Use qwen3-asr throughout speech integrations

ATHENA.md

@@ -95,8 +95,8 @@ nicht direkt. Kein automatischer Host-Neustart ist vorgesehen.
 - Qwen-Image-2.1 INT8: Bildgenerierung und Editing auf der RTX 5080; das Textmodell wird dafür kurz entladen und danach
   automatisch wiederhergestellt
 - Qwen3-TTS 1.7B: RTX 3060; kein Piper-Fallback
-- Qwen3-ASR 0.6B Q8: CPU, hinter dem bestehenden OpenAI-kompatiblen
+- Qwen3-ASR 0.6B Q8: CPU, hinter dem OpenAI-kompatiblen
-  Transkriptionsendpunkt. `whisper-1` bleibt nur als API-Kompatibilitätsname.
+  Transkriptionsendpunkt mit dem Modellnamen `qwen3-asr`.
 - EmbeddingGemma 300M Q8: CPU, OpenAI-kompatibel auf Port 8082; kein GPU-Zugriff
 
 Die geprüften Live-Werte stehen in [docs/LIVE_STATE.md](docs/LIVE_STATE.md).

README.md

@@ -145,8 +145,8 @@ Der Router stellt Sprache OpenAI-kompatibel bereit: Sprachausgabe über
 `/v1/audio/speech` und Spracherkennung über `/v1/audio/transcriptions`.
 Spracherkennung nutzt Qwen3-ASR-0.6B Q8 auf der CPU; das Modell liegt unter
 `/data/models/qwen3-asr-0.6b-q8`. Der Adapter liefert reinen Text unter
-`qwen3-asr` und dem bisherigen `whisper-1`-Kompatibilitätsnamen, sodass
+`qwen3-asr`; OpenClaw und die Voice-Brücke verwenden denselben Modellnamen.
-OpenClaw nicht neu konfiguriert werden muss. Whisper-Container, Image und
+Whisper-Container, Image und
 Modellvolume sind entfernt. Audiodaten werden lokal auf Athena verarbeitet.
 Für OpenClaw Talk liegt der lokale
 Realtime-Provider unter

dev/mock_stt_worker.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """Mock-STT-Worker für lokale Tests.
 
-Simuliert den Whisper-STT-Worker:
+Simuliert den Qwen3-ASR-Adapter:
   GET  /status      → ready: true
   POST /transcribe  → liefert festes Transkript
 

