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Migration Guide: From Praat Scripts to pladdrr

Fredrik Nylén

2026-09-10

Source: vignettes/migration-from-praat.Rmd
migration-from-praat.Rmd

Introduction

This guide helps you translate Praat scripts into R code using pladdrr. The package provides a direct object-oriented interface to Praat’s functionality.

Key Principles

1. Object Creation

Praat Script:

sound = Read from file: "audio.wav"

pladdrr:

library(pladdrr)
#> pladdrr: direct access to Praat's core algorithms from R.
#> See ?pladdrr for an overview, or citation("pladdrr") for citation details.
sound <- Sound$new(system.file("extdata", "test.wav", package = "pladdrr"))

2. Method Calls

Praat commands become method calls following a consistent naming pattern:

Praat Command pladdrr Method Pattern
To Pitch... to_pitch() To X → to_x()
To Formant (burg)... to_formant_burg() To X (y) → to_x_y()
Get mean... get_mean() Get x → get_x()
Set value... set_value() Set x → set_x()

3. Parameters

Praat’s positional parameters become named parameters in R:

Praat:

pitch = To Pitch: 0.01, 75, 600

pladdrr:

pitch <- sound$to_pitch(time_step = 0.01, pitch_floor = 75, pitch_ceiling = 600)

Common Workflows

Basic Pitch Analysis

Praat Script:

sound = Read from file: "audio.wav"
pitch = To Pitch: 0.01, 75, 600
mean_f0 = Get mean: 0, 0, "Hertz"
std_f0 = Get standard deviation: 0, 0, "Hertz"

pladdrr:

sound <- Sound$new(system.file("extdata", "test.wav", package = "pladdrr"))
pitch <- sound$to_pitch(time_step = 0.01, pitch_floor = 75, pitch_ceiling = 600)
mean_f0 <- pitch$get_mean(from_time = 0, to_time = 0, unit = "hertz")
std_f0 <- pitch$get_standard_deviation(from_time = 0, to_time = 0,
  unit = "hertz")

Formant Extraction

Praat Script:

sound = Read from file: "vowel.wav"
formant = To Formant (burg): 0.01, 5, 5500, 0.025, 50
f1 = Get value at time: 1, 0.5, "Hertz", "Linear"
f2 = Get value at time: 2, 0.5, "Hertz", "Linear"

pladdrr:

sound <- Sound$new(system.file("extdata", "test.wav", package = "pladdrr"))
formant <- sound$to_formant_burg(
  time_step = 0.01,
  max_number_of_formants = 5,
  maximum_formant = 5500,
  window_length = 0.025,
  pre_emphasis_from = 50
)
f1 <- formant$get_value_at_time(formant_number = 1, time = 0.5, unit = "hertz")
f2 <- formant$get_value_at_time(formant_number = 2, time = 0.5, unit = "hertz")

Intensity Measurements

Praat Script:

sound = Read from file: "audio.wav"
intensity = To Intensity: 100, 0.01, "yes"
mean_intensity = Get mean: 0, 0, "energy"
max_intensity = Get maximum: 0, 0, "Parabolic"

pladdrr:

sound <- Sound$new(system.file("extdata", "test.wav", package = "pladdrr"))
intensity <- sound$to_intensity(minimum_pitch = 100, time_step = 0.01,
  subtract_mean = TRUE)
mean_intensity <- intensity$get_mean(from_time = 0, to_time = 0,
  averaging_method = "energy")
max_intensity <- intensity$get_maximum(from_time = 0, to_time = 0,
  interpolation = "parabolic")

Spectral Analysis

Praat Script:

sound = Read from file: "audio.wav"
spectrum = To Spectrum: "yes"
cog = Get centre of gravity: 2.0

pladdrr:

sound <- Sound$new(system.file("extdata", "test.wav", package = "pladdrr"))
spectrum <- sound$to_spectrum(fast = TRUE)
cog <- spectrum$get_centre_of_gravity(power = 2.0)

TextGrid Manipulation

Praat Script:

textgrid = Create TextGrid: 0, 1, "words phones", "phones"
Insert boundary: 1, 0.5
Set interval text: 1, 1, "hello"

pladdrr:

textgrid <- TextGrid$create(tmin = 0, tmax = 1, tier_names = "words phones",
  point_tiers = "phones")
textgrid$insert_boundary(tier = 1, time = 0.5)
textgrid$set_interval_text(tier = 1, interval_number = 1, text = "hello")

