hyprvoice/docs/config.md

Configuration Reference

This document covers manual configuration of hyprvoice via the config.toml file. For most users, the interactive wizard is recommended:

hyprvoice configure

Configuration is stored in ~/.config/hyprvoice/config.toml and changes are applied immediately without restarting the daemon.

Table of Contents

• Unified Provider System
• Transcription Providers
  • Cloud Providers
  • Local Transcription (whisper-cpp)
  • Streaming Transcription
• Language Configuration
• Model Management
• LLM Post-Processing
• Keywords
• Recording Configuration
• Text Injection
• Notifications
• Example Configurations
• Migration from Old Config Format

Unified Provider System

Hyprvoice uses a unified provider system where API keys are configured once and shared between transcription and LLM features:

# Configure API keys for providers you want to use
[providers.openai]
  api_key = "sk-..."           # Or set OPENAI_API_KEY env var

[providers.groq]
  api_key = "gsk_..."          # Or set GROQ_API_KEY env var

[providers.mistral]
  api_key = "..."              # Or set MISTRAL_API_KEY env var

[providers.elevenlabs]
  api_key = "..."              # Or set ELEVENLABS_API_KEY env var

[providers.deepgram]
  api_key = "..."              # Or set DEEPGRAM_API_KEY env var

API key resolution order:

1. [providers.X] section in config
2. Environment variable (OPENAI_API_KEY, GROQ_API_KEY, etc.)

Transcription Providers

Hyprvoice supports multiple transcription backends. See docs/providers.md for detailed comparisons.

Cloud Providers

OpenAI Whisper API

Cloud-based transcription using OpenAI's Whisper API:

[transcription]
provider = "openai"
language = ""                   # Empty for auto-detect, or "en", "es", "fr", etc.
model = "whisper-1"

Features:

• High-quality transcription
• Supports 50+ languages
• Auto-detection or specify language for better accuracy

Groq Whisper API (Transcription)

Fast cloud-based transcription using Groq's Whisper API:

[transcription]
provider = "groq-transcription"
language = ""                   # Empty for auto-detect, or "en", "es", "fr", etc.
model = "whisper-large-v3"      # Or "whisper-large-v3-turbo" for faster processing

Features:

• Ultra-fast transcription (significantly faster than OpenAI)
• Same Whisper model quality
• Supports 50+ languages
• Free tier available with generous limits

Groq Translation API

Fast translation of audio to English using Groq's Whisper API:

[transcription]
provider = "groq-translation"
language = "es"                 # Optional: hint source language for better accuracy
model = "whisper-large-v3"

Features:

• Translates any language audio → English text
• Ultra-fast processing
• Language field hints at source language (improves accuracy)
• Always outputs English regardless of input language

Mistral Voxtral

Transcription using Mistral's Voxtral API, excellent for European languages:

[transcription]
provider = "mistral-transcription"
language = ""
model = "voxtral-mini-latest"   # Or "voxtral-mini-2507"

ElevenLabs Scribe

Transcription using ElevenLabs' Scribe API with 57+ language support:

[transcription]
provider = "elevenlabs"
language = ""
model = "scribe_v1"             # Or "scribe_v2" for lower latency

Features:

• 57+ languages supported
• Both batch and streaming models available
• Ultra-low latency streaming options

Deepgram Nova

Fast streaming transcription using Deepgram's Nova models:

[providers.deepgram]
  api_key = "..."               # Or set DEEPGRAM_API_KEY env var

[transcription]
provider = "deepgram"
language = ""
model = "nova-3"                # Or "nova-2" for different language support

Features:

• All models are streaming-only
• Nova-3: 42 languages, best accuracy
• Nova-2: 33 languages, faster with filler word detection
• Excellent for real-time transcription and live captions

Local Transcription (whisper-cpp)

Run Whisper models locally on your machine. No API keys, no network latency, complete privacy.

