Qwen3-TTS auf der DGX: Startskript, Deploy-Config, Node-Client
- dgx/start-qwen3-tts.sh startet CustomVoice (:8011) oder Base/Klon (:8012) via vllm-omni im Image vllm-node-030-omni - dgx/qwen3_tts_spark.yaml: V1-Runner (MRV2 scheitert am vLLM-dev-Build) und 0.08/0.06 Speicher statt 2x 0.3 - SpeechSynthesis_Qwen.js als Ersatz fuer das Azure-Skript: Stimmenauswahl inkl. Klonstimmen, <break>-Pausen, Musik, Ausgabe als WAV/G.722/MP3 Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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(async function () {
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"use strict";
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// Qwen3-TTS (vllm-omni auf der DGX) statt Azure. Gleicher Ablauf wie
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// SpeechSynthesis_SSML.js, fragt aber nacheinander ab:
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// 1. Stimme -> Klonstimmen aus voice_clone/ oder feste Qwen-Stimmen
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// 2. Text -> <break time="500ms"/> bzw. <break time="1s"/> fuer Pausen
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// (Qwen3-TTS kann kein SSML, andere Tags werden entfernt)
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// 3. Datei -> Endung bestimmt das Format (Enter = tts_asterisk.wav):
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// .wav WAV 8 kHz mono (Asterisk)
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// .g722 G.722 16 kHz roh (Asterisk)
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// .mp3 MP3 (WhatsApp, E-Mail)
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//
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// Aufruf (Musik wie bisher):
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// node SpeechSynthesis_Qwen.js -> background.wav, falls vorhanden
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// node SpeechSynthesis_Qwen.js meine-musik.mp3 -> diese Musik
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// node SpeechSynthesis_Qwen.js --no-music -> ohne Musik (auch: none, -n)
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//
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// Neue Klonstimme: Aufnahme (max. 30 s) als voice_clone/<name>.wav ablegen,
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// exaktes Transkript als voice_clone/<name>.txt daneben -> erscheint in der Liste.
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//
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// Ohne Rueckfragen (z. B. in Skripten, Text dann per stdin):
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// QWEN_TTS_VOICE=<name oder nummer> QWEN_TTS_OUT=<datei>
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// QWEN_TTS_REF_AUDIO=<aufnahme> [QWEN_TTS_REF_TEXT=<transkript.txt>] (Klon ausserhalb voice_clone/)
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// QWEN_TTS_INSTRUCT=<Stilanweisung, nur feste Stimmen> QWEN_TTS_URL=<api>
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const readline = require("readline");
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const { execFile } = require("child_process");
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const fs = require("fs");
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const path = require("path");
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const host = "http://10.146.97.103";
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const sampleRate = 24000;
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const rawFile = "tts_raw.wav";
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const arg = process.argv[2];
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const noMusic = ["--no-music", "none", "-n"].includes(arg);
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const musicFile = noMusic ? null : (arg || "background.wav");
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const musicVolume = 0.15;
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const introMs = 2000; // Musik-Vorlauf vor der Sprache
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// Klonstimmen: Audiodatei mit gleichnamiger .txt in voice_clone/ ("alex_ref.wav" -> "alex")
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const cloneDir = path.join(__dirname, "voice_clone");
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const txtFor = (f) => f.replace(/\.[^./]+$/, "") + ".txt";
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const clones = (fs.existsSync(cloneDir) ? fs.readdirSync(cloneDir) : [])
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.filter((f) => /\.(wav|mp3|m4a|ogg|flac)$/i.test(f) && fs.existsSync(path.join(cloneDir, txtFor(f))))
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.map((f) => ({ name: f.replace(/(_ref)?\.[^.]+$/, ""), file: path.join(cloneDir, f) }));
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// Feste Stimmen des CustomVoice-Modells (per QWEN_TTS_VOICE auch: ono_anna, sohee, eric, dylan)
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const builtins = ["serena", "vivian", "ryan", "aiden", "uncle_fu"];
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const voices = [...clones, ...builtins.map((name) => ({ name }))];
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function outArgs(file) {
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if (/\.g722$/i.test(file)) return ["-ac", "1", "-ar", "16000", "-c:a", "g722", "-f", "g722", file];
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if (/\.mp3$/i.test(file)) return ["-ac", "1", "-ar", "44100", "-c:a", "libmp3lame", "-b:a", "96k", file];
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return ["-acodec", "pcm_s16le", "-ar", "8000", "-ac", "1", file];
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}
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function ffmpeg(args) {
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return new Promise((resolve, reject) => {
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execFile("ffmpeg", ["-y", "-loglevel", "error", ...args], (error, stdout, stderr) => {
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if (error) reject(new Error(stderr || error.message));
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else resolve();
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});
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});
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}
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// "Hallo <break time="1s"/> Welt" -> [{text:"Hallo"}, {pauseMs:1000}, {text:"Welt"}]
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function parseInput(input) {
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const parts = [];
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const re = /<break\s+time\s*=\s*["']?([\d.]+)\s*(ms|s)["']?\s*\/?>/gi;
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let last = 0, m;
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const pushText = (t) => {
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t = t.replace(/<[^>]+>/g, " ").replace(/\s+/g, " ").trim();
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if (t) parts.push({ text: t });
