Veloura

Veloura is a reusable Python audio transition engine for smooth queue playback. It provides FFmpeg-backed PCM decoding, equal-power crossfades, transition analysis, beat-aware planning, and a small CLI for local inspection.

Veloura is framework-agnostic. Use it in streamer tools, radio pipelines, desktop music apps, Discord/Twitch bots, or backend automation without tying your project to one bot implementation.

Features

• Equal-power crossfade mixing for signed 16-bit PCM audio
• Lossless file transition rendering to FLAC, WAV, ALAC, or AIFF
• Discord-independent PCM queue player with snapshots and playback controls
• Public Small Listening Model planner for automatic pair-specific crossfade timing
• Smart transition planning based on track duration, silence trim, and loudness
• Beat/BPM analysis with beat-aware transition plans
• Project-local or user-cache transition analysis storage
• Optional yt-dlp stream resolution for URLs and search queries
• Optional Discord audio source compatibility
• Pure-Python PCM fallback for Python builds without audioop

Requirements

• Python 3.12 or newer
• Veloura installs imageio-ffmpeg by default and uses its bundled FFmpeg executable when system ffmpeg is not available
• System ffplay is only needed for the standalone local player example
• yt-dlp only when resolving online stream/search inputs
• discord.py and PyNaCl only when using the Discord audio source directly

0.6.5 SLM Auto Timing Snapshot

Installation

Install the core package:

pip install veloura-audio

Install stream resolution support:

pip install "veloura-audio[stream]"

Install Discord voice support:

pip install "veloura-audio[discord]"

Install every optional integration:

pip install "veloura-audio[all]"

Verify the installed package:

python -c "import veloura; print(veloura.__version__)"
python -m veloura doctor

The PyPI distribution is named veloura-audio; the Python import package is veloura:

import veloura
from veloura.audio import AudioTrack, QueuePlayer

Quick Start

from veloura.audio import AudioTrack, QueuePlayer, transition_preset

config = transition_preset("streamer")

track = AudioTrack.from_source(
    "/music/current-song.flac",
    title="Artist - Current Song",
    duration=184,
)

player = QueuePlayer(volume=0.65, crossfade_seconds=config.base_crossfade_seconds)
player.enqueue(track)

frame = player.read_frame()

For online sources, install veloura-audio[stream] and resolve a playable stream:

import asyncio

from veloura.audio import resolve_stream_track, transition_preset


async def main():
    track = await resolve_stream_track(
        "artist song official audio",
        transition_config=transition_preset("streamer"),
    )
    print(track.title, track.stream_url)


asyncio.run(main())

CLI

Veloura exposes the same core tools through python -m veloura or the veloura console script:

python -m veloura presets
python -m veloura doctor
python -m veloura prepare ./song.mp3 --preset streamer
python -m veloura analyze ./song.mp3
python -m veloura plan ./current.mp3 ./next.mp3 --preset broadcast
python -m veloura render-transition ./song-a.flac ./song-b.flac ./transition.flac

For YouTube/search inputs, install veloura-audio[stream] and add --resolve:

python -m veloura plan "current song" "next song" --preset streamer --resolve

Transition analysis is cached under ~/.cache/veloura by default, or under the directory set in VELOURA_CACHE_DIR. Use --cache-dir for a project-local cache, or --no-cache when comparing fresh analysis.

Presets

• streamer: balanced transitions for livestream/background music
• broadcast: longer, smoother radio-style blends
• low-latency: shorter analysis windows for weaker machines or fast queues
• automix: beat-aware pair planning with conservative tempo matching

Compatibility aliases such as streamer-safe, broadcast-smooth, auto-mix, slm, veloura-auto, and fast remain available for older integrations.

Veloura SLM Auto Timing

Veloura's public SLM means Small Listening Model. It is deterministic, local-only, and does not call any external AI service. It chooses a pair-specific crossfade duration from track duration, safety caps, and optional beat profiles:

from veloura.audio import AudioTrack, plan_slm_transition, transition_preset

config = transition_preset("slm")
current = AudioTrack.from_source("track-a.flac", title="Track A", duration=184)
next_track = AudioTrack.from_source("track-b.flac", title="Track B", duration=196)

plan = plan_slm_transition(current, next_track, config)
print(plan.crossfade_seconds, plan.reason)

Use this when your app wants Veloura to choose transition timing instead of asking users to set crossfade_seconds by hand.

Use prepare_automix_transition_pair(...) when you have two explicit AudioTrack objects and want to prepare their transition before playback. It analyzes beat windows, applies a pair-specific crossfade length, trims weak intro audio on confident matches, uses the SLM crossfade estimate, and nudges tempo only within a small safe range.

Apps that manage playback through QueuePlayer can call player.prepare_next_transition_pair(...) as a queue convenience method when the current and next track are already inside the player. Discord bots can keep using CrossfadeAudioSource as an adapter for Discord voice playback.

