feat: Add provider selection modal and enhance voice profile handling for Supertonic integration

abogen/web/conversion_runner.py

@@ -42,6 +42,7 @@ from abogen.utils import (
 )
 from abogen.voice_cache import ensure_voice_assets
 from abogen.voice_formulas import extract_voice_ids, get_new_voice
+from abogen.voice_profiles import load_profiles, normalize_profile_entry
 from abogen.pronunciation_store import increment_usage
 from abogen.llm_client import LLMClientError
 from abogen.tts_supertonic import DEFAULT_SUPERTONIC_VOICES, SupertonicPipeline
@@ -67,6 +68,58 @@ def _supertonic_voice_from_spec(spec: Any, fallback: str) -> str:
     return str(fallback or "").strip() or "M1"
 
 
+def _split_speaker_reference(value: Any) -> tuple[Optional[str], str]:
+    raw = str(value or "").strip()
+    if not raw or ":" not in raw:
+        return None, raw
+    prefix, remainder = raw.split(":", 1)
+    prefix = prefix.strip().lower()
+    if prefix not in {"speaker", "profile"}:
+        return None, raw
+    name = remainder.strip()
+    return (name or None), raw
+
+
+def _formula_from_kokoro_entry(entry: Mapping[str, Any]) -> str:
+    voices = entry.get("voices") or []
+    if not voices:
+        return ""
+    total = 0.0
+    parts: list[tuple[str, float]] = []
+    for item in voices:
+        if not isinstance(item, (list, tuple)) or len(item) < 2:
+            continue
+        name = str(item[0] or "").strip()
+        try:
+            weight = float(item[1])
+        except (TypeError, ValueError):
+            continue
+        if not name or weight <= 0:
+            continue
+        parts.append((name, weight))
+        total += weight
+    if total <= 0 or not parts:
+        return ""
+
+    def _format_weight(value: float) -> str:
+        normalized = value / total if total else 0.0
+        return (f"{normalized:.4f}").rstrip("0").rstrip(".") or "0"
+
+    return "+".join(f"{name}*{_format_weight(weight)}" for name, weight in parts)
+
+
+def _infer_provider_from_spec(value: Any, fallback: str = "kokoro") -> str:
+    raw = str(value or "").strip()
+    if not raw:
+        return fallback
+    upper = raw.upper()
+    if upper in DEFAULT_SUPERTONIC_VOICES:
+        return "supertonic"
+    if "*" in raw or "+" in raw:
+        return "kokoro"
+    return fallback
+
+
 class _JobCancelled(Exception):
     """Raised internally to abort a conversion when the client cancels."""
 