Prerequisites:

1. Install whisper-cli: https://github.com/ggerganov/whisper.cpp
2. Download a model: hyprvoice model download base.en

[transcription]
provider = "whisper-cpp"
language = ""                   # Empty for auto-detect
model = "base.en"               # English-only model (fastest)
threads = 0                     # 0 = auto (uses NumCPU - 1)

Available models:

Model	Size	Languages	Best For
tiny.en	75MB	English only	Quick tests, low-power devices
base.en	142MB	English only	Daily use, good balance
small.en	466MB	English only	Better accuracy
medium.en	1.5GB	English only	Best English accuracy
tiny	75MB	57 languages	Quick multilingual
base	142MB	57 languages	Daily multilingual use
small	466MB	57 languages	Better multilingual
medium	1.5GB	57 languages	Great accuracy
large-v3	3GB	57 languages	Best accuracy

Threads configuration:

• threads = 0 (default): auto-detects, uses NumCPU - 1 to leave one core free
• threads = 4: explicitly use 4 threads
• Higher thread count = faster transcription but more CPU usage

Streaming Transcription

For real-time transcription, use streaming models:

# ElevenLabs streaming
[transcription]
provider = "elevenlabs"
model = "scribe_v1-streaming"   # Or "scribe_v2-streaming" for <150ms latency

# Deepgram streaming (all models are streaming)
[transcription]
provider = "deepgram"
model = "nova-3"

# OpenAI Realtime
[transcription]
provider = "openai"
model = "gpt-4o-realtime-preview"

Streaming models:

Provider	Model	Latency	Languages
ElevenLabs	scribe_v1-streaming	Low	57+
ElevenLabs	scribe_v2-streaming	<150ms	57+
Deepgram	nova-3	Low	42
Deepgram	nova-2	Very Low	33
OpenAI	gpt-4o-realtime-preview	Low	57

Language Configuration

Configure the expected spoken language for better accuracy:

[transcription]
language = ""                   # Empty for auto-detect (recommended)
# Or specify a language code:
# language = "en"               # English
# language = "es"               # Spanish
# language = "fr"               # French
# language = "zh"               # Chinese
# language = "ja"               # Japanese

Recommendations:

• Use auto-detect (language = "") for most cases - it works well
• Specify a language if you always speak the same language (slight accuracy boost)
• Required for English-only models if you speak English

Supported Languages

Hyprvoice supports 57 languages:

Afrikaans (af), Arabic (ar), Armenian (hy), Azerbaijani (az), Belarusian (be), Bosnian (bs), Bulgarian (bg), Catalan (ca), Chinese (zh), Croatian (hr), Czech (cs), Danish (da), Dutch (nl), English (en), Estonian (et), Finnish (fi), French (fr), Galician (gl), German (de), Greek (el), Hebrew (he), Hindi (hi), Hungarian (hu), Icelandic (is), Indonesian (id), Italian (it), Japanese (ja), Kannada (kn), Kazakh (kk), Korean (ko), Latvian (lv), Lithuanian (lt), Macedonian (mk), Malay (ms), Marathi (mr), Maori (mi), Nepali (ne), Norwegian (no), Persian (fa), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Serbian (sr), Slovak (sk), Slovenian (sl), Spanish (es), Swahili (sw), Swedish (sv), Tagalog (tl), Tamil (ta), Thai (th), Turkish (tr), Ukrainian (uk), Urdu (ur), Vietnamese (vi), Welsh (cy)

Language-Model Compatibility

Some models only support English. Hyprvoice validates compatibility:

English-only models:

Provider	Model
Groq	distil-whisper-large-v3-en
whisper-cpp	tiny.en, base.en, small.en, medium.en

Deepgram models support fewer languages than the full 57 - see providers.md.

Validation behavior:

1. At config time (TUI/validation): Selecting an English-only model with a non-English language shows an error and prevents saving
2. At runtime (safety net): If config was manually edited to an invalid combination, hyprvoice logs a warning, sends a desktop notification, and falls back to auto-detect

# This combination will be rejected:
[transcription]
provider = "groq-transcription"
model = "distil-whisper-large-v3-en"   # English only!
language = "es"                         # Error: model does not support Spanish

Model Management

Manage local whisper models with CLI commands:

List Models

# List all models
hyprvoice model list

# Filter by provider
hyprvoice model list --provider whisper-cpp

# Filter by type
hyprvoice model list --type transcription

Shows installed status [x] for local models and model details.

