readtextgrid

readtextgrid parses Praat textgrids into tidy R dataframes.

Features

Simple: Minimal package with two core functions (read_textgrid() and read_textgrid_lines()).
Tidy: Returns rectangular tibbles ready for downstream processing with dplyr and tidyr.
Flexible: Supports both long and short textgrid file formats.
Fast: Uses a compiled C++ tokenizer for high-throughput parsing.

Installation

Install readtextgrid from CRAN:

install.packages("readtextgrid")

Development version. Install precompiled version of readtextgrid from R-universe:

install.packages(
  "readtextgrid", 
  repos = c("https://tjmahr.r-universe.dev", "https://cloud.r-project.org")
)

Basic usage

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>                      <int> <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>              NA

The 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:

file filename of the textgrid. By default this column uses the filename in path. A user can override this value by setting the file argument in read_textgrid(path, file), which can be useful if textgrids are stored in speaker-specific folders.
tier_num the number of the tier (as in the left margin of Praat’s textgrid editor)
tier_name the name of the tier (as in the right margin of Praat’s textgrid editor)
tier_type the 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_xmax start and end times of the tier in seconds
xmin, xmax start and end times of the textgrid interval or point tier annotation in seconds
text the text in the annotation
annotation_num the number of the annotation in that tier (1 for the first annotation, etc.)

Reading in directories of textgrids

Suppose we 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()–map the read_textgrid() function over the paths—to read all these textgrids into R and combine them from a list to a single dataframe with purrr::list_rbind(). But note that this way doesn’t track any speaker information.

library(purrr)

paths |> 
  map(read_textgrid) |> 
  list_rbind()
#> # A tibble: 150 × 10
#>    file           tier_num tier_name tier_type    tier_xmin tier_xmax  xmin
#>    <chr>             <int> <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 rows

By default, read_textgrid() uses the file basename (the file-path minus the directory part) for the file column. But we can manually set the file value. Here, we use purrr::map2() to map the function over read_textgrid(path, file) over path and file pairs. Then we add the speaker information with some dataframe manipulation functions.

library(dplyr)

# This tells read_textgrid() to set the file column to the full path
data <- map2(paths, paths, read_textgrid) |> 
  list_rbind() |> 
  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>             <int> <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 rows

Another strategy would be to read the textgrid dataframes into a list column and tidyr::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>    <int> <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>

Pivoting nested intervals in textgrids

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 respects 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 <int>, 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 <int>, 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 rows

Some remarks:

Each tier in tiers becomes a batch of columns. For the rows for the words tier become the batch of columns words (the original text value), words_xmin, words_xmax, etc.
The columns in join_cols should uniquely identify a textgrid file, so the combination of speaker and file is needed in the case where different speakers have the same file.
The tier names in tiers should be given in the order of their nesting from outside to inside (e.g., words contain phones). 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 <int>, words_tier_type <chr>,
#> #   tier_xmin <dbl>, tier_xmax <dbl>

Speeding things up

Do you have thousands of textgrids to read? The following workflow can speed things up. We are going to read the textgrids in parallel. Below are two approaches:

future backend and furrr frontend
mirai backend and purrr frontend

The backend manages the parallel computation, and the frontend provides the syntax for calling a function with parallelism.

Approach 1: We tell future to use a multisession plan for parallelism, so the computations are done on separate R sessions in the background. The syntax is like the above purrr code, but we 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)
)

Approach 2: We have mirai set up 4 daemons (background processes), and then we use purrr’s in_parallel() helper to signal to map() that the function should be run in parallel. We need to give all the information needed for the daemons to run the function, so we 1) provide a complete function definition (including function(x) ...) and 2) spell out the package namespace readtextgrid::read_textgrid().

mirai::daemons(4)
data_nested <- tibble(
  speaker = basename(dirname(paths)),
  data = map(paths, in_parallel(function(x) readtextgrid::read_textgrid(x)))
)
mirai::daemons(0)