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};
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while ((m = re.exec(input)) !== null) {
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pushText(input.slice(last, m.index));
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parts.push({ pauseMs: Math.round(parseFloat(m[1]) * (m[2].toLowerCase() === "s" ? 1000 : 1)) });
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last = re.lastIndex;
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}
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pushText(input.slice(last));
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return parts;
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}
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// Aufnahme (beliebiges Format) -> 24 kHz mono WAV als data-URL fuer ref_audio
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async function loadRefAudio(file) {
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const refWav = "tts_ref.wav";
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await ffmpeg(["-i", file, "-ac", "1", "-ar", String(sampleRate), refWav]);
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const dataUrl = "data:audio/wav;base64," + fs.readFileSync(refWav).toString("base64");
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fs.unlinkSync(refWav);
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return dataUrl;
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}
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// Zeilenweise lesen statt rl.question: funktioniert auch mit per Pipe uebergebenem Text
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const rl = readline.createInterface({ input: process.stdin, output: process.stdout });
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const lines = rl[Symbol.asyncIterator]();
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async function ask(question) {
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process.stdout.write(question);
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const { value, done } = await lines.next();
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return done ? "" : value.trim();
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}
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// 1. Stimme
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let voice;
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if (process.env.QWEN_TTS_REF_AUDIO) {
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voice = { name: path.basename(process.env.QWEN_TTS_REF_AUDIO), file: process.env.QWEN_TTS_REF_AUDIO };
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} else {
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let choice = process.env.QWEN_TTS_VOICE;
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if (!choice) {
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console.log("Stimmen:");
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voices.forEach((v, i) => console.log(` ${i + 1}) ${v.name}${v.file ? "" : " (feste Qwen-Stimme)"}`));
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choice = (await ask("Stimme [1]: ")) || "1";
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}
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const n = Number(choice);
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voice = Number.isInteger(n) ? voices[n - 1]
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: voices.find((v) => v.name === choice.toLowerCase()) || { name: choice.toLowerCase() };
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if (!voice) {
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console.error(`Ungueltige Auswahl: ${choice}`);
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rl.close();
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return;
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}
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}
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const refTextFile = voice.file && (process.env.QWEN_TTS_REF_TEXT ||
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[txtFor(voice.file), path.join(path.dirname(voice.file), "referenztext.txt")].find((f) => fs.existsSync(f)));
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const apiUrl = process.env.QWEN_TTS_URL || `${host}:${voice.file ? 8012 : 8011}/v1/audio/speech`;
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const instructions = process.env.QWEN_TTS_INSTRUCT || "";
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// 2. Text, 3. Datei
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const userInput = await ask("Text (<break time=\"500ms\"/> fuer Pausen):\n> ");
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const parts = parseInput(userInput);
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if (!parts.some((p) => p.text)) {
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console.error("Kein Text eingegeben.");
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rl.close();
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return;
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}
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const outFile = process.env.QWEN_TTS_OUT ||
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(await ask("Datei [tts_asterisk.wav] (.wav / .g722 / .mp3): ")) || "tts_asterisk.wav";
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rl.close();
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let clone = null; // { audio, text } im Klon-Modus
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async function synthesize(text, file) {
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const body = { input: text, language: "German", response_format: "wav", sample_rate: sampleRate };
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if (clone) {
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// Base-Modell kennt keine instructions
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Object.assign(body, { task_type: "Base", ref_audio: clone.audio, ref_text: clone.text });
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} else {
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body.voice = voice.name;
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if (instructions) body.instructions = instructions;
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}
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const res = await fetch(apiUrl, {
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method: "POST",
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify(body)
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});
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if (!res.ok) throw new Error(`HTTP ${res.status}: ${await res.text()}`);
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fs.writeFileSync(file, Buffer.from(await res.arrayBuffer()));
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}
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const started = Date.now();
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console.log(`Synthesizing (Qwen3-TTS, ${voice.file ? `Klon: ${voice.name}` : `Stimme: ${voice.name}`})...`);