dev/test_chunked.py

@@ -220,7 +220,7 @@ def main() -> None:
     # Test 1: Multipart + Content-Length (bestehender Pfad)
     # ------------------------------------------------------------------
     print("Test 1: Multipart + Content-Length")
-    mp = build_multipart({"model": "whisper-1", "language": "de"},
+    mp = build_multipart({"model": "qwen3-asr", "language": "de"},
                          file_data=fake_webm)
     status, body = http_request(
         "POST", PORTS["router"], "/v1/audio/transcriptions",
@@ -235,7 +235,7 @@ def main() -> None:
     # Test 2: Multipart + Transfer-Encoding chunked (einfach)
     # ------------------------------------------------------------------
     print("Test 2: Multipart + chunked (einfach)")
-    mp = build_multipart({"model": "whisper-1"}, file_data=fake_webm)
+    mp = build_multipart({"model": "qwen3-asr"}, file_data=fake_webm)
     chunked = build_chunked_body([mp])
     raw = (
         b"POST /v1/audio/transcriptions HTTP/1.1\r\n"
@@ -254,7 +254,7 @@ def main() -> None:
     # Test 3: Mehrere unterschiedlich große Chunks
     # ------------------------------------------------------------------
     print("Test 3: Mehrere unterschiedlich große Chunks")
-    mp = build_multipart({"model": "whisper-1"}, file_data=fake_webm)
+    mp = build_multipart({"model": "qwen3-asr"}, file_data=fake_webm)
     # In 5 Chunks aufteilen (unterschiedlich groß)
     chunks = []
     sizes = [10, 50, 7, 100, 33]
@@ -283,7 +283,7 @@ def main() -> None:
     # Test 4: Boundary über Chunk-Grenzen verteilt
     # ------------------------------------------------------------------
     print("Test 4: Boundary über Chunk-Grenzen verteilt")
-    mp = build_multipart({"model": "whisper-1"}, file_data=fake_webm)
+    mp = build_multipart({"model": "qwen3-asr"}, file_data=fake_webm)
     # Boundary-String finden und Chunk-Grenze genau dorthin setzen
     boundary_str = b"--testboundary123"
     idx = mp.find(boundary_str, 10)  # zweite Boundary (vor file)
@@ -310,7 +310,7 @@ def main() -> None:
     # Test 5: Chunk Extensions
     # ------------------------------------------------------------------
     print("Test 5: Chunk Extensions")
-    mp = build_multipart({"model": "whisper-1"}, file_data=fake_webm)
+    mp = build_multipart({"model": "qwen3-asr"}, file_data=fake_webm)
     chunks_ext = [
         (mp[:20], "ext1=value1"),
         (mp[20:60], None),
@@ -335,7 +335,7 @@ def main() -> None:
     #         hier explizit mit Trailer)
     # ------------------------------------------------------------------
     print("Test 6: 0-Chunk mit Trailer")
-    mp = build_multipart({"model": "whisper-1"}, file_data=fake_webm)
+    mp = build_multipart({"model": "qwen3-asr"}, file_data=fake_webm)
     chunked = build_chunked_body([mp])
     # Trailer hinzufügen
     chunked_with_trailer = chunked.replace(
@@ -376,7 +376,7 @@ def main() -> None:
     print("Test 8: Uploadgrößenlimit")
     # MAX_UPLOAD_SIZE = 1 MB, also 2 MB senden
     big_data = b"A" * (2 * 1024 * 1024)
-    mp = build_multipart({"model": "whisper-1"}, file_data=big_data)
+    mp = build_multipart({"model": "qwen3-asr"}, file_data=big_data)
     chunked = build_chunked_body([mp[:1024 * 1024], mp[1024 * 1024:]])
     raw = (
         b"POST /v1/audio/transcriptions HTTP/1.1\r\n"
@@ -402,7 +402,7 @@ def main() -> None:
                 + b"WEBM_OPUS_AUDIO_DATA" * 100)
     boundary = "950bd961b24c4a32801e31b128c85e09"
     mp = build_multipart(
-        {"model": "whisper-1", "language": "de"},
+        {"model": "qwen3-asr", "language": "de"},
         file_data=webm_data,
         filename="recording.webm",
         boundary=boundary,

dev/test_multipart.py

@@ -71,13 +71,13 @@ def test_quoted_boundary():
     boundary = "----WebKitFormBoundary7MA4YWxkTrZu0gW"
     webm = b"\x1a\x45\xdf\xa3" + b"\x00\x01\x02\x03\xff\xfe\xfd" * 50
     body, ct = build(
-        [("model", "whisper-1", None), ("file", webm, "t.webm")],
+        [("model", "qwen3-asr", None), ("file", webm, "t.webm")],
         boundary, quoted=True,
     )
     fd, fn, fl = parse_multipart(body, ct)
     assert fd == webm, "file_data mismatch"
     assert fn == "t.webm", f"filename mismatch: {fn!r}"
-    assert fl["model"] == "whisper-1", f"model mismatch: {fl!r}"
+    assert fl["model"] == "qwen3-asr", f"model mismatch: {fl!r}"
     print("  quoted boundary: OK")
 
 
@@ -109,7 +109,7 @@ def test_openwebui_style():
         f"\r\n--{boundary}\r\n"
         f'Content-Disposition: form-data; name="model"\r\n'
         f"\r\n"
-        f"whisper-1\r\n"
+        f"qwen3-asr\r\n"
         f"--{boundary}\r\n"
         f'Content-Disposition: form-data; name="temperature"\r\n'
         f"\r\n"
@@ -119,7 +119,7 @@ def test_openwebui_style():
     fd, fn, fl = parse_multipart(body, ct)
     assert fd == webm, "file_data mismatch"
     assert fn == "rec.webm", f"filename mismatch: {fn!r}"
-    assert fl["model"] == "whisper-1", f"model mismatch: {fl!r}"
+    assert fl["model"] == "qwen3-asr", f"model mismatch: {fl!r}"
     assert fl["temperature"] == "0.0", f"temperature mismatch: {fl!r}"
     print("  Open-WebUI-artig: OK")
 
