voicepad-core 0.1.1

Core audio processing for Voicepad.

voicepad-core

Core Python library for voice recording, GPU-accelerated transcription, and system diagnostics.

Install

pip install voicepad-core

For GPU support (4-5x faster):

pip install voicepad-core[gpu]

Requirements: Python 3.13+

Quick Start

from voicepad_core import AudioRecorder, transcribe_audio, get_config

# Load configuration
config = get_config()

# Record audio
recorder = AudioRecorder(config)
audio_file = recorder.start_recording()
# Press Ctrl+C to stop

# Transcribe the audio file
output_file = config.markdown_path / "transcript.md"
stats = transcribe_audio(audio_file, output_file, config)
print(f"Transcribed: {stats['word_count']} words")

Key Components

Audio Recording

from voicepad_core import AudioRecorder, get_config

config = get_config()
recorder = AudioRecorder(config)
audio_file = recorder.start_recording()  # Press Ctrl+C to stop

Transcription

from voicepad_core import transcribe_audio, get_config

config = get_config()
stats = transcribe_audio(
    audio_file=Path("recording.wav"),
    output_file=Path("transcript.md"),
    config=config
)

System Diagnostics

from voicepad_core import gpu_diagnostics, get_ram_info, get_cpu_info

gpu = gpu_diagnostics()
ram = get_ram_info()
cpu = get_cpu_info()

print(f"GPU available: {gpu.faster_whisper_gpu.success}")
print(f"Available RAM: {ram.available_gb} GB")

Model Recommendations

from voicepad_core import (
    get_model_recommendation,
    get_available_models,
    get_ram_info,
    get_cpu_info,
    gpu_diagnostics
)
from voicepad_core.diagnostics.models import SystemInfo

system_info = SystemInfo(
    ram=get_ram_info(),
    cpu=get_cpu_info(),
    gpu_diagnostics=gpu_diagnostics()
)
recommendation = get_model_recommendation(system_info, get_available_models())
print(f"Recommended model: {recommendation.recommended_model}")

Configuration

Create voicepad.yaml:

recordings_path: data/recordings
markdown_path: data/markdown
input_device_index: null
transcription_model: tiny
transcription_device: auto
transcription_compute_type: auto

Documentation

• API Reference - Complete API docs
• GPU Acceleration - GPU setup guide
• Main README - Project overview

Supported Models

All OpenAI Whisper models are supported:

• tiny - Fastest (39M)
• base - Fast (74M)
• small - Balanced (244M)
• medium - Accurate (769M)
• large-v3 - Most accurate (1.5B)
• turbo - Latest generation (809M)

Use get_available_models() to list all models.

Requirements

• Python 3.13+
• Audio device for recording
• GPU (optional) for 4-5x faster transcription