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const segFiles = [];
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const ffArgs = [];
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const labels = [];
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try {
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if (voice.file) {
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if (!refTextFile) throw new Error(`Kein Transkript zu ${voice.file} gefunden (.txt daneben oder QWEN_TTS_REF_TEXT)`);
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clone = {
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audio: await loadRefAudio(voice.file),
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text: fs.readFileSync(refTextFile, "utf8").trim()
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};
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}
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for (const p of parts) {
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if (p.text) {
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const f = `tts_seg_${segFiles.length}.wav`;
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await synthesize(p.text, f);
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segFiles.push(f);
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ffArgs.push("-i", f);
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} else {
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ffArgs.push("-f", "lavfi", "-t", String(p.pauseMs / 1000),
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"-i", `anullsrc=r=${sampleRate}:cl=mono`);
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}
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labels.push(`[${labels.length}:a]`);
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}
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await ffmpeg([...ffArgs, "-filter_complex",
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`${labels.join("")}concat=n=${labels.length}:v=0:a=1`, rawFile]);
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} catch (err) {
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console.error("TTS failed:", err.message);
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return;
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} finally {
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segFiles.forEach((f) => fs.existsSync(f) && fs.unlinkSync(f));
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}
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const hasMusic = !noMusic && fs.existsSync(musicFile);
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if (!noMusic && !hasMusic) {
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console.warn(`Hinweis: "${musicFile}" nicht gefunden -> ohne Musik.`);
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}
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console.log(
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"Qwen3-TTS done → converting" +
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(hasMusic ? ` (mit Musik: ${musicFile})...` : " (ohne Musik)...")
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);
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try {
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if (hasMusic) {
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await ffmpeg(["-i", rawFile, "-stream_loop", "-1", "-i", musicFile, "-filter_complex",
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`[0:a]adelay=${introMs}:all=1[sp];` +
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`[1:a]volume=${musicVolume}[bg];` +
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`[sp][bg]amix=inputs=2:duration=first:dropout_transition=0,` +
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`areverse,afade=t=in:d=2,areverse`,
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...outArgs(outFile)]);
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} else {
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await ffmpeg(["-i", rawFile, ...outArgs(outFile)]);
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}
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} catch (err) {
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console.error("FFmpeg error:", err.message);
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return;
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}
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console.log(`Fertig: ${outFile} (${((Date.now() - started) / 1000).toFixed(1)} s)`);
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fs.unlinkSync(rawFile);
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})();
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@@ -0,0 +1,163 @@
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# DGX-Spark-Variante von vllm_omni/deploy/qwen3_tts.yaml (vllm-omni 0.30.0):
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# * model_runner v1 statt v2 -- MRV2 importiert RoutedExpertsTensors, das im
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# vLLM-dev-Build des Images (vllm-node-030-omni) fehlt -> ImportError.
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# * gpu_memory_utilization 0.08 / 0.06 statt 2x 0.3 (Unified Memory ist geteilt).
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# * max_num_seqs 8 statt 64.
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#
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# Qwen3-TTS deploy: talker → code2wav via shared-memory chunk streaming.
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# Verified on 1x H100.
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#
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# Runner selection:
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# Model Runner V2 (MRV2) is EXPERIMENTAL and is the default runner on CUDA
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# for this model. Set `model_runner: v1` to opt out. The `platforms:`
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# sections at the end of this file keep V1 on NPU, XPU, ROCm and MUSA.
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# See docs/configuration/stage_configs.md ("Qwen3-TTS with Model Runner V2").
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#
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# Fields omitted from a stage fall back to StageDeployConfig defaults (see
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# vllm_omni/config/stage_config.py). Notable choices for this model:
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# * Stage 0 (talker) sets max_num_batched_tokens=512 for async-chunk
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# latency tuning (not correctness) — small per-step batches keep
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# first-chunk latency low, and the MRV2 profiles rely on that bound.
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# * Stage 1 (code2wav) sets max_num_batched_tokens=65536 for correctness:
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# codec prefill length (Q * num_frames) exceeds the 32k default.
