readtextgrid parses Praat textgrids into R dataframes.
Install from CRAN:
install.packages("readtextgrid")Install the development version from Github:
install.packages("remotes")
remotes::install_github("tjmahr/readtextgrid")Here is the example textgrid created by Praat. It was created using
New -> Create TextGrid... with default settings in Praat.
This textgrid is bundled with this R package. We can locate the file
with example_textgrid(). We read in the textgrid with
read_textgrid().
library(readtextgrid)
# Locates path to an example textgrid bundled with this package
tg <- example_textgrid()
read_textgrid(path = tg)
#> # A tibble: 3 × 10
#> file tier_num tier_name tier_type tier_xmin tier_xmax
#> <chr> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 Mary_John_bell.TextGrid 1 Mary IntervalTier 0 1
#> 2 Mary_John_bell.TextGrid 2 John IntervalTier 0 1
#> 3 Mary_John_bell.TextGrid 3 bell TextTier 0 1
#> xmin xmax text annotation_num
#> <dbl> <dbl> <chr> <int>
#> 1 0 1 "" 1
#> 2 0 1 "" 1
#> 3 NA NA <NA> NAThe dataframe contains one row per annotation: one row for each interval
on an interval tier and one row for each point on a point tier. If a
point tier has no points, it is represented with single row with NA
values.
The columns encode the following information:
filefilename of the textgrid. By default this column uses the filename inpath. A user can override this value by setting thefileargument inread_textgrid(path, file), which can be useful if textgrids are stored in speaker-specific folders.tier_numthe number of the tier (as in the left margin of the textgrid editor)tier_namethe name of the tier (as in the right margin of the textgrid editor)tier_typethe type of the tier."IntervalTier"for interval tiers and"TextTier"for point tiers (this is the terminology used inside of the textgrid file format).tier_xmin,tier_xmaxstart and end times of the tier in secondsxmin,xmaxstart and end times of the textgrid interval or point tier annotation in secondstextthe text in the annotationannotation_numthe number of the annotation in that tier (1 for the first annotation, etc.)
Suppose you have data on multiple speakers with one folder of textgrids
per speaker. As an example, this package has a folder called
speaker_data bundled with it representing 5 five textgrids from 2
speakers.
speaker-data
+-- speaker001
| +-- s2T01.TextGrid
| +-- s2T02.TextGrid
| +-- s2T03.TextGrid
| +-- s2T04.TextGrid
| \-- s2T05.TextGrid
\-- speaker002
+-- s2T01.TextGrid
+-- s2T02.TextGrid
+-- s2T03.TextGrid
+-- s2T04.TextGrid
\-- s2T05.TextGrid
First, we create a vector of file-paths to read into R.
# Get the path of the folder bundled with the package
data_dir <- system.file(package = "readtextgrid", "speaker-data")
# Get the full paths to all the textgrids
paths <- list.files(
path = data_dir,
pattern = "TextGrid$",
full.names = TRUE,
recursive = TRUE
)We can use purrr::map_dfr()–map the read_textgrid function over
the paths and combine the dataframes (_dfr)—to read all these
textgrids into R. But note that this way loses the speaker information.
library(purrr)
map_dfr(paths, read_textgrid)
#> # A tibble: 150 × 10
#> file tier_num tier_name tier_type tier_xmin tier_xmax xmin
#> <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 s2T01.TextGrid 1 words IntervalTier 0 1.35 0
#> 2 s2T01.TextGrid 1 words IntervalTier 0 1.35 0.297
#> 3 s2T01.TextGrid 1 words IntervalTier 0 1.35 0.522
#> 4 s2T01.TextGrid 1 words IntervalTier 0 1.35 0.972
#> 5 s2T01.TextGrid 2 phones IntervalTier 0 1.35 0
#> 6 s2T01.TextGrid 2 phones IntervalTier 0 1.35 0.297
#> 7 s2T01.TextGrid 2 phones IntervalTier 0 1.35 0.36
#> 8 s2T01.TextGrid 2 phones IntervalTier 0 1.35 0.495
#> 9 s2T01.TextGrid 2 phones IntervalTier 0 1.35 0.522
#> 10 s2T01.TextGrid 2 phones IntervalTier 0 1.35 0.621
#> xmax text annotation_num
#> <dbl> <chr> <int>
#> 1 0.297 "" 1
#> 2 0.522 "bird" 2
#> 3 0.972 "house" 3
#> 4 1.35 "" 4
#> 5 0.297 "sil" 1
#> 6 0.36 "B" 2
#> 7 0.495 "ER1" 3
#> 8 0.522 "D" 4
#> 9 0.621 "HH" 5
#> 10 0.783 "AW1" 6
#> # ℹ 140 more rowsWe can use purrr::map2_dfr() and some dataframe manipulation to add
the speaker information.
