2026-01-07 23:29:46 +05:00
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use std::sync::mpsc::Receiver;
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2023-06-11 19:17:50 +05:00
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use std::time::SystemTime;
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2026-04-23 11:07:42 +03:00
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use jarvis_core::{audio_buffer::AudioRingBuffer, audio_processing, audio_processing::vad::listen_window::{ListenWindow, WindowDecision}, audio_processing::vad::webrtc::WebRtcVad, commands, config, listener, recorder, stt, COMMANDS_LIST, intent, voices, ipc::{self, IpcEvent}, i18n, slots};
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2026-01-13 02:21:59 +05:00
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use rand::seq::SliceRandom;
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2023-06-11 19:17:50 +05:00
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2026-01-07 00:15:36 +05:00
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use crate::should_stop;
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2026-01-08 00:35:21 +05:00
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// VAD state machine
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#[derive(Debug, Clone, Copy, PartialEq)]
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enum VadState {
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WaitingForVoice,
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VoiceActive,
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}
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2026-02-18 21:08:48 +05:00
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pub fn start(text_cmd_rx: Receiver<String>, rt: &tokio::runtime::Runtime) -> Result<(), ()> {
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main_loop(text_cmd_rx, rt)
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2023-06-11 19:17:50 +05:00
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}
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2026-02-18 21:08:48 +05:00
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fn main_loop(text_cmd_rx: Receiver<String>, rt: &tokio::runtime::Runtime) -> Result<(), ()> {
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2026-01-08 00:35:21 +05:00
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let frame_length: usize = 512;
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let sample_rate: usize = 16000;
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2023-06-11 19:17:50 +05:00
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let mut frame_buffer: Vec<i16> = vec![0; frame_length];
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2026-01-08 00:35:21 +05:00
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2026-02-11 07:21:50 +05:00
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// ring buffer: keeps last 5 seconds of audio (pre-roll)
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let mut audio_buffer = AudioRingBuffer::new(5.0, frame_length, sample_rate);
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2026-02-08 06:37:39 +05:00
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2026-01-08 00:35:21 +05:00
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// VAD state
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let mut vad_state = VadState::WaitingForVoice;
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2026-01-06 23:32:58 +05:00
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let mut silence_frames: u32 = 0;
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2026-01-08 00:35:21 +05:00
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// how many frames of silence before we consider speech ended
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// 1.5 seconds = 1.5 * (16000 / 512) ≈ 47 frames
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let silence_threshold: u32 = ((1.5 * sample_rate as f32) / frame_length as f32) as u32;
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2026-01-07 23:29:46 +05:00
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voices::play_greet();
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2023-06-11 19:17:50 +05:00
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match recorder::start_recording() {
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2026-02-08 06:37:39 +05:00
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Ok(_) => info!("Recording started. Microphone: {}",
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recorder::get_audio_device_name(recorder::get_selected_microphone_index())),
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2023-06-11 19:17:50 +05:00
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Err(_) => {
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error!("Cannot start recording.");
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2026-01-08 00:35:21 +05:00
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return Err(());
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2023-06-11 19:17:50 +05:00
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}
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}
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2026-01-07 00:15:36 +05:00
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ipc::send(IpcEvent::Idle);
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2026-01-08 00:35:21 +05:00
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// ### WAKE WORD DETECTION LOOP
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2023-06-11 19:17:50 +05:00
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'wake_word: loop {
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2026-01-07 00:15:36 +05:00
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if should_stop() {
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info!("Stop signal received, shutting down...");
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2026-01-07 23:29:46 +05:00
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voices::play_goodbye();
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2026-01-07 00:15:36 +05:00
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ipc::send(IpcEvent::Stopping);
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break;
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}
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2026-01-07 23:29:46 +05:00
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if let Ok(text) = text_cmd_rx.try_recv() {
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process_text_command(&text, &rt);
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continue 'wake_word;
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}
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2023-06-11 19:17:50 +05:00
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recorder::read_microphone(&mut frame_buffer);
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2026-01-06 23:32:58 +05:00
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let processed = audio_processing::process(&frame_buffer);
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2026-01-08 00:35:21 +05:00
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match vad_state {
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VadState::WaitingForVoice => {
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// always buffer audio
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audio_buffer.push(&frame_buffer);
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if processed.is_voice {
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// voice started! flush buffer to Vosk
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info!("VAD: Voice started, flushing {} buffered frames", audio_buffer.len());
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for buffered_frame in audio_buffer.drain_all() {
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listener::data_callback(&buffered_frame);
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}
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vad_state = VadState::VoiceActive;
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silence_frames = 0;
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}
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}
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VadState::VoiceActive => {
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2026-02-08 06:37:39 +05:00
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// dual-feed: speech recognizer gets frames in parallel with wake word detector
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let _ = stt::recognize(&frame_buffer, false);
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2026-01-08 00:35:21 +05:00
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// feed to wake word detector
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if let Some(_keyword_index) = listener::data_callback(&frame_buffer) {
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// WAKE WORD DETECTED!
