Automatic Speech Recognition
Transformers
GGUF
Safetensors
Arabic
English
asr
speech-recognition
arabic
arabic-asr
dialectal-arabic
emirati
gulf-arabic
streaming
realtime
llama-cpp
audar
conversational
Instructions to use mradermacher/Audar-ASR-V1-Turbo-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/Audar-ASR-V1-Turbo-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mradermacher/Audar-ASR-V1-Turbo-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/Audar-ASR-V1-Turbo-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/Audar-ASR-V1-Turbo-GGUF 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 mradermacher/Audar-ASR-V1-Turbo-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/Audar-ASR-V1-Turbo-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mradermacher/Audar-ASR-V1-Turbo-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/Audar-ASR-V1-Turbo-GGUF:Q4_K_M
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 mradermacher/Audar-ASR-V1-Turbo-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/Audar-ASR-V1-Turbo-GGUF:Q4_K_M
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 mradermacher/Audar-ASR-V1-Turbo-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/Audar-ASR-V1-Turbo-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/Audar-ASR-V1-Turbo-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/Audar-ASR-V1-Turbo-GGUF with Ollama:
ollama run hf.co/mradermacher/Audar-ASR-V1-Turbo-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use mradermacher/Audar-ASR-V1-Turbo-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/Audar-ASR-V1-Turbo-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/Audar-ASR-V1-Turbo-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/Audar-ASR-V1-Turbo-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Audar-ASR-V1-Turbo-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
- Xet hash:
- 222b735725778cd336a20cd61dc8586f2fa60ce258cb00ac73b3403a2df174c5
- Size of remote file:
- 1.24 GB
- SHA256:
- 613657f9e81431c40ee12aa186b3e7d61897f2700e09ac98ec93e8df9e232eb0
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