"""
Data reading and writing.
Authors
* Mirco Ravanelli 2020
* Aku Rouhe 2020
* Ju-Chieh Chou 2020
* Samuele Cornell 2020
* Abdel HEBA 2020
* Gaelle Laperriere 2021
* Sahar Ghannay 2021
* Sylvain de Langen 2022
"""
import os
import torch
import logging
import numpy as np
import pickle
import hashlib
import csv
import time
import torchaudio
import json
import re
from speechbrain.utils.torch_audio_backend import check_torchaudio_backend
check_torchaudio_backend()
logger = logging.getLogger(__name__)
[docs]def load_data_json(json_path, replacements={}):
"""Loads JSON and recursively formats string values.
Arguments
----------
json_path : str
Path to CSV file.
replacements : dict
(Optional dict), e.g., {"data_folder": "/home/speechbrain/data"}.
This is used to recursively format all string values in the data.
Returns
-------
dict
JSON data with replacements applied.
Example
-------
>>> json_spec = '''{
... "ex1": {"files": ["{ROOT}/mic1/ex1.wav", "{ROOT}/mic2/ex1.wav"], "id": 1},
... "ex2": {"files": [{"spk1": "{ROOT}/ex2.wav"}, {"spk2": "{ROOT}/ex2.wav"}], "id": 2}
... }
... '''
>>> tmpfile = getfixture('tmpdir') / "test.json"
>>> with open(tmpfile, "w") as fo:
... _ = fo.write(json_spec)
>>> data = load_data_json(tmpfile, {"ROOT": "/home"})
>>> data["ex1"]["files"][0]
'/home/mic1/ex1.wav'
>>> data["ex2"]["files"][1]["spk2"]
'/home/ex2.wav'
"""
with open(json_path, "r") as f:
out_json = json.load(f)
_recursive_format(out_json, replacements)
return out_json
def _recursive_format(data, replacements):
# Data: dict or list, replacements : dict
# Replaces string keys in replacements by their values
# at all levels of data (in str values)
# Works in-place.
if isinstance(data, dict):
for key, item in data.items():
if isinstance(item, dict) or isinstance(item, list):
_recursive_format(item, replacements)
elif isinstance(item, str):
data[key] = item.format_map(replacements)
# If not dict, list or str, do nothing
if isinstance(data, list):
for i, item in enumerate(data):
if isinstance(item, dict) or isinstance(item, list):
_recursive_format(item, replacements)
elif isinstance(item, str):
data[i] = item.format_map(replacements)
# If not dict, list or str, do nothing
[docs]def load_data_csv(csv_path, replacements={}):
"""Loads CSV and formats string values.
Uses the SpeechBrain legacy CSV data format, where the CSV must have an
'ID' field.
If there is a field called duration, it is interpreted as a float.
The rest of the fields are left as they are (legacy _format and _opts fields
are not used to load the data in any special way).
Bash-like string replacements with $to_replace are supported.
Arguments
----------
csv_path : str
Path to CSV file.
replacements : dict
(Optional dict), e.g., {"data_folder": "/home/speechbrain/data"}
This is used to recursively format all string values in the data.
Returns
-------
dict
CSV data with replacements applied.
Example
-------
>>> csv_spec = '''ID,duration,wav_path
... utt1,1.45,$data_folder/utt1.wav
... utt2,2.0,$data_folder/utt2.wav
... '''
>>> tmpfile = getfixture("tmpdir") / "test.csv"
>>> with open(tmpfile, "w") as fo:
... _ = fo.write(csv_spec)
>>> data = load_data_csv(tmpfile, {"data_folder": "/home"})
>>> data["utt1"]["wav_path"]
'/home/utt1.wav'
"""
with open(csv_path, newline="") as csvfile:
result = {}
reader = csv.DictReader(csvfile, skipinitialspace=True)
variable_finder = re.compile(r"\$([\w.]+)")
for row in reader:
# ID:
try:
data_id = row["ID"]
del row["ID"] # This is used as a key in result, instead.
except KeyError:
raise KeyError(
"CSV has to have an 'ID' field, with unique ids"
" for all data points"
)
if data_id in result:
raise ValueError(f"Duplicate id: {data_id}")
# Replacements:
for key, value in row.items():
try:
row[key] = variable_finder.sub(
lambda match: str(replacements[match[1]]), value
)
except KeyError:
raise KeyError(
f"The item {value} requires replacements "
"which were not supplied."
