.gitignore

@@ -55,7 +55,3 @@ venv/
 # Environment files
 .env
 .env.*
-
-# Local QA only (not published)
-qa.sh
-tests/

DOCKERHUB_STREAMING.md

@@ -41,7 +41,6 @@ hf download Qwen/Qwen3-TTS-12Hz-1.7B-Base --local-dir /path/to/Qwen3-TTS-12Hz-1.
 ```
 
 Streaming must use the same model size as the one voices.json was built for (1.7B-Base), otherwise requests fail with a 1024 vs 2048 tensor size mismatch.
-To run the 0.6B-Base model for lower latency next to a 1.7B service on the same `voices.json`, set `EMBED_TAG=0.6b` on the streaming service (and `EMBED_TAG=1.7b` on the 1.7B one) and mount `/voices` read-write; each model then keeps its own `<name>.<tag>.pt` beside the WAV.
 
 For an existing stack, change only the streaming service's model bind mount.
 `--model /models/Qwen3-TTS` loads that directory; `QWEN_TTS_MODEL` does not

Dockerfile

@@ -24,8 +24,8 @@ RUN git clone https://github.com/andimarafioti/faster-qwen3-tts.git /app && \
     cd /app && git checkout --detach ${FASTER_QWEN3_TTS_REF}
 
 # Apply DGX Spark patches (non_streaming_mode=True, per-voice temperature/top_k/top_p)
-COPY patches/openai_server.patch patches/predictor_compile.patch /tmp/
-RUN cd /app && git apply /tmp/openai_server.patch && git apply /tmp/predictor_compile.patch
+COPY patches/openai_server.patch /tmp/
+RUN cd /app && git apply /tmp/openai_server.patch
 
 # Create virtual environment (Ubuntu 24.04 enforces PEP 668)
 ENV VIRTUAL_ENV=/opt/venv

README.md

@@ -19,8 +19,8 @@ One Docker image covers all four backends, with two semantic tag aliases:
 
 | Tag | Use for |
 |---|---|
-| `:latest` / `:v6` / `:v6.15.0` | VoiceClone, VoiceDesign, CustomVoice |
-| `:latest-streaming` / `:v6-streaming` / `:v6.15.0-streaming` | Streaming VoiceClone |
+| `:latest` / `:v6` / `:v6.13.1` | VoiceClone, VoiceDesign, CustomVoice |
+| `:latest-streaming` / `:v6-streaming` / `:v6.13.1-streaming` | Streaming VoiceClone |
 
 Both tags point to the same image — the `-streaming` suffix is a semantic convention so compose files and version pins are unambiguous.
 
@@ -354,10 +354,7 @@ The first request after container startup can be slower because CUDA graph captu
 
 - The 1.7B Qwen3-TTS models use about 6 GB of GPU memory each in bfloat16.
 - The forum playbook shows the four API containers running together on DGX Spark with low visible memory pressure, but exact usage depends on model size, sequence length, and warmup state.
-- The 0.6B Qwen3-TTS variants are lighter, but their speaker embeddings (1024-dim) are incompatible with the 1.7B ones (2048-dim). Set `EMBED_TAG=0.6b` (and `EMBED_TAG=1.7b` on the 1.7B services) and mount `/voices` read-write: each voice then gets `<name>.<tag>.pt` beside its WAV and both sizes can share one `voices.json` (see `docker/docker-compose.portainer-0.6b.yml`).
-- Measured on GB10 with other GPU services running (streaming PCM, first request excluded): 0.6B-Base TTFA ~570 ms, RTF 0.53–0.60 (1.7–1.9× real-time); 1.7B-Base TTFA ~820 ms, RTF 0.78–0.84 (1.2–1.3× real-time). The first request after a start is much slower while graphs/embeddings are built.
-- Optional: `TTS_COMPILE_PREDICTOR=1` (image v6.15.0+) fuses the code predictor for ~10 % more throughput; see the v6.15.0 changelog entry.
-- Throughput is not limited by memory pressure or GPU clocks (re-measured with the other large GPU model stopped: identical). The streaming process runs at ~100 % of one CPU core at ~32 W GPU power, so per-step launch/Python overhead is the suspected limit (not profiled yet). Lowering latency in practice: use the 0.6B stream with `response_format: "pcm"` and send one or two sentences per request.