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info!("Wake word activated!");
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ipc::send(IpcEvent::WakeWordDetected);
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stt::reset_wake_recognizer();
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audio_processing::reset();
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2026-02-08 06:37:39 +05:00
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// brief sniff to keep feeding STT while transitioning
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let sniff_frames = ((0.3 * sample_rate as f32) / frame_length as f32) as u32;
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for _ in 0..sniff_frames {
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recorder::read_microphone(&mut frame_buffer);
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audio_processing::process(&frame_buffer);
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stt::recognize(&frame_buffer, false);
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}
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2026-01-08 00:35:21 +05:00
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ipc::send(IpcEvent::Listening);
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2026-02-08 06:37:39 +05:00
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recognize_command(&mut frame_buffer, &rt, frame_length, sample_rate, true);
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2026-01-08 00:35:21 +05:00
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// reset state after command
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vad_state = VadState::WaitingForVoice;
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silence_frames = 0;
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audio_buffer.clear();
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stt::reset_wake_recognizer();
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2026-02-08 06:37:39 +05:00
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stt::reset_speech_recognizer(); // NOW reset, after command is done
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2026-01-08 00:35:21 +05:00
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audio_processing::reset();
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ipc::send(IpcEvent::Idle);
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continue 'wake_word;
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}
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// track silence
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if processed.is_voice {
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silence_frames = 0;
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} else {
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silence_frames += 1;
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if silence_frames > silence_threshold {
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debug!("VAD: Silence timeout, returning to wait state");
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vad_state = VadState::WaitingForVoice;
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silence_frames = 0;
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stt::reset_wake_recognizer();
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2026-02-08 06:37:39 +05:00
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stt::reset_speech_recognizer(); // reset since we were dual-feeding
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2026-01-08 00:35:21 +05:00
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}
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}
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}
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2026-01-06 23:32:58 +05:00
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}
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2026-01-08 00:35:21 +05:00
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}
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2026-01-06 23:32:58 +05:00
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2026-01-08 00:35:21 +05:00
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recorder::stop_recording().ok();
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ipc::send(IpcEvent::Stopping);
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2026-01-07 23:29:46 +05:00
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2026-01-08 00:35:21 +05:00
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Ok(())
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}
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2023-06-11 19:17:50 +05:00
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2026-01-07 00:15:36 +05:00
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2026-01-08 00:35:21 +05:00
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// Voice recognition for command after wake word
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fn recognize_command(
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frame_buffer: &mut [i16],
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rt: &tokio::runtime::Runtime,
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frame_length: usize,
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sample_rate: usize,
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2026-02-08 06:37:39 +05:00
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prefed_audio: bool
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2026-01-08 00:35:21 +05:00
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) {
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let mut audio_buffer = AudioRingBuffer::new(2.0, frame_length, sample_rate);
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2026-02-08 06:37:39 +05:00
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let mut vad_state = if prefed_audio {
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VadState::VoiceActive
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} else {
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VadState::WaitingForVoice
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};
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2026-01-08 00:35:21 +05:00
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let mut silence_frames: u32 = 0;
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let mut start = SystemTime::now();
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2026-02-08 06:37:39 +05:00
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let mut first_recognition = prefed_audio;
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2026-04-23 11:07:42 +03:00
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let mut webrtc_vad = WebRtcVad::with_aggressiveness(config::VAD_AGGRESSIVENESS);
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let mut window = ListenWindow::new(jarvis_core::audio_processing::vad::webrtc::FRAME_MS);
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2026-02-11 07:21:50 +05:00
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let silence_threshold: u32 = ((5.0 * sample_rate as f32) / frame_length as f32) as u32;
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2026-01-08 00:35:21 +05:00
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loop {
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if crate::should_stop() {
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return;
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}
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recorder::read_microphone(frame_buffer);
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let processed = audio_processing::process(frame_buffer);
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2026-04-23 11:07:42 +03:00
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let mut vad_finalized: Option<String> = None;
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let mut vad_hard_cap = false;
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for is_speech in webrtc_vad.push_samples(frame_buffer) {
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match window.push(is_speech) {
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WindowDecision::KeepListening => {}
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WindowDecision::Close => {
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if window.had_speech() {
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info!(
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"VAD: silence after speech ({} ms speech, {} ms silence), finalizing.",
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window.speech_ms(), window.silence_ms()
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);
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vad_finalized = stt::finalize_speech();
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if vad_finalized.is_none() {
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return;
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}
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break;
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}
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}
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WindowDecision::HardCap => {
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info!("VAD: hard cap reached ({} ms), returning to wake word mode.", window.elapsed_ms());
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vad_hard_cap = true;
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break;
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}
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}
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}
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if vad_hard_cap {
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return;
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}
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2026-01-08 00:35:21 +05:00
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match vad_state {
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VadState::WaitingForVoice => {
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audio_buffer.push(frame_buffer);
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2026-04-23 11:07:42 +03:00
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2026-01-08 00:35:21 +05:00
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if processed.is_voice {
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// flush buffer to STT
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for buffered_frame in audio_buffer.drain_all() {
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stt::recognize(&buffered_frame, false);
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2026-01-07 00:15:36 +05:00
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}
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2026-01-08 00:35:21 +05:00