)
# Duration:
if "duration" in row:
row["duration"] = float(row["duration"])
result[data_id] = row
return result
[docs]def read_audio_info(path) -> "torchaudio.backend.common.AudioMetaData":
"""Retrieves audio metadata from a file path. Behaves identically to
torchaudio.info, but attempts to fix metadata (such as frame count) that is
otherwise broken with certain torchaudio version and codec combinations.
Note that this may cause full file traversal in certain cases!
Arguments
----------
path : str
Path to the audio file to examine.
Returns
-------
torchaudio.backend.common.AudioMetaData
Same value as returned by `torchaudio.info`, but may eventually have
`num_frames` corrected if it otherwise would have been `== 0`.
NOTE
----
Some codecs, such as MP3, require full file traversal for accurate length
information to be retrieved.
In these cases, you may as well read the entire audio file to avoid doubling
the processing time.
"""
_path_no_ext, path_ext = os.path.splitext(path)
if path_ext == ".mp3":
# Additionally, certain affected versions of torchaudio fail to
# autodetect mp3.
# HACK: here, we check for the file extension to force mp3 detection,
# which prevents an error from occuring in torchaudio.
info = torchaudio.info(path, format="mp3")
else:
info = torchaudio.info(path)
# Certain file formats, such as MP3, do not provide a reliable way to
# query file duration from metadata (when there is any).
# For MP3, certain versions of torchaudio began returning num_frames == 0.
#
# https://github.com/speechbrain/speechbrain/issues/1925
# https://github.com/pytorch/audio/issues/2524
#
# Accomodate for these cases here: if `num_frames == 0` then maybe something
# has gone wrong.
# If some file really had `num_frames == 0` then we are not doing harm
# double-checking anyway. If I am wrong and you are reading this comment
# because of it: sorry
if info.num_frames == 0:
channels_data, sample_rate = torchaudio.load(path, normalize=False)
info.num_frames = channels_data.size(1)
info.sample_rate = sample_rate # because we might as well
return info
[docs]def read_audio(waveforms_obj):
"""General audio loading, based on a custom notation.
Expected use case is in conjunction with Datasets
specified by JSON.
The parameter may just be a path to a file:
`read_audio("/path/to/wav1.wav")`
Alternatively, you can specify more options in a dict, e.g.:
```
# load a file from sample 8000 through 15999
read_audio({
"file": "/path/to/wav2.wav",
"start": 8000,
"stop": 16000
})
```
Which codecs are supported depends on your torchaudio backend.
Refer to `torchaudio.load` documentation for further details.
Arguments
----------
waveforms_obj : str, dict
Path to audio or dict with the desired configuration.
Keys for the dict variant:
- `"file"` (str): Path to the audio file.
- `"start"` (int, optional): The first sample to load.
If unspecified, load from the very first frame.
- `"stop"` (int, optional): The last sample to load (exclusive).
If unspecified or equal to start, load from `start` to the end.
Will not fail if `stop` is past the sample count of the file and will
return less frames.
Returns
-------
torch.Tensor
1-channel: audio tensor with shape: `(samples, )`.
>=2-channels: audio tensor with shape: `(samples, channels)`.
Example
-------
>>> dummywav = torch.rand(16000)
>>> import os
>>> tmpfile = str(getfixture('tmpdir') / "wave.wav")
>>> write_audio(tmpfile, dummywav, 16000)
>>> asr_example = { "wav": tmpfile, "spk_id": "foo", "words": "foo bar"}
>>> loaded = read_audio(asr_example["wav"])
>>> loaded.allclose(dummywav.squeeze(0),atol=1e-4) # replace with eq with sox_io backend
True
"""
if isinstance(waveforms_obj, str):
audio, _ = torchaudio.load(waveforms_obj)
else:
path = waveforms_obj["file"]
start = waveforms_obj.get("start", 0)
# To match past SB behavior, `start == stop` or omitted `stop` means to
# load all frames from `start` to the file end.
stop = waveforms_obj.get("stop", start)
if start < 0:
raise ValueError(
f"Invalid sample range (start < 0): {start}..{stop}!"
)
if stop < start:
# Could occur if the user tried one of two things:
# - specify a negative value as an attempt to index from the end;
# - specify -1 as an attempt to load up to the last sample.
raise ValueError(
f"Invalid sample range (stop < start): {start}..{stop}!\n"
'Hint: Omit "stop" if you want to read to the end of file.'