Lossless Transition Rendering

For local renderers, music apps, and release-prep workflows, Veloura can render two sources into a lossless transition file:

python -m veloura render-transition ./track-a.flac ./track-b.flac ./transition.flac --crossfade 8

Supported output extensions are .flac, .wav, .m4a, .alac, .aif, and .aiff. The renderer decodes inputs to high-precision float PCM inside FFmpeg, uses an equal-power-style crossfade curve, and writes a lossless output codec.

Programmatic use:

from veloura.audio import LosslessTransitionConfig, render_lossless_transition

render_lossless_transition(
    "track-a.flac",
    "track-b.flac",
    "transition.flac",
    LosslessTransitionConfig(crossfade_seconds=8),
)

This is lossless file transition processing, not bit-perfect copying, because crossfading intentionally changes the waveform. Discord voice output is still encoded by Discord.

Discord Bot Integration

Keep your bot commands, queue state, and permissions in your Discord project. Use Veloura as the audio transition layer:

from veloura.audio import CrossfadeAudioSource, resolve_stream_track, transition_preset

config = transition_preset("streamer")
source = CrossfadeAudioSource(
    crossfade_seconds=config.base_crossfade_seconds,
    max_queue_size=50,
)

track = await resolve_stream_track(
    "artist song official audio",
    transition_config=config,
    timeout=35,
)

source.enqueue(track)
voice_client.play(source)

Slash Command Example

Veloura includes a minimal Discord slash-command bot at examples/discord_slash_bot.py. It provides /play, /queue, /now, /skip, /stop, and /volume.

pip install "veloura-audio[all]"
export DISCORD_TOKEN="your-bot-token"
export DISCORD_GUILD_ID="your-test-server-id"
python examples/discord_slash_bot.py

DISCORD_GUILD_ID is optional, but recommended for development because server slash-command sync is much faster than global sync.

When inviting the bot, enable the bot and applications.commands scopes and grant Connect/Speak voice permissions.

The example includes public-bot guardrails: same-voice-channel controls, optional DJ role checks, mention escaping, queue caps, per-user /play cooldowns, resolver timeouts, and bounded analysis cache storage. Useful environment variables:

• VELOURA_DJ_ROLE_ID: require a Discord role for /play, /skip, /stop, and /volume.
• VELOURA_MAX_QUEUE_SIZE: cap pending tracks per server. Default: 50.
• VELOURA_PLAY_COOLDOWN_SECONDS: per-user /play cooldown. Default: 5.
• VELOURA_RESOLVE_TIMEOUT_SECONDS: cap stream lookup and analysis waits. Default: 35.
• VELOURA_CACHE_MAX_ENTRIES: cap transition analysis cache files. Default: 1000.
• VELOURA_CACHE_TTL_SECONDS: expire old cache files. Default: 604800.

For public bots, still treat user search terms and URLs as untrusted input. Keep permission checks in your app, rate-limit stream resolution, and avoid exposing yt-dlp resolution to users who should not be able to trigger network lookups.

Standalone Example

The example player resolves local files or stream queries, prepares transition analysis, mixes the queue, and pipes PCM into ffplay. This example needs a system FFmpeg install with ffplay available:

python examples/streamer_player.py ./song-a.mp3 ./song-b.mp3 --preset streamer
python examples/streamer_player.py ./song-a.mp3 ./song-b.mp3 --cache-dir ./veloura-cache

Free Transition Demos

The website includes small playable transition clips rendered from CC0 music sources. One demo is rendered through QueuePlayer; the lossless demo is rendered through python -m veloura render-transition into FLAC and WAV output. Regenerate them with:

python examples/generate_transition_demo_audio.py

Demo music sources:

• Empacotatron by Fupi: https://opengameart.org/content/empacotatron
• Rhythm Garden by congusbongus: https://opengameart.org/content/rhythm-garden

Both source pages list the license as CC0. Attribution is not required by CC0, but Veloura credits the sources so the demo has clear provenance.

Troubleshooting

• Run python -m veloura doctor to verify FFmpeg and optional integrations.
• Set VELOURA_FFMPEG=/path/to/ffmpeg if you want Veloura to use a specific FFmpeg executable instead of the bundled provider.
• Set VELOURA_YTDLP_SOURCE_ADDRESS only when you need yt-dlp to use a specific outbound network interface.
• Pass max_queue_size to CrossfadeAudioSource or QueuePlayer when the queue is exposed to public users.
• Use FileAnalysisCache(max_entries=..., ttl_seconds=...) for long-running bots or services.
• Install system FFmpeg if you want to use the standalone ffplay example.
• Install veloura-audio[stream] when resolving YouTube URLs or search queries through yt-dlp.
• Install veloura-audio[discord] when using CrossfadeAudioSource directly with Discord voice playback.
• Run python -m veloura presets to confirm the CLI entry point is installed.