@@ -128,13 +128,13 @@ def test_file_before_model():
     """File-Feld vor model-Feld."""
     boundary = "boundary123"
     body, ct = build(
-        [("file", b"DATA", "f.wav"), ("model", "whisper-1", None)],
+        [("file", b"DATA", "f.wav"), ("model", "qwen3-asr", None)],
         boundary, quoted=False,
     )
     fd, fn, fl = parse_multipart(body, ct)
     assert fd == b"DATA", "file_data mismatch"
     assert fn == "f.wav", f"filename mismatch: {fn!r}"
-    assert fl["model"] == "whisper-1", f"model mismatch: {fl!r}"
+    assert fl["model"] == "qwen3-asr", f"model mismatch: {fl!r}"
     print("  File vor model: OK")
 
 
@@ -142,13 +142,13 @@ def test_file_after_model():
     """model-Feld vor File-Feld."""
     boundary = "boundary456"
     body, ct = build(
-        [("model", "whisper-1", None), ("file", b"DATA", "g.wav")],
+        [("model", "qwen3-asr", None), ("file", b"DATA", "g.wav")],
         boundary, quoted=False,
     )
     fd, fn, fl = parse_multipart(body, ct)
     assert fd == b"DATA", "file_data mismatch"
     assert fn == "g.wav", f"filename mismatch: {fn!r}"
-    assert fl["model"] == "whisper-1", f"model mismatch: {fl!r}"
+    assert fl["model"] == "qwen3-asr", f"model mismatch: {fl!r}"
     print("  File nach model: OK")
 
 
@@ -182,13 +182,13 @@ def test_extra_headers_ignored():
         f"\r\n--{boundary}\r\n"
         f'Content-Disposition: form-data; name="model"\r\n'
         f"\r\n"
-        f"whisper-1\r\n"
+        f"qwen3-asr\r\n"
         f"--{boundary}--\r\n"
     ).encode()
     fd, fn, fl = parse_multipart(body, ct)
     assert fd == webm, "file_data mismatch"
     assert fn == "x.webm", f"filename mismatch: {fn!r}"
-    assert fl["model"] == "whisper-1", f"model mismatch: {fl!r}"
+    assert fl["model"] == "qwen3-asr", f"model mismatch: {fl!r}"
     print("  Extra-Header ignoriert: OK")
 
 
@@ -198,7 +198,7 @@ def test_all_fields():
     body, ct = build(
         [
             ("file", b"AUDIO", "a.webm"),
-            ("model", "whisper-1", None),
+            ("model", "qwen3-asr", None),
             ("language", "de", None),
             ("prompt", "Kontext", None),
             ("response_format", "verbose_json", None),
@@ -209,7 +209,7 @@ def test_all_fields():
     fd, fn, fl = parse_multipart(body, ct)
     assert fd == b"AUDIO", "file_data mismatch"
     assert fn == "a.webm", f"filename mismatch: {fn!r}"
-    assert fl["model"] == "whisper-1", f"model mismatch: {fl!r}"
+    assert fl["model"] == "qwen3-asr", f"model mismatch: {fl!r}"
     assert fl["language"] == "de", f"language mismatch: {fl!r}"
     assert fl["prompt"] == "Kontext", f"prompt mismatch: {fl!r}"
     assert fl["response_format"] == "verbose_json", f"response_format mismatch: {fl!r}"