)
# Requested to load until a specific frame?
if start != stop:
num_frames = stop - start
audio, fs = torchaudio.load(
path, num_frames=num_frames, frame_offset=start
)
else:
# Load to the end.
audio, fs = torchaudio.load(path, frame_offset=start)
audio = audio.transpose(0, 1)
return audio.squeeze(1)
[docs]def read_audio_multichannel(waveforms_obj):
"""General audio loading, based on a custom notation.
Expected use case is in conjunction with Datasets
specified by JSON.
The custom notation:
The annotation can be just a path to a file:
"/path/to/wav1.wav"
Multiple (possibly multi-channel) files can be specified, as long as they
have the same length:
{"files": [
"/path/to/wav1.wav",
"/path/to/wav2.wav"
]
}
Or you can specify a single file more succinctly:
{"files": "/path/to/wav2.wav"}
Offset number samples and stop number samples also can be specified to read
only a segment within the files.
{"files": [
"/path/to/wav1.wav",
"/path/to/wav2.wav"
]
"start": 8000
"stop": 16000
}
Arguments
----------
waveforms_obj : str, dict
Audio reading annotation, see above for format.
Returns
-------
torch.Tensor
Audio tensor with shape: (samples, ).
Example
-------
>>> dummywav = torch.rand(16000, 2)
>>> import os
>>> tmpfile = str(getfixture('tmpdir') / "wave.wav")
>>> write_audio(tmpfile, dummywav, 16000)
>>> asr_example = { "wav": tmpfile, "spk_id": "foo", "words": "foo bar"}
>>> loaded = read_audio(asr_example["wav"])
>>> loaded.allclose(dummywav.squeeze(0),atol=1e-4) # replace with eq with sox_io backend
True
"""
if isinstance(waveforms_obj, str):
audio, _ = torchaudio.load(waveforms_obj)
return audio.transpose(0, 1)
files = waveforms_obj["files"]
if not isinstance(files, list):
files = [files]
waveforms = []
start = waveforms_obj.get("start", 0)
# Default stop to start -> if not specified, num_frames becomes 0,
# which is the torchaudio default
stop = waveforms_obj.get("stop", start - 1)
num_frames = stop - start
for f in files:
audio, fs = torchaudio.load(
f, num_frames=num_frames, frame_offset=start
)
waveforms.append(audio)
out = torch.cat(waveforms, 0)
return out.transpose(0, 1)
[docs]def write_audio(filepath, audio, samplerate):
"""Write audio on disk. It is basically a wrapper to support saving
audio signals in the speechbrain format (audio, channels).
Arguments
---------
filepath: path
Path where to save the audio file.
audio : torch.Tensor
Audio file in the expected speechbrain format (signal, channels).
samplerate: int
Sample rate (e.g., 16000).
Example
-------
>>> import os
>>> tmpfile = str(getfixture('tmpdir') / "wave.wav")
>>> dummywav = torch.rand(16000, 2)
>>> write_audio(tmpfile, dummywav, 16000)
>>> loaded = read_audio(tmpfile)
>>> loaded.allclose(dummywav,atol=1e-4) # replace with eq with sox_io backend
True
"""
if len(audio.shape) == 2:
audio = audio.transpose(0, 1)
elif len(audio.shape) == 1:
audio = audio.unsqueeze(0)
torchaudio.save(filepath, audio, samplerate)
[docs]def load_pickle(pickle_path):
"""Utility function for loading .pkl pickle files.
Arguments
---------
pickle_path : str
Path to pickle file.
Returns
-------
out : object
Python object loaded from pickle.
"""
with open(pickle_path, "rb") as f:
out = pickle.load(f)
return out
[docs]def to_floatTensor(x: (list, tuple, np.ndarray)):
"""
Arguments
---------
x : (list, tuple, np.ndarray)
Input data to be converted to torch float.
Returns
-------
tensor : torch.tensor
Data now in torch.tensor float datatype.
"""
if isinstance(x, torch.Tensor):
return x.float()
if isinstance(x, np.ndarray):
return torch.from_numpy(x).float()
else:
return torch.tensor(x, dtype=torch.float)
[docs]def to_doubleTensor(x: (list, tuple, np.ndarray)):
"""
Arguments
---------
x : (list, tuple, np.ndarray)
Input data to be converted to torch double.