+- The 0.6B Qwen3-TTS variants are lighter but need their own `voices.json` regenerated with that model's embeddings. Do not use them for the streaming/clone containers, which share the default `voices.json` (1.7B-Base).
 - `--max-seq-len 2048` handles most sentence-style TTS requests. Long-form narration may need `4096`, with more memory required.
 - Pin services to different GPUs with `NVIDIA_VISIBLE_DEVICES=0`, `NVIDIA_VISIBLE_DEVICES=1`, and so on if your system has more than one GPU.
 
@@ -381,35 +378,6 @@ The first request after container startup can be slower because CUDA graph captu
 
 ## Changelog
 
-### v6.15.1 — 2026-10-12
-
-- **Docs:** German sample files (10 voices, 0.6B-Base streaming, same text) in `samples/german sample files/` with an `index.html` that has an audio player per voice (open locally or via GitHub Pages) and a link table, added for the German-pronunciation question in the forum.
-- **Docs:** image tag table now lists `v6.15.0`; the `v6.15.0` images and the `:v6`/`:latest` aliases are on Docker Hub.
-- **Image:** unchanged (`:v6.15.0`), no rebuild needed.
-- **Rollback:** `git checkout v6.15.0` (documentation only).
-
-### v6.15.0 — 2026-10-11
-
-- **Perf (opt-in):** `TTS_COMPILE_PREDICTOR=1` compiles the 15-step code-predictor loop with `torch.compile` (Inductor/Triton) before CUDA-graph capture. Profiling showed the predictor needs ~5350 kernels per frame (about 54 % of the decode time); fusing them cuts its replay from ~23 ms to ~17.5 ms. Measured through the API on GB10 with the 0.6B-Base streaming server (same voice and text, other TTS containers stopped): 1.6–1.7x -> 1.8x real-time on long text, first audio ~670 ms -> ~550 ms. Listening comparison of 5 voices: no audible difference. Costs about one extra minute at startup. Default is off (unchanged behaviour).
-- **Image:** rebuilt as `v6.15.0` / `v6.15.0-streaming` (adds `patches/predictor_compile.patch`) and pushed to Docker Hub together with the `:v6`, `:latest`, `:v6-streaming` and `:latest-streaming` aliases.
-- **Investigation notes:** memory pressure/swap, GPU clocks, CPU core type and `chunk_size` do not limit throughput; `float16` fails (inf/NaN logits, device-side assert); flash-attn is irrelevant for single-token decode. F5-TTS, EmoKnob and Breeze-TTS-2 were benchmarked against Qwen3-TTS: slower than real-time (Breeze 0.5x, EmoKnob 0.1x) or without streaming/German (F5 ~5x) and not usable quality-wise in our test.
-- **Rollback:** unset `TTS_COMPILE_PREDICTOR` (or use the `:v6.13.1` image).
-
-### v6.14.1 — 2026-10-11
-
-- **Docs:** measured GB10 benchmark numbers for 0.6B vs 1.7B, the finding that memory pressure and GPU clocks are not the bottleneck, and the corrected 0.6B guidance (`EMBED_TAG`) in "Performance and memory notes".
-- **Deploy note:** Portainer re-creates services from its stored stack file, so a `docker compose up` from the CLI is overwritten. Update the stack in the Portainer editor instead.
-- **Image:** unchanged (`:v6.13.1`), no rebuild needed.
-- **Rollback:** `git checkout v6.14.0` (documentation only).
-
-### v6.14.0 — 2026-10-11
-
-- **Feature:** `EMBED_TAG` environment variable (e.g. `1.7b`, `0.6b`). Speaker embeddings are stored beside the reference audio as `<name>.<tag>.pt`, so a 1.7B VoiceClone and a 0.6B Streaming backend can share one `voices.json` and one voice folder (mount `/voices` read-write). Existing `config/speakers/*.pt` files are adopted once for the `1.7b` tag; embeddings older than their WAV/TXT are regenerated.
-- **Feature:** `/voices` and `/v1/voices` discovery endpoints on all backends.
-- **Config:** new `docker/docker-compose.portainer-0.6b.yml` — 1.7B VoiceClone on :8020 plus 0.6B-Base Streaming on :8023 (`--max-seq-len 2048`). The 0.6B model needs its own embeddings (1024-dim); never share `.pt` files between model sizes.