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# * Stage 0 does not set enforce_eager — talker runs cudagraph by default.
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# Stage 1 defaults to false so Code2Wav can use its inner cudagraph;
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# set true to disable that path in eager mode.
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model_runner: v1
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async_chunk: true
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connectors:
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connector_of_shared_memory:
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name: SharedMemoryConnector
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extra:
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codec_streaming: true
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connector_get_sleep_s: 0.01
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connector_get_max_wait_first_chunk: 3000
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connector_get_max_wait: 300
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# Must match the decoder sliding attention window.
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codec_chunk_frames: 25
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codec_left_context_frames: 72
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# Emit only the first audio chunk early, then return to codec_chunk_frames.
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initial_codec_chunk_frames: 1
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# Chunk ramp (opt-in): gradual size increase for first N chunks to
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# eliminate the IC→steady cliff (1→25). Uncomment to enable.
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# Each entry is the size of that chunk index; indices past the table
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# fall back to codec_chunk_frames. When enabled, ramp overrides
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# initial_codec_chunk_frames (including the per-request API field).
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# Example:
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# codec_chunk_ramp: [4, 4, 8, 16, 25]
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# Adaptive chunk ramp (opt-in, takes precedence over fixed codec_chunk_ramp):
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# Chunk 0 uses dynamic IC; chunk 1+ sizes from live buffer-depth feedback.
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# Self-tunes across hardware (NPU/NVIDIA) and load conditions.
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# codec_chunk_adaptive: true
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# codec_chunk_min_frames: 2
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# Code2Wav decode + transfer margin
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# codec_chunk_safety_margin_ms: 50
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# EWMA->delta divisor (delta=max(delta_min, int(ewma/divisor))); higher=gentler ramp
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# codec_chunk_ramp_divisor: 15
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# min chunk-size growth per emit
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# codec_chunk_ramp_delta_min: 2
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# Decoder CUDA Graph sizing:
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# * async_chunk=true derives all stateful ICL/xvec shapes from
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# codec_chunk_ramp when enabled, otherwise from
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# initial_codec_chunk_frames, codec_chunk_frames, and sliding_window;
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# decode_cudagraph_capture_sizes is ignored in this mode.
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# * async_chunk=false captures stateless shapes 150 and
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# decode_chunk_frames + decode_left_context_frames (325 by default).
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# Extra stateless shapes may be added (union, not intersection) with:
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# decode_cudagraph_capture_sizes: [97, 400]
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# Graph batch size defaults to 1. Add larger buckets only when needed:
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# decode_cudagraph_batch_sizes: [1, 2, 4]
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# Limit eager batches to reduce temporary stateful cache allocations; CUDA Graph batching uses the buckets above.
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decode_batch_max_size: 4
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stages:
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- stage_id: 0
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max_num_seqs: 8
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gpu_memory_utilization: 0.08
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trust_remote_code: true
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# Omni prefix cache (reuse of talker codec rows on shared prefixes). Off
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# by default; set true when text repeats across requests (same voice or
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# shared ref_audio). The talker opts out of hidden-state caching and
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# defers codes.audio writes, so per-step cost is small. Stage 1 (codec
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# decoder) is not a supported prefix-cache stage: keep it false there.
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# See docs/design/feature/prefix_caching.md for the stage matrix.
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enable_prefix_caching: false
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async_scheduling: true
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# Bound per-step prefill work (validated MRV2 basic profile). Small
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# per-step batches keep first-chunk latency low.
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max_num_batched_tokens: 512
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max_model_len: 4096
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devices: "0"
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output_connectors:
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to_stage_1: connector_of_shared_memory
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default_sampling_params:
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temperature: 0.9
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top_k: 50
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max_tokens: 4096
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# Official qwen-tts generates the talker with min_new_tokens=2; without
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# it, checkpoints that sample codec EOS on the first frame return empty
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# audio (HTTP 400). See #4962.
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min_tokens: 2
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repetition_penalty: 1.05
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# Suppress codebook-0 silence tokens for the first N decode frames of a
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# request (#4966). Applies to every request on this stage, so intended
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# for x-vector-only (Base voice-clone) deploys. 0 = off.
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silence_ban_frames: 0
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subtalker_sampling_params:
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do_sample: true
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temperature: 0.9
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top_k: 50
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top_p: 1.0
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- stage_id: 1
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# Decoder compute dtype on GPU/NPU; 310P requires FP16 and overrides this.