Returns
-------
tensor : torch.tensor
Data now in torch.tensor double datatype.
"""
if isinstance(x, torch.Tensor):
return x.double()
if isinstance(x, np.ndarray):
return torch.from_numpy(x).double()
else:
return torch.tensor(x, dtype=torch.double)
[docs]def to_longTensor(x: (list, tuple, np.ndarray)):
"""
Arguments
---------
x : (list, tuple, np.ndarray)
Input data to be converted to torch long.
Returns
-------
tensor : torch.tensor
Data now in torch.tensor long datatype.
"""
if isinstance(x, torch.Tensor):
return x.long()
if isinstance(x, np.ndarray):
return torch.from_numpy(x).long()
else:
return torch.tensor(x, dtype=torch.long)
[docs]def convert_index_to_lab(batch, ind2lab):
"""Convert a batch of integer IDs to string labels.
Arguments
---------
batch : list
List of lists, a batch of sequences.
ind2lab : dict
Mapping from integer IDs to labels.
Returns
-------
list
List of lists, same size as batch, with labels from ind2lab.
Example
-------
>>> ind2lab = {1: "h", 2: "e", 3: "l", 4: "o"}
>>> out = convert_index_to_lab([[4,1], [1,2,3,3,4]], ind2lab)
>>> for seq in out:
... print("".join(seq))
oh
hello
"""
return [[ind2lab[int(index)] for index in seq] for seq in batch]
[docs]def relative_time_to_absolute(batch, relative_lens, rate):
"""Converts SpeechBrain style relative length to the absolute duration.
Operates on batch level.
Arguments
---------
batch : torch.tensor
Sequences to determine the duration for.
relative_lens : torch.tensor
The relative length of each sequence in batch. The longest sequence in
the batch needs to have relative length 1.0.
rate : float
The rate at which sequence elements occur in real-world time. Sample
rate, if batch is raw wavs (recommended) or 1/frame_shift if batch is
features. This has to have 1/s as the unit.
Returns
------:
torch.tensor
Duration of each sequence in seconds.
Example
-------
>>> batch = torch.ones(2, 16000)
>>> relative_lens = torch.tensor([3./4., 1.0])
>>> rate = 16000
>>> print(relative_time_to_absolute(batch, relative_lens, rate))
tensor([0.7500, 1.0000])
"""
max_len = batch.shape[1]
durations = torch.round(relative_lens * max_len) / rate
return durations
[docs]class IterativeCSVWriter:
"""Write CSV files a line at a time.
Arguments
---------
outstream : file-object
A writeable stream
data_fields : list
List of the optional keys to write. Each key will be expanded to the
SpeechBrain format, producing three fields: key, key_format, key_opts.
Example
-------
>>> import io
>>> f = io.StringIO()
>>> writer = IterativeCSVWriter(f, ["phn"])
>>> print(f.getvalue())
ID,duration,phn,phn_format,phn_opts
>>> writer.write("UTT1",2.5,"sil hh ee ll ll oo sil","string","")
>>> print(f.getvalue())
ID,duration,phn,phn_format,phn_opts
UTT1,2.5,sil hh ee ll ll oo sil,string,
>>> writer.write(ID="UTT2",phn="sil ww oo rr ll dd sil",phn_format="string")
>>> print(f.getvalue())
ID,duration,phn,phn_format,phn_opts
UTT1,2.5,sil hh ee ll ll oo sil,string,
UTT2,,sil ww oo rr ll dd sil,string,
>>> writer.set_default('phn_format', 'string')
>>> writer.write_batch(ID=["UTT3","UTT4"],phn=["ff oo oo", "bb aa rr"])
>>> print(f.getvalue())
ID,duration,phn,phn_format,phn_opts
UTT1,2.5,sil hh ee ll ll oo sil,string,
UTT2,,sil ww oo rr ll dd sil,string,
UTT3,,ff oo oo,string,
UTT4,,bb aa rr,string,
"""
def __init__(self, outstream, data_fields, defaults={}):
self._outstream = outstream
self.fields = ["ID", "duration"] + self._expand_data_fields(data_fields)
self.defaults = defaults
self._outstream.write(",".join(self.fields))
[docs] def set_default(self, field, value):
"""Sets a default value for the given CSV field.
Arguments
---------
field : str
A field in the CSV.
value
The default value.
