Audio-to-Audio
Moshi
English
speech-to-speech
full-duplex
spoken-dialogue
conversational-ai
voice-agent
voice-assistant
real-time
indian-english
indian-accent
india
customer-support
call-center
barge-in
mimi
lora
audio
Instructions to use IOTEverythin/roxi-duplex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Moshi
How to use IOTEverythin/roxi-duplex with Moshi:
# pip install moshi # Run the interactive web server python -m moshi.server --hf-repo "IOTEverythin/roxi-duplex" # Then open https://localhost:8998 in your browser
# pip install moshi import torch from moshi.models import loaders # Load checkpoint info from HuggingFace checkpoint = loaders.CheckpointInfo.from_hf_repo("IOTEverythin/roxi-duplex") # Load the Mimi audio codec mimi = checkpoint.get_mimi(device="cuda") mimi.set_num_codebooks(8) # Encode audio (24kHz, mono) wav = torch.randn(1, 1, 24000 * 10) # [batch, channels, samples] with torch.no_grad(): codes = mimi.encode(wav.cuda()) decoded = mimi.decode(codes) - Notebooks
- Google Colab
- Kaggle
File size: 550 Bytes
b2421c3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | data: {train_data: "/home/coherent_pro_full", eval_data: "", shuffle: true}
moshi_paths: {hf_repo_id: "kyutai/moshiko-pytorch-bf16"}
full_finetuning: false
lora: {enable: true, rank: 64, scaling: 2., ft_embed: false}
first_codebook_weight_multiplier: 100.
text_padding_weight: .5
duration_sec: 20
batch_size: 1
max_steps: 1500
gradient_checkpointing: true
optim: {lr: 2.e-5, weight_decay: 0.1, pct_start: 0.05}
seed: 0
log_freq: 50
eval_freq: 20000
do_eval: false
do_ckpt: true
ckpt_freq: 1500
save_adapters: true
run_dir: "/home/moshi_run_pro_full"
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