NeMo
Safetensors
GGUF
English
audio
audio-annotation
speech-recognition
speaker-diarization
emotion-recognition
sound-event-detection
vocal-burst
pipeline
mirror
imatrix
conversational
Instructions to use laion/universal-audio-annotation-pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use laion/universal-audio-annotation-pipeline with NeMo:
# tag did not correspond to a valid NeMo domain.
- llama-cpp-python
How to use laion/universal-audio-annotation-pipeline with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="laion/universal-audio-annotation-pipeline", filename="models/gemma-4-12b-it-gguf/gemma-4-12b-it-Q8_0.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use laion/universal-audio-annotation-pipeline with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf laion/universal-audio-annotation-pipeline:Q8_0 # Run inference directly in the terminal: llama-cli -hf laion/universal-audio-annotation-pipeline:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf laion/universal-audio-annotation-pipeline:Q8_0 # Run inference directly in the terminal: llama-cli -hf laion/universal-audio-annotation-pipeline:Q8_0
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 laion/universal-audio-annotation-pipeline:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf laion/universal-audio-annotation-pipeline:Q8_0
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 laion/universal-audio-annotation-pipeline:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf laion/universal-audio-annotation-pipeline:Q8_0
Use Docker
docker model run hf.co/laion/universal-audio-annotation-pipeline:Q8_0
- LM Studio
- Jan
- Ollama
How to use laion/universal-audio-annotation-pipeline with Ollama:
ollama run hf.co/laion/universal-audio-annotation-pipeline:Q8_0
- Unsloth Studio
How to use laion/universal-audio-annotation-pipeline 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 laion/universal-audio-annotation-pipeline 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 laion/universal-audio-annotation-pipeline to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for laion/universal-audio-annotation-pipeline to start chatting
- Pi
How to use laion/universal-audio-annotation-pipeline with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf laion/universal-audio-annotation-pipeline:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "laion/universal-audio-annotation-pipeline:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use laion/universal-audio-annotation-pipeline with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf laion/universal-audio-annotation-pipeline:Q8_0
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default laion/universal-audio-annotation-pipeline:Q8_0
Run Hermes
hermes
- Docker Model Runner
How to use laion/universal-audio-annotation-pipeline with Docker Model Runner:
docker model run hf.co/laion/universal-audio-annotation-pipeline:Q8_0
- Lemonade
How to use laion/universal-audio-annotation-pipeline with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull laion/universal-audio-annotation-pipeline:Q8_0
Run and chat with the model
lemonade run user.universal-audio-annotation-pipeline-Q8_0
List all available models
lemonade list
| from typing import Optional, List | |
| from dataclasses import dataclass, field | |
| from transformers import PretrainedConfig, Qwen3Config | |
| class MossAudioEncoderConfig: | |
| d_model: int = 1280 | |
| output_dim: int = 1280 | |
| num_mel_bins: int = 128 | |
| encoder_layers: int = 32 | |
| encoder_attention_heads: int = 20 | |
| encoder_ffn_dim: int = 5120 | |
| downsample_rate: int = 8 | |
| downsample_hidden_size: int = 480 | |
| encoder_attention_window_size: int = 100 | |
| max_source_positions: int = 1500 | |
| dropout: float = 0.1 | |
| attention_dropout: float = 0.1 | |
| activation_dropout: float = 0.0 | |
| activation_function: str = "gelu" | |
| layer_norm_eps: float = 1e-5 | |
| _attn_implementation: str = "eager" | |
| pretrained_path: str = "" | |
| n_window: int = 200 | |
| conv_chunksize: int = 64 | |
| deepstack_encoder_layer_indexes: List[int] = field(default_factory=lambda: [8, 16, 24]) | |
| def from_dict(cls, config_dict): | |
| if config_dict is None: | |
| return cls() | |
| allowed_keys = set(cls.__dataclass_fields__.keys()) | |
| filtered = {k: v for k, v in config_dict.items() if k in allowed_keys} | |
| return cls(**filtered) | |
| def to_dict(self): | |
| return { | |
| "d_model": self.d_model, | |
| "output_dim": self.output_dim, | |
| "num_mel_bins": self.num_mel_bins, | |
| "encoder_layers": self.encoder_layers, | |
| "encoder_attention_heads": self.encoder_attention_heads, | |
| "encoder_ffn_dim": self.encoder_ffn_dim, | |
| "downsample_rate": self.downsample_rate, | |
| "downsample_hidden_size": self.downsample_hidden_size, | |
| "encoder_attention_window_size": self.encoder_attention_window_size, | |
| "max_source_positions": self.max_source_positions, | |
| "dropout": self.dropout, | |
| "attention_dropout": self.attention_dropout, | |
| "activation_dropout": self.activation_dropout, | |
| "activation_function": self.activation_function, | |
| "layer_norm_eps": self.layer_norm_eps, | |
| "_attn_implementation": self._attn_implementation, | |
| "pretrained_path": self.pretrained_path, | |
| "n_window": self.n_window, | |
| "conv_chunksize": self.conv_chunksize, | |
| "deepstack_encoder_layer_indexes": list(self.deepstack_encoder_layer_indexes or []), | |
| } | |
| class MossAudioConfig(PretrainedConfig): | |
| model_type = "moss_audio" | |
| is_composition = True | |
| def __init__( | |
| self, | |
| audio_config=None, | |
| language_config=None, | |
| adapter_hidden_size=8192, | |
| ignore_index=-100, | |
| deepstack_num_inject_layers: Optional[int] = None, | |
| **kwargs, | |
| ): | |
| if isinstance(audio_config, dict): | |
| audio_config = MossAudioEncoderConfig.from_dict(audio_config) | |
| elif audio_config is None: | |
| audio_config = MossAudioEncoderConfig() | |
| if isinstance(language_config, dict): | |
| language_config = Qwen3Config(**language_config) | |
| elif language_config is None: | |
| language_config = Qwen3Config() | |
| self.audio_config = audio_config | |
| self.language_config = language_config | |
| self.adapter_hidden_size = adapter_hidden_size | |
| self.ignore_index = ignore_index | |
| self.deepstack_num_inject_layers = deepstack_num_inject_layers | |
| _propagate_keys = { | |
| "num_hidden_layers", "eos_token_id", "bos_token_id", "vocab_size", | |
| "tie_word_embeddings", | |
| } | |
| for key in ("num_hidden_layers", "eos_token_id", "bos_token_id", "vocab_size"): | |
| kwargs.setdefault(key, getattr(language_config, key, None)) | |
| kwargs.setdefault("tie_word_embeddings", False) | |
| if hasattr(language_config, "to_dict"): | |
| _lang_keys = set(language_config.to_dict().keys()) | |
| for key in list(kwargs.keys()): | |
| if key in _lang_keys and key not in _propagate_keys: | |
| kwargs.pop(key) | |
| super().__init__(**kwargs) | |
| def to_dict(self): | |
| output = super().to_dict() | |
| output["audio_config"] = ( | |
| self.audio_config.to_dict() if hasattr(self.audio_config, "to_dict") else self.audio_config | |
| ) | |
| output["language_config"] = ( | |
| self.language_config.to_dict() | |
| if hasattr(self.language_config, "to_dict") | |
| else self.language_config | |
| ) | |
| output["adapter_hidden_size"] = self.adapter_hidden_size | |
| output["ignore_index"] = self.ignore_index | |
| output["deepstack_num_inject_layers"] = self.deepstack_num_inject_layers | |
| return output | |
| __all__ = ["MossAudioEncoderConfig", "MossAudioConfig"] | |