"""
if field not in self.fields:
raise ValueError(f"{field} is not a field in this CSV!")
self.defaults[field] = value
[docs] def write(self, *args, **kwargs):
"""Writes one data line into the CSV.
Arguments
---------
*args
Supply every field with a value in positional form OR.
**kwargs
Supply certain fields by key. The ID field is mandatory for all
lines, but others can be left empty.
"""
if args and kwargs:
raise ValueError(
"Use either positional fields or named fields, but not both."
)
if args:
if len(args) != len(self.fields):
raise ValueError("Need consistent fields")
to_write = [str(arg) for arg in args]
if kwargs:
if "ID" not in kwargs:
raise ValueError("I'll need to see some ID")
full_vals = self.defaults.copy()
full_vals.update(kwargs)
to_write = [str(full_vals.get(field, "")) for field in self.fields]
self._outstream.write("\n")
self._outstream.write(",".join(to_write))
[docs] def write_batch(self, *args, **kwargs):
"""Writes a batch of lines into the CSV.
Here each argument should be a list with the same length.
Arguments
---------
*args
Supply every field with a value in positional form OR.
**kwargs
Supply certain fields by key. The ID field is mandatory for all
lines, but others can be left empty.
"""
if args and kwargs:
raise ValueError(
"Use either positional fields or named fields, but not both."
)
if args:
if len(args) != len(self.fields):
raise ValueError("Need consistent fields")
for arg_row in zip(*args):
self.write(*arg_row)
if kwargs:
if "ID" not in kwargs:
raise ValueError("I'll need to see some ID")
keys = kwargs.keys()
for value_row in zip(*kwargs.values()):
kwarg_row = dict(zip(keys, value_row))
self.write(**kwarg_row)
@staticmethod
def _expand_data_fields(data_fields):
expanded = []
for data_field in data_fields:
expanded.append(data_field)
expanded.append(data_field + "_format")
expanded.append(data_field + "_opts")
return expanded
[docs]def write_txt_file(data, filename, sampling_rate=None):
"""Write data in text format.
Arguments
---------
data : str, list, torch.tensor, numpy.ndarray
The data to write in the text file.
filename : str
Path to file where to write the data.
sampling_rate : None
Not used, just here for interface compatibility.
Returns
-------
None
Example
-------
>>> tmpdir = getfixture('tmpdir')
>>> signal=torch.tensor([1,2,3,4])
>>> write_txt_file(signal, tmpdir / 'example.txt')
"""
del sampling_rate # Not used.
# Check if the path of filename exists
os.makedirs(os.path.dirname(filename), exist_ok=True)
with open(filename, "w") as fout:
if isinstance(data, torch.Tensor):
data = data.tolist()
if isinstance(data, np.ndarray):
data = data.tolist()
if isinstance(data, list):
for line in data:
print(line, file=fout)
if isinstance(data, str):
print(data, file=fout)
[docs]def write_stdout(data, filename=None, sampling_rate=None):
"""Write data to standard output.
Arguments
---------
data : str, list, torch.tensor, numpy.ndarray
The data to write in the text file.
filename : None
Not used, just here for compatibility.
sampling_rate : None
Not used, just here for compatibility.
Returns
-------
None
Example
-------
>>> tmpdir = getfixture('tmpdir')
>>> signal = torch.tensor([[1,2,3,4]])
>>> write_stdout(signal, tmpdir / 'example.txt')
[1, 2, 3, 4]
"""
# Managing Torch.Tensor
if isinstance(data, torch.Tensor):
data = data.tolist()
# Managing np.ndarray
if isinstance(data, np.ndarray):
data = data.tolist()
if isinstance(data, list):
for line in data:
print(line)
if isinstance(data, str):
print(data)
[docs]def length_to_mask(length, max_len=None, dtype=None, device=None):
"""Creates a binary mask for each sequence.
Reference: https://discuss.pytorch.org/t/how-to-generate-variable-length-mask/23397/3
Arguments
---------
length : torch.LongTensor
Containing the length of each sequence in the batch. Must be 1D.
max_len : int
Max length for the mask, also the size of the second dimension.
dtype : torch.dtype, default: None
The dtype of the generated mask.
device: torch.device, default: None
The device to put the mask variable.
Returns
-------
mask : tensor
The binary mask.
