROOT, lazy=True, stream=False):
-        super(LJSpeech, self).__init__(lazy, stream)
+    def __init__(self, root):
+        super(LJSpeech, self).__init__()
         self.root = root
         self.metadata = self._prepare_metadata() # we can do this just for luck
-
-        if self.stream:
-            self.examples_generator = self._read() 
         
     def _prepare_metadata(self):
         # if pure-stream case, each _prepare_metadata returns a generator
@@ -26,11 +22,6 @@ class LJSpeech(Dataset):
         metadata = pd.read_csv(csv_path, sep="|", header=None, quoting=3,
                                names=["fname", "raw_text", "normalized_text"])
         return metadata
-    
-    def _read(self):
-        for _, metadatum in self.metadata.iterrows():
-            example = self._get_example(metadatum)
-            yield example
             
     def _get_example(self, metadatum):
         """All the code for generating an Example from a metadatum. If you want a 
@@ -62,44 +53,30 @@ class LJSpeech(Dataset):
         phonemes = np.array(g2p.en.text_to_sequence(normalized_text), dtype=np.int64)
         return (mag, mel, phonemes) # maybe we need to implement it as a map in the future
 
+    def _batch_examples(self, minibatch):
+        mag_batch = []
+        mel_batch = []
+        phoneme_batch = []
+        for example in minibatch:
+            mag, mel, phoneme = example
+            mag_batch.append(mag)
+            mel_batch.append(mel)
+            phoneme_batch.append(phoneme)
+        mag_batch = SpecBatcher(pad_value=0.)(mag_batch)
+        mel_batch = SpecBatcher(pad_value=0.)(mel_batch)
+        phoneme_batch = TextIDBatcher(pad_id=0)(phoneme_batch)
+        return (mag_batch, mel_batch, phoneme_batch)
+
     def __getitem__(self, index):
-        if self.stream:
-            raise ValueError("__getitem__ is invalid in stream mode")
         metadatum = self.metadata.iloc[index]
         example = self._get_example(metadatum)
         return example
     
     def __iter__(self):
-        if self.stream:
-            for example in self.examples_generator:
-                yield example
-        else:
-            for i in range(len(self)):
-                yield self[i]
+        for i in range(len(self)):
+            yield self[i]
     
     def __len__(self):
-        if self.stream:
-            raise ValueError("__len__ is invalid in stream mode")
         return len(self.metadata)
 
 
-def fn(minibatch):
-    mag_batch = []
-    mel_batch = []
-    phoneme_batch = []
-    for example in minibatch:
-        mag, mel, phoneme = example
-        mag_batch.append(mag)
-        mel_batch.append(mel)
-        phoneme_batch.append(phoneme)
-    mag_batch = spec_collate(mag_batch)
-    mel_batch = spec_collate(mel_batch)
-    phoneme_batch = text_collate(phoneme_batch)
-    return (mag_batch, mel_batch, phoneme_batch)
-
-if __name__ == "__main__":
-    ljspeech = LJSpeech(LJSPEECH_ROOT)
-    ljspeech_loader = DataLoader(ljspeech, batch_size=16, shuffle=True, collate_fn=fn)
-    for i, batch in enumerate(ljspeech_loader):
-        print(i)
-

parakeet/g2p/__init__.py

@@ -12,22 +12,22 @@ and the property:
 - n_vocab
 
 """
-from g2p import en
+from . import en
 
 # optinoal Japanese frontend
 try:
-    from g2p import jp
+    from . import jp
 except ImportError:
     jp = None
 
 try:
-    from g2p import ko
+    from . import ko
 except ImportError:
     ko = None
 
 # if you are going to use the frontend, you need to modify _characters in symbol.py:
 # _characters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!\'(),-.:;? ' + '¡¿ñáéíóúÁÉÍÓÚÑ'
 try:
-    from g2p import es
+    from . import es
 except ImportError:
     es = None

parakeet/g2p/en/__init__.py

@@ -1,8 +1,7 @@
 # coding: utf-8
 
-from g2p.text.symbols import symbols
-from g2p import text
-from g2p.text import sequence_to_text
+from ..text.symbols import symbols
+from ..text import sequence_to_text
 
 import nltk
 from random import random
@@ -30,7 +29,7 @@ def mix_pronunciation(text, p):
 def text_to_sequence(text, p=0.0):
     if p >= 0:
         text = mix_pronunciation(text, p)
-    from g2p.text import text_to_sequence
+    from ..text import text_to_sequence
     text = text_to_sequence(text, ["english_cleaners"])
     return text
 

parakeet/g2p/es/__init__.py

@@ -1,6 +1,6 @@
 # coding: utf-8
-from g2p.text.symbols import symbols
-from g2p.text import sequence_to_text
+from ..text.symbols import symbols
+from ..text import sequence_to_text
 
 import nltk
 from random import random
@@ -9,7 +9,7 @@ n_vocab = len(symbols)
 
 
 def text_to_sequence(text, p=0.0):
-    from g2p.text import text_to_sequence
+    from ..text import text_to_sequence
     text = text_to_sequence(text, ["basic_cleaners"])
     return text
 

parakeet/g2p/text/__init__.py

@@ -1,6 +1,6 @@
 import re
-from g2p.text import cleaners
-from g2p.text.symbols import symbols
+from . import cleaners
+from .symbols import symbols
 
 
 # Mappings from symbol to numeric ID and vice versa:

tests/test_ljspeech.py

@@ -0,0 +1,10 @@
+from parakeet.data.ljspeech import LJSpeech
+from parakeet.data.datacargo import DataCargo
+
+from pathlib import Path
+
+LJSPEECH_ROOT = Path("/Users/chenfeiyu/projects/LJSpeech-1.1")
+ljspeech = LJSpeech(LJSPEECH_ROOT)
+ljspeech_cargo = DataCargo(ljspeech, batch_size=16, shuffle=True)
+for i, batch in enumerate(ljspeech_cargo):
+    print(i)