Download Models

# Download a whisper model
hyprvoice model download base.en

# Download with progress
hyprvoice model download large-v3

Cloud models (OpenAI, Groq, etc.) don't require download - this is for local models only.

Remove Models

# Remove a downloaded model
hyprvoice model remove base.en

LLM Post-Processing

LLM post-processing is enabled by default and significantly improves transcription quality. After transcription, the text is processed by an LLM to:

• Remove stutters and repeated words ("I I I want" → "I want")
• Add proper punctuation
• Fix grammar errors
• Remove filler words ("um", "uh", "like", "you know", etc.)

Basic Configuration

[llm]
  enabled = true               # Disable with false if you want raw transcriptions
  provider = "openai"          # "openai" or "groq"
  model = "gpt-4o-mini"        # OpenAI: "gpt-4o-mini", Groq: "llama-3.3-70b-versatile"

Post-Processing Options

All options are enabled by default. Disable specific ones as needed:

[llm.post_processing]
  remove_stutters = true       # "I I I want" → "I want"
  add_punctuation = true       # Adds periods, commas, etc.
  fix_grammar = true           # Fixes grammatical errors
  remove_filler_words = true   # Removes "um", "uh", "like", "you know"

Custom Prompts

Add custom instructions for specific use cases:

[llm.custom_prompt]
  enabled = true
  prompt = "Format as bullet points"

Use cases for custom prompts:

• "Format as bullet points" - for note-taking
• "Keep technical terms exactly as spoken" - for programming dictation
• "Use formal language" - for professional documents
• "Translate to Spanish" - for translation workflows

LLM Provider Recommendations

Provider	Model	Best For
OpenAI	gpt-4o-mini	Best quality/cost balance (default)
Groq	llama-3.3-70b-versatile	Fastest processing, free tier

Keywords

Keywords help both transcription and LLM understand domain-specific terms, names, and technical vocabulary:

keywords = ["Hyprland", "Wayland", "PipeWire", "Claude", "TypeScript"]

How keywords work:

• Transcription: Passed as initial_prompt to Whisper, improving recognition of these terms
• LLM: Included in the system prompt to ensure correct spelling

When to use keywords:

• Names of people, companies, or products
• Technical terminology specific to your field
• Acronyms or abbreviations
• Words commonly misheard by speech-to-text

Recording Configuration

Audio capture settings:

[recording]
sample_rate = 16000        # Audio sample rate in Hz (16000 recommended for speech)
channels = 1               # Number of audio channels (1 = mono, 2 = stereo)
format = "s16"             # Audio format (s16 = 16-bit signed integers)
buffer_size = 8192         # Internal buffer size in bytes (larger = less CPU, more latency)
device = ""                # PipeWire device name (empty = default microphone)
channel_buffer_size = 30   # Audio frame buffer size (frames to buffer)
timeout = "5m"             # Maximum recording duration (e.g., "30s", "2m", "5m")

Recording Timeout

• Prevents accidental long recordings that could consume resources
• Default: 5 minutes ("5m")
• Format: Go duration strings like "30s", "2m", "10m"
• Recording automatically stops when timeout is reached

Text Injection

Configurable text injection with multiple backends:

[injection]
backends = ["ydotool", "wtype", "clipboard"]  # Ordered fallback chain
ydotool_timeout = "5s"
wtype_timeout = "5s"
clipboard_timeout = "3s"

Injection Backends

• ydotool: Uses ydotool (requires ydotoold daemon for ydotool v1.0.0+). Most compatible with Chromium/Electron apps.
• wtype: Uses wtype for Wayland. May have issues with some Chromium-based apps (known upstream bug).
• clipboard: Copies text to clipboard only. Most reliable, but requires manual paste.

