Instructions to use ncoder-ai/VibeVoice-Large-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ncoder-ai/VibeVoice-Large-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="ncoder-ai/VibeVoice-Large-AWQ")# Load model directly from transformers import VibeVoiceForConditionalGenerationInference model = VibeVoiceForConditionalGenerationInference.from_pretrained("ncoder-ai/VibeVoice-Large-AWQ", dtype="auto") - VibeVoice
How to use ncoder-ai/VibeVoice-Large-AWQ with VibeVoice:
import torch, soundfile as sf, librosa, numpy as np from vibevoice.processor.vibevoice_processor import VibeVoiceProcessor from vibevoice.modular.modeling_vibevoice_inference import VibeVoiceForConditionalGenerationInference # Load voice sample (should be 24kHz mono) voice, sr = sf.read("path/to/voice_sample.wav") if voice.ndim > 1: voice = voice.mean(axis=1) if sr != 24000: voice = librosa.resample(voice, sr, 24000) processor = VibeVoiceProcessor.from_pretrained("ncoder-ai/VibeVoice-Large-AWQ") model = VibeVoiceForConditionalGenerationInference.from_pretrained( "ncoder-ai/VibeVoice-Large-AWQ", torch_dtype=torch.bfloat16 ).to("cuda").eval() model.set_ddpm_inference_steps(5) inputs = processor(text=["Speaker 0: Hello!\nSpeaker 1: Hi there!"], voice_samples=[[voice]], return_tensors="pt") audio = model.generate(**inputs, cfg_scale=1.3, tokenizer=processor.tokenizer).speech_outputs[0] sf.write("output.wav", audio.cpu().numpy().squeeze(), 24000) - Notebooks
- Google Colab
- Kaggle
File size: 3,243 Bytes
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"acostic_vae_dim": 64,
"acoustic_tokenizer_config": {
"causal": true,
"channels": 1,
"conv_bias": true,
"conv_norm": "none",
"corpus_normalize": 0.0,
"decoder_depths": null,
"decoder_n_filters": 32,
"decoder_ratios": [
8,
5,
5,
4,
2,
2
],
"disable_last_norm": true,
"encoder_depths": "3-3-3-3-3-3-8",
"encoder_n_filters": 32,
"encoder_ratios": [
8,
5,
5,
4,
2,
2
],
"fix_std": 0.5,
"layer_scale_init_value": 1e-06,
"layernorm": "RMSNorm",
"layernorm_elementwise_affine": true,
"layernorm_eps": 1e-05,
"mixer_layer": "depthwise_conv",
"model_type": "vibevoice_acoustic_tokenizer",
"pad_mode": "constant",
"std_dist_type": "gaussian",
"torch_dtype": "float16",
"vae_dim": 64,
"weight_init_value": 0.01
},
"acoustic_vae_dim": 64,
"architectures": [
"VibeVoiceForConditionalGenerationInference"
],
"decoder_config": {
"attention_dropout": 0.0,
"hidden_act": "silu",
"hidden_size": 3584,
"initializer_range": 0.02,
"intermediate_size": 18944,
"max_position_embeddings": 32768,
"max_window_layers": 28,
"model_type": "qwen2",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000.0,
"sliding_window": null,
"torch_dtype": "float16",
"use_cache": true,
"use_mrope": false,
"use_sliding_window": false,
"vocab_size": 152064
},
"diffusion_head_config": {
"ddpm_batch_mul": 4,
"ddpm_beta_schedule": "cosine",
"ddpm_num_inference_steps": 20,
"ddpm_num_steps": 1000,
"diffusion_type": "ddpm",
"head_ffn_ratio": 3.0,
"head_layers": 4,
"hidden_size": 3584,
"latent_size": 64,
"model_type": "vibevoice_diffusion_head",
"prediction_type": "v_prediction",
"rms_norm_eps": 1e-05,
"speech_vae_dim": 64,
"torch_dtype": "float16"
},
"model_type": "vibevoice",
"quantization_config": {
"bits": 4,
"group_size": 128,
"modules_to_not_convert": [
"acoustic_tokenizer",
"semantic_tokenizer",
"acoustic_connector",
"semantic_connector",
"prediction_head",
"lm_head"
],
"quant_method": "awq",
"version": "gemm",
"zero_point": true
},
"semantic_tokenizer_config": {
"causal": true,
"channels": 1,
"conv_bias": true,
"conv_norm": "none",
"corpus_normalize": 0.0,
"disable_last_norm": true,
"encoder_depths": "3-3-3-3-3-3-8",
"encoder_n_filters": 32,
"encoder_ratios": [
8,
5,
5,
4,
2,
2
],
"fix_std": 0,
"layer_scale_init_value": 1e-06,
"layernorm": "RMSNorm",
"layernorm_elementwise_affine": true,
"layernorm_eps": 1e-05,
"mixer_layer": "depthwise_conv",
"model_type": "vibevoice_semantic_tokenizer",
"pad_mode": "constant",
"std_dist_type": "none",
"torch_dtype": "float16",
"vae_dim": 128,
"weight_init_value": 0.01
},
"semantic_vae_dim": 128,
"tie_word_embeddings": false,
"torch_dtype": "float16",
"transformers_version": "4.51.3"
}
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