Glossolalia Dial LoRA

Use

Load through the app's patched F5-TTS, which wires the LoRA + LevelEmbed and exposes set_dial(0..4):

Clone the repo first so its patches/ module is on your path, then run from inside it:

git clone https://github.com/akshan-main/glossolalia && cd glossolalia

from f5_tts.api import F5TTS
import patches  # local folder from the cloned repo; adds load_lora + set_dial to F5TTS
tts = F5TTS(model="F5TTS_v1_Base")
tts.load_lora("akshan-main/glossolalia-dial-lora")
tts.set_dial(4)  # 0 clean, 4 full glossolalia
tts.infer(ref_file="voice.wav", ref_text="...", gen_text="she sells seashells by the seashore", file_wave="out.wav")

Training

30k clips made by corrupting 3000 public-domain sentences' phonemes at five rising rates and having base F5-TTS read each in two reference voices. The model only sees the clean sentence plus the dial value and learns the slide from the dial alone. Pipeline: github.com/akshan-main/glossolalia. Inputs: akshan-main/glossolalia-inputs.

--- license: apache-2.0 base_model: SWivid/F5-TTS library_name: peft pipeline_tag: text-to-speech tags: - f5-tts - lora - text-to-speech - voice-cloning - glossolalia --- # Glossolalia Dial LoRA The trained "dial" behind [Glossolalia Dial](https://huggingface.co/spaces/build-small-hackathon/glossolalia): a rank-16 LoRA on F5-TTS attention plus a co-trained scalar-to-vector network (LevelEmbed) that adds a dial value into the model's timestep embedding. One control grades a typed sentence from clean speech (dial 0) to wordless glossolalia (dial 4), in the same cloned voice. ## Files - `adapter_model.safetensors`, `adapter_config.json`: the rank-16 LoRA (PEFT). - `level_to_time.safetensors`, `level_to_time.json`: the LevelEmbed MLP that maps the dial value into the time embedding. ## Use Load through the app's patched F5-TTS, which wires the LoRA + LevelEmbed and exposes `set_dial(0..4)`: Clone the repo first so its `patches/` module is on your path, then run from inside it: ```bash git clone https://github.com/akshan-main/glossolalia && cd glossolalia ``` ```python from f5_tts.api import F5TTS import patches # local folder from the cloned repo; adds load_lora + set_dial to F5TTS tts = F5TTS(model="F5TTS_v1_Base") tts.load_lora("akshan-main/glossolalia-dial-lora") tts.set_dial(4) # 0 clean, 4 full glossolalia tts.infer(ref_file="voice.wav", ref_text="...", gen_text="she sells seashells by the seashore", file_wave="out.wav") ``` ## Training 30k clips made by corrupting 3000 public-domain sentences' phonemes at five rising rates and having base F5-TTS read each in two reference voices. The model only sees the clean sentence plus the dial value and learns the slide from the dial alone. Pipeline: [github.com/akshan-main/glossolalia](https://github.com/akshan-main/glossolalia). Inputs: [akshan-main/glossolalia-inputs](https://huggingface.co/datasets/akshan-main/glossolalia-inputs).