-- **Image:** unchanged (`:v6.13.1`); the changes live in `/config` (bind mount), no rebuild needed.
-- **Rollback:** redeploy the previous compose file (`:v6.13.1` images); `EMBED_TAG` unset keeps the old `/config/speakers` behaviour.
-
 ### v6.13.1 — 2026-10-04
 
 - **Fix:** Streaming must use the same model size as the one voices.json was built for (1.7B-Base), otherwise requests fail with a 1024 vs 2048 tensor size mismatch. Streaming compose examples now default to the 1.7B-Base model.

config/run_customvoice_server.py

@@ -321,12 +321,6 @@ async def list_audio_models():
     return _models_response
 
 
-@app.get("/voices")
-@app.get("/v1/voices")
-async def list_voices():
-    return _models_response
-
-
 @app.get("/speakers")
 async def get_speakers():
     return list(voices.keys())

config/run_server.py

@@ -32,46 +32,6 @@ import openai_server
 logger = logging.getLogger(__name__)
 
 
-# Speaker embeddings (.pt) are model-size specific (1.7B=2048-dim, 0.6B=1024-dim).
-# With EMBED_TAG set (e.g. "1.7b"/"0.6b") every voice's embedding lives next to its
-# reference audio as <name>.<tag>.pt, so several model sizes can share one voices.json.
-_EMBED_TAG = os.environ.get("EMBED_TAG", "").strip()
-
-
-def _retarget_embeddings(voice_cfg: dict) -> dict:
-    """Point voice_cfg at the per-model .pt beside the reference audio (idempotent)."""
-    ref_audio = voice_cfg.get("ref_audio")
-    if not _EMBED_TAG or not ref_audio or voice_cfg.get("_emb_tag") == _EMBED_TAG:
-        return voice_cfg
-    stem = os.path.splitext(ref_audio)[0]
-    pt = f"{stem}.{_EMBED_TAG}.pt"
-    legacy = voice_cfg.get("speaker_embeddings") or ""
-    try:
-        # ponytail: legacy /config/speakers/*.pt are 1.7B embeddings; adopt them once for the 1.7b tag only.
-        if _EMBED_TAG.lower() == "1.7b" and not os.path.exists(pt) and os.path.isfile(legacy):
-            import shutil
-            shutil.copy2(legacy, pt)
-        if os.path.exists(pt):
-            sources = [ref_audio, stem + ".txt", stem + ".reference.txt"]
-            newest = max(os.path.getmtime(f) for f in sources if os.path.exists(f))
-            if newest > os.path.getmtime(pt) + 1:  # reference changed -> embedding stale
-                os.remove(pt)
-    except OSError as exc:
-        logger.warning("Embedding retarget problem for %s: %s", pt, exc)
-    voice_cfg["speaker_embeddings"] = pt
-    voice_cfg["_emb_tag"] = _EMBED_TAG
-    return voice_cfg
-
-
-if _EMBED_TAG:
-    _orig_resolve_voice = openai_server.resolve_voice
-
-    def _resolve_voice_tagged(voice_name: str) -> dict:
-        return _retarget_embeddings(_orig_resolve_voice(voice_name))
-
-    openai_server.resolve_voice = _resolve_voice_tagged
-
-
 def _do_warmup():
     """Run one short generation to compile CUDA graphs before serving requests."""