library(dplyr)
# This tells read_textgrid() to set the file column to the full path
data <- map2_dfr(paths, paths, read_textgrid) |>
mutate(
# basename() removes the folder part from a path,
# dirname() removes the file part from a path
speaker = basename(dirname(file)),
file = basename(file),
) |>
select(
speaker, everything()
)
data
#> # A tibble: 150 × 11
#> speaker file tier_num tier_name tier_type tier_xmin tier_xmax
#> <chr> <chr> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 speaker001 s2T01.TextGrid 1 words IntervalTier 0 1.35
#> 2 speaker001 s2T01.TextGrid 1 words IntervalTier 0 1.35
#> 3 speaker001 s2T01.TextGrid 1 words IntervalTier 0 1.35
#> 4 speaker001 s2T01.TextGrid 1 words IntervalTier 0 1.35
#> 5 speaker001 s2T01.TextGrid 2 phones IntervalTier 0 1.35
#> 6 speaker001 s2T01.TextGrid 2 phones IntervalTier 0 1.35
#> 7 speaker001 s2T01.TextGrid 2 phones IntervalTier 0 1.35
#> 8 speaker001 s2T01.TextGrid 2 phones IntervalTier 0 1.35
#> 9 speaker001 s2T01.TextGrid 2 phones IntervalTier 0 1.35
#> 10 speaker001 s2T01.TextGrid 2 phones IntervalTier 0 1.35
#> xmin xmax text annotation_num
#> <dbl> <dbl> <chr> <int>
#> 1 0 0.297 "" 1
#> 2 0.297 0.522 "bird" 2
#> 3 0.522 0.972 "house" 3
#> 4 0.972 1.35 "" 4
#> 5 0 0.297 "sil" 1
#> 6 0.297 0.36 "B" 2
#> 7 0.36 0.495 "ER1" 3
#> 8 0.495 0.522 "D" 4
#> 9 0.522 0.621 "HH" 5
#> 10 0.621 0.783 "AW1" 6
#> # ℹ 140 more rowsAnother strategy would be to read the textgrid dataframes into a list
column and unnest() them.
# Read dataframes into a list column
data_nested <- tibble(
speaker = basename(dirname(paths)),
data = map(paths, read_textgrid)
)
# We have one row per textgrid dataframe because `data` is a list column
data_nested
#> # A tibble: 10 × 2
#> speaker data
#> <chr> <list>
#> 1 speaker001 <tibble [13 × 10]>
#> 2 speaker001 <tibble [15 × 10]>
#> 3 speaker001 <tibble [16 × 10]>
#> 4 speaker001 <tibble [12 × 10]>
#> 5 speaker001 <tibble [19 × 10]>
#> 6 speaker002 <tibble [13 × 10]>
#> 7 speaker002 <tibble [15 × 10]>
#> 8 speaker002 <tibble [16 × 10]>
#> 9 speaker002 <tibble [12 × 10]>
#> 10 speaker002 <tibble [19 × 10]>
# promote the nested dataframes into the main dataframe
tidyr::unnest(data_nested, "data")
#> # A tibble: 150 × 11
#> speaker file tier_num tier_name tier_type tier_xmin tier_xmax xmin xmax
#> <chr> <chr> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 speaker001 s2T0… 1 words Interval… 0 1.35 0 0.297
#> 2 speaker001 s2T0… 1 words Interval… 0 1.35 0.297 0.522
#> 3 speaker001 s2T0… 1 words Interval… 0 1.35 0.522 0.972
#> 4 speaker001 s2T0… 1 words Interval… 0 1.35 0.972 1.35
#> 5 speaker001 s2T0… 2 phones Interval… 0 1.35 0 0.297
#> 6 speaker001 s2T0… 2 phones Interval… 0 1.35 0.297 0.36
#> 7 speaker001 s2T0… 2 phones Interval… 0 1.35 0.36 0.495
#> 8 speaker001 s2T0… 2 phones Interval… 0 1.35 0.495 0.522
#> 9 speaker001 s2T0… 2 phones Interval… 0 1.35 0.522 0.621
#> 10 speaker001 s2T0… 2 phones Interval… 0 1.35 0.621 0.783
#> # ℹ 140 more rows
#> # ℹ 2 more variables: text <chr>, annotation_num <int>In the textgrids above, there is a natural nesting or hierarchy to the
tiers. Intervals in words tier contain intervals in the phones tier.