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dtype: bfloat16
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# Tuned for Base voice clone; CustomVoice / VoiceDesign are TTFA-optimal at 1.
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max_num_seqs: 8
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gpu_memory_utilization: 0.06
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enforce_eager: false
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trust_remote_code: true
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enable_prefix_caching: false
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async_scheduling: true
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# Must be divisible by num_code_groups and cover (left_context + chunk).
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# Prefill length is Q * num_frames (e.g. 16 * 2148 = 34368); keep
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# headroom past 32k.
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max_num_batched_tokens: 65536
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# async_chunk appends windows per step; max_model_len must cover the
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# accumulated flat codec stream.
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max_model_len: 65536
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devices: "0"
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input_connectors:
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from_stage_0: connector_of_shared_memory
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default_sampling_params:
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temperature: 0.0
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top_p: 1.0
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top_k: -1
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max_tokens: 65536
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repetition_penalty: 1.0
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platforms:
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rocm:
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# MRV2 is not qualified on ROCm; keep the legacy V1 runner.
|
||||
model_runner: v1
|
||||
stages:
|
||||
- stage_id: 1
|
||||
# MIOpen Conv1d cannot be invoked while vLLM captures the outer
|
||||
# Code2Wav model graph. Keep CUDA's graph-enabled default, but run
|
||||
# Stage 1 eager on ROCm so decoder convolutions execute normally.
|
||||
enforce_eager: true
|
||||
npu:
|
||||
model_runner: v1
|
||||
stages:
|
||||
- stage_id: 0
|
||||
enforce_eager: false
|
||||
compilation_config:
|
||||
cudagraph_mode: PIECEWISE
|
||||
- stage_id: 1
|
||||
compilation_config:
|
||||
cudagraph_mode: PIECEWISE
|
||||
xpu:
|
||||
model_runner: v1
|
||||
musa:
|
||||
model_runner: v1
|
||||
Executable
+34
@@ -0,0 +1,34 @@
|
||||
#!/bin/bash
|
||||
# Qwen3-TTS 1.7B auf der DGX Spark (admin@10.146.97.103) via vllm-omni.
|
||||
# OpenAI-kompatibel: POST http://10.146.97.103:<port>/v1/audio/speech
|
||||
#
|
||||
# ./start-qwen3-tts.sh -> CustomVoice (9 feste Stimmen) :8011
|
||||
# ./start-qwen3-tts.sh base -> Base (Voice-Cloning, ref_audio) :8012
|
||||
#
|
||||
# Liegt auf der DGX unter ~/qwen3-tts/ zusammen mit qwen3_tts_spark.yaml
|
||||
# (eigene Deploy-Config: V1-Runner + 0.08/0.06 Speicher, siehe Kopf der YAML).
|
||||
set -eu
|
||||
CFG_DIR=/home/admin/qwen3-tts
|
||||
IMAGE=vllm-node-030-omni # vllm-omni 0.30.0 + librosa/soundfile, bringt qwen3_tts mit
|
||||
|
||||
case "${1:-customvoice}" in
|
||||
customvoice) NAME=qwen3-tts-customvoice; PORT=8011; MODEL=Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice ;;
|
||||
base) NAME=qwen3-tts-base; PORT=8012; MODEL=Qwen/Qwen3-TTS-12Hz-1.7B-Base ;;
|
||||
*) echo "Aufruf: $0 [customvoice|base]" >&2; exit 1 ;;
|
||||
esac
|
||||
|
||||
if docker ps --format '{{.Names}}' | grep -qx "$NAME"; then
|
||||
echo "$NAME laeuft bereits"; exit 0
|
||||
fi
|
||||
docker rm "$NAME" >/dev/null 2>&1 || true
|
||||
|
||||
docker run -d --name "$NAME" --network host --ipc host --gpus all \
|
||||
-v /home/admin/.cache/huggingface:/root/.cache/huggingface \
|
||||
-v "$CFG_DIR":/cfg:ro \
|
||||
-e VLLM_DISABLE_COMPILE_CACHE=1 \
|
||||
-e HF_HUB_OFFLINE=1 \
|
||||
"$IMAGE" \
|
||||
vllm-omni serve "$MODEL" --omni --port "$PORT" \
|
||||
--deploy-config /cfg/qwen3_tts_spark.yaml
|
||||
|
||||
echo "gestartet: docker logs -f $NAME"
|
||||
Reference in new issue
Block a user