Initial Commit
This commit is contained in:
@@ -0,0 +1,57 @@
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package me.neurodock.glados;
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import me.neurodock.core.PrintAdvanceMessageHandler;
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import me.neurodock.core.ToolCallingRender;
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import me.neurodock.ollama.OllamaFunctionArgument;
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import me.neurodock.ollama.OllamaFunctionTool;
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import me.neurodock.ollama.OllamaPerameter;
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import me.neurodock.ollama.OllamaToolResponse;
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import me.neurodock.ollama.exceptions.OllamaToolErrorException;
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import org.json.JSONObject;
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import java.util.Objects;
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public class AnnouncerVoiceCalling extends OllamaFunctionTool {
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Main.PlaybackHandler pb;
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public AnnouncerVoiceCalling(Main.PlaybackHandler printAdvanceMessageHandler) {
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this.pb = printAdvanceMessageHandler;
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}
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@org.jetbrains.annotations.NotNull
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@Override
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public String name() {
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return "pa_announcer";
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}
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@Override
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public @org.jetbrains.annotations.NotNull OllamaPerameter parameters() {
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return OllamaPerameter.builder()
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.addProperty("message", OllamaPerameter.OllamaPerameterBuilder.Type.STRING, "Message to be announced", true)
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.build();
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}
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@Override
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public ToolCallingRender renderCalling(JSONObject calling) {
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return new ToolCallingRender.Suppress();
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}
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@Override
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public @org.jetbrains.annotations.NotNull OllamaToolResponse function(OllamaFunctionArgument... ollamaFunctionArguments) {
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if (ollamaFunctionArguments.length != 1) {
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throw new OllamaToolErrorException(name(), "Number of arguments to be announced must be 1");
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}
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if(!(Objects.equals(ollamaFunctionArguments[0].argument(), "message")))
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{
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throw new OllamaToolErrorException(name(), "Message to be announced must be message");
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}
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if(ollamaFunctionArguments[0].value() instanceof String str) {
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pb.playAnnouncer(str);
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return new OllamaToolResponse(name(), "success");
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}
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throw new OllamaToolErrorException(name(), "Message to be announced must be message");
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}
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}
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@@ -0,0 +1,212 @@
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package me.neurodock.glados;
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import io.github.jvoiceproject.piperjni.PiperJNI;
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import io.github.jvoiceproject.piperjni.PiperVoice;
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import me.neurodock.core.Core;
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import me.neurodock.core.LaunchOptions;
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import me.neurodock.core.PrintAdvanceMessageHandler;
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import me.neurodock.ollama.OllamaMessage;
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import me.neurodock.ollama.OllamaMessageRole;
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import me.neurodock.ollama.OllamaObject;
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import org.json.JSONObject;
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import javax.sound.sampled.AudioFormat;
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import javax.sound.sampled.AudioSystem;
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import javax.sound.sampled.LineUnavailableException;
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import javax.sound.sampled.TargetDataLine;
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import java.io.IOException;
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import java.io.OutputStream;
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import java.nio.file.Path;
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import java.nio.file.Paths;
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import java.util.ArrayList;
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import java.util.List;
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public class Main {
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static void main(String[] args) throws LineUnavailableException {
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new Main();
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}
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public Main() throws LineUnavailableException {
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PlaybackHandler pb = new PlaybackHandler();
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Path socketPath = Paths.get("/tmp/whisper.sock");
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//pb.playGLaDOS("I'm Glaudos");
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Core core = new Core(pb, "127.0.0.1");
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LaunchOptions.getInstance().setLoadOld(false);
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core.setOllamaObjectNoMemory(OllamaObject.builder()
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.setModel("llama3.2")
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//.addMessage(new OllamaMessage(OllamaMessageRole.SYSTEM, "You are GLaDOS."))
