def __init__(
self,
data,
dynamic_items=[],
output_keys=[],
):
self.data = data
self.data_ids = list(self.data.keys())
static_keys = list(self.data[self.data_ids[0]].keys())
if "id" in static_keys:
raise ValueError("The key 'id' is reserved for the data point id.")
else:
static_keys.append("id")
self.pipeline = DataPipeline(static_keys, dynamic_items)
self.set_output_keys(output_keys)
class DynamicItemDataset(Dataset):
"""Dataset that reads, wrangles and produces dicts
Each data point dict provides some items (by key), for example a path to a
wavefile with the key "wav_file". When a data point is fetched from this
Dataset, more items are produced dynamically, based on pre-existing items
and other dynamic created items. For example, a dynamic item could take the
wavfile path and load the audio from disk.
The dynamic items can depend on other dynamic items: a suitable evaluation
order is used automatically, as long as there are no circular dependencies.
A specified list of keys is collected in the output dict. These can be items
in the original data or dynamic items. If some dynamic items are not
requested, nor depended on by other requested items, they won't be computed.
So for example if a user simply wants to iterate over the text, the
time-consuming audio loading can be skipped.
About the format:
Takes a dict of dicts as the collection of data points to read/wrangle.
The top level keys are data point IDs.
Each data point (example) dict should have the same keys, corresponding to
different items in that data point.
Altogether the data collection could look like this:
>>> data = {
... "spk1utt1": {
... "wav_file": "/path/to/spk1utt1.wav",
... "text": "hello world",
... "speaker": "spk1",
... },
... "spk1utt2": {
... "wav_file": "/path/to/spk1utt2.wav",
... "text": "how are you world",
... "speaker": "spk1",
... }
... }
NOTE
----
The top level key, the data point id, is implicitly added as an item
in the data point, with the key "id"
Each dynamic item is configured by three things: a key, a func, and a list
of argkeys. The key should be unique among all the items (dynamic or not) in
each data point. The func is any callable, and it returns the dynamic item's
value. The callable is called with the values of other items as specified
by the argkeys list (as positional args, passed in the order specified by
argkeys).
The dynamic_items configuration could look like this:
>>> import torch
>>> dynamic_items = [
... {"func": lambda l: torch.Tensor(l),
... "takes": ["wav_loaded"],
... "provides": "wav"},
... {"func": lambda path: [ord(c)/100 for c in path], # Fake "loading"
... "takes": ["wav_file"],
... "provides": "wav_loaded"},
... {"func": lambda t: t.split(),
... "takes": ["text"],
... "provides": "words"}]
With these, different views of the data can be loaded:
>>> from speechbrain.dataio.dataloader import SaveableDataLoader
>>> from speechbrain.dataio.batch import PaddedBatch
>>> dataset = DynamicItemDataset(data, dynamic_items)
>>> dataloader = SaveableDataLoader(dataset, collate_fn=PaddedBatch,
... batch_size=2)
>>> # First, create encoding for words:
>>> dataset.set_output_keys(["words"])
>>> encoding = {}
>>> next_id = 1
>>> for batch in dataloader:
... for sent in batch.words:
... for word in sent:
... if word not in encoding:
... encoding[word] = next_id
... next_id += 1
>>> # Next, add an encoded words_tensor dynamic item:
>>> dataset.add_dynamic_item(
... func = lambda ws: torch.tensor([encoding[w] for w in ws],
... dtype=torch.long),
... takes = ["words"],
... provides = "words_encoded")
>>> # Now we can get word and audio tensors:
>>> dataset.set_output_keys(["id", "wav", "words_encoded"])
>>> batch = next(iter(dataloader))
>>> batch.id
['spk1utt1', 'spk1utt2']
>>> batch.wav # +ELLIPSIS
PaddedData(data=tensor([[0.4700, 1.1200, ...
