chatterbox

chatterbox is an R package that is an R port of resemble AI's chatterbox library. It is written entirely in R using torch and has no Python dependencies.

Installation

You can install the development version of chatterbox from GitHub with:

remotes::install_github("cornball-ai/chatterbox")

Usage

# Set timeout to 10 minutes to allow model download
options(timeout = 600)

library(chatterbox)

# Load model
model <- chatterbox("cuda")
model <- load_chatterbox(model)

# Generate speech
jfk <- system.file("audio", "jfk.mp3", package = "chatterbox")
result <- generate(model, "Hello, this is a test!", jfk)
write_audio(result$audio, result$sample_rate, "output.wav")

# Or one-liner:
quick_tts("Hello world!", "ref.wav", "out.wav")

Differences from the Python implementation

This package targets behavioral parity with chatterbox-tts 0.1.7, with a few deliberate differences:

• No audio watermark. Python chatterbox embeds Resemble's Perth imperceptible watermark in every generated clip; this port does not. If you need provenance marking for generated audio, add it downstream.
• A reference voice is required. Python falls back to a builtin default voice (conds.pt); the R API asks for reference audio explicitly and skips that ~105 MB download.
• Reliability extras. generate() reports eos_found, n_tokens, and audio_sec, normalizes problem text by default (normalize_text = TRUE), and stops degenerate token loops early. Python 0.1.4 (English) generates until the token cap in those cases.
• Backend token caps. The pure-R and backend = "jit" paths generate up to max_new_tokens (default 1000, ~40 s; jit auto-sizes its KV cache so generation always completes). traced = TRUE is limited by its pre-allocated 350-position cache (roughly 10 s of audio per call). Long texts: tts_chunked().
• Performance depends on torch's GC settings. With torch's default allocator settings, autoregressive inference spends most of its time in R garbage collection. Run chatterbox_gc_options() for the recommended options() snippet (set before torch loads), and see the performance vignette for measurements.
• Voice conversion (vc.py) and the multilingual model are not ported.