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
training config
Browse files- train_config.yaml +19 -0
train_config.yaml
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data: {train_data: "/home/coherent_pro_full", eval_data: "", shuffle: true}
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moshi_paths: {hf_repo_id: "kyutai/moshiko-pytorch-bf16"}
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full_finetuning: false
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lora: {enable: true, rank: 64, scaling: 2., ft_embed: false}
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first_codebook_weight_multiplier: 100.
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text_padding_weight: .5
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duration_sec: 20
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batch_size: 1
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max_steps: 1500
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gradient_checkpointing: true
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optim: {lr: 2.e-5, weight_decay: 0.1, pct_start: 0.05}
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seed: 0
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log_freq: 50
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eval_freq: 20000
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do_eval: false
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do_ckpt: true
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ckpt_freq: 1500
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save_adapters: true
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run_dir: "/home/moshi_run_pro_full"
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