sonarwise

Pluggable audio perception engine. Hear. Search. Retrieve.

Features

• Transcription : Whisper, Faster Whisper, or bring your own ASR
• Audio Embeddings : CLAP joint text-audio space for semantic search
• Speaker Diarization : know who said what (pyannote)
• Speaker Registry : track speakers across files by voiceprint
• Audio Event Detection : detect alarms, machinery, glass breaks, and 50+ sound types
• Live Streaming : real-time transcription with keyword and event callbacks
• Export : SRT, VTT, JSON, CSV, TXT, meeting notes
• Pluggable Architecture : every component swappable via base classes
• CLI : full command-line interface

Install

# Core (no ML dependencies)
pip install sonarwise

# With all features
pip install sonarwise[all]

# Pick what you need
pip install sonarwise[whisper]          # Whisper transcription
pip install sonarwise[faster-whisper]   # Faster Whisper (CTranslate2)
pip install sonarwise[clap]            # CLAP audio embeddings
pip install sonarwise[diarization]     # Speaker diarization
pip install sonarwise[speaker]         # Speaker identification
pip install sonarwise[events]          # Audio event detection
pip install sonarwise[live]            # Live microphone capture

Requires: ffmpeg (sudo apt install ffmpeg or brew install ffmpeg)

Quick Start

from sonarwise import SonarWise

sw = SonarWise(diarization=True, events=True)

# Index audio
sw.index("meeting.wav")
sw.index_folder("./recordings/")

# Search by text
results = sw.query("budget discussion", top_k=5)
for r in results:
    print(f"[{r.speaker_name}] {r.transcript} (score: {r.score})")

# Search by audio similarity
results = sw.query_audio("alarm_clip.wav", top_k=5)

# Search by speaker
results = sw.query("budget", speaker="Ant", top_k=5)

# Search by event
results = sw.query_events(event="machine_fault", top_k=5)

Speaker Intelligence

# Register a speaker
sw.register_speaker("Ant", reference_audio="ant_voice.wav")

# Query by speaker
results = sw.query("budget", speaker="Ant")

# Speaker timeline
timeline = sw.speaker_timeline("meeting.wav")

# Speaker stats
stats = sw.speaker_stats("meeting.wav")

# Find speaker across files
presence = sw.find_speaker_across(speaker="Ant", folders=["./meetings/"])

Live Mode

sw = SonarWise(mode="live", diarization=True, events=True)

@sw.on("transcript")
def on_speech(segment):
    print(f"[{segment.speaker_name}] {segment.transcript}")

@sw.on("keyword", words=["budget", "deadline", "risk"])
def on_keyword(segment):
    send_alert(segment.transcript)

@sw.on("sound_event", events=["alarm", "glass_break"])
def on_danger(event):
    trigger_alert(event)

sw.listen(source="microphone")

Plug Any Model

Every component is swappable:

from sonarwise import SonarWise
from sonarwise.core.transcriber import BaseTranscriber

class MyTranscriber(BaseTranscriber):
    def transcribe(self, audio):
        return my_model.process(audio)

sw = SonarWise(transcriber=MyTranscriber())

Pluggable slots:

CLI

sonarwise index meeting.wav
sonarwise query "budget discussion"
sonarwise speakers meeting.wav
sonarwise timeline meeting.wav
sonarwise listen --source microphone --on-keyword "budget,risk"
sonarwise export meeting.wav --format srt
sonarwise stats

Export

sw.export("meeting.wav", format="srt", output="subtitles.srt")
sw.export("meeting.wav", format="json", output="segments.json")
sw.export("meeting.wav", format="txt", output="transcript.txt")
sw.export("meeting.wav", format="notes", output="meeting_notes.md")

Ecosystem

sonarwise is part of the VK-Ant AI perception ecosystem:

License

Apache 2.0

Author

Built by Venkatkumar Rajan

• GitHub: https://github.com/VK-Ant
• Portfolio: https://vk-ant.github.io/Venkatkumar