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vad_state = VadState::VoiceActive;
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silence_frames = 0;
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2026-02-08 06:37:39 +05:00
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} else {
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silence_frames += 1;
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2026-04-23 11:07:42 +03:00
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2026-02-08 06:37:39 +05:00
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if silence_frames > silence_threshold {
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info!("Long silence detected, returning to wake word mode.");
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return;
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}
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2026-01-08 00:35:21 +05:00
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}
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}
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VadState::VoiceActive => {
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2026-04-23 11:07:42 +03:00
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// feed to STT (or use VAD-forced finalization)
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let recognized = vad_finalized.take().or_else(|| stt::recognize(frame_buffer, false));
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if let Some(mut recognized_voice) = recognized {
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2026-01-08 00:35:21 +05:00
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info!("Recognized voice: {}", recognized_voice);
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ipc::send(IpcEvent::SpeechRecognized {
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text: recognized_voice.clone(),
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});
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recognized_voice = recognized_voice.to_lowercase();
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// check if wake word repeated (reactivate)
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2026-02-08 06:37:39 +05:00
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let wake_phrases = config::get_wake_phrases(&i18n::get_language());
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let contains_wake = wake_phrases.iter().any(|wp| recognized_voice.contains(wp));
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if contains_wake {
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// strip the wake word
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let mut remaining = recognized_voice.clone();
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for wp in wake_phrases {
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remaining = remaining.replace(wp, "");
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}
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let remaining = remaining.trim();
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if remaining.is_empty() {
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if first_recognition {
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// leftover wake word from dual-feed, just discard it
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info!("Discarding initial wake word from prefed audio");
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first_recognition = false;
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stt::reset_speech_recognizer();
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voices::play_reply();
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vad_state = VadState::WaitingForVoice;
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silence_frames = 0;
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start = SystemTime::now();
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audio_buffer.clear();
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2026-04-23 11:07:42 +03:00
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webrtc_vad.reset();
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window = ListenWindow::new(jarvis_core::audio_processing::vad::webrtc::FRAME_MS);
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2026-02-08 06:37:39 +05:00
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continue;
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}
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// just wake word, no command - reactivate
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info!("Wake word repeated during chaining, reactivating...");
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voices::play_reply();
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stt::reset_speech_recognizer();
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ipc::send(IpcEvent::Listening);
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2026-04-23 11:07:42 +03:00
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2026-02-08 06:37:39 +05:00
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vad_state = VadState::WaitingForVoice;
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silence_frames = 0;
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start = SystemTime::now();
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audio_buffer.clear();
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2026-04-23 11:07:42 +03:00
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webrtc_vad.reset();
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window = ListenWindow::new(jarvis_core::audio_processing::vad::webrtc::FRAME_MS);
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2026-02-08 06:37:39 +05:00
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continue;
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} else {
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// wake word + command in one phrase - execute the command part
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info!("Wake word + command during chaining: '{}'", remaining);
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recognized_voice = remaining.to_string();
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// fall through to command execution below
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}
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2026-01-06 23:32:58 +05:00
|
|
|
}
|
2026-02-08 06:37:39 +05:00
|
|
|
|
|
|
|
|
first_recognition = false;
|
2026-04-23 02:16:02 +03:00
|
|
|
|
|
|
|
|
if crate::llm_fallback::is_enabled() {
|
|
|
|
|
if let Some(prompt) = crate::llm_fallback::extract_prompt(&recognized_voice) {
|
|
|
|
|
crate::llm_fallback::handle(&prompt);
|
|
|
|
|
stt::reset_speech_recognizer();
|
|
|
|
|
vad_state = VadState::WaitingForVoice;
|
|
|
|
|
silence_frames = 0;
|
|
|
|
|
start = SystemTime::now();
|
|
|
|
|
audio_buffer.clear();
|
2026-04-23 11:07:42 +03:00
|
|
|
webrtc_vad.reset();
|
|
|
|
|
window = ListenWindow::new(jarvis_core::audio_processing::vad::webrtc::FRAME_MS);
|
2026-04-23 02:16:02 +03:00
|
|
|
ipc::send(IpcEvent::Listening);
|
|
|
|
|
continue;
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
|
2026-01-08 00:35:21 +05:00
|
|
|
// filter activation phrases
|
2026-01-13 02:21:59 +05:00
|
|
|
// for tbr in config::ASSISTANT_PHRASES_TBR {
|
|
|
|
|
// recognized_voice = recognized_voice.replace(tbr, "");
|
|
|
|
|
// }
|
|
|
|
|
for tbr in config::get_phrases_to_remove(&i18n::get_language()) {
|
2026-01-08 00:35:21 +05:00
|
|
|
recognized_voice = recognized_voice.replace(tbr, "");
|
2023-06-11 19:17:50 +05:00
|
|
|
}
|
2026-01-13 02:21:59 +05:00
|
|
|
|
2026-01-08 00:35:21 +05:00
|
|
|
recognized_voice = recognized_voice.trim().to_string();
|
|
|
|
|
|
2026-02-11 07:21:50 +05:00
|
|
|
if recognized_voice.len() < 5 {
|
|
|
|
|
debug!("Ignoring too short recognition: '{}'", recognized_voice);
|
|
|
|
|
continue;
|
|
|
|
|
}
|
|
|
|
|
|
2026-01-08 00:35:21 +05:00
|
|
|
if recognized_voice.is_empty() {
|
|
|
|
|
continue;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
// execute command and check if we should chain
|
|
|
|
|
let should_chain = execute_command(&recognized_voice, rt);
|
|
|
|
|
|
feat: TTS backend abstraction + 4 'imba' features + Lua packs
Big push driven by user feedback ("делай имбу") and web research on what
voice assistants need to be the ideal:
TTS backend abstraction (P0.1)
- new module crates/jarvis-core/src/tts/{mod,sapi,piper,silero}.rs
- TtsBackend trait with SapiBackend (current PowerShell), PiperBackend
(rhasspy/piper, neural quality), SileroBackend (python subprocess)
- JARVIS_TTS env var picks (sapi|piper|silero). Auto-detect Piper if
binary + voice present in tools/piper/. Falls back to SAPI on missing.
- SpeakOpts {lang, detached, raw} replaces ad-hoc args. text_utils
sanitiser applied unless raw=true.
- llm_fallback + lua/api/tts both routed through tts::backend().
- tools/piper/install.ps1 downloads piper.exe + ru_RU-irina-medium.onnx
from rhasspy releases + huggingface. Smoke-test included.
- tools/silero/silero_tts.py helper (PyTorch); rust spawns it as subprocess.
IMBA-1 Agentic LLM router
- crates/jarvis-app/src/llm_router.rs
- When fuzzy/intent matcher fails, LLM picks the closest command from the
full registry. Returns JSON {command_id, confidence, reason}.
- Threshold-gated re-dispatch via substitute phrase. JARVIS_LLM_ROUTER=1
enables; JARVIS_LLM_ROUTER_THRESHOLD overrides 0.55 default.
- Inserted in app.rs::execute_command between "no match" and existing
llm_fallback chat fallback.
IMBA-2 Long-term memory
- crates/jarvis-core/src/long_term_memory.rs — JSON store at
APP_CONFIG_DIR/long_term_memory.json. Atomic write-through.
- remember/recall/search/forget/all/build_context API.
- Lua bindings: jarvis.memory.* (5 functions).
- llm_fallback auto-injects relevant facts (substring search of prompt)
into system message before LLM call.
- Pack resources/commands/memory_pack/ with 4 commands: remember, recall,
forget, list.
IMBA-3 Profile switching (work/game/sleep/driving/default)
- crates/jarvis-core/src/profiles.rs — JSON profiles at APP_CONFIG_DIR/profiles/
Auto-seeds 5 defaults on first run with personality + allow/deny lists +
greetings + emoji icons.
- active_profile.txt persists choice across restart.
- Lua bindings: jarvis.profile.{active,set,list,allows,active_name}.
- llm_fallback prepends profile personality to system prompt.
- Pack resources/commands/profile_switch/ with 6 voice triggers.
IMBA-4 Multimodal screenshot + vision LLM
- crates/jarvis-core/src/lua/api/vision.rs — gated on HTTP sandbox.
- jarvis.vision.screenshot() captures via PowerShell System.Drawing.
- jarvis.vision.describe(prompt?) sends base64 PNG to Groq vision model
(default llama-3.2-11b-vision-preview, override via GROQ_VISION_MODEL).
- Pack resources/commands/vision/ with 2 commands: describe + read_error.
P0.2 Continuous conversation grace window
- config::CONVERSATION_GRACE_MS = 30_000.