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.keep_alive(10)
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.build());
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//core.addTool(new AnnouncerVoiceCalling(pb), Core.Source.CTP);
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try(WhisperSocketServer server = new WhisperSocketServer(socketPath, event ->
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{
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core.getOllamaObject().addMessage(new OllamaMessage(OllamaMessageRole.USER, event.text()));
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core.qurryOllama().thenApply((json) ->
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{
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String message = json.getJSONObject("message").getString("content");
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if(json.getJSONObject("message").has("content") && !message.isBlank()) {
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message = message.replaceAll("(?i)GLaDOS", "Glaudos").replaceAll("\"", " quote ");
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}
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json.getJSONObject("message").put("content", message);
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return json;
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}).thenAccept(core::handleResponse).join();
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}))
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{
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server.start();
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Thread.currentThread().join();
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}catch (IOException _) {
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} catch (InterruptedException e) {
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throw new RuntimeException(e);
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}
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}
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public class PlaybackHandler implements PrintAdvanceMessageHandler
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{
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public static final String GLaDOS_PIPER_MODEL = ".local/share/piper/glados/glados_piper_medium.onnx";
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public static final String PORTAL_ANNOUNCER_PIPER_MODEL = ".local/share/piper/portal-announcer/announcer.onnx";
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public static final int SENTENCE_PAUSE_DURATION_MS = 500;
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@Override
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public void printMessage(OllamaMessage ollamaMessage) {
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System.out.println(ollamaMessage.toString());
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switch(ollamaMessage.getRole())
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{
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case ASSISTANT -> playGLaDOS(ollamaMessage.getContent());
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case TOOL -> playAnnouncer("Tool: " + new JSONObject(ollamaMessage.getContent()).get("result"));
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case USER -> System.out.println("User> "+ollamaMessage.getContent());
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case SYSTEM -> playAnnouncer(ollamaMessage.getContent());
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}
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}
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@Override
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public void printErrorMessage(OllamaMessage ollamaMessage) {
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playAnnouncer("Error: "+ollamaMessage.getContent());
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}
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@Override
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public void printToolCalling(String s) {
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playAnnouncer(s);
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}
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public void playGLaDOS(String msg) {
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try (PiperJNI piper = new PiperJNI()) {
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piper.initialize(true);
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try (PiperVoice voice = piper.loadVoice(
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Paths.get(System.getenv("HOME"), GLaDOS_PIPER_MODEL),
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Paths.get(System.getenv("HOME"),GLaDOS_PIPER_MODEL+".json"),
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0)) {
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int sampleRate = voice.getSampleRate();
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short[] samples = synthesizeWithPauses(piper, voice, msg, SENTENCE_PAUSE_DURATION_MS);
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//piper.textToAudio(voice, msg);
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playAudio(samples, sampleRate);
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} finally {
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piper.terminate();
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}
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}catch (Exception e) {
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System.out.println("Critial Error on piper-tts!");
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System.out.println(e.getMessage());
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e.printStackTrace(System.out);
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System.exit(-1);
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}
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}
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public void playAnnouncer(String msg) {
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try (PiperJNI piper = new PiperJNI()) {
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piper.initialize(true);
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try (PiperVoice voice = piper.loadVoice(
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Paths.get(System.getenv("HOME"), PORTAL_ANNOUNCER_PIPER_MODEL),
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Paths.get(System.getenv("HOME"),PORTAL_ANNOUNCER_PIPER_MODEL+".json"),
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0)) {
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int sampleRate = voice.getSampleRate();
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short[] samples = synthesizeWithPauses(piper, voice, msg, SENTENCE_PAUSE_DURATION_MS);
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playAudio(samples, sampleRate);
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} finally {
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piper.terminate();
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}
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}catch (Exception e) {
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System.out.println("Critial Error on piper-tts!");
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System.out.println(e.getMessage());
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e.printStackTrace(System.out);
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System.exit(-1);
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}
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}
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public short[] synthesizeWithPauses(PiperJNI piper, PiperVoice voice, String text, int pauseMs) throws PiperJNI.NotInitialized, IOException {
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// Split, but keep the delimiter so we know which pause length to use
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String[] chunks = text.split("(?<=\\.\\.\\.)|(?<=[.!?])\\s+|(?<=,)\\s+");
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List<short[]> pieces = new ArrayList<>();
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for (String chunk : chunks) {
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if (chunk.isBlank()) continue;
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String trimmed = chunk.trim();
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pieces.add(piper.textToAudio(voice, trimmed));
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int thisPauseMs;
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if (trimmed.endsWith("...")) {
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thisPauseMs = pauseMs;
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} else if (trimmed.endsWith(",")) {
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thisPauseMs = pauseMs / 2;
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} else if (trimmed.matches(".*[.!?]$")) {
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thisPauseMs = pauseMs;
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} else {
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thisPauseMs = 0; // no trailing punctuation, no forced pause
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}
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if (thisPauseMs > 0) {
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int pauseSamples = (voice.getSampleRate() * thisPauseMs) / 1000;
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pieces.add(new short[pauseSamples]);
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}
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}
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int total = pieces.stream().mapToInt(p -> p.length).sum();
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short[] combined = new short[total];
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int offset = 0;
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for (short[] p : pieces) {
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System.arraycopy(p, 0, combined, offset, p.length);
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offset += p.length;
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}
