Automatic Speech Recognition
Transformers
Safetensors
VibeVoice
multilingual
vibevoice_asr
bitsandbytes
4-bit precision
quantized
diarization
Instructions to use Dubedo/VibeVoice-ASR-HF-NF4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dubedo/VibeVoice-ASR-HF-NF4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Dubedo/VibeVoice-ASR-HF-NF4")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Dubedo/VibeVoice-ASR-HF-NF4") model = AutoModelForMultimodalLM.from_pretrained("Dubedo/VibeVoice-ASR-HF-NF4", device_map="auto") - VibeVoice
How to use Dubedo/VibeVoice-ASR-HF-NF4 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("Dubedo/VibeVoice-ASR-HF-NF4") model = VibeVoiceForConditionalGenerationInference.from_pretrained( "Dubedo/VibeVoice-ASR-HF-NF4", 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: 442 Bytes
289d51e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"quantization_method": "bitsandbytes",
"bits": 4,
"quant_type": "nf4",
"double_quant": true,
"modules_not_quantized": [
"acoustic_tokenizer_encoder",
"semantic_tokenizer_encoder",
"acoustic_projection",
"semantic_projection",
"lm_head"
],
"modules_quantized": [
"language_model (excluding lm_head)"
],
"base_model": "microsoft/VibeVoice-ASR-HF",
"transformers_version": "5.3.0"
} |