>>> batch.words_encoded
PaddedData(data=tensor([[1, 2, 0, 0],
[3, 4, 5, 2]]), lengths=tensor([0.5000, 1.0000]))
>>> # Output keys can also be a map:
>>> dataset.set_output_keys({"id":"id", "signal": "wav", "words": "words_encoded"})
>>> batch = next(iter(dataloader))
>>> batch.words
PaddedData(data=tensor([[1, 2, 0, 0],
[3, 4, 5, 2]]), lengths=tensor([0.5000, 1.0000]))
Arguments
---------
data : dict
Dictionary containing single data points (e.g. utterances).
dynamic_items : list, optional
Configuration for the dynamic items produced when fetchin an example.
List of DynamicItems or dicts with the format
func: <callable> # To be called
takes: <list> # key or list of keys of args this takes
provides: key # key or list of keys that this provides
output_keys : dict, list, optional
List of keys (either directly available in data or dynamic items)
to include in the output dict when data points are fetched.
If a dict is given; it is used to map internal keys to output keys.
From the output_keys dict key:value pairs the key appears outside,
and value is the internal key.
"""
def __init__(
self,
data,
dynamic_items=[],
output_keys=[],
):
self.data = data
self.data_ids = list(self.data.keys())
static_keys = list(self.data[self.data_ids[0]].keys())
if "id" in static_keys:
raise ValueError("The key 'id' is reserved for the data point id.")
else:
static_keys.append("id")
self.pipeline = DataPipeline(static_keys, dynamic_items)
self.set_output_keys(output_keys)
def __len__(self):
return len(self.data_ids)
def __getitem__(self, index):
data_id = self.data_ids[index]
data_point = self.data[data_id]
data_point["id"] = data_id
return self.pipeline.compute_outputs(data_point)
def add_dynamic_item(self, func, takes=None, provides=None):
"""Makes a new dynamic item available on the dataset.
Two calling conventions. For DynamicItem objects, just use:
add_dynamic_item(dynamic_item)
But otherwise, should use:
add_dynamic_item(func, takes, provides)
See `speechbrain.utils.data_pipeline`
Arguments
---------
func : callable, DynamicItem
If a DynamicItem is given, adds that directly. Otherwise a
DynamicItem is created, and this specifies the callable to use. If
a generator function is given, then create a GeneratorDynamicItem.
Otherwise creates a normal DynamicItem.
takes : list, str
List of keys. When func is called, each key is resolved to
either an entry in the data or the output of another dynamic_item.
The func is then called with these as positional arguments,
in the same order as specified here.
A single arg can be given directly.
provides : str
Unique key or keys that this provides.
"""
self.pipeline.add_dynamic_item(func, takes, provides)
def set_output_keys(self, keys):
"""Use this to change the output keys
These are the keys that are actually evaluated when a data point
is fetched from the dataset.
Arguments
---------
keys : dict, list
List of of keys (str) to produce in output.
If a dict is given; it is used to map internal keys to output keys.
From the output_keys dict key:value pairs the key appears outside,
and value is the internal key.
"""
self.pipeline.set_output_keys(keys)
@contextlib.contextmanager
def output_keys_as(self, keys):
"""Context manager to temporarily set output keys
Example
-------
>>> dataset = DynamicItemDataset({"a":{"x":1,"y":2},"b":{"x":3,"y":4}},
... output_keys = ["x"])
>>> with dataset.output_keys_as(["y"]):
... print(dataset[0])
{'y': 2}
>>> print(dataset[0])
{'x': 1}
NOTE
----
Not thread-safe. While in this context manager, the output keys
are affected for any call.
"""
saved_output = self.pipeline.output_mapping
self.pipeline.set_output_keys(keys)
yield self
self.pipeline.set_output_keys(saved_output)
def filtered_sorted(
self,
key_min_value={},
key_max_value={},
key_test={},
sort_key=None,
reverse=False,
select_n=None,
):
"""Get a filtered and/or sorted version of this, shares static data.
The reason to implement these operations in the same method is that
computing some dynamic items may be expensive, and this way the
filtering and sorting steps don't need to compute the dynamic items
twice.
Arguments
---------
key_min_value : dict
Map from key (in data or in dynamic items) to limit, will only keep
data_point if data_point[key] >= limit
key_max_value : dict
Map from key (in data or in dynamic items) to limit, will only keep
data_point if data_point[key] <= limit
key_test : dict
Map from key (in data or in dynamic items) to func, will only keep
data_point if bool(func(data_point[key])) == True
sort_key : None, str
If not None, sort by data_point[sort_key]. Default is ascending
order.
reverse : bool
If True, sort in descending order.
select_n : None, int
If not None, only keep (at most) the first n filtered data_points.
