kwslib 0.0.1

Python client library for KWS Platform API

KWS Library (kwslib)

Python client library for interacting with the KWS Platform backend API.

Features

• Complete API Coverage: Wraps all backend API endpoints
• MinIO Integration: Download .wav and .npz files for training
• Telegram Notifications: Optional notifications for training jobs
• Easy to Use: Simple, intuitive API design
• Type Hints: Full type annotations for better IDE support

Installation

pip install kwslib

Or from source:

git clone <repository>
cd KWS_Lib
pip install -e .

Quick Start

Basic Usage

from kwslib import KWSClient

# Initialize client
client = KWSClient(base_url="http://localhost:8000")

# Login
client.login(username="admin", password="password")

# List datasets
datasets = client.datasets.list()
print(f"Found {datasets['total']} datasets")

# Get dataset details
dataset = client.datasets.get(dataset_id=1)
print(f"Dataset: {dataset['name']}")

Download Dataset Split Files for Training

from kwslib import KWSClient, DatasetSplitFilesClient

# Initialize API client
api = KWSClient(base_url="http://localhost:8000")
api.login(username="admin", password="password")

# Initialize files client (uses API, no direct MinIO connection)
files_client = DatasetSplitFilesClient(api)

# List all files in split
files_info = files_client.list_files(split_id=1, file_type="npz")
print(f"Found {files_info['total_files']} files")

# Download all .npz files
files_client.download_all_npz(
    split_id=1,
    output_dir="features"
)

# Download all .wav files
files_client.download_all_wav(
    split_id=1,
    output_dir="audio"
)

# Or download as ZIP
files_client.download_all_files_zip(
    split_id=1,
    file_type="npz",
    output_path="features.zip"
)

# Get presigned URLs (for Google Colab)
urls = files_client.get_file_urls(split_id=1, file_type="npz")
for file_info in urls["files"]:
    print(f"{file_info['file_name']}: {file_info['url']}")

With Telegram Notifications

from kwslib import KWSClient, TelegramNotifier

# Initialize
client = KWSClient(base_url="http://localhost:8000")
client.login(username="admin", password="password")

notifier = TelegramNotifier(
    bot_token="YOUR_BOT_TOKEN",
    chat_id="YOUR_CHAT_ID"
)

# Create experiment run
run = client.experiments.create_run(
    experiment_id=1,
    name="Training Run 1",
    model_id=1,
    dataset_split_id=1
)

# Wait for completion
job_id = run.get("job_id")
status = client.jobs.wait_for_completion(job_id)

# Send notification
if status["status"] == "completed":
    notifier.send(f"Training completed! Results: {status['result']}")
else:
    notifier.send(f"Training failed: {status.get('error')}")

API Modules

Authentication

• client.auth.login() - Login
• client.auth.logout() - Logout
• client.auth.get_me() - Get current user info

Datasets

• client.datasets.list() - List datasets
• client.datasets.get() - Get dataset
• client.datasets.create() - Create dataset
• client.datasets.update() - Update dataset
• client.datasets.delete() - Delete dataset
• client.datasets.list_versions() - List versions
• client.datasets.create_version() - Create version

Models

• client.models.list() - List models
• client.models.get() - Get model
• client.models.create() - Create model
• client.models.list_model_inits() - List model architectures

Experiments

• client.experiments.list() - List experiments
• client.experiments.create() - Create experiment
• client.experiments.create_run() - Create experiment run
• client.experiments.list_runs() - List experiment runs

Dataset Splits

• client.dataset_splits.list() - List splits
• client.dataset_splits.create() - Create split
• client.dataset_splits.download() - Download split as ZIP
• client.dataset_splits.generate() - Generate split

Audio

• client.audio.list_keyword_samples() - List keyword audio
• client.audio.upload_keyword_sample() - Upload audio
• client.audio.get_keyword_sample_url() - Get presigned URL

Features

• client.features.get_keyword_features() - Get features
• client.features.extract_keyword_features() - Extract features

Jobs

• client.jobs.get() - Get job status
• client.jobs.list() - List jobs
• client.jobs.wait_for_completion() - Wait for job completion

Dataset Split Files Client

• files_client.list_files() - List all files in split
• files_client.download_wav() - Download a .wav file
• files_client.download_npz() - Download and load a .npz file
• files_client.download_all_wav() - Download all .wav files
• files_client.download_all_npz() - Download all .npz files
• files_client.download_all_files_zip() - Download all files as ZIP
• files_client.get_file_urls() - Get presigned URLs for all files

Telegram Notifier

• notifier.send() - Send message
• notifier.send_file() - Send file
• notifier.send_photo() - Send photo

Examples

Complete Training Workflow

from kwslib import KWSClient, DatasetSplitFilesClient, TelegramNotifier

# Setup
api = KWSClient(base_url="http://localhost:8000")
api.login(username="admin", password="password")

files_client = DatasetSplitFilesClient(api)

notifier = TelegramNotifier(
    bot_token="YOUR_TOKEN",
    chat_id="YOUR_CHAT_ID"
)

# 1. Create dataset split
split = api.dataset_splits.create(
    dataset_version_id=1,
    name="train_split",
    config_name="train",
    seed=42,
    split_option="fixed",
    fixed_count=1000,
    keyword_ids=[1, 2, 3]
)

split_id = split["dataset_splits_id"]

# 2. Generate split
job = api.dataset_splits.generate(split_id)
job_id = job["job_id"]

# 3. Wait for completion
status = api.jobs.wait_for_completion(job_id)
notifier.send(f"Split generation: {status['status']}")

# 4. Download features (via API, no direct MinIO access)
features_dir = f"features/split_{split_id}"
files_client.download_all_npz(
    split_id=split_id,
    output_dir=features_dir
)

# 5. Create experiment run
run = api.experiments.create_run(
    experiment_id=1,
    name="Training Run",
    model_id=1,
    dataset_split_id=split_id
)

notifier.send(f"Training started: {run['name']}")

Google Colab Usage

# In Google Colab, use presigned URLs for direct download
from kwslib import KWSClient, DatasetSplitFilesClient

api = KWSClient(base_url="https://your-api.com")
api.login(username="admin", password="password")

files_client = DatasetSplitFilesClient(api)

# Get presigned URLs
urls = files_client.get_file_urls(split_id=1, file_type="npz")

# Download in Colab
import urllib.request
for file_info in urls["files"]:
    urllib.request.urlretrieve(
        file_info["url"],
        f"/content/{file_info['file_name']}"
    )

Google Colab Usage

# In Google Colab, use presigned URLs for direct download
from kwslib import KWSClient, DatasetSplitFilesClient

api = KWSClient(base_url="https://your-api.com")
api.login(username="admin", password="password")

files_client = DatasetSplitFilesClient(api)

# Get presigned URLs
urls = files_client.get_file_urls(split_id=1, file_type="npz")

# Download in Colab
import urllib.request
for file_info in urls["files"]:
    urllib.request.urlretrieve(
        file_info["url"],
        f"/content/{file_info['file_name']}"
    )

Configuration

Environment Variables

You can set default values using environment variables:

export KWS_BASE_URL="http://localhost:8000"
export KWS_USERNAME="admin"
export KWS_PASSWORD="password"
export MINIO_ENDPOINT="localhost:9000"
export MINIO_ACCESS_KEY="minioadmin"
export MINIO_SECRET_KEY="minioadmin"

License

MIT License

Contributing

Contributions are welcome! Please open an issue or submit a pull request.