- app.rs: after command result, if grace_ms > 0 keep listening WITHOUT
re-wake for the grace duration. Existing CMS_WAIT_DELAY back-dated so
the existing timeout fires at start + grace_ms.
Tests: 24/24 jarvis-core unit tests pass (including 5 text_utils).
Build: cargo build --release -p jarvis-app and -p jarvis-gui both succeed
on Windows MSVC (VS 2026 Enterprise vcvars64).
Notes for setup:
- Piper voice install: pwsh tools/piper/install.ps1 (downloads ~90 MB).
- GROQ_TOKEN needed for IMBA-1 (router) and IMBA-4 (vision).
- All features are opt-in via env vars or auto-detect; existing SAPI +
fuzzy match path remains the default.
2026-05-15 15:32:44 +03:00
|
|
|
let grace_ms = jarvis_core::config::CONVERSATION_GRACE_MS;
|
|
|
|
|
if should_chain || grace_ms > 0 {
|
|
|
|
|
// Either explicit chain OR P0.2 continuous-conversation grace window.
|
|
|
|
|
// Reset listening state and keep going without re-wake.
|
|
|
|
|
if should_chain {
|
|
|
|
|
info!("Chain requested, continuing to listen...");
|
|
|
|
|
} else {
|
|
|
|
|
info!(
|
|
|
|
|
"Continuous conversation: {}ms grace window for follow-up.",
|
|
|
|
|
grace_ms
|
|
|
|
|
);
|
|
|
|
|
}
|
2026-01-08 00:35:21 +05:00
|
|
|
stt::reset_speech_recognizer();
|
|
|
|
|
vad_state = VadState::WaitingForVoice;
|
|
|
|
|
silence_frames = 0;
|
feat: TTS backend abstraction + 4 'imba' features + Lua packs
Big push driven by user feedback ("делай имбу") and web research on what
voice assistants need to be the ideal:
TTS backend abstraction (P0.1)
- new module crates/jarvis-core/src/tts/{mod,sapi,piper,silero}.rs
- TtsBackend trait with SapiBackend (current PowerShell), PiperBackend
(rhasspy/piper, neural quality), SileroBackend (python subprocess)
- JARVIS_TTS env var picks (sapi|piper|silero). Auto-detect Piper if
binary + voice present in tools/piper/. Falls back to SAPI on missing.
- SpeakOpts {lang, detached, raw} replaces ad-hoc args. text_utils
sanitiser applied unless raw=true.
- llm_fallback + lua/api/tts both routed through tts::backend().
- tools/piper/install.ps1 downloads piper.exe + ru_RU-irina-medium.onnx
from rhasspy releases + huggingface. Smoke-test included.
- tools/silero/silero_tts.py helper (PyTorch); rust spawns it as subprocess.
IMBA-1 Agentic LLM router
- crates/jarvis-app/src/llm_router.rs
- When fuzzy/intent matcher fails, LLM picks the closest command from the
full registry. Returns JSON {command_id, confidence, reason}.
- Threshold-gated re-dispatch via substitute phrase. JARVIS_LLM_ROUTER=1
enables; JARVIS_LLM_ROUTER_THRESHOLD overrides 0.55 default.
- Inserted in app.rs::execute_command between "no match" and existing
llm_fallback chat fallback.
IMBA-2 Long-term memory
- crates/jarvis-core/src/long_term_memory.rs — JSON store at
APP_CONFIG_DIR/long_term_memory.json. Atomic write-through.
- remember/recall/search/forget/all/build_context API.
- Lua bindings: jarvis.memory.* (5 functions).
- llm_fallback auto-injects relevant facts (substring search of prompt)
into system message before LLM call.
- Pack resources/commands/memory_pack/ with 4 commands: remember, recall,
forget, list.
IMBA-3 Profile switching (work/game/sleep/driving/default)
- crates/jarvis-core/src/profiles.rs — JSON profiles at APP_CONFIG_DIR/profiles/
Auto-seeds 5 defaults on first run with personality + allow/deny lists +
greetings + emoji icons.
- active_profile.txt persists choice across restart.
- Lua bindings: jarvis.profile.{active,set,list,allows,active_name}.
- llm_fallback prepends profile personality to system prompt.
- Pack resources/commands/profile_switch/ with 6 voice triggers.
IMBA-4 Multimodal screenshot + vision LLM
- crates/jarvis-core/src/lua/api/vision.rs — gated on HTTP sandbox.
- jarvis.vision.screenshot() captures via PowerShell System.Drawing.
- jarvis.vision.describe(prompt?) sends base64 PNG to Groq vision model
(default llama-3.2-11b-vision-preview, override via GROQ_VISION_MODEL).
- Pack resources/commands/vision/ with 2 commands: describe + read_error.
P0.2 Continuous conversation grace window
- config::CONVERSATION_GRACE_MS = 30_000.
- app.rs: after command result, if grace_ms > 0 keep listening WITHOUT
re-wake for the grace duration. Existing CMS_WAIT_DELAY back-dated so
the existing timeout fires at start + grace_ms.
Tests: 24/24 jarvis-core unit tests pass (including 5 text_utils).
Build: cargo build --release -p jarvis-app and -p jarvis-gui both succeed
on Windows MSVC (VS 2026 Enterprise vcvars64).
Notes for setup:
- Piper voice install: pwsh tools/piper/install.ps1 (downloads ~90 MB).
- GROQ_TOKEN needed for IMBA-1 (router) and IMBA-4 (vision).
- All features are opt-in via env vars or auto-detect; existing SAPI +
fuzzy match path remains the default.
