Text Generation
Transformers
Burmese
English
myanmar
burmese
llm
chat
instruction-following
conversational
autoregressive
Instructions to use amkyawdev/myanmar-ghost with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amkyawdev/myanmar-ghost with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="amkyawdev/myanmar-ghost") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("amkyawdev/myanmar-ghost", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use amkyawdev/myanmar-ghost with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "amkyawdev/myanmar-ghost" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "amkyawdev/myanmar-ghost", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/amkyawdev/myanmar-ghost
- SGLang
How to use amkyawdev/myanmar-ghost with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "amkyawdev/myanmar-ghost" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "amkyawdev/myanmar-ghost", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "amkyawdev/myanmar-ghost" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "amkyawdev/myanmar-ghost", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use amkyawdev/myanmar-ghost with Docker Model Runner:
docker model run hf.co/amkyawdev/myanmar-ghost
| """Test audio processor module.""" | |
| import sys | |
| from pathlib import Path | |
| sys.path.insert(0, str(Path(__file__).parent.parent)) | |
| import numpy as np | |
| from src.data_processing.audio_processor import AudioProcessor | |
| def test_audio_processor_init(): | |
| """Test AudioProcessor initialization.""" | |
| processor = AudioProcessor(sample_rate=16000) | |
| assert processor.sample_rate == 16000 | |
| print("✓ AudioProcessor init test passed") | |
| def test_normalize_audio(): | |
| """Test audio normalization.""" | |
| processor = AudioProcessor() | |
| audio = np.array([0.5, -0.5, 1.0, -1.0]) | |
| normalized = processor.normalize_audio(audio) | |
| assert np.abs(normalized).max() <= 1.0 | |
| print("✓ Normalize audio test passed") | |
| def test_remove_silence(): | |
| """Test silence removal.""" | |
| processor = AudioProcessor() | |
| # Create audio with silence | |
| audio = np.concatenate([ | |
| np.zeros(1000), # silence | |
| np.random.randn(5000), # speech | |
| np.zeros(500), # silence | |
| ]) | |
| cleaned = processor.remove_silence(audio, threshold_db=40) | |
| assert len(cleaned) < len(audio) | |
| print("✓ Remove silence test passed") | |
| def test_prosody_extraction(): | |
| """Test prosody feature extraction.""" | |
| processor = AudioProcessor() | |
| # Generate synthetic audio | |
| duration = 1.0 | |
| sample_rate = 16000 | |
| t = np.linspace(0, duration, int(sample_rate * duration)) | |
| audio = np.sin(2 * np.pi * 200 * t) * 0.5 # 200Hz tone | |
| prosody = processor.extract_prosody_features(audio) | |
| assert "mean_pitch" in prosody | |
| assert "mean_energy" in prosody | |
| print("✓ Prosody extraction test passed") | |
| if __name__ == "__main__": | |
| test_audio_processor_init() | |
| test_normalize_audio() | |
| test_remove_silence() | |
| test_prosody_extraction() | |
| print("\n✅ All audio processor tests passed!") | |