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return combined;
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}
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public byte[] shortsToBytes(short[] samples) {
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byte[] bytes = new byte[samples.length * 2];
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for (int i = 0; i < samples.length; i++) {
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bytes[i * 2] = (byte) (samples[i] & 0xFF); // low byte
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bytes[i * 2 + 1] = (byte) ((samples[i] >> 8) & 0xFF); // high byte
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}
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return bytes;
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}
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public void playAudio(short[] samples, int sampleRate) throws IOException, InterruptedException {
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ProcessBuilder pb = new ProcessBuilder(
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"pw-play",
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"--rate=" + sampleRate,
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"--channels=1",
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"--format=s16",
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"--volume=0.3",
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"--raw",
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"-"
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);
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pb.redirectErrorStream(true);
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Process proc = pb.start();
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try (OutputStream os = proc.getOutputStream()) {
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os.write(shortsToBytes(samples));
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}
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proc.waitFor();
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}
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}
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}
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@@ -0,0 +1,100 @@
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package me.neurodock.glados;
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import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
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import com.fasterxml.jackson.databind.ObjectMapper;
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import java.io.BufferedReader;
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import java.io.IOException;
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import java.net.StandardProtocolFamily;
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import java.net.UnixDomainSocketAddress;
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import java.nio.channels.Channels;
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import java.nio.channels.ServerSocketChannel;
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import java.nio.channels.SocketChannel;
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import java.nio.charset.StandardCharsets;
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import java.nio.file.Files;
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import java.nio.file.Path;
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import java.util.concurrent.ExecutorService;
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import java.util.concurrent.Executors;
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import java.util.function.Consumer;
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public class WhisperSocketServer implements AutoCloseable {
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@JsonIgnoreProperties(ignoreUnknown = true)
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public record TranscriptionEvent(String type, String text, double timestampt, String language) {};
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private final Path socketPath;
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private final Consumer<TranscriptionEvent> onTranscription;
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private final ObjectMapper mapper = new ObjectMapper();
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private final ExecutorService executor = Executors.newCachedThreadPool();
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private ServerSocketChannel serverChannel;
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private volatile boolean running = true;
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public WhisperSocketServer(Path socketPath, Consumer<TranscriptionEvent> onTranscription) {
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this.socketPath = socketPath;
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this.onTranscription = onTranscription;
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}
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public void start() throws IOException {
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Files.deleteIfExists(socketPath);
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UnixDomainSocketAddress address = UnixDomainSocketAddress.of(socketPath);
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serverChannel = ServerSocketChannel.open(StandardProtocolFamily.UNIX);
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serverChannel.bind(address);
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System.out.println("[whisper-socket] listening on "+socketPath);
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executor.submit(this::acceptLoop);
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}
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private void acceptLoop() {
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while (running) {
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try {
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SocketChannel client = serverChannel.accept();
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System.out.println("[whisper-socket] python client connected");
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executor.submit(() -> handleClient(client));
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}catch(IOException e)
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{
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if(running)
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{
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System.err.println("[whisper-socket] accept failed: " + e.getMessage());
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}
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}
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}
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}
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private void handleClient(SocketChannel client) {
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try(BufferedReader reader = new BufferedReader(Channels.newReader(client, StandardCharsets.UTF_8))){
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String line;
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while((line = reader.readLine()) != null)
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{
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if(line.isBlank()) continue;
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try {
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TranscriptionEvent event = mapper.readValue(line, TranscriptionEvent.class);
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onTranscription.accept(event);
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} catch (Exception parseEx) {
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System.err.println("[whisper-socket] bad line: " + line + " (" + parseEx.getMessage() + ")");
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}
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}
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} catch (IOException e) {
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System.err.println("[whisper-socket] client read error: " + e.getMessage());
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} finally {
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System.out.println("[whisper-socket] python client disconnected");
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try {
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client.close();
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} catch (IOException ignored) {
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}
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}
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}
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@Override
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public void close() throws IOException {
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running = false;
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executor.shutdownNow();
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if (serverChannel != null) {
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serverChannel.close();
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}
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Files.deleteIfExists(socketPath);
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}
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}
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@@ -0,0 +1,193 @@
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#!/usr/bin/env python3
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"""
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whisper_daemon.py - Live microphone transcription via OpenAI Whisper (PyTorch/ROCm backend)
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Captures mic audio, segments it into utterances using WebRTC VAD (rather than
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blindly transcribing fixed windows), runs each finished segment through Whisper,
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and streams the resulting text as newline-delimited JSON over a Unix domain socket.