2026-05-15 15:32:44 +03:00
|
|
|
// For grace-mode, shorten the deadline to grace_ms (instead of CMS_WAIT_DELAY).
|
|
|
|
|
// We set `start` such that the existing timeout check (line ~360) fires at
|
|
|
|
|
// start + CMS_WAIT_DELAY = now + grace_ms.
|
|
|
|
|
if !should_chain {
|
|
|
|
|
let cms = jarvis_core::config::CMS_WAIT_DELAY.as_millis() as u64;
|
|
|
|
|
if grace_ms < cms {
|
|
|
|
|
// back-date `start` so the existing timeout fires at now + grace_ms
|
|
|
|
|
let backdate_ms = cms - grace_ms;
|
|
|
|
|
start = SystemTime::now()
|
|
|
|
|
.checked_sub(std::time::Duration::from_millis(backdate_ms))
|
|
|
|
|
.unwrap_or_else(SystemTime::now);
|
|
|
|
|
} else {
|
|
|
|
|
start = SystemTime::now();
|
|
|
|
|
}
|
|
|
|
|
} else {
|
|
|
|
|
start = SystemTime::now();
|
|
|
|
|
}
|
2026-01-08 00:35:21 +05:00
|
|
|
audio_buffer.clear();
|
2026-04-23 11:07:42 +03:00
|
|
|
webrtc_vad.reset();
|
|
|
|
|
window = ListenWindow::new(jarvis_core::audio_processing::vad::webrtc::FRAME_MS);
|
2026-01-08 00:35:21 +05:00
|
|
|
ipc::send(IpcEvent::Listening);
|
|
|
|
|
continue;
|
|
|
|
|
} else {
|
feat: TTS backend abstraction + 4 'imba' features + Lua packs
Big push driven by user feedback ("делай имбу") and web research on what
voice assistants need to be the ideal:
TTS backend abstraction (P0.1)
- new module crates/jarvis-core/src/tts/{mod,sapi,piper,silero}.rs
- TtsBackend trait with SapiBackend (current PowerShell), PiperBackend
(rhasspy/piper, neural quality), SileroBackend (python subprocess)
- JARVIS_TTS env var picks (sapi|piper|silero). Auto-detect Piper if
binary + voice present in tools/piper/. Falls back to SAPI on missing.
- SpeakOpts {lang, detached, raw} replaces ad-hoc args. text_utils
sanitiser applied unless raw=true.
- llm_fallback + lua/api/tts both routed through tts::backend().
- tools/piper/install.ps1 downloads piper.exe + ru_RU-irina-medium.onnx
from rhasspy releases + huggingface. Smoke-test included.
- tools/silero/silero_tts.py helper (PyTorch); rust spawns it as subprocess.
IMBA-1 Agentic LLM router
- crates/jarvis-app/src/llm_router.rs
- When fuzzy/intent matcher fails, LLM picks the closest command from the
full registry. Returns JSON {command_id, confidence, reason}.
- Threshold-gated re-dispatch via substitute phrase. JARVIS_LLM_ROUTER=1
enables; JARVIS_LLM_ROUTER_THRESHOLD overrides 0.55 default.
- Inserted in app.rs::execute_command between "no match" and existing
llm_fallback chat fallback.
IMBA-2 Long-term memory
- crates/jarvis-core/src/long_term_memory.rs — JSON store at
APP_CONFIG_DIR/long_term_memory.json. Atomic write-through.
- remember/recall/search/forget/all/build_context API.
- Lua bindings: jarvis.memory.* (5 functions).
- llm_fallback auto-injects relevant facts (substring search of prompt)
into system message before LLM call.
- Pack resources/commands/memory_pack/ with 4 commands: remember, recall,
forget, list.
IMBA-3 Profile switching (work/game/sleep/driving/default)
- crates/jarvis-core/src/profiles.rs — JSON profiles at APP_CONFIG_DIR/profiles/
Auto-seeds 5 defaults on first run with personality + allow/deny lists +
greetings + emoji icons.
- active_profile.txt persists choice across restart.
- Lua bindings: jarvis.profile.{active,set,list,allows,active_name}.
- llm_fallback prepends profile personality to system prompt.
- Pack resources/commands/profile_switch/ with 6 voice triggers.
IMBA-4 Multimodal screenshot + vision LLM
- crates/jarvis-core/src/lua/api/vision.rs — gated on HTTP sandbox.
- jarvis.vision.screenshot() captures via PowerShell System.Drawing.
- jarvis.vision.describe(prompt?) sends base64 PNG to Groq vision model
(default llama-3.2-11b-vision-preview, override via GROQ_VISION_MODEL).
- Pack resources/commands/vision/ with 2 commands: describe + read_error.
P0.2 Continuous conversation grace window
- config::CONVERSATION_GRACE_MS = 30_000.
- app.rs: after command result, if grace_ms > 0 keep listening WITHOUT
re-wake for the grace duration. Existing CMS_WAIT_DELAY back-dated so
the existing timeout fires at start + grace_ms.
Tests: 24/24 jarvis-core unit tests pass (including 5 text_utils).
Build: cargo build --release -p jarvis-app and -p jarvis-gui both succeed
on Windows MSVC (VS 2026 Enterprise vcvars64).
Notes for setup:
- Piper voice install: pwsh tools/piper/install.ps1 (downloads ~90 MB).
- GROQ_TOKEN needed for IMBA-1 (router) and IMBA-4 (vision).
- All features are opt-in via env vars or auto-detect; existing SAPI +
fuzzy match path remains the default.