Example
-------
>>> length=torch.Tensor([1,2,3])
>>> mask=length_to_mask(length)
>>> mask
tensor([[1., 0., 0.],
[1., 1., 0.],
[1., 1., 1.]])
"""
assert len(length.shape) == 1
if max_len is None:
max_len = length.max().long().item() # using arange to generate mask
mask = torch.arange(
max_len, device=length.device, dtype=length.dtype
).expand(len(length), max_len) < length.unsqueeze(1)
if dtype is None:
dtype = length.dtype
if device is None:
device = length.device
mask = torch.as_tensor(mask, dtype=dtype, device=device)
return mask
[docs]def read_kaldi_lab(kaldi_ali, kaldi_lab_opts):
"""Read labels in kaldi format.
Uses kaldi IO.
Arguments
---------
kaldi_ali : str
Path to directory where kaldi alignments are stored.
kaldi_lab_opts : str
A string that contains the options for reading the kaldi alignments.
Returns
-------
lab : dict
A dictionary containing the labels.
Note
----
This depends on kaldi-io-for-python. Install it separately.
See: https://github.com/vesis84/kaldi-io-for-python
Example
-------
This example requires kaldi files.
```
lab_folder = '/home/kaldi/egs/TIMIT/s5/exp/dnn4_pretrain-dbn_dnn_ali'
read_kaldi_lab(lab_folder, 'ali-to-pdf')
```
"""
# EXTRA TOOLS
try:
import kaldi_io
except ImportError:
raise ImportError("Could not import kaldi_io. Install it to use this.")
# Reading the Kaldi labels
lab = {
k: v
for k, v in kaldi_io.read_vec_int_ark(
"gunzip -c "
+ kaldi_ali
+ "/ali*.gz | "
+ kaldi_lab_opts
+ " "
+ kaldi_ali
+ "/final.mdl ark:- ark:-|"
)
}
return lab
[docs]def get_md5(file):
"""Get the md5 checksum of an input file.
Arguments
---------
file : str
Path to file for which compute the checksum.
Returns
-------
md5
Checksum for the given filepath.
Example
-------
>>> get_md5('tests/samples/single-mic/example1.wav')
'c482d0081ca35302d30d12f1136c34e5'
"""
# Lets read stuff in 64kb chunks!
BUF_SIZE = 65536
md5 = hashlib.md5()
# Computing md5
with open(file, "rb") as f:
while True:
data = f.read(BUF_SIZE)
if not data:
break
md5.update(data)
return md5.hexdigest()
[docs]def save_md5(files, out_file):
"""Saves the md5 of a list of input files as a pickled dict into a file.
Arguments
---------
files : list
List of input files from which we will compute the md5.
outfile : str
The path where to store the output pkl file.
Returns
-------
None
Example:
>>> files = ['tests/samples/single-mic/example1.wav']
>>> tmpdir = getfixture('tmpdir')
>>> save_md5(files, tmpdir / "md5.pkl")
"""
# Initialization of the dictionary
md5_dict = {}
# Computing md5 for all the files in the list
for file in files:
md5_dict[file] = get_md5(file)
# Saving dictionary in pkl format
save_pkl(md5_dict, out_file)
[docs]def save_pkl(obj, file):
"""Save an object in pkl format.
Arguments
---------
obj : object
Object to save in pkl format
file : str
Path to the output file
sampling_rate : int
Sampling rate of the audio file, TODO: this is not used?
Example
-------
>>> tmpfile = getfixture('tmpdir') / "example.pkl"
>>> save_pkl([1, 2, 3, 4, 5], tmpfile)
>>> load_pkl(tmpfile)
[1, 2, 3, 4, 5]
"""
with open(file, "wb") as f:
pickle.dump(obj, f)
[docs]def load_pkl(file):
"""Loads a pkl file.
For an example, see `save_pkl`.
Arguments
---------
file : str
Path to the input pkl file.
Returns
-------
The loaded object.
"""
# Deals with the situation where two processes are trying
# to access the same label dictionary by creating a lock
count = 100
while count > 0:
if os.path.isfile(file + ".lock"):
time.sleep(1)
count -= 1
else:
break
try:
open(file + ".lock", "w").close()
with open(file, "rb") as f:
return pickle.load(f)
finally:
if os.path.isfile(file + ".lock"):
os.remove(file + ".lock")
[docs]def prepend_bos_token(label, bos_index):
"""Create labels with <bos> token at the beginning.