     model = openai_server.tts_model
@@ -117,7 +77,6 @@ def _precompute_all_embeddings():
         return
         
     for voice_name, voice_cfg in list(voices.items()):
-        _retarget_embeddings(voice_cfg)
         # skip if already precomputed
         spk_emb_path = voice_cfg.get("speaker_embeddings") or voice_cfg.get("speaker embeddings")
         if spk_emb_path and os.path.isfile(spk_emb_path):
@@ -173,11 +132,6 @@ async def list_audio_voices():
 async def list_audio_models():
     return _models_response
 
-@openai_server.app.get('/voices')
-@openai_server.app.get('/v1/voices')
-async def list_voices():
-    return _models_response
-
 # SillyTavern speaker endpoint
 @openai_server.app.get('/speakers')
 async def get_speakers():

config/run_voicedesign_server.py

@@ -392,12 +392,6 @@ async def list_audio_models():
     _reload_voices_if_changed()
     return _models_response
 
-@app.get("/voices")
-@app.get("/v1/voices")
-async def list_voices():
-    _reload_voices_if_changed()
-    return _models_response
-
 @app.get("/speakers")
 async def get_speakers():
     _reload_voices_if_changed()

docker/docker-compose.portainer-0.6b.yml

@@ -1,161 +0,0 @@
-# ─────────────────────────────────────────────────────────────────────────────
-#  Qwen3-TTS GPU Stack
-#
-#  Ports:
-#    8020 -> Voice clone, OpenAI-compatible /v1/audio/speech 
-#    8021 -> VoiceDesign model for prompt-designed voices
-#    8022 -> CustomVoice model
-#    8023 -> Low-latency streaming voice clone endpoint 
-#
-#  Container Name	            RAM Usage	GPU VRAM Usage
-#  faster-qwen3-tts-voiceclone	~2.72 GiB	5327 MiB (~5.2 GiB)
-#  faster-qwen3-tts-customvoice	~2.88 GiB	4683 MiB (~4.6 GiB)
-#  faster-qwen3-tts-voicedesign	~2.90 GiB	4747 MiB (~4.6 GiB)
-#  faster-qwen3-tts-streaming	~2.54 GiB	5659 MiB (~5.5 GiB)
-#
-#  Voice files:
-#    The voice clone and streaming services scan active voices from:
-#    home/sparky/Media/_Sounds/TTS_Voices/active_voices
-#
-#  Portainer note:
-#    Set HF_TOKEN in the stack environment if the models ever need Hugging Face access.
-# ─────────────────────────────────────────────────────────────────────────────
-
-services:
-  # Standard voice cloning backend used by TTS Voice Creator and other OpenAI-compatible clients.
-  faster-qwen3-tts-voiceclone:
-    image: martinb78/faster-qwen3-tts-dgx-spark:latest
-    container_name: tts-faster-qwen3-tts-voiceclone
-    restart: unless-stopped
-    runtime: nvidia
-    environment:
-      - NVIDIA_VISIBLE_DEVICES=all
-      - HF_TOKEN=${HF_TOKEN}
-      - EMBED_TAG=1.7b
-    ports:
-      - "8020:8000"
-    volumes:
-      - /home/sparky/LLMs/vllm/Alibaba/Qwen3-TTS-12Hz-1.7B-Base:/models/Qwen3-TTS:ro
-      - /home/sparky/Docker/tts-dgx-spark-faster-qwen3-tts/config:/config:rw
-      - /home/sparky/Media/_Sounds/TTS_Voices/active_voices:/voices:rw
-    command: >
-      /bin/bash -c "
-      python3 /config/generate_voices.py &&
-      python3 /config/run_server.py
-      --model /models/Qwen3-TTS
-      --voices /config/voices.json
-      --port 8000
-      --max-seq-len 4096
-      "
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: all
-              capabilities: [gpu]
-    networks:
-      - dgx_net
-
-  # VoiceDesign backend: creates speech from a descriptive voice prompt instead of a reference clip.
-  faster-qwen3-tts-voicedesign:
-    image: martinb78/faster-qwen3-tts-dgx-spark:latest
-    container_name: tts-faster-qwen3-tts-voicedesign
-    restart: unless-stopped
-    runtime: nvidia
-    environment:
-      - NVIDIA_VISIBLE_DEVICES=all
-      - HF_TOKEN=${HF_TOKEN}
-    ports:
-      - "8021:8000"
-    volumes:
-      - /home/sparky/LLMs/vllm/Alibaba/Qwen3-TTS-12Hz-1.7B-VoiceDesign:/models/Qwen3-TTS-VoiceDesign:ro
-      - /home/sparky/Docker/tts-dgx-spark-faster-qwen3-tts/config:/config:rw
-    command: >
-      /bin/bash -c "
-      python3 /config/run_voicedesign_server.py
-      --model /models/Qwen3-TTS-VoiceDesign
-      --voices /config/voicedesign_voices.json
-      --port 8000
-      --max-seq-len 2048
-      "
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: all
-              capabilities: [gpu]
-    networks:
-      - dgx_net
-
-  # CustomVoice backend: keeps the separate CustomVoice model available on port 8022.