The possible sorting is applied, but only on the first n data
points found. Meant for debuggging.
Returns
-------
FilteredSortedDynamicItemDataset
Shares the static data, but has its own output keys and
dynamic items (initially deep copied from this, so they have the
same dynamic items available)
NOTE
----
Temporarily changes the output keys!
"""
filtered_sorted_ids = self._filtered_sorted_ids(
key_min_value,
key_max_value,
key_test,
sort_key,
reverse,
select_n,
)
return FilteredSortedDynamicItemDataset(
self, filtered_sorted_ids) # NOTE: defined below
def _filtered_sorted_ids(
self,
key_min_value={},
key_max_value={},
key_test={},
sort_key=None,
reverse=False,
select_n=None,
):
"""Returns a list of data ids, fulfilling the sorting and filtering"""
def combined_filter(computed):
for key, limit in key_min_value.items():
# NOTE: docstring promises >= so using that.
# Mathematically could also use < for nicer syntax, but
# maybe with some super special weird edge case some one can
# depend on the >= operator
if computed[key] >= limit:
continue
return False
for key, limit in key_max_value.items():
if computed[key] <= limit:
continue
return False
for key, func in key_test.items():
if bool(func(computed[key])):
continue
return False
return True
temp_keys = (set(key_min_value.keys())
| set(key_max_value.keys())
| set(key_test.keys())
| set([] if sort_key is None else [sort_key]))
filtered_ids = []
with self.output_keys_as(temp_keys):
for i, data_id in enumerate(self.data_ids):
if select_n is not None and len(filtered_ids) == select_n:
break
data_point = self.data[data_id]
data_point["id"] = data_id
computed = self.pipeline.compute_outputs(data_point)
if combined_filter(computed):
if sort_key is not None:
# Add (main sorting index, current index, data_id)
# So that we maintain current sorting and don't compare
# data_id values ever.
filtered_ids.append((computed[sort_key], i, data_id))
else:
filtered_ids.append(data_id)
if sort_key is not None:
filtered_sorted_ids = [
tup[2] for tup in sorted(filtered_ids, reverse=reverse)
]
else:
filtered_sorted_ids = filtered_ids
return filtered_sorted_ids
@classmethod
def from_json(cls,
json_path,
replacements={},
dynamic_items=[],
output_keys=[]):
"""Load a data prep JSON file and create a Dataset based on it."""
data = load_data_json(json_path, replacements)
return cls(data, dynamic_items, output_keys)
@classmethod
def from_csv(cls,
csv_path,
replacements={},
dynamic_items=[],
output_keys=[]):
"""Load a data prep CSV file and create a Dataset based on it."""
