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Native R torch implementation of OpenAI Whisper for speech-to-text transcription.
install.packages("whisper")Or install the development version from GitHub:
remotes::install_github("cornball-ai/whisper")library(whisper)
# Transcribe the bundled JFK "Ask not" speech (prompts to download model on first use)
jfk <- system.file("audio", "jfk.mp3", package = "whisper")
result <- transcribe(jfk)
result$text
#> "Ask not what your country can do for you, ask what you can do for your country."On first use, you’ll be prompted to download the model:
Download 'tiny' model (~151 MB) from HuggingFace? (Yes/no/cancel)# Download a model explicitly
download_whisper_model("tiny")
# List available models
list_whisper_models()
#> [1] "tiny" "base" "small" "medium" "large-v3"
# Check which models are downloaded
list_downloaded_models()
# Check if a specific model exists locally
model_exists("tiny")# Basic transcription
result <- transcribe("audio.wav")
print(result$text)
# Specify model size
result <- transcribe("audio.wav", model = "small")
# Force CPU (useful if CUDA has issues)
result <- transcribe("audio.wav", device = "cpu")
# Non-English audio (specify language for better accuracy)
allende <- system.file("audio", "allende.mp3", package = "whisper")
result <- transcribe(allende, language = "es")
# Translate to English (quality is model-dependent; larger models work better)
result <- transcribe(allende, task = "translate", language = "es", model = "small")# Segment-level timestamps
result <- transcribe("audio.wav", timestamps = TRUE)
result$segments
#> start end text
#> 1 0.00 7.44 Ask not what your country...
# Word-level timestamps (via cross-attention DTW alignment)
result <- transcribe("audio.wav", word_timestamps = TRUE)
result$words
#> word start end
#> 1 Ask 0.00 0.54
#> 2 not 0.54 1.16
#> 3 what 1.16 2.46
#> ...Both work with the pipeline API for repeated transcription:
pipe <- whisper_pipeline("tiny")
result <- pipe$transcribe("audio.wav", word_timestamps = TRUE)
result$words| Model | Parameters | Disk (fp32) | English WER | Peak VRAM (CUDA fp16) | Speed* |
|---|---|---|---|---|---|
| tiny | 39M | 151 MB | ~9% | 564 MiB | 5.5s |
| base | 74M | 290 MB | ~7% | 734 MiB | 1.9s |
| small | 244M | 967 MB | ~5% | 1,454 MiB | 3.6s |
| medium | 769M | 3.0 GB | ~4% | 3,580 MiB | 8.6s |
| large-v3 | 1550M | 6.2 GB | ~3% | 3,892 MiB | 16.7s |
*Speed measured on RTX 5060 Ti transcribing a 17s audio clip with
word_timestamps = TRUE. Peak VRAM includes ~364 MiB torch
CUDA context overhead.
Models are downloaded from HuggingFace and cached in
~/.cache/huggingface/ unless otherwise specified.
MIT
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.