Fallback Chain

Backends are tried in order. The first successful one wins. Example configurations:

# Clipboard only (safest, always works)
backends = ["clipboard"]

# wtype with clipboard fallback
backends = ["wtype", "clipboard"]

# Full fallback chain (default) - best compatibility
backends = ["ydotool", "wtype", "clipboard"]

# ydotool only (if you have it set up)
backends = ["ydotool"]

ydotool Setup

ydotool requires the ydotoold daemon running (for ydotool v1.0.0+) and access to /dev/uinput:

# Start ydotool daemon (systemd)
systemctl --user enable --now ydotool

# Or add user to input group
sudo usermod -aG input $USER
# Then logout/login

# For Hyprland, add to config to set correct keyboard layout:
# device:ydotoold-virtual-device {
#     kb_layout = us
# }

Notifications

Desktop notification settings:

[notifications]
enabled = true             # Enable/disable notifications
type = "desktop"           # "desktop", "log", or "none"

Notification Types

• desktop: Use notify-send for desktop notifications
• log: Log messages to console only
• none: Disable all notifications

Custom Notification Messages

You can customize notification text via the [notifications.messages] section:

[notifications.messages]
  [notifications.messages.recording_started]
    title = "Hyprvoice"
    body = "Recording Started"
  [notifications.messages.transcribing]
    title = "Hyprvoice"
    body = "Recording Ended... Transcribing"
  [notifications.messages.llm_processing]
    title = "Hyprvoice"
    body = "Processing..."
  [notifications.messages.config_reloaded]
    title = "Hyprvoice"
    body = "Config Reloaded"
  [notifications.messages.operation_cancelled]
    title = "Hyprvoice"
    body = "Operation Cancelled"
  [notifications.messages.recording_aborted]
    body = "Recording Aborted"
  [notifications.messages.injection_aborted]
    body = "Injection Aborted"

Emoji-only example (for minimal pill-style notifications):

[notifications.messages.recording_started]
  title = ""
  body = "🎙️"

Example Configurations

Fast Transcription Only (No LLM)

[providers.groq]
  api_key = "gsk_..."

[transcription]
  provider = "groq-transcription"
  model = "whisper-large-v3-turbo"

[llm]
  enabled = false

High Quality with OpenAI (Default)

[providers.openai]
  api_key = "sk-..."

[transcription]
  provider = "openai"
  model = "whisper-1"

[llm]
  enabled = true
  provider = "openai"
  model = "gpt-4o-mini"

Budget-Friendly with Groq

[providers.groq]
  api_key = "gsk_..."

[transcription]
  provider = "groq-transcription"
  model = "whisper-large-v3-turbo"

[llm]
  enabled = true
  provider = "groq"
  model = "llama-3.3-70b-versatile"

Mixed Providers (Groq Transcription + OpenAI LLM)

[providers.openai]
  api_key = "sk-..."

[providers.groq]
  api_key = "gsk_..."

[transcription]
  provider = "groq-transcription"
  model = "whisper-large-v3-turbo"

[llm]
  enabled = true
  provider = "openai"
  model = "gpt-4o-mini"

Local Transcription (Privacy-First)

# No API keys needed!

[transcription]
  provider = "whisper-cpp"
  model = "base.en"
  threads = 0                   # Auto-detect (NumCPU - 1)

[llm]
  enabled = false               # No LLM for full privacy

Real-Time Streaming with Deepgram

[providers.deepgram]
  api_key = "..."

[transcription]
  provider = "deepgram"
  model = "nova-3"              # All Deepgram models are streaming

[llm]
  enabled = false               # Streaming doesn't need LLM post-processing

Ultra-Low Latency Streaming

[providers.elevenlabs]
  api_key = "..."

[transcription]
  provider = "elevenlabs"
  model = "scribe_v2-streaming" # <150ms latency

[llm]
  enabled = false

Migration from Old Config Format

If you're upgrading from an older version with transcription.api_key:

Old format (still works):

[transcription]
  provider = "openai"
  api_key = "sk-..."  # Legacy location
  model = "whisper-1"

New format (recommended):

[providers.openai]
  api_key = "sk-..."  # Unified location

[transcription]
  provider = "openai"
  model = "whisper-1"

[llm]
  enabled = true
  provider = "openai"
  model = "gpt-4o-mini"

Run hyprvoice configure to interactively update your config to the new format.

Configuration Hot-Reloading

The daemon automatically watches the config file for changes and applies them immediately:

• Notification settings: Applied instantly
• Injection settings: Applied to current and future operations
• Recording/Transcription/LLM settings: Applied to new recording sessions
• Invalid configs: Rejected with error notification, daemon continues with previous config