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Architecture assumption: this process is a CLIENT. It connects OUT to a Unix
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socket that the Java program is listening on (and will retry/reconnect if the
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Java side isn't up yet). Flip SocketClient -> a socketserver if you'd rather
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have Python own the socket and Java connect in.
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"""
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import argparse
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import json
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import queue
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import socket
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import sys
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import threading
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import time
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import numpy as np
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import sounddevice as sd
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import torch
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import webrtcvad
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import whisper
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SAMPLE_RATE = 16000
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FRAME_MS = 30 # webrtcvad only accepts 10/20/30ms frames
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FRAME_SAMPLES = int(SAMPLE_RATE * FRAME_MS / 1000)
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SILENCE_TIMEOUT_MS = 700 # trailing silence before an utterance is finalized
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MAX_SEGMENT_MS = 15000 # hard cap so long uninterrupted speech still flushes
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class SocketClient:
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"""Unix socket client with blocking connect-retry and reconnect-on-send-failure."""
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def __init__(self, sock_path: str, retry_interval: float = 2.0):
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self.sock_path = sock_path
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self.retry_interval = retry_interval
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self.sock = None
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self.lock = threading.Lock()
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self._connect()
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def _connect(self):
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while self.sock is None:
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try:
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s = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
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s.connect(self.sock_path)
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self.sock = s
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print(f"[socket] connected to {self.sock_path}", file=sys.stderr)
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except (FileNotFoundError, ConnectionRefusedError) as e:
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print(f"[socket] waiting for {self.sock_path} ({e})", file=sys.stderr)
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time.sleep(self.retry_interval)
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def send(self, obj: dict):
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data = (json.dumps(obj) + "\n").encode("utf-8")
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with self.lock:
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try:
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self.sock.sendall(data)
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except (BrokenPipeError, OSError) as e:
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print(f"[socket] send failed ({e}), reconnecting", file=sys.stderr)
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try:
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self.sock.close()
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except OSError:
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pass
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self.sock = None
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self._connect()
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self.sock.sendall(data)
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def frame_generator(audio_queue: "queue.Queue"):
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"""Re-chunk arbitrary-sized audio callback buffers into fixed VAD-sized frames."""
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buf = np.zeros((0,), dtype=np.int16)
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while True:
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chunk = audio_queue.get()
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if chunk is None:
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return
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buf = np.concatenate([buf, chunk])
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while len(buf) >= FRAME_SAMPLES:
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frame = buf[:FRAME_SAMPLES]
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buf = buf[FRAME_SAMPLES:]
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yield frame
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||||
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def vad_segmenter(audio_queue: "queue.Queue", vad_aggressiveness: int = 2):
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"""
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Consume raw frames, use WebRTC VAD to detect speech, and yield complete
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utterances (numpy int16 arrays) once trailing silence or max length hits.