data = load_data_csv(csv_path, replacements)
return cls(data, dynamic_items, output_keys)
@classmethod
def from_arrow_dataset(cls,
dataset,
replacements={},
dynamic_items=[],
output_keys=[]):
"""loading a prepared huggingface dataset"""
# define an unbound method to generate puesdo keys
def keys(self):
return [i for i in range(dataset.__len__())]
# bind this method to arrow dataset
dataset.keys = MethodType(keys, dataset)
return cls(dataset, dynamic_items, output_keys)
def test_data_pipeline():
from speechbrain.utils.data_pipeline import DataPipeline
pipeline = DataPipeline(
["text"],
dynamic_items=[
{"func": lambda x: x.lower(), "takes": ["text"], "provides": "foo"},
{"func": lambda x: x[::-1], "takes": "foo", "provides": ["bar"]},
],
output_keys=["text", "foo", "bar"],
)
result = pipeline({"text": "Test"})
print(result)
assert result["bar"] == "tset"
pipeline = DataPipeline(["foo", "bar"])
pipeline.add_dynamic_item(
func=lambda x, y: x + y, takes=["foo", "bar"], provides="foobar"
)
pipeline.set_output_keys(["bar", "foobar"])
result = pipeline({"foo": 1, "bar": 2})
assert result["foobar"] == 3
pipeline = DataPipeline(["foo", "bar"])
from unittest.mock import MagicMock, Mock
watcher = Mock()
pipeline.add_dynamic_item(
provides="foobar", func=watcher, takes=["foo", "bar"]
)
result = pipeline({"foo": 1, "bar": 2})
assert not watcher.called
pipeline = DataPipeline(["foo", "bar"])
watcher = MagicMock(return_value=3)
pipeline.add_dynamic_item(watcher, ["foo", "bar"], "foobar")
pipeline.add_dynamic_item(lambda x: x, ["foobar"], ["truebar"])
pipeline.set_output_keys(("truebar",))
result = pipeline({"foo": 1, "bar": 2})
assert watcher.called
assert result["truebar"] == 3
pipeline = DataPipeline(["foo", "bar"])
watcher = MagicMock(return_value=3)
pipeline.add_dynamic_item(
func=watcher, takes=["foo", "bar"], provides="foobar"
)
pipeline.add_dynamic_item(
func=lambda x: x, takes=["foo"], provides="truebar"
)
pipeline.set_output_keys(("truebar",))
result = pipeline({"foo": 1, "bar": 2})
assert not watcher.called
assert result["truebar"] == 1
pipeline = DataPipeline(["foo", "bar"])
watcher = MagicMock(return_value=3)
pipeline.add_dynamic_item(
func=watcher, takes=["foo", "bar"], provides="foobar"
)
pipeline.set_output_keys(("foobar", "foo"))
result = pipeline({"foo": 1, "bar": 2})
assert watcher.called
assert "foo" in result
assert "foobar" in result
assert "bar" not in result
# Can change the outputs (continues previous tests)
watcher.reset_mock()
pipeline.set_output_keys(["bar"])
result = pipeline({"foo": 1, "bar": 2})
assert not watcher.called
assert "foo" not in result
assert "foobar" not in result
assert "bar" in result
# Finally, can also still request any specific key:
computed = pipeline.compute_specific(["foobar"], {"foo": 1, "bar": 2})
assert watcher.called
assert computed["foobar"] == 3
# Output can be a mapping:
# (Key appears outside, value is internal)
pipeline.set_output_keys({"signal": "foobar"})
result = pipeline({"foo": 1, "bar": 2})
assert result["signal"] == 3
def test_MIMO_pipeline():
from speechbrain.utils.data_pipeline import DataPipeline, takes, provides
@takes("text", "other-text")
@provides("reversed", "concat")
def text_pipeline(text, other):
return text[::-1], text + other
@takes("reversed", "concat")
@provides("reversed_twice", "double_concat")
def second_pipeline(rev, concat):
yield rev[::-1]
yield concat + concat
@provides("hello-world")
def provider():
yield "hello-world"
@takes("hello-world", "reversed_twice")
@provides("message")
def messenger(hello, name):
return f"{hello}, {name}"
pipeline = DataPipeline(
["text", "other-text"],
dynamic_items=[second_pipeline, text_pipeline],
output_keys=["text", "reversed", "reversed_twice"],
)
result = pipeline({"text": "abc", "other-text": "def"})
assert result["reversed"] == "cba"
assert result["reversed_twice"] == "abc"
result = pipeline.compute_specific(
["concat"], {"text": "abc", "other-text": "def"}
)
assert result["concat"] == "abcdef"
result = pipeline.compute_specific(
["double_concat"], {"text": "abc", "other-text": "def"}
)
assert result["double_concat"] == "abcdefabcdef"
assert "concat" not in result
# Add messenger but not provider, so "hello-world" is unaccounted for:
pipeline.add_dynamic_item(messenger)
with pytest.raises(RuntimeError):
pipeline.compute_specific(
["message"], {"text": "abc", "other-text": "def"}
)
# Now add provider, so that the unaccounted for hello-world key gets accounted for.
pipeline.add_dynamic_item(provider)
result = pipeline.compute_specific(
["message"], {"text": "abc", "other-text": "def"}
)
assert result["message"] == "hello-world, abc"