From 87e5216cd287f411b2ffab04dbc435f48c1d4aae Mon Sep 17 00:00:00 2001 From: historia Date: Wed, 19 Aug 2026 03:55:13 -0400 Subject: refactor: config.py simplified --- README.md | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) (limited to 'README.md') diff --git a/README.md b/README.md index 3bd999f..d4e339b 100644 --- a/README.md +++ b/README.md @@ -189,17 +189,10 @@ Third-party wheels: https://mjunya.com/flash-attention-prebuild-wheels/ (hosted Transcription affects the output a lot. Whisper is okay, but does not give perfect transcription. A manual transcription passed via `--transcription` is better. -Keep `CHUNK_SIZE_WORDS` small (default 40). Every API call is a single model generation: long generations lose prosody, can degrade into garbled audio, and text past the model's token limit is never spoken. If parts of a book sound flat, monotone, or garbled, the chunk size is the first thing to check. - -`MIN_DELAY_BETWEEN_CHUNKS` only matters for hosted demos (rate limits); a local server needs no delay (default 0). - -Manual transcription, imperfect whisper transcription, and `--no-transcription` each provide different results. Usually the most accurate transcription is the best, but sometimes `--no-transcription` can produce a flat tone that might be preferable for certain voices. +If you're cloning one language and outputting another language, `--no-transcription` will remove the accent. Alternatively, setting the "wrong" output `--language` can add an accent. Even tiny amounts of pause between phrases in the sample audio can have a big impact. Try increasing or decreasing them. -Setting `--language` to the "wrong" language for English text can produce an accent. It is not as strong as cloning a voice with the desired accent. - -The built-in "custom" voices are mediocre. I get *much* better results cloning anything. ## License -- cgit v1.2.3