2026-05-15 15:32:44 +03:00
|
|
|
info!("Conversation grace disabled, returning to wake word mode.");
|
2026-01-08 00:35:21 +05:00
|
|
|
return;
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
// track silence
|
|
|
|
|
if processed.is_voice {
|
|
|
|
|
silence_frames = 0;
|
|
|
|
|
} else {
|
|
|
|
|
silence_frames += 1;
|
|
|
|
|
|
|
|
|
|
if silence_frames > silence_threshold {
|
|
|
|
|
info!("Long silence detected, returning to wake word mode.");
|
|
|
|
|
return;
|
2023-06-11 19:17:50 +05:00
|
|
|
}
|
|
|
|
|
}
|
2025-12-11 23:43:50 +05:00
|
|
|
}
|
2026-01-08 00:35:21 +05:00
|
|
|
}
|
|
|
|
|
|
|
|
|
|
// timeout
|
|
|
|
|
if let Ok(elapsed) = start.elapsed() {
|
|
|
|
|
if elapsed > config::CMS_WAIT_DELAY {
|
|
|
|
|
info!("Command timeout, returning to wake word mode.");
|
|
|
|
|
return;
|
|
|
|
|
}
|
2023-06-11 19:17:50 +05:00
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
|
2026-01-07 23:29:46 +05:00
|
|
|
|
|
|
|
|
fn process_text_command(text: &str, rt: &tokio::runtime::Runtime) {
|
|
|
|
|
info!("Processing text command: {}", text);
|
2026-04-23 02:16:02 +03:00
|
|
|
|
2026-01-07 23:29:46 +05:00
|
|
|
ipc::send(IpcEvent::SpeechRecognized { text: text.to_string() });
|
2026-04-23 02:16:02 +03:00
|
|
|
|
|
|
|
|
if crate::llm_fallback::is_enabled() {
|
|
|
|
|
if let Some(prompt) = crate::llm_fallback::extract_prompt(text) {
|
|
|
|
|
crate::llm_fallback::handle(&prompt);
|
|
|
|
|
ipc::send(IpcEvent::Idle);
|
|
|
|
|
return;
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
|
2026-01-07 23:29:46 +05:00
|
|
|
let mut filtered = text.to_lowercase();
|
2026-01-13 02:21:59 +05:00
|
|
|
// for tbr in config::ASSISTANT_PHRASES_TBR {
|
|
|
|
|
// filtered = filtered.replace(tbr, "");
|
|
|
|
|
// }
|
|
|
|
|
for tbr in config::get_phrases_to_remove(&i18n::get_language()) {
|
2026-01-07 23:29:46 +05:00
|
|
|
filtered = filtered.replace(tbr, "");
|
|
|
|
|
}
|
2026-01-13 02:21:59 +05:00
|
|
|
|
2026-01-07 23:29:46 +05:00
|
|
|
let filtered = filtered.trim();
|
|
|
|
|
|
|
|
|
|
if filtered.is_empty() {
|
|
|
|
|
ipc::send(IpcEvent::Idle);
|
|
|
|
|
return;
|
|
|
|
|
}
|
|
|
|
|
|
2026-01-08 00:35:21 +05:00
|
|
|
// text commands never chain
|
2026-01-07 23:29:46 +05:00
|
|
|
execute_command(filtered, rt);
|
|
|
|
|
}
|
|
|
|
|
|
2026-01-08 00:35:21 +05:00
|
|
|
|
|
|
|
|
// Execute command, returns true if chaining should continue
|
|
|
|
|
fn execute_command(text: &str, rt: &tokio::runtime::Runtime) -> bool {
|
2026-01-07 23:29:46 +05:00
|
|
|
let commands_list = match COMMANDS_LIST.get() {
|
|
|
|
|
Some(c) => c,
|
|
|
|
|
None => {
|
|
|
|
|
ipc::send(IpcEvent::Error { message: "Commands not loaded".to_string() });
|
|
|
|
|
ipc::send(IpcEvent::Idle);
|
2026-01-08 00:35:21 +05:00
|
|
|
return false;
|
2026-01-07 23:29:46 +05:00
|
|
|
}
|
|
|
|
|
};
|
|
|
|
|
|
|
|
|
|
let cmd_result = if let Some((intent_id, confidence)) =
|
|
|
|
|
rt.block_on(intent::classify(text))
|
|
|
|
|
{
|
|
|
|
|
info!("Intent recognized: {} (confidence: {:.2})", intent_id, confidence);
|
|
|
|
|
intent::get_command_by_intent(commands_list, &intent_id)
|
|
|
|
|
} else {
|
|
|
|
|
info!("Intent not recognized, trying levenshtein fallback...");
|
|
|
|
|
commands::fetch_command(text, commands_list)
|
|
|
|
|
};
|
|
|
|
|
|
|
|
|
|
if let Some((cmd_path, cmd_config)) = cmd_result {
|
|
|
|
|
info!("Command found: {:?}", cmd_path);
|
|
|
|
|
|
2026-02-11 07:21:50 +05:00
|
|
|
// extract slots if needed
|
|
|
|
|
let extracted_slots = if !cmd_config.slots.is_empty() {
|
|
|
|
|
let s = slots::extract(text, &cmd_config.slots);
|
|
|
|
|
if !s.is_empty() {
|
|
|
|
|
info!("Extracted slots: {:?}", s);
|
|
|
|
|
}
|
|
|
|
|
Some(s)
|
|
|
|
|
} else {
|
|
|
|
|
None
|
|
|
|
|
};
|
|
|
|
|
|
|
|
|
|
match commands::execute_command(&cmd_path, &cmd_config, Some(&text), extracted_slots.as_ref()) {
|
2026-01-08 00:35:21 +05:00
|
|
|
Ok(chain) => {
|
2026-01-07 23:29:46 +05:00
|
|
|
info!("Command executed successfully");
|
2026-01-13 02:21:59 +05:00
|
|
|
// voices::play_ok();
|
|
|
|
|
voices::play_random_from(cmd_config.get_sounds(&i18n::get_language()).as_slice());
|
2026-01-07 23:29:46 +05:00
|
|
|
ipc::send(IpcEvent::CommandExecuted {
|
|
|
|
|
id: cmd_config.id.clone(),
|
|
|
|
|
success: true,
|
|
|
|
|
});
|
2026-01-08 00:35:21 +05:00
|
|
|
ipc::send(IpcEvent::Idle);
|
|
|
|
|
return chain; // return chain status from command
|
2026-01-07 23:29:46 +05:00
|
|
|
}
|
|
|
|
|
Err(msg) => {
|
|
|
|
|
error!("Error executing command: {}", msg);
|
|
|
|
|
voices::play_error();
|
|
|
|
|
ipc::send(IpcEvent::CommandExecuted {
|
|
|
|
|
id: cmd_config.id.clone(),
|
|
|
|
|
success: false,
|
|
|
|
|
});
|
|
|
|
|
ipc::send(IpcEvent::Error { message: msg.to_string() });
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
} else {
|
|
|
|
|
info!("No command found for: {}", text);
|
feat: LLM auto-fallback + codegen pack + OCR pack
LLM auto-fallback (jarvis-app/src/app.rs + jarvis-core/src/config.rs):
- When neither intent classifier nor levenshtein finds a command match,
route the utterance straight to the LLM instead of just playing the
"not found" sound. Triggers ("скажи X", "answer Y") still work and
take precedence — they short-circuit before command lookup.
- Two new config knobs:
LLM_AUTO_FALLBACK = true — master toggle
LLM_AUTO_FALLBACK_MIN_CHARS = 4 — suppress for very short utterances
so background noise doesn't burn
Groq quota
- Requires GROQ_TOKEN; if absent, behaviour is unchanged (play_not_found).
codegen/ command pack:
- Phrases: "напиши код X", "сгенерируй скрипт Y", "write code Z", etc.