docs/CONTAINER_INVENTORY.md

@@ -39,7 +39,7 @@ nicht automatisch ein ungenutzter Rest.
 | `mike-ai-tts-gateway` | kein eigenes Modell | Normalisiert Text, konvertiert Ausgabeformate und stellt Qwen3-TTS sowie natives PCM-Streaming über eine stabile interne API bereit. |
 | `mike-ai-voice-studio` | `k2-fsa/OmniVoice` 0.2.1 mit Whisper-ASR | Erzeugt Text-to-Speech mit einer Referenzstimme; kein Audio-to-Audio-Voice-Changer. |
 | `mike-ai-qwen-asr` | Qwen3-ASR 0.6B Q8 | CPU-Inferenz für lokale deutsche Spracherkennung. |
-| `mike-ai-qwen-asr-worker` | kein eigenes Modell | Audio-Adapter für `/v1/audio/transcriptions`; `whisper-1` ist nur ein API-Kompatibilitätsname. |
+| `mike-ai-qwen-asr-worker` | kein eigenes Modell | Audio-Adapter für `/v1/audio/transcriptions` mit dem Modellnamen `qwen3-asr`. |
 | `mike-ai-wireguard-gateway` | kein Modell | Veröffentlicht Dashboard und Fachoberflächen ausschließlich über den privaten WireGuard-Pfad. |
 | `mike-ai-xvc-studio` | `chenxie95/X-VC`, GLM-4-Voice-Tokenizer und optional Resemble Enhance | Wandelt eine vorhandene Sprachaufnahme anhand einer Referenzstimme in Audio zu Audio um; gibt das native 16-kHz-Ergebnis und optional eine neural restaurierte 44,1-kHz-Fassung aus. |
 | `mike-ai-yue2-playground` | Image `mike-ai/yue2:3b-0.1.6` | Vorhandener, gestoppter Playground; in diesem Abgleich nicht funktional getestet. |

docs/LIVE_STATE.md

@@ -2,9 +2,9 @@
 
 Nachtrag vom 25. September 2026: Die produktive Spracherkennung läuft über
 Qwen3-ASR 0.6B Q8 auf der CPU (`mike-ai-qwen-asr` und
-`mike-ai-qwen-asr-worker`). Der Router bietet weiterhin `whisper-1` als
+`mike-ai-qwen-asr-worker`). Der Router bietet nur `qwen3-asr` als STT-Modell
-Kompatibilitätsnamen und zusätzlich `qwen3-asr` an. Beide wurden mit einer
+an; OpenClaw und die Voice-Brücke verwenden denselben Namen. Eine M4A-Aufnahme
-M4A-Aufnahme über `/v1/audio/transcriptions` geprüft. Whisper-Container,
+wurde über `/v1/audio/transcriptions` geprüft. Whisper-Container,
 Images und Modellvolume wurden entfernt. Das aktive Ultra-Profil wurde bei
 dieser Umstellung nicht gewechselt. Die Angaben zu Whisper weiter unten
 beschreiben den historischen Stand vom 21. September.

integrations/openclaw-athena-talk/README.md

@@ -60,7 +60,8 @@ openclaw plugins install . --force --accept-capabilities
 openclaw plugins inspect athena-talk --runtime --json
 ```
 
-The plugin registers **Athena Qwen3-ASR (Diktieren)** as a separate
+Version 1.3.1 sends `qwen3-asr` throughout dictation and Talk. The plugin
+registers **Athena Qwen3-ASR (Diktieren)** as a separate
 realtime transcription provider through OpenClaw's official plugin API. In the
 browser composer, hold the microphone for dictation, then release it to send
 the 8 kHz G.711 audio through the Gateway. Short recordings are converted to
@@ -95,8 +96,7 @@ An M4A voice note uploaded as a chat attachment does **not** use the realtime
 dictation provider above. OpenClaw processes it through its built-in
 `tools.media.audio` path. On the Unraid installation, automatic provider
 selection hit `SsrFBlockedError` for the private Athena address. Configure the
-existing OpenAI-compatible provider and retain the `whisper-1` API alias for
+existing OpenAI-compatible provider with the `qwen3-asr` model:
-Qwen3-ASR:
 
