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: 1,014 Bytes
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"dim": 4096,
"text_card": 32000,
"existing_text_padding_id": 3,
"n_q": 16,
"dep_q": 8,
"card": 2048,
"num_heads": 32,
"num_layers": 32,
"hidden_scale": 4.125,
"causal": true,
"layer_scale": null,
"context": 3000,
"max_period": 10000,
"gating": "silu",
"norm": "rms_norm_f32",
"positional_embedding": "rope",
"depformer_dim": 1024,
"depformer_dim_feedforward": 4224,
"depformer_num_heads": 16,
"depformer_num_layers": 6,
"depformer_layer_scale": null,
"depformer_multi_linear": true,
"depformer_context": 8,
"depformer_max_period": 10000,
"depformer_gating": "silu",
"depformer_pos_emb": "none",
"depformer_weights_per_step": true,
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"lora": true,
"lora_rank": 64,
"lora_scaling": 2.0
} |