It is often necessary to group intervals by their parent intervals
(group phones by words). This package provides the
pivot_textgrid_tiers() function to convert textgrids into a wide
format in a way that respect the nesting/hierarchy of tiers.
data_wide <- pivot_textgrid_tiers(
data,
tiers = c("words", "phones"),
join_cols = c("speaker", "file")
)
data_wide
#> # A tibble: 108 × 18
#> speaker file words words_xmin words_xmax words_xmid words_annotation_num
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <int>
#> 1 speaker001 s2T01… "" 0 0.297 0.149 1
#> 2 speaker001 s2T01… "bir… 0.297 0.522 0.410 2
#> 3 speaker001 s2T01… "bir… 0.297 0.522 0.410 2
#> 4 speaker001 s2T01… "bir… 0.297 0.522 0.410 2
#> 5 speaker001 s2T01… "hou… 0.522 0.972 0.747 3
#> 6 speaker001 s2T01… "hou… 0.522 0.972 0.747 3
#> 7 speaker001 s2T01… "hou… 0.522 0.972 0.747 3
#> 8 speaker001 s2T01… "" 0.972 1.35 1.16 4
#> 9 speaker001 s2T01… "" 0.972 1.35 1.16 4
#> 10 speaker001 s2T02… "" 0 0.297 0.149 1
#> # ℹ 98 more rows
#> # ℹ 11 more variables: words_tier_num <dbl>, words_tier_type <chr>,
#> # tier_xmin <dbl>, tier_xmax <dbl>, phones <chr>, phones_xmin <dbl>,
#> # phones_xmax <dbl>, phones_xmid <dbl>, phones_annotation_num <int>,
#> # phones_tier_num <dbl>, phones_tier_type <chr>
# more clearly,
data_wide |>
select(
speaker, file, words, phones,
words_xmin, words_xmax, phones_xmin, phones_xmax
)
#> # A tibble: 108 × 8
#> speaker file words phones words_xmin words_xmax phones_xmin phones_xmax
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 speaker001 s2T01.… "" "sil" 0 0.297 0 0.297
#> 2 speaker001 s2T01.… "bir… "B" 0.297 0.522 0.297 0.36
#> 3 speaker001 s2T01.… "bir… "ER1" 0.297 0.522 0.36 0.495
#> 4 speaker001 s2T01.… "bir… "D" 0.297 0.522 0.495 0.522
#> 5 speaker001 s2T01.… "hou… "HH" 0.522 0.972 0.522 0.621
#> 6 speaker001 s2T01.… "hou… "AW1" 0.522 0.972 0.621 0.783
#> 7 speaker001 s2T01.… "hou… "S" 0.522 0.972 0.783 0.972
#> 8 speaker001 s2T01.… "" "sp" 0.972 1.35 0.972 1.33
#> 9 speaker001 s2T01.… "" "" 0.972 1.35 1.33 1.35
#> 10 speaker001 s2T02.… "" "sil" 0 0.297 0 0.297
#> # ℹ 98 more rowsSome remarks:
- Each tier in
tiersbecomes a batch of columns. For the rows forwordsbecomewords(the originaltextvalue),words_xmin,words_xmax, etc. - The columns in
join_colsshould uniquely identify a textgrid file, so the combination ofspeakerandfileis needed in the case where different speakers have the same file. - The tier names in
tiersshould be given in the order of their nesting from outside to inside (e.g.,wordscontainphones). Behind the scenes,dplyr::left_join(..., relationship = "one-to-many")is used to constrain how intervals are combined.
This function also works on a single tiers value. In this case, the
function returns just the intervals in that tier with the columns
renamed and prefixed.
data |>
pivot_textgrid_tiers(
tiers = "words",
join_cols = c("speaker", "file")
)
#> # A tibble: 42 × 11
#> speaker file words words_xmin words_xmax words_xmid words_annotation_num
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <int>
#> 1 speaker001 s2T01… "" 0 0.297 0.149 1
#> 2 speaker001 s2T01… "bir… 0.297 0.522 0.410 2
#> 3 speaker001 s2T01… "hou… 0.522 0.972 0.747 3
#> 4 speaker001 s2T01… "" 0.972 1.35 1.16 4
#> 5 speaker001 s2T02… "" 0 0.297 0.149 1
#> 6 speaker001 s2T02… "cow… 0.297 0.702 0.500 2
#> 7 speaker001 s2T02… "boo… 0.702 1.17 0.936 3
#> 8 speaker001 s2T02… "" 1.17 1.59 1.38 4
#> 9 speaker001 s2T03… "" 0 0.369 0.184 1
#> 10 speaker001 s2T03… "hug" 0.369 0.657 0.513 2
#> # ℹ 32 more rows
#> # ℹ 4 more variables: words_tier_num <dbl>, words_tier_type <chr>,
#> # tier_xmin <dbl>, tier_xmax <dbl>Do you have thousands of textgrids to read? The following workflow can
speed things up. We are going to read the textgrids in parallel. We
use the future package to manage the parallel computation. We use the
furrr package to get future-friendly versions of the purrr functions. We
tell future to use a multisession plan for parallelism: Do the extra
computation on separate R sessions in the background. Then everything
else is the same. Just replace map() with future_map().