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||||
"""
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vad = webrtcvad.Vad(vad_aggressiveness)
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voiced_frames = []
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silence_ms = 0
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||||
speech_ms = 0
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triggered = False
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||||
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for frame in frame_generator(audio_queue):
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is_speech = vad.is_speech(frame.tobytes(), SAMPLE_RATE)
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if not triggered:
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||||
if is_speech:
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triggered = True
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||||
voiced_frames = [frame]
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speech_ms = FRAME_MS
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||||
silence_ms = 0
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else:
|
||||
voiced_frames.append(frame)
|
||||
speech_ms += FRAME_MS
|
||||
if is_speech:
|
||||
silence_ms = 0
|
||||
else:
|
||||
silence_ms += FRAME_MS
|
||||
|
||||
if silence_ms >= SILENCE_TIMEOUT_MS or speech_ms >= MAX_SEGMENT_MS:
|
||||
segment = np.concatenate(voiced_frames)
|
||||
triggered = False
|
||||
voiced_frames = []
|
||||
silence_ms = 0
|
||||
speech_ms = 0
|
||||
yield segment
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Live Whisper transcription -> Unix socket")
|
||||
parser.add_argument("--socket", default="/tmp/whisper.sock",
|
||||
help="Unix socket path to connect to (Java side should be listening here)")
|
||||
parser.add_argument("--model", default="small",
|
||||
help="Whisper model size: tiny/base/small/medium/large-v3")
|
||||
parser.add_argument("--device", default=None,
|
||||
help="Force device string (cuda/cpu). Default: auto-detect via torch.cuda.is_available() "
|
||||
"(ROCm builds of torch report themselves as 'cuda').")
|
||||
parser.add_argument("--vad-aggressiveness", type=int, default=2, choices=[0, 1, 2, 3],
|
||||
help="0=least aggressive filtering (more false positives), 3=most aggressive")
|
||||
parser.add_argument("--language", default=None, help="Force language code e.g. 'en'. Default: auto-detect.")
|
||||
parser.add_argument("--input-device", type=int, default=None,
|
||||
help="sounddevice input device index, see --list-devices")
|
||||
parser.add_argument("--list-devices", action="store_true")
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.list_devices:
|
||||
print(sd.query_devices())
|
||||
return
|
||||
|
||||
device = args.device or ("cuda" if torch.cuda.is_available() else "cpu")
|
||||
print(f"[whisper] loading model '{args.model}' on device '{device}'", file=sys.stderr)
|
||||
model = whisper.load_model(args.model, device=device)
|
||||
|
||||
client = SocketClient(args.socket)
|
||||
audio_queue: "queue.Queue" = queue.Queue()
|
||||
|
||||
def audio_callback(indata, frames, time_info, status):
|
||||
if status:
|
||||
print(f"[audio] {status}", file=sys.stderr)
|
||||
audio_queue.put(indata[:, 0].copy())
|
||||
|
||||
stream = sd.InputStream(
|
||||
samplerate=SAMPLE_RATE,
|
||||
channels=1,
|
||||
dtype="int16",
|
||||
blocksize=FRAME_SAMPLES,
|
||||
device=args.input_device,
|
||||
callback=audio_callback,
|
||||
)
|
||||
|
||||
print("[whisper] listening... (Ctrl+C to stop)", file=sys.stderr)
|
||||
with stream:
|
||||
try:
|
||||
for segment in vad_segmenter(audio_queue, args.vad_aggressiveness):
|
||||
audio_f32 = segment.astype(np.float32) / 32768.0
|
||||
result = model.transcribe(
|
||||
audio_f32,
|
||||
language=args.language,
|
||||
fp16=(device != "cpu"),
|
||||
condition_on_previous_text=False,
|
||||
)
|
||||
text = result.get("text", "").strip()
|
||||
if text:
|
||||
payload = {
|
||||
"type": "transcript",
|
||||
"text": text,
|
||||
"timestamp": time.time(),
|
||||
"language": result.get("language"),
|
||||
}
|
||||
print(f"[transcript] {text}")
|
||||
client.send(payload)
|
||||
except KeyboardInterrupt:
|
||||
print("\n[whisper] stopping", file=sys.stderr)
|
||||
audio_queue.put(None)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user