- Strips the trigger from the recognized phrase, sends what remains to
Groq with a strict system prompt ("return ONLY code, no fences, no
commentary") at temperature 0.2.
- Parses the JSON content, unescapes \n / \" / \\ / \t, strips any
remaining ```lang ... ``` fences, drops the result into the clipboard
via jarvis.system.clipboard.set.
- Notifies a 120-char preview + plays ok-sound. Works for any language
the model handles (Python by default if unspecified).
- GROQ_TOKEN / GROQ_MODEL / GROQ_BASE_URL read from env at call time —
same envvars the voice-loop fallback already uses.
ocr/ command pack:
- Phrases: "прочитай экран", "что на экране", "read screen", etc.
- Captures the primary screen via System.Windows.Forms + System.Drawing
to a temp PNG, then shells to tesseract.exe (-l rus+eng for ru/ua,
-l eng for en).
- Resolves Tesseract by PATH first, then C:\Program Files\Tesseract-OCR
and the x86 install dir; if none works the user gets a friendly
"winget install UB-Mannheim.TesseractOCR" hint in a notification.
- Recognized text goes to clipboard + a 200-char preview notification.
All three features are portable (env-var resolution, no hardcoded user
paths). Command-pack total is now 11. cargo test -p jarvis-core --lib
commands::tests passes 3/3.
2026-05-15 01:39:21 +03:00
|
|
|
|
feat: TTS backend abstraction + 4 'imba' features + Lua packs
Big push driven by user feedback ("делай имбу") and web research on what
voice assistants need to be the ideal:
TTS backend abstraction (P0.1)
- new module crates/jarvis-core/src/tts/{mod,sapi,piper,silero}.rs
- TtsBackend trait with SapiBackend (current PowerShell), PiperBackend
(rhasspy/piper, neural quality), SileroBackend (python subprocess)
- JARVIS_TTS env var picks (sapi|piper|silero). Auto-detect Piper if
binary + voice present in tools/piper/. Falls back to SAPI on missing.
- SpeakOpts {lang, detached, raw} replaces ad-hoc args. text_utils
sanitiser applied unless raw=true.
- llm_fallback + lua/api/tts both routed through tts::backend().
- tools/piper/install.ps1 downloads piper.exe + ru_RU-irina-medium.onnx
from rhasspy releases + huggingface. Smoke-test included.
- tools/silero/silero_tts.py helper (PyTorch); rust spawns it as subprocess.
IMBA-1 Agentic LLM router
- crates/jarvis-app/src/llm_router.rs
- When fuzzy/intent matcher fails, LLM picks the closest command from the
full registry. Returns JSON {command_id, confidence, reason}.
- Threshold-gated re-dispatch via substitute phrase. JARVIS_LLM_ROUTER=1
enables; JARVIS_LLM_ROUTER_THRESHOLD overrides 0.55 default.
- Inserted in app.rs::execute_command between "no match" and existing
llm_fallback chat fallback.
IMBA-2 Long-term memory
- crates/jarvis-core/src/long_term_memory.rs — JSON store at
APP_CONFIG_DIR/long_term_memory.json. Atomic write-through.
- remember/recall/search/forget/all/build_context API.
- Lua bindings: jarvis.memory.* (5 functions).
- llm_fallback auto-injects relevant facts (substring search of prompt)
into system message before LLM call.
- Pack resources/commands/memory_pack/ with 4 commands: remember, recall,
forget, list.
IMBA-3 Profile switching (work/game/sleep/driving/default)
- crates/jarvis-core/src/profiles.rs — JSON profiles at APP_CONFIG_DIR/profiles/
Auto-seeds 5 defaults on first run with personality + allow/deny lists +
greetings + emoji icons.
- active_profile.txt persists choice across restart.
- Lua bindings: jarvis.profile.{active,set,list,allows,active_name}.
- llm_fallback prepends profile personality to system prompt.
- Pack resources/commands/profile_switch/ with 6 voice triggers.
IMBA-4 Multimodal screenshot + vision LLM
- crates/jarvis-core/src/lua/api/vision.rs — gated on HTTP sandbox.
- jarvis.vision.screenshot() captures via PowerShell System.Drawing.
- jarvis.vision.describe(prompt?) sends base64 PNG to Groq vision model
(default llama-3.2-11b-vision-preview, override via GROQ_VISION_MODEL).
- Pack resources/commands/vision/ with 2 commands: describe + read_error.
P0.2 Continuous conversation grace window
- config::CONVERSATION_GRACE_MS = 30_000.
- app.rs: after command result, if grace_ms > 0 keep listening WITHOUT
re-wake for the grace duration. Existing CMS_WAIT_DELAY back-dated so
the existing timeout fires at start + grace_ms.
Tests: 24/24 jarvis-core unit tests pass (including 5 text_utils).
Build: cargo build --release -p jarvis-app and -p jarvis-gui both succeed
on Windows MSVC (VS 2026 Enterprise vcvars64).
Notes for setup:
- Piper voice install: pwsh tools/piper/install.ps1 (downloads ~90 MB).
- GROQ_TOKEN needed for IMBA-1 (router) and IMBA-4 (vision).
- All features are opt-in via env vars or auto-detect; existing SAPI +
fuzzy match path remains the default.
2026-05-15 15:32:44 +03:00
|
|
|
// IMBA-1: Agentic LLM router — try to map unknown phrase to a known command
|
|
|
|
|
// by asking the LLM. This is the killer feature competitors don't have.
|
|
|
|
|
if crate::llm_router::is_enabled() {
|
|
|
|
|
if let Some(routed) = crate::llm_router::try_route(text) {
|
|
|
|
|
info!(
|
|
|
|
|
"Router → {} ({}%): {} | substitute='{}'",
|
|
|
|
|
routed.command_id,
|
|
|
|
|
(routed.confidence * 100.0) as u32,
|
|
|
|
|
routed.reason,
|
|
|
|
|
routed.substitute_phrase
|
|
|
|
|
);
|
|
|
|
|
|
|
|
|
|
// Re-dispatch with the canonical phrase for the chosen command.
|
|
|
|
|
// Guard against infinite recursion: pass a marker if needed.
|
|
|
|
|
if routed.substitute_phrase != text {
|
|
|
|
|
return execute_command(&routed.substitute_phrase, rt);
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
|
feat: LLM auto-fallback + codegen pack + OCR pack
LLM auto-fallback (jarvis-app/src/app.rs + jarvis-core/src/config.rs):
- When neither intent classifier nor levenshtein finds a command match,
route the utterance straight to the LLM instead of just playing the
"not found" sound. Triggers ("скажи X", "answer Y") still work and
take precedence — they short-circuit before command lookup.