Batch Processing

Praat Script Approach 

Create Strings as file list: "fileList", "*.wav"
numberOfFiles = Get number of strings

for ifile to numberOfFiles
    selectObject: "Strings fileList"
    fileName$ = Get string: ifile
    sound = Read from file: fileName$
    
    pitch = To Pitch: 0.01, 75, 600
    mean_f0 = Get mean: 0, 0, "Hertz"
    
    appendFileLine: "results.txt", fileName$, tab$, mean_f0
    
    removeObject: sound, pitch
endfor

pladdrr Approach 

library(pladdrr)

# Get list of WAV files
files <- list.files(pattern = "\\.wav$", full.names = TRUE)

# Process each file
results <- lapply(files, function(filepath) {
  sound <- Sound$new(filepath)
  pitch <- sound$to_pitch(time_step = 0.01, pitch_floor = 75,
    pitch_ceiling = 600)
  mean_f0 <- pitch$get_mean(from_time = 0, to_time = 0, unit = "hertz")
  
  data.frame(
    file = basename(filepath),
    mean_f0 = mean_f0
  )
})

# Combine results
results_df <- do.call(rbind, results)
write.csv(results_df, file.path(tempdir(), "results.csv"), row.names = FALSE)

Advanced Features in pladdrr

Integration with tidyverse 

library(pladdrr)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(purrr)

results <- tibble(file = list.files(pattern = "\\.wav$")) %>%
  mutate(
    sound = map(file, Sound$new),
    pitch = map(sound,
      ~.$to_pitch(time_step = 0.01, pitch_floor = 75, pitch_ceiling = 600)),
    mean_f0 = map_dbl(pitch,
      ~.$get_mean(from_time = 0, to_time = 0, unit = "hertz")),
    sd_f0 = map_dbl(pitch,
      ~.$get_standard_deviation(from_time = 0, to_time = 0, unit = "hertz"))
  ) %>%
  select(file, mean_f0, sd_f0)

Visualization with ggplot2 

library(ggplot2)

# Extract pitch contour
sound <- Sound$new(system.file("extdata", "test.wav", package = "pladdrr"))
pitch <- sound$to_pitch(time_step = 0.01, pitch_floor = 75, pitch_ceiling = 600)
pitch_data <- pitch$as_data_frame()

# Plot
ggplot(pitch_data, aes(x = time, y = frequency)) +
  geom_line() +
  labs(title = "Pitch Contour", x = "Time (s)", y = "Frequency (Hz)") +
  theme_minimal()

Advantages Over Praat Scripts

  1. Type Safety: R catches type errors at runtime
  2. Code Completion: RStudio provides autocomplete for all methods
  3. Integration: Works with R’s data analysis ecosystem (dplyr, purrr, ggplot2, etc.)
  4. Reproducibility: Version control and package management
  5. Performance: Direct C++ binding (no scripting overhead)
  6. Memory Management: Automatic cleanup of Praat objects

Common Pitfalls

1. Object Lifetime

Praat uses explicit object selection and removal:

selectObject: sound
removeObject: sound

pladdrr uses R’s garbage collection (automatic):

# Objects are automatically cleaned up when no longer referenced
sound <- Sound$new(system.file("extdata", "test.wav", package = "pladdrr"))
# 'sound' is freed when it goes out of scope or is reassigned

2. Time Ranges

Praat uses 0, 0 to mean “entire range”:

mean_f0 = Get mean: 0, 0, "Hertz"

pladdrr follows the same convention:

mean_f0 <- pitch$get_mean(from_time = 0, to_time = 0, unit = "hertz")

3. Unit Strings

Use lowercase for units in pladdrr:

# Correct
pitch$get_mean(unit = "hertz")
#> [1] 440.0102

# Also works (case-insensitive in many methods)
pitch$get_mean(unit = "Hertz")
#> [1] 440.0102

Getting Help

Conclusion

Most translations follow a simple pattern: convert Praat commands to lowercase method names with underscores, and use named parameters.

For complex workflows, pladdrr can be combined with R’s data manipulation and visualization packages (e.g. dplyr, purrr, ggplot2).