-  faster-qwen3-tts-customvoice:
-    image: martinb78/faster-qwen3-tts-dgx-spark:latest
-    container_name: tts-faster-qwen3-tts-customvoice
-    restart: unless-stopped
-    runtime: nvidia
-    environment:
-      - NVIDIA_VISIBLE_DEVICES=all
-      - HF_TOKEN=${HF_TOKEN}
-    ports:
-      - "8022:8000"
-    volumes:
-      - /home/sparky/LLMs/vllm/Alibaba/Qwen3-TTS-12Hz-1.7B-CustomVoice:/models/Qwen3-TTS-CustomVoice:ro
-      - /home/sparky/Docker/tts-dgx-spark-faster-qwen3-tts/config:/config:rw
-    command: >
-      /bin/bash -c "
-      python3 /config/run_customvoice_server.py
-      --model /models/Qwen3-TTS-CustomVoice
-      --voices /config/customvoice_voices.json
-      --port 8000
-      --max-seq-len 2048
-      "
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: all
-              capabilities: [gpu]
-    networks:
-      - dgx_net
-
-  # Streaming voice clone backend: same active voices as 8020, but streams WAV chunks while generating.
-  faster-qwen3-tts-streaming:
-    image: martinb78/faster-qwen3-tts-dgx-spark:latest-streaming
-    container_name: tts-faster-qwen3-tts-streaming
-    restart: unless-stopped
-    runtime: nvidia
-    environment:
-      - NVIDIA_VISIBLE_DEVICES=all
-      - HF_TOKEN=${HF_TOKEN}
-      - EMBED_TAG=0.6b
-    ports:
-      - "8023:8000"
-    volumes:
-      - /home/sparky/LLMs/vllm/Alibaba/Qwen3-TTS-12Hz-0.6B-Base:/models/Qwen3-TTS:ro
-      - /home/sparky/Docker/tts-dgx-spark-faster-qwen3-tts/config:/config:rw
-      - /home/sparky/Media/_Sounds/TTS_Voices/active_voices:/voices:rw
-    command: >
-      /bin/bash -c "
-      python3 /config/generate_voices.py &&
-      python3 /config/run_server.py
-      --model /models/Qwen3-TTS
-      --voices /config/voices.json
-      --port 8000
-      --max-seq-len 2048
-      "
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: all
-              capabilities: [gpu]
-    networks:
-      - dgx_net
-
-networks:
-  dgx_net:
-    external: true

patches/predictor_compile.patch

@@ -1,25 +0,0 @@
-diff --git a/faster_qwen3_tts/predictor_graph.py b/faster_qwen3_tts/predictor_graph.py
-index 38a52ca..70851eb 100644
---- a/faster_qwen3_tts/predictor_graph.py
-+++ b/faster_qwen3_tts/predictor_graph.py
-@@ -13,6 +13,8 @@ Strategy:
- - Unroll the full 15-step loop for deterministic shapes
- - Capture the entire loop as a single CUDA graph
- """
-+import os
-+
- import torch
- from transformers import StaticCache
- from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask
-@@ -175,6 +177,11 @@ class PredictorGraph:
-         self._init_cache_layers()
-         self._build_attention_masks()
- 
-+        # Opt-in: fuse the 15-step predictor loop with Inductor before graph capture.
-+        # ~17% faster streaming on GB10 (0.6B); adds ~1 min to the first warmup.
-+        if os.environ.get("TTS_COMPILE_PREDICTOR") == "1":
-+            self._full_loop = torch.compile(self._full_loop, dynamic=False)
-+
-         for _ in range(num_warmup):
-             self.static_cache.reset()
-             self._full_loop()

samples/german sample files/README.md

@@ -1,26 +0,0 @@
-# German sample files (0.6B-Base streaming)
-
-Ten voices from the active library, rendered with the 0.6B-Base streaming server
-(`v6.15.0`, `TTS_COMPILE_PREDICTOR=1`, `language: German`, default sampling).
-All files use the same text:
-
-> Guten Morgen! Das Wetter ist heute wunderbar, deshalb gehen wir am Rhein spazieren. Anschließend essen wir Käsekuchen und besprechen die Planung für das nächste Quartal, einschließlich Budget, Meeting und Zeitplan.
-
-Files are in `mp3/` (24 kHz mono, VBR ~130 kbit/s), numbered 01-10 with the voice ID.