- Two new config knobs:
LLM_AUTO_FALLBACK = true — master toggle
LLM_AUTO_FALLBACK_MIN_CHARS = 4 — suppress for very short utterances
so background noise doesn't burn
Groq quota
- Requires GROQ_TOKEN; if absent, behaviour is unchanged (play_not_found).
codegen/ command pack:
- Phrases: "напиши код X", "сгенерируй скрипт Y", "write code Z", etc.
- Strips the trigger from the recognized phrase, sends what remains to
Groq with a strict system prompt ("return ONLY code, no fences, no
commentary") at temperature 0.2.
- Parses the JSON content, unescapes \n / \" / \\ / \t, strips any
remaining ```lang ... ``` fences, drops the result into the clipboard
via jarvis.system.clipboard.set.
- Notifies a 120-char preview + plays ok-sound. Works for any language
the model handles (Python by default if unspecified).
- GROQ_TOKEN / GROQ_MODEL / GROQ_BASE_URL read from env at call time —
same envvars the voice-loop fallback already uses.
ocr/ command pack:
- Phrases: "прочитай экран", "что на экране", "read screen", etc.
- Captures the primary screen via System.Windows.Forms + System.Drawing
to a temp PNG, then shells to tesseract.exe (-l rus+eng for ru/ua,
-l eng for en).
- Resolves Tesseract by PATH first, then C:\Program Files\Tesseract-OCR
and the x86 install dir; if none works the user gets a friendly
"winget install UB-Mannheim.TesseractOCR" hint in a notification.
- Recognized text goes to clipboard + a 200-char preview notification.
All three features are portable (env-var resolution, no hardcoded user
paths). Command-pack total is now 11. cargo test -p jarvis-core --lib
commands::tests passes 3/3.
2026-05-15 01:39:21 +03:00
|
|
|
if jarvis_core::config::LLM_AUTO_FALLBACK
|
|
|
|
|
&& crate::llm_fallback::is_enabled()
|
|
|
|
|
&& text.chars().count() >= jarvis_core::config::LLM_AUTO_FALLBACK_MIN_CHARS
|
|
|
|
|
{
|
|
|
|
|
info!("Auto-routing to LLM (no command match): {}", text);
|
|
|
|
|
crate::llm_fallback::handle(text);
|
|
|
|
|
} else {
|
|
|
|
|
voices::play_not_found();
|
|
|
|
|
ipc::send(IpcEvent::Error {
|
|
|
|
|
message: format!("Command not found: {}", text)
|
|
|
|
|
});
|
|
|
|
|
}
|
2026-01-07 23:29:46 +05:00
|
|
|
}
|
feat: LLM auto-fallback + codegen pack + OCR pack
LLM auto-fallback (jarvis-app/src/app.rs + jarvis-core/src/config.rs):
- When neither intent classifier nor levenshtein finds a command match,
route the utterance straight to the LLM instead of just playing the
"not found" sound. Triggers ("скажи X", "answer Y") still work and
take precedence — they short-circuit before command lookup.
- Two new config knobs:
LLM_AUTO_FALLBACK = true — master toggle
LLM_AUTO_FALLBACK_MIN_CHARS = 4 — suppress for very short utterances
so background noise doesn't burn
Groq quota
- Requires GROQ_TOKEN; if absent, behaviour is unchanged (play_not_found).
codegen/ command pack:
- Phrases: "напиши код X", "сгенерируй скрипт Y", "write code Z", etc.
- Strips the trigger from the recognized phrase, sends what remains to
Groq with a strict system prompt ("return ONLY code, no fences, no
commentary") at temperature 0.2.
- Parses the JSON content, unescapes \n / \" / \\ / \t, strips any
remaining ```lang ... ``` fences, drops the result into the clipboard
via jarvis.system.clipboard.set.
- Notifies a 120-char preview + plays ok-sound. Works for any language
the model handles (Python by default if unspecified).
- GROQ_TOKEN / GROQ_MODEL / GROQ_BASE_URL read from env at call time —
same envvars the voice-loop fallback already uses.
ocr/ command pack:
- Phrases: "прочитай экран", "что на экране", "read screen", etc.
- Captures the primary screen via System.Windows.Forms + System.Drawing
to a temp PNG, then shells to tesseract.exe (-l rus+eng for ru/ua,
-l eng for en).
- Resolves Tesseract by PATH first, then C:\Program Files\Tesseract-OCR
and the x86 install dir; if none works the user gets a friendly
"winget install UB-Mannheim.TesseractOCR" hint in a notification.
- Recognized text goes to clipboard + a 200-char preview notification.
All three features are portable (env-var resolution, no hardcoded user
paths). Command-pack total is now 11. cargo test -p jarvis-core --lib
commands::tests passes 3/3.
2026-05-15 01:39:21 +03:00
|
|
|
|
2026-01-07 23:29:46 +05:00
|
|
|
ipc::send(IpcEvent::Idle);
|
2026-01-08 00:35:21 +05:00
|
|
|
false // no chain on error or not found
|
2026-01-07 23:29:46 +05:00
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
2026-04-27 21:07:33 +03:00
|
|
|
pub fn close(code: i32, label: &'static str) {
|
|
|
|
|
eprintln!("[jarvis-app] app::close called: code={} label={}", code, label);
|
|
|
|
|
error!("Closing application: code={} label={}", code, label);
|
2026-01-07 23:29:46 +05:00
|
|
|
voices::play_goodbye();
|
2026-01-07 00:15:36 +05:00
|
|
|
ipc::send(IpcEvent::Stopping);
|
2023-06-11 19:17:50 +05:00
|
|
|
std::process::exit(code);
|
2026-01-08 00:35:21 +05:00
|
|
|
}
|