Another way to eke out performance is to set the encoding. 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 <- withr::with_seed(1, sample(paths, 100, replace = TRUE))

mirai::daemons(4)
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"), 
  mirai_guess = purrr::map(
    paths_bench, 
    in_parallel(function(x) readtextgrid::read_textgrid(x))
  ),
  mirai_set = purrr::map(
    paths_bench, 
    in_parallel(function(x) readtextgrid::read_textgrid(x, encoding = "UTF-8"))
  ),
  check = TRUE
)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 6 × 6
#>   expression        min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr>   <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 lapply_guess    1.17s    1.17s     0.852   13.32MB     5.96
#> 2 lapply_set   883.69ms 883.69ms     1.13     5.41MB     6.79
#> 3 future_guess 407.83ms 421.37ms     2.37   627.53KB     2.37
#> 4 future_set   356.49ms 358.09ms     2.79   627.53KB     2.79
#> 5 mirai_guess  315.85ms 338.82ms     2.95  1006.66KB     0   
#> 6 mirai_set    258.63ms 259.45ms     3.85  1006.66KB     0
mirai::daemons(0)

Legacy behavior and supported textgrid formats

The original version of this package assumed the textgrid text files followed a “long” format with helpful labels and annotations. For example, in the following textgrid, each number has a label that makes it easy and fast to parse the textgrid with regular expressions:

File type = "ooTextFile"
Object class = "TextGrid"

xmin = 0 
xmax = 1 
tiers? <exists> 
size = 1 
item []: 
    item [1]:
        class = "IntervalTier" 
        name = "Mary" 
        xmin = 0 
        xmax = 1 
        intervals: size = 1 
        intervals [1]:
            xmin = 0 
            xmax = 1 
            text = ""

The original version of the parser designed for this textgrid format is still provided with the legacy_read_textgrid() and legacy_read_textgrid_lines() functions.

Version 0.2.0 of readtextgrid added a C++ based parser that can handle many more textgrid formats. For example, it can “short” format textgrids like the following:

File type = "ooTextFile"
Object class = "TextGrid"

0
1
<exists>
1
"IntervalTier"
"Mary"
0
1
1
0
1
""

The “long” format textgrids are outputted in Praat with Save > Save as text file..., and the “short” format textgrids are outputted with Save > Save as short textfile....

readtextgrid’s parser can also handle esoteric features like comments (that start with !) or arbitrary text attached to a number, as in the following example;:

File type = "ooTextFile"
Object class = "TextGrid"

! info about the grid
0s 1s <exists> 1
! info about the tier
"IntervalTier" "Mary" 0s 1s 1 ! type, name, xmin, xmax, size
0s 1s "" ! interval xmin, xmax, size

Because the new parser uses C++ for tokenization—that is, the part scans the contents character by character and determines whether the inputs are strings, numbers, or skipped—it is much faster the legacy version.

paths_bench <- withr::with_seed(2, sample(paths, 10, replace = TRUE))

bench::mark(
  current = lapply(paths_bench, read_textgrid),
  legacy = lapply(paths_bench, legacy_read_textgrid),
  min_iterations = 10, 
  filter_gc = FALSE,
  check = TRUE
)
#> # A tibble: 2 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 current       114ms    123ms      8.11    1.31MB     4.86
#> 2 legacy        332ms    342ms      2.89   19.57MB     6.06

Other tips

Helpful columns

The following columns are often helpful:

duration of an interval
xmid midpoint of an interval
total_annotations total 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          <int> 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…

Launching Praat

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
)

Acknowledgments

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.

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man		man
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.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
LICENSE.md		LICENSE.md
NAMESPACE		NAMESPACE
NEWS.md		NEWS.md
README.Rmd		README.Rmd
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readtextgrid

Features

Installation

Basic usage

Reading in directories of textgrids

Pivoting nested intervals in textgrids

Speeding things up

Legacy behavior and supported textgrid formats

Other tips

Helpful columns

Launching Praat

Acknowledgments

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Releases 3

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Contributors 4

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tjmahr/readtextgrid

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readtextgrid

Features

Installation

Basic usage

Reading in directories of textgrids

Pivoting nested intervals in textgrids

Speeding things up

Legacy behavior and supported textgrid formats

Other tips

Helpful columns

Launching Praat

Acknowledgments

About

Resources

License

Code of conduct

Uh oh!

Stars

Watchers

Forks

Releases 3

Packages 0

Uh oh!

Contributors 4

Uh oh!

Languages

Packages