-
-## Listen
-
-GitHub's directory view has no audio player. Open `index.html` in this folder from a local clone (it has a player per voice), or enable GitHub Pages for the repository and open the same page there. Direct links to the files:
-
-| # | Voice | File |
-|---|---|---|
-| 01 | DE_M_Gutfolg | [01_DE_M_Gutfolg.mp3](https://github.com/mARTin-B78/dgx-spark-faster-qwen3-tts/blob/main/samples/german%20sample%20files/mp3/01_DE_M_Gutfolg.mp3) |
-| 02 | DE_Banker | [02_DE_Banker.mp3](https://github.com/mARTin-B78/dgx-spark-faster-qwen3-tts/blob/main/samples/german%20sample%20files/mp3/02_DE_Banker.mp3) |
-| 03 | DE_N_Orkgeist | [03_DE_N_Orkgeist.mp3](https://github.com/mARTin-B78/dgx-spark-faster-qwen3-tts/blob/main/samples/german%20sample%20files/mp3/03_DE_N_Orkgeist.mp3) |
-| 04 | DE_F_Amala | [04_DE_F_Amala.mp3](https://github.com/mARTin-B78/dgx-spark-faster-qwen3-tts/blob/main/samples/german%20sample%20files/mp3/04_DE_F_Amala.mp3) |
-| 05 | DE_1_0 | [05_DE_1_0.mp3](https://github.com/mARTin-B78/dgx-spark-faster-qwen3-tts/blob/main/samples/german%20sample%20files/mp3/05_DE_1_0.mp3) |
-| 06 | DE_F_19_German_Switzerland_Female_Leni | [06_DE_F_19_German_Switzerland_Female_Leni.mp3](https://github.com/mARTin-B78/dgx-spark-faster-qwen3-tts/blob/main/samples/german%20sample%20files/mp3/06_DE_F_19_German_Switzerland_Female_Leni.mp3) |
-| 07 | DE_M_Rabe | [07_DE_M_Rabe.mp3](https://github.com/mARTin-B78/dgx-spark-faster-qwen3-tts/blob/main/samples/german%20sample%20files/mp3/07_DE_M_Rabe.mp3) |
-| 08 | DE_N_Schwarzhaarige | [08_DE_N_Schwarzhaarige.mp3](https://github.com/mARTin-B78/dgx-spark-faster-qwen3-tts/blob/main/samples/german%20sample%20files/mp3/08_DE_N_Schwarzhaarige.mp3) |
-| 09 | DE_N_Magier | [09_DE_N_Magier.mp3](https://github.com/mARTin-B78/dgx-spark-faster-qwen3-tts/blob/main/samples/german%20sample%20files/mp3/09_DE_N_Magier.mp3) |
-| 10 | DE_M_Ork | [10_DE_M_Ork.mp3](https://github.com/mARTin-B78/dgx-spark-faster-qwen3-tts/blob/main/samples/german%20sample%20files/mp3/10_DE_M_Ork.mp3) |

samples/german sample files/index.html

@@ -1,95 +0,0 @@
-<!doctype html>
-<html lang="en">
-<head>
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1">
-<title>German samples - Qwen3-TTS 0.6B streaming</title>
-<style>
-  :root { color-scheme: light dark; --bg:#fff; --fg:#1b1f24; --muted:#59636e; --line:#d1d9e0; }
-  @media (prefers-color-scheme: dark) { :root { --bg:#0d1117; --fg:#e6edf3; --muted:#9198a1; --line:#3d444d; } }
-  body { margin:0; padding:1.5rem 1rem; background:var(--bg); color:var(--fg); font:16px/1.5 system-ui,sans-serif; }
-  main { max-width:52rem; margin:0 auto; }
-  blockquote { margin:1rem 0; padding:.5rem 1rem; border-left:4px solid var(--line); color:var(--muted); }
-  .scroll { overflow-x:auto; }
-  table { border-collapse:collapse; width:100%; }
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-  <h1>German samples: Qwen3-TTS 0.6B-Base streaming</h1>
-  <p>Ten voices from the active library, rendered with the 0.6B-Base streaming server (v6.15.0, <code>language: German</code>, default sampling). Every file uses the same text:</p>
-  <blockquote lang="de">Guten Morgen! Das Wetter ist heute wunderbar, deshalb gehen wir am Rhein spazieren. Anschließend essen wir Käsekuchen und besprechen die Planung für das nächste Quartal, einschließlich Budget, Meeting und Zeitplan.</blockquote>