 ```json5
 {
@@ -113,7 +113,7 @@ Qwen3-ASR:
       models: [
         {
           provider: "openai",
-          model: "whisper-1",
+          model: "qwen3-asr",
           baseUrl: "http://192.168.1.212:8081/v1",
           capabilities: ["audio"],
         },

integrations/openclaw-athena-talk/dist/index.js

@@ -262,7 +262,7 @@ class AthenaTranscriptionSession {
     async transcribe(audio, prompt) {
         const form = new FormData();
         form.append("file", new Blob([Uint8Array.from(wavFromMulaw8k(audio))], { type: "audio/wav" }), "dictation.wav");
-        form.append("model", "whisper-1");
+        form.append("model", "qwen3-asr");
         form.append("language", this.config.language);
         if (prompt)
             form.append("prompt", prompt);
@@ -471,7 +471,7 @@ class AthenaTalkBridge {
         const form = new FormData();
         const wavBytes = Uint8Array.from(wavFromPcm16(pcm));
         form.append("file", new Blob([wavBytes], { type: "audio/wav" }), "talk.wav");
-        form.append("model", "whisper-1");
+        form.append("model", "qwen3-asr");
         form.append("language", this.cfg.language);
         const response = await fetch(`${this.cfg.baseUrl}/audio/transcriptions`, {
             method: "POST",
@@ -568,8 +568,8 @@ export default definePluginEntry({
         api.registerRealtimeTranscriptionProvider({
             id: "athena-talk",
             label: "Athena Qwen3-ASR (Diktieren)",
-            defaultModel: "whisper-1",
+            defaultModel: "qwen3-asr",
-            models: ["whisper-1"],
+            models: ["qwen3-asr"],
             autoSelectOrder: 1,
             resolveConfig: ({ cfg, rawConfig }) => resolveTranscriptionConfig(cfg, rawConfig),
             isConfigured: ({ providerConfig }) => Boolean(record(providerConfig).baseUrl),

integrations/openclaw-athena-talk/index.ts

@@ -289,7 +289,7 @@ class AthenaTranscriptionSession {
   private async transcribe(audio: Buffer, prompt: string): Promise<string> {
     const form = new FormData();
     form.append("file", new Blob([Uint8Array.from(wavFromMulaw8k(audio))], { type: "audio/wav" }), "dictation.wav");
-    form.append("model", "whisper-1");
+    form.append("model", "qwen3-asr");
     form.append("language", this.config.language);
     if (prompt) form.append("prompt", prompt);
     const response = await fetch(`${this.config.baseUrl}/audio/transcriptions`, {
@@ -490,7 +490,7 @@ class AthenaTalkBridge {
     const form = new FormData();
     const wavBytes = Uint8Array.from(wavFromPcm16(pcm));
     form.append("file", new Blob([wavBytes], { type: "audio/wav" }), "talk.wav");
-    form.append("model", "whisper-1");
+    form.append("model", "qwen3-asr");
     form.append("language", this.cfg.language);
     const response = await fetch(`${this.cfg.baseUrl}/audio/transcriptions`, {
       method: "POST",
@@ -579,8 +579,8 @@ export default definePluginEntry({
     api.registerRealtimeTranscriptionProvider({
       id: "athena-talk",
       label: "Athena Qwen3-ASR (Diktieren)",
-      defaultModel: "whisper-1",
+      defaultModel: "qwen3-asr",
-      models: ["whisper-1"],
+      models: ["qwen3-asr"],
       autoSelectOrder: 1,
       resolveConfig: ({ cfg, rawConfig }) => resolveTranscriptionConfig(cfg, rawConfig),
       isConfigured: ({ providerConfig }) => Boolean(record(providerConfig).baseUrl),

integrations/openclaw-athena-talk/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "@casaderoll/openclaw-athena-talk",
-  "version": "1.3.0",
+  "version": "1.3.1",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "@casaderoll/openclaw-athena-talk",
-      "version": "1.3.0",
+      "version": "1.3.1",
       "devDependencies": {
         "@types/node": "^24.0.0",
         "openclaw": "2026.9.4",

integrations/openclaw-athena-talk/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@casaderoll/openclaw-athena-talk",
-  "version": "1.3.0",
+  "version": "1.3.1",
   "private": true,
   "description": "Local OpenClaw Talk provider backed by Athena Qwen3-ASR and Qwen3-TTS",
   "type": "module",