library(future)
library(furrr)
plan(multisession, workers = 4)
data_nested <- tibble(
speaker = basename(dirname(paths)),
data = future_map(paths, read_textgrid)
)By default, readtextgrid uses readr::guess_encoding() to determine the
encoding of the textgrid before reading it in. But if you know the
encoding beforehand, you can skip this guessing. In my limited testing,
I found that setting the encoding could reduce benchmark times by
3–4% compared to guessing the encoding.
Here, we read 100 textgrids using different approaches to benchmark the results.
paths_bench <- sample(paths, 100, replace = TRUE)
bench::mark(
lapply_guess = lapply(paths_bench, read_textgrid),
lapply_set = lapply(paths_bench, read_textgrid, encoding = "UTF-8"),
future_guess = future_map(paths_bench, read_textgrid),
future_set = future_map(paths_bench, read_textgrid, encoding = "UTF-8"),
min_iterations = 5,
check = TRUE
)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 4 × 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <bch:tm> <bch:tm> <dbl> <bch:byt> <dbl>
#> 1 lapply_guess 3.96s 4.03s 0.245 175.5MB 1.67
#> 2 lapply_set 3.73s 3.78s 0.264 167.62MB 1.48
#> 3 future_guess 1.37s 1.39s 0.717 5.09MB 0.143
#> 4 future_set 1.28s 1.33s 0.757 5.09MB 0The following columns are often helpful:
durationof an intervalxmidmidpoint of an intervaltotal_annotationstotal number of annotations on a tier
Here is how to create them:
data |>
# grouping needed for counting annotations per tier per file per speaker
group_by(speaker, file, tier_num) |>
mutate(
duration = xmax - xmin,
xmid = xmin + (xmax - xmin) / 2,
total_annotations = sum(!is.na(annotation_num))
) |>
ungroup() |>
glimpse()
#> Rows: 150
#> Columns: 14
#> $ speaker <chr> "speaker001", "speaker001", "speaker001", "speaker00…
#> $ file <chr> "s2T01.TextGrid", "s2T01.TextGrid", "s2T01.TextGrid"…
#> $ tier_num <dbl> 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2…
#> $ tier_name <chr> "words", "words", "words", "words", "phones", "phone…
#> $ tier_type <chr> "IntervalTier", "IntervalTier", "IntervalTier", "Int…
#> $ tier_xmin <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
#> $ tier_xmax <dbl> 1.348571, 1.348571, 1.348571, 1.348571, 1.348571, 1.…
#> $ xmin <dbl> 0.000, 0.297, 0.522, 0.972, 0.000, 0.297, 0.360, 0.4…
#> $ xmax <dbl> 0.297000, 0.522000, 0.972000, 1.348571, 0.297000, 0.…
#> $ text <chr> "", "bird", "house", "", "sil", "B", "ER1", "D", "HH…
#> $ annotation_num <int> 1, 2, 3, 4, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 1…
#> $ duration <dbl> 0.29700000, 0.22500000, 0.45000000, 0.37657143, 0.29…
#> $ xmid <dbl> 0.148500, 0.409500, 0.747000, 1.160286, 0.148500, 0.…
#> $ total_annotations <int> 4, 4, 4, 4, 9, 9, 9, 9, 9, 9, 9, 9, 9, 4, 4, 4, 4, 1…This tip is written from the perspective of a Windows user who uses git bash for a terminal.
To open textgrids in Praat, you can tell R to call Praat from the
command line. You have to know where the location of the Praat binary is
though. I like to keep a copy in my project directories. So, assuming
that Praat.exe in my working folder, the following would open the 10
textgrids in paths in Praat.
system2(
command = "./Praat.exe",
args = c("--open", paths),
wait = FALSE
)readtextgrid supports textgrids created by Praat by using
Save as text file.... It uses a parsing strategy based on regular
expressions targeting indentation patterns and text flags in the file
format. The formal specification of the textgrid
format,
however, is much more flexible. As a result, not every textgrid that
Praat can open—especially the minimal “short text” files—is compatible
with this package.
readtextgrid was created to process data from the WISC Lab project. Thus, development of this package was supported by NIH R01DC009411 and NIH R01DC015653.
Please note that the ‘readtextgrid’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.