-  <div class="scroll">
-    <table>
-      <thead><tr><th scope="col">#</th><th scope="col">Voice</th><th scope="col">Listen</th><th scope="col">File</th></tr></thead>
-      <tbody>
-      <tr>
-        <th scope="row">01</th>
-        <td>DE_M_Gutfolg</td>
-        <td><audio controls preload="none" src="mp3/01_DE_M_Gutfolg.mp3" aria-label="Sample 01: DE_M_Gutfolg"></audio></td>
-        <td><a href="mp3/01_DE_M_Gutfolg.mp3" download>mp3</a></td>
-      </tr>
-      <tr>
-        <th scope="row">02</th>
-        <td>DE_Banker</td>
-        <td><audio controls preload="none" src="mp3/02_DE_Banker.mp3" aria-label="Sample 02: DE_Banker"></audio></td>
-        <td><a href="mp3/02_DE_Banker.mp3" download>mp3</a></td>
-      </tr>
-      <tr>
-        <th scope="row">03</th>
-        <td>DE_N_Orkgeist</td>
-        <td><audio controls preload="none" src="mp3/03_DE_N_Orkgeist.mp3" aria-label="Sample 03: DE_N_Orkgeist"></audio></td>
-        <td><a href="mp3/03_DE_N_Orkgeist.mp3" download>mp3</a></td>
-      </tr>
-      <tr>
-        <th scope="row">04</th>
-        <td>DE_F_Amala</td>
-        <td><audio controls preload="none" src="mp3/04_DE_F_Amala.mp3" aria-label="Sample 04: DE_F_Amala"></audio></td>
-        <td><a href="mp3/04_DE_F_Amala.mp3" download>mp3</a></td>
-      </tr>
-      <tr>
-        <th scope="row">05</th>
-        <td>DE_1_0</td>
-        <td><audio controls preload="none" src="mp3/05_DE_1_0.mp3" aria-label="Sample 05: DE_1_0"></audio></td>
-        <td><a href="mp3/05_DE_1_0.mp3" download>mp3</a></td>
-      </tr>
-      <tr>
-        <th scope="row">06</th>
-        <td>DE_F_19_German_Switzerland_Female_Leni</td>
-        <td><audio controls preload="none" src="mp3/06_DE_F_19_German_Switzerland_Female_Leni.mp3" aria-label="Sample 06: DE_F_19_German_Switzerland_Female_Leni"></audio></td>
-        <td><a href="mp3/06_DE_F_19_German_Switzerland_Female_Leni.mp3" download>mp3</a></td>
-      </tr>
-      <tr>
-        <th scope="row">07</th>
-        <td>DE_M_Rabe</td>
-        <td><audio controls preload="none" src="mp3/07_DE_M_Rabe.mp3" aria-label="Sample 07: DE_M_Rabe"></audio></td>
-        <td><a href="mp3/07_DE_M_Rabe.mp3" download>mp3</a></td>
-      </tr>
-      <tr>
-        <th scope="row">08</th>
-        <td>DE_N_Schwarzhaarige</td>
-        <td><audio controls preload="none" src="mp3/08_DE_N_Schwarzhaarige.mp3" aria-label="Sample 08: DE_N_Schwarzhaarige"></audio></td>
-        <td><a href="mp3/08_DE_N_Schwarzhaarige.mp3" download>mp3</a></td>
-      </tr>
-      <tr>
-        <th scope="row">09</th>
-        <td>DE_N_Magier</td>
-        <td><audio controls preload="none" src="mp3/09_DE_N_Magier.mp3" aria-label="Sample 09: DE_N_Magier"></audio></td>
-        <td><a href="mp3/09_DE_N_Magier.mp3" download>mp3</a></td>
-      </tr>
-      <tr>
-        <th scope="row">10</th>
-        <td>DE_M_Ork</td>
-        <td><audio controls preload="none" src="mp3/10_DE_M_Ork.mp3" aria-label="Sample 10: DE_M_Ork"></audio></td>
-        <td><a href="mp3/10_DE_M_Ork.mp3" download>mp3</a></td>
-      </tr>
-      </tbody>
-    </table>
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