espnet/yodas-granary
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How to use LocalAI-io/vibevoice-asr-it-yodas-only-lora 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("LocalAI-io/vibevoice-asr-it-yodas-only-lora")
model = VibeVoiceForConditionalGenerationInference.from_pretrained(
"LocalAI-io/vibevoice-asr-it-yodas-only-lora", 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)LoRA adapter for microsoft/VibeVoice-ASR, fine-tuned on YODAS-Granary Italian (asr_only ~87k + ast 200k cap, conversational YouTube audio).
from peft import PeftModel
from vibevoice.modular.modeling_vibevoice_asr import VibeVoiceASRForConditionalGeneration
model = VibeVoiceASRForConditionalGeneration.from_pretrained("microsoft/VibeVoice-ASR", dtype="bfloat16")
model = PeftModel.from_pretrained(model, "LocalAI-io/vibevoice-asr-it-yodas-only-lora")
For a pre-merged model see LocalAI-io/vibevoice-asr-it-yodas-only — same weights, faster to load.
Base model
microsoft/VibeVoice-ASR