Arguments
---------
label : torch.IntTensor
Containing the original labels. Must be of size: [batch_size, max_length].
bos_index : int
The index for <bos> token.
Returns
-------
new_label : tensor
The new label with <bos> at the beginning.
Example
-------
>>> label=torch.LongTensor([[1,0,0], [2,3,0], [4,5,6]])
>>> new_label=prepend_bos_token(label, bos_index=7)
>>> new_label
tensor([[7, 1, 0, 0],
[7, 2, 3, 0],
[7, 4, 5, 6]])
"""
new_label = label.long().clone()
batch_size = label.shape[0]
bos = new_label.new_zeros(batch_size, 1).fill_(bos_index)
new_label = torch.cat([bos, new_label], dim=1)
return new_label
[docs]def append_eos_token(label, length, eos_index):
"""Create labels with <eos> token appended.
Arguments
---------
label : torch.IntTensor
Containing the original labels. Must be of size: [batch_size, max_length]
length : torch.LongTensor
Containing the original length of each label sequences. Must be 1D.
eos_index : int
The index for <eos> token.
Returns
-------
new_label : tensor
The new label with <eos> appended.
Example
-------
>>> label=torch.IntTensor([[1,0,0], [2,3,0], [4,5,6]])
>>> length=torch.LongTensor([1,2,3])
>>> new_label=append_eos_token(label, length, eos_index=7)
>>> new_label
tensor([[1, 7, 0, 0],
[2, 3, 7, 0],
[4, 5, 6, 7]], dtype=torch.int32)
"""
new_label = label.int().clone()
batch_size = label.shape[0]
pad = new_label.new_zeros(batch_size, 1)
new_label = torch.cat([new_label, pad], dim=1)
new_label[torch.arange(batch_size), length.long()] = eos_index
return new_label
[docs]def merge_char(sequences, space="_"):
"""Merge characters sequences into word sequences.
Arguments
---------
sequences : list
Each item contains a list, and this list contains a character sequence.
space : string
The token represents space. Default: _
Returns
-------
The list contains word sequences for each sentence.
Example
-------
>>> sequences = [["a", "b", "_", "c", "_", "d", "e"], ["e", "f", "g", "_", "h", "i"]]
>>> results = merge_char(sequences)
>>> results
[['ab', 'c', 'de'], ['efg', 'hi']]
"""
results = []
for seq in sequences:
words = "".join(seq).split(space)
results.append(words)
return results
[docs]def merge_csvs(data_folder, csv_lst, merged_csv):
"""Merging several csv files into one file.
Arguments
---------
data_folder : string
The folder to store csv files to be merged and after merging.
csv_lst : list
Filenames of csv file to be merged.
merged_csv : string
The filename to write the merged csv file.
Example
-------
>>> tmpdir = getfixture('tmpdir')
>>> os.symlink(os.path.realpath("tests/samples/annotation/speech.csv"), tmpdir / "speech.csv")
>>> merge_csvs(tmpdir,
... ["speech.csv", "speech.csv"],
... "test_csv_merge.csv")
"""
write_path = os.path.join(data_folder, merged_csv)
if os.path.isfile(write_path):
logger.info("Skipping merging. Completed in previous run.")
with open(os.path.join(data_folder, csv_lst[0])) as f:
header = f.readline()
lines = []
for csv_file in csv_lst:
with open(os.path.join(data_folder, csv_file)) as f:
for i, line in enumerate(f):
if i == 0:
# Checking header
if line != header:
raise ValueError(
"Different header for " f"{csv_lst[0]} and {csv}."
)
continue
lines.append(line)
with open(write_path, "w") as f:
f.write(header)
for line in lines:
f.write(line)
logger.info(f"{write_path} is created.")
[docs]def split_word(sequences, space="_"):
"""Split word sequences into character sequences.
Arguments
---------
sequences: list
Each item contains a list, and this list contains a words sequence.
space: string
The token represents space. Default: _
Returns
-------
The list contains word sequences for each sentence.
Example
-------
>>> sequences = [['ab', 'c', 'de'], ['efg', 'hi']]
>>> results = split_word(sequences)
>>> results
[['a', 'b', '_', 'c', '_', 'd', 'e'], ['e', 'f', 'g', '_', 'h', 'i']]
"""
results = []
for seq in sequences:
chars = list(space.join(seq))
results.append(chars)
return results