VibeVoice Acoustic Tokenizer#
VibeVoice is a novel framework designed for generating expressive, long-form, multi-speaker conversational audio, such as podcasts, from text. It addresses significant challenges in traditional Text-to-Speech (TTS) systems, particularly in scalability, speaker consistency, and natural turn-taking.
A core innovation of VibeVoice is its use of continuous speech tokenizers (Acoustic and Semantic) operating at an ultra-low frame rate of 7.5 Hz. These tokenizers efficiently preserve audio fidelity while significantly boosting computational efficiency for processing long sequences. VibeVoice employs a next-token diffusion framework, leveraging a Large Language Model (LLM) to understand textual context and dialogue flow, and a diffusion head to generate high-fidelity acoustic details.
The speech tokenizer is a key component for both VibeVoice TTS and ASR .
➡️ Technical Report: VibeVoice Technical Report
➡️ Project Page: microsoft/VibeVoice
Image: Tokenizer Comparison
Models#
Model Context Length Length (min) Weight VibeVoice-Realtime-0.5B 8K ~10 min HF link VibeVoice-1.5B 64K ~90 min HF link VibeVoice-ASR 64K ~60 min HF link VibeVoice-AcousticTokenizer - - This model
Usage#
Setup#
Until the VibeVoice acoustic tokenizer is part of an official Transformers release, it can be used by installing from the source code:
pip install git+https://github.com/huggingface/transformers.gitExample#
Encoding and decoding
import torch
from scipy.io import wavfile
from transformers import AutoFeatureExtractor, VibeVoiceAcousticTokenizerModel
from transformers.audio_utils import load_audio_librosa
model_id = "microsoft/VibeVoice-AcousticTokenizer"
# load model
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
model = VibeVoiceAcousticTokenizerModel.from_pretrained(model_id, device_map="auto")
print("Model loaded on device:", model.device)
print("Model dtype:", model.dtype)
# load audio
audio = load_audio_librosa(
"https://huggingface.co/datasets/bezzam/vibevoice_samples/resolve/main/voices/en-Alice_woman.wav",
sampling_rate=feature_extractor.sampling_rate,
)
# preprocess audio
inputs = feature_extractor(
audio,
sampling_rate=feature_extractor.sampling_rate,
pad_to_multiple_of=3200,
).to(model.device, model.dtype)
print("Input audio shape:", inputs.input_values.shape)
# Input audio shape: torch.Size([1, 1, 224000])
with torch.no_grad():
# set VAE sampling to False for deterministic output
encoded_outputs = model.encode(inputs.input_values, sample=False)
print("Latent shape:", encoded_outputs.latents.shape)
# Latent shape: torch.Size([1, 70, 64])
decoded_outputs = model.decode(**encoded_outputs)
print("Reconstructed audio shape:", decoded_outputs.audio.shape)
# Reconstructed audio shape: torch.Size([1, 1, 224000])
# Save audio
output_fp = "vibevoice_acoustic_tokenizer_reconstructed.wav"
wavfile.write(output_fp, feature_extractor.sampling_rate, decoded_outputs.audio.squeeze().float().cpu().numpy())
print(f"Reconstructed audio saved to : {output_fp}")Original audio
Encoded/decoded audio
Streaming
For streaming ASR or TTS, where cached states need to be tracked, the use_cache parameter can be used when encoding or decoding audio:
import torch
from scipy.io import wavfile
from transformers import AutoFeatureExtractor, VibeVoiceAcousticTokenizerModel
from transformers.audio_utils import load_audio_librosa
model_id = "microsoft/VibeVoice-AcousticTokenizer"
# load model
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
model = VibeVoiceAcousticTokenizerModel.from_pretrained(model_id, device_map="auto")
print("Model loaded on device:", model.device)
print("Model dtype:", model.dtype)
# load audio
audio = load_audio_librosa(
"https://huggingface.co/datasets/bezzam/vibevoice_samples/resolve/main/voices/en-Alice_woman.wav",
sampling_rate=feature_extractor.sampling_rate,
)
# preprocess audio
inputs = feature_extractor(
audio,
sampling_rate=feature_extractor.sampling_rate,
pad_to_multiple_of=3200,
).to(model.device, model.dtype)
print("Input audio shape:", inputs.input_values.shape)
# Input audio shape: torch.Size([1, 1, 224000])
# chache will be initialized after a first pass
encoder_cache = None
decoder_cache = None
with torch.no_grad():
# set VAE sampling to False for deterministic output
encoded_outputs = model.encode(inputs.input_values, sample=False, padding_cache=encoder_cache, use_cache=True)
print("Latent shape:", encoded_outputs.latents.shape)
# Latent shape: torch.Size([1, 70, 64])
decoded_outputs = model.decode(encoded_outputs.latents, padding_cache=decoder_cache, use_cache=True)
print("Reconstructed audio shape:", decoded_outputs.audio.shape)
# Reconstructed audio shape: torch.Size([1, 1, 224000])
# `padding_cache` can be extracted from the outputs for subsequent passes
encoder_cache = encoded_outputs.padding_cache
print("Number of cached encoder layers:", len(encoder_cache.per_layer_in_channels))
# Number of cached encoder layers: 34
decoder_cache = decoded_outputs.padding_cache
print("Number of cached decoder layers:", len(decoder_cache.per_layer_in_channels))
# Number of cached decoder layers: 34
# Save audio
output_fp = "vibevoice_acoustic_tokenizer_reconstructed.wav"
wavfile.write(output_fp, feature_extractor.sampling_rate, decoded_outputs.audio.squeeze().float().cpu().numpy())
print(f"Reconstructed audio saved to : {output_fp}")
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