integrations/openclaw-athena-talk/test-transcription.mjs

@@ -11,7 +11,7 @@ async function waitFor(predicate, timeoutMs = 2000) {
   }
 }
 
-test("dictation registers separately and sends G.711 audio to Athena Whisper", async () => {
+test("dictation registers separately and sends G.711 audio to Athena Qwen3-ASR", async () => {
   let transcription;
   plugin.register({
     registerRealtimeTranscriptionProvider: (value) => { transcription = value; },
@@ -37,7 +37,7 @@ test("dictation registers separately and sends G.711 audio to Athena Whisper", a
     assert.equal(wav.readUInt16LE(34), 16);
     assert.equal(wav.length, 48);
     assert.equal(form.get("language"), "de");
-    assert.equal(form.get("model"), "whisper-1");
+    assert.equal(form.get("model"), "qwen3-asr");
     res.writeHead(200, { "Content-Type": "application/json" }).end(JSON.stringify({ text: "Hallo Athena" }));
   });
   await new Promise((resolve) => server.listen(0, "127.0.0.1", resolve));

platform/hermes/config.yaml

@@ -133,8 +133,8 @@ display:
   language: "de"
   show_reasoning: false
 
-# Speech input stays local and private. A fixed German hint avoids Whisper
+# Speech input stays local and private. The fixed German hint helps with
-# interpreting short utterances as English while retaining the fast base model.
+# short utterances and product names.
 stt:
   enabled: true
   provider: "local"

router/ai_profile_router.py

@@ -219,7 +219,7 @@ TTS_DEFAULT_FORMAT = "mp3"
 STT_WORKER_URL = os.environ.get("STT_WORKER_URL", "http://127.0.0.1:8084")
 STT_TIMEOUT = float(os.environ.get("STT_TIMEOUT", "120"))  # s, pro Transkription
 STT_CONNECT_TIMEOUT = float(os.environ.get("STT_CONNECT_TIMEOUT", "5"))
-STT_MODEL = "whisper-1"  # OpenClaw/OpenAI compatibility alias; Qwen3-ASR serves it
+STT_MODEL = "qwen3-asr"
 
 # Maximale Upload-Größe (Bytes) – verhindert unbegrenzten RAM-Verbrauch.
 # 50 MB ist für Audio-Dateien (WebM/Opus, WAV, MP3) mehr als ausreichend.
@@ -2848,12 +2848,6 @@ class Handler(BaseHTTPRequestHandler):
                 "owned_by": "qwen3-asr",
                 "type": "transcription",
             })
-            models.append({
-                "id": "qwen3-asr",
-                "object": "model",
-                "owned_by": "qwen3-asr",
-                "type": "transcription",
-            })
         if tts.get("ready"):
             models.append({
                 "id": TTS_MODEL,
@@ -2974,7 +2968,7 @@ class Handler(BaseHTTPRequestHandler):
 
         # Modell-Validierung
         model = fields.get("model", STT_MODEL)
-        if model not in (STT_MODEL, "whisper", "qwen3-asr"):
+        if model != STT_MODEL:
             self._send_error(400, f"unbekanntes Modell: {model!r} "
                                   f"(erwartet: {STT_MODEL})",
                              "invalid_request_error", "unknown_model")

scripts/speech-roundtrip.py

@@ -47,7 +47,7 @@ def main() -> int:
     body = (
         f"--{boundary}\r\n"
         'Content-Disposition: form-data; name="model"\r\n\r\n'
-        "whisper-1\r\n"
+        "qwen3-asr\r\n"
         f"--{boundary}\r\n"
         'Content-Disposition: form-data; name="language"\r\n\r\n'
         "de\r\n"

services/athena-realtime-voice/server.py

@@ -281,7 +281,7 @@ class RealtimeSession:
         try:
             form = FormData()
             form.add_field("file", make_wav(pcm), filename="talk.wav", content_type="audio/wav")
-            form.add_field("model", "whisper-1")
+            form.add_field("model", "qwen3-asr")
             form.add_field("language", os.environ.get("STT_LANGUAGE", "de"))
             async with self.http.post(
                 os.environ["ATHENA_API_BASE_URL"].rstrip("/") + "/audio/transcriptions",