Automatic Speech Recognition
Safetensors
GGUF
VibeVoice
ggml
ASR
quantization
cpu-inference
bitnet
multilingual
conversational
Instructions to use microsoft/VibeVoice-ASR-BitNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use microsoft/VibeVoice-ASR-BitNet with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="microsoft/VibeVoice-ASR-BitNet", filename="vibeasr-lm-i2_s-embed-q6_k.gguf", )
llm.create_chat_completion( messages = "\"sample1.flac\"" )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use microsoft/VibeVoice-ASR-BitNet with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf microsoft/VibeVoice-ASR-BitNet:Q6_K # Run inference directly in the terminal: llama cli -hf microsoft/VibeVoice-ASR-BitNet:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf microsoft/VibeVoice-ASR-BitNet:Q6_K # Run inference directly in the terminal: llama cli -hf microsoft/VibeVoice-ASR-BitNet:Q6_K
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf microsoft/VibeVoice-ASR-BitNet:Q6_K # Run inference directly in the terminal: ./llama-cli -hf microsoft/VibeVoice-ASR-BitNet:Q6_K
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf microsoft/VibeVoice-ASR-BitNet:Q6_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf microsoft/VibeVoice-ASR-BitNet:Q6_K
Use Docker
docker model run hf.co/microsoft/VibeVoice-ASR-BitNet:Q6_K
- LM Studio
- Jan
- Ollama
How to use microsoft/VibeVoice-ASR-BitNet with Ollama:
ollama run hf.co/microsoft/VibeVoice-ASR-BitNet:Q6_K
- Unsloth Studio
How to use microsoft/VibeVoice-ASR-BitNet with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for microsoft/VibeVoice-ASR-BitNet to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for microsoft/VibeVoice-ASR-BitNet to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for microsoft/VibeVoice-ASR-BitNet to start chatting
- Atomic Chat new
- Docker Model Runner
How to use microsoft/VibeVoice-ASR-BitNet with Docker Model Runner:
docker model run hf.co/microsoft/VibeVoice-ASR-BitNet:Q6_K
- Lemonade
How to use microsoft/VibeVoice-ASR-BitNet with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull microsoft/VibeVoice-ASR-BitNet:Q6_K
Run and chat with the model
lemonade run user.VibeVoice-ASR-BitNet-Q6_K
List all available models
lemonade list
File size: 3,527 Bytes
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"acoustic_tokenizer_config": {
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"channels": 1,
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"conv_norm": "none",
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"decoder_depths": null,
"decoder_n_filters": 32,
"decoder_ratios": [
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"disable_last_norm": true,
"dtype": "bfloat16",
"encoder_depths": "3-3-3-3-3-3-8",
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"fix_std": 0.5,
"layer_scale_init_value": 1e-06,
"layernorm": "RMSNorm",
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"layernorm_eps": 1e-05,
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"model_type": "vibevoice_acoustic_tokenizer",
"pad_mode": "constant",
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"vae_dim": 64,
"weight_init_value": 0.01
},
"acoustic_vae_dim": 64,
"architectures": [
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"decoder_config": {
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"max_position_embeddings": 65536,
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"sliding_window": null,
"tie_word_embeddings": true,
"use_cache": true,
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"diffusion_head_config": {
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"ddpm_beta_schedule": "cosine",
"ddpm_num_inference_steps": 20,
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"diffusion_type": "ddpm",
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"hidden_size": 1536,
"latent_size": 64,
"model_type": "vibepod_diffusion_head",
"prediction_type": "v_prediction",
"rms_norm_eps": 1e-05,
"speech_vae_dim": 64
},
"dtype": "float32",
"model_type": "vibevoice",
"semantic_tokenizer_config": {
"causal": true,
"channels": 1,
"conv_bias": true,
"conv_norm": "none",
"corpus_normalize": 0.0,
"disable_last_norm": true,
"dtype": "bfloat16",
"encoder_depths": "3-3-3-3-3-3-8",
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"fix_std": 0,
"layer_scale_init_value": 1e-06,
"layernorm": "RMSNorm",
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"weight_init_value": 0.01
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"semantic_vae_dim": 128,
"transformers_version": "4.57.6"
}
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