@@ -1380,14 +1433,76 @@ def run_conversion_job(job: Job) -> None:
     audio_output_path: Optional[Path] = None
     extraction: Optional[Any] = None
     pipeline: Any = None
+    pipelines: Dict[str, Any] = {}
+    kokoro_cache_ready = False
+    normalized_profiles: Dict[str, Dict[str, Any]] = {}
     chunk_groups: Dict[int, List[Dict[str, Any]]] = {}
     active_chapter_configs: List[Dict[str, Any]] = []
     usage_counter: Dict[str, int] = defaultdict(int)
     override_token_map: Dict[str, str] = {}
     try:
-        pipeline = _load_pipeline(job)
-        if getattr(job, "tts_provider", "kokoro") == "kokoro":
-            _initialize_voice_cache(job)
+        # Load saved speakers once so we can resolve speaker: references during conversion.
+        try:
+            profiles = load_profiles()
+        except Exception:
+            profiles = {}
+        for name, entry in (profiles or {}).items():
+            normalized = normalize_profile_entry(entry)
+            if normalized:
+                normalized_profiles[str(name)] = normalized
+
+        def get_pipeline(provider: str) -> Any:
+            nonlocal kokoro_cache_ready
+            provider_norm = str(provider or "kokoro").strip().lower() or "kokoro"
+            if provider_norm not in {"kokoro", "supertonic"}:
+                provider_norm = "kokoro"
+
+            existing = pipelines.get(provider_norm)
+            if existing is not None:
+                return existing
+
+            if provider_norm == "supertonic":
+                pipelines[provider_norm] = SupertonicPipeline(
+                    sample_rate=SAMPLE_RATE,
+                    auto_download=True,
+                    total_steps=int(getattr(job, "supertonic_total_steps", 5) or 5),
+                )
+                return pipelines[provider_norm]
+
+            # Kokoro
+            cfg = load_config()
+            disable_gpu = not job.use_gpu or not cfg.get("use_gpu", True)
+            device = "cpu"
+            if not disable_gpu:
+                device = _select_device()
+            _np, KPipeline = load_numpy_kpipeline()
+            pipelines[provider_norm] = KPipeline(lang_code=job.language, repo_id="hexgrad/Kokoro-82M", device=device)
+            if not kokoro_cache_ready:
+                _initialize_voice_cache(job)
+                kokoro_cache_ready = True
+            return pipelines[provider_norm]
+
+        def resolve_voice_target(raw_spec: str) -> tuple[str, str, Optional[float], Optional[int]]:
+            """Return (provider, voice_spec, speed_override, steps_override)."""
+            spec = str(raw_spec or "").strip()
+            speaker_name, _ = _split_speaker_reference(spec)
+            if speaker_name and speaker_name in normalized_profiles:
+                entry = normalized_profiles[speaker_name]
+                provider = str(entry.get("provider") or "kokoro").strip().lower() or "kokoro"
+                if provider == "supertonic":
+                    voice = str(entry.get("voice") or getattr(job, "voice", "M1") or "M1").strip() or "M1"
+                    steps = int(entry.get("total_steps") or getattr(job, "supertonic_total_steps", 5) or 5)
+                    speed = float(entry.get("speed") or getattr(job, "speed", 1.0) or 1.0)
+                    return "supertonic", _supertonic_voice_from_spec(voice, getattr(job, "voice", "M1")), speed, steps
+                formula = _formula_from_kokoro_entry(entry)
+                return "kokoro", formula or spec, None, None
+
+            fallback_provider = str(getattr(job, "tts_provider", "kokoro") or "kokoro").strip().lower() or "kokoro"
+            inferred = _infer_provider_from_spec(spec, fallback=fallback_provider)
+            if inferred == "supertonic":
+                return "supertonic", _supertonic_voice_from_spec(spec, getattr(job, "voice", "M1")), None, None
+            return "kokoro", spec, None, None
+
         extraction = extract_from_path(job.stored_path)
         file_type = _infer_file_type(job.stored_path)
         pronunciation_rules = _compile_pronunciation_rules(job.pronunciation_overrides)
@@ -1507,9 +1622,10 @@ def run_conversion_job(job: Job) -> None:
 
         base_voice_spec = _job_voice_fallback(job)
         voice_cache: Dict[str, Any] = {}
-        if getattr(job, "tts_provider", "kokoro") == "kokoro":
-            if base_voice_spec and "*" not in base_voice_spec:
-                voice_cache[base_voice_spec] = _resolve_voice(pipeline, base_voice_spec, job.use_gpu)
+        base_provider, base_voice_resolved, _, _ = resolve_voice_target(base_voice_spec)
+        if base_provider == "kokoro" and base_voice_resolved and "*" not in base_voice_resolved:
+            kokoro_pipeline = get_pipeline("kokoro")
+            voice_cache[f"kokoro:{base_voice_resolved}"] = _resolve_voice(kokoro_pipeline, base_voice_resolved, job.use_gpu)
         processed_chars = 0
         subtitle_index = 1
         current_time = 0.0
@@ -1538,6 +1654,9 @@ def run_conversion_job(job: Job) -> None:
             chapter_sink: Optional[AudioSink],
             preview_prefix: Optional[str] = None,
             split_pattern: Optional[str] = SPLIT_PATTERN,
+            tts_provider: Optional[str] = None,
+            speed_override: Optional[float] = None,
+            supertonic_steps_override: Optional[int] = None,
         ) -> int:
             nonlocal processed_chars, subtitle_index, current_time
             source_text = str(text or "")
@@ -1560,21 +1679,23 @@ def run_conversion_job(job: Job) -> None:
                 raise
             local_segments = 0
 
-            provider = getattr(job, "tts_provider", "kokoro")
+            provider = str(tts_provider or getattr(job, "tts_provider", "kokoro") or "kokoro").strip().lower() or "kokoro"
             if provider == "supertonic":
+                supertonic_pipeline = get_pipeline("supertonic")
                 voice_name = _supertonic_voice_from_spec(voice_choice, getattr(job, "voice", "M1"))
-                segment_iter = pipeline(
+                segment_iter = supertonic_pipeline(
                     normalized,
                     voice=voice_name,
-                    speed=job.speed,
+                    speed=float(speed_override if speed_override is not None else job.speed),
                     split_pattern=split_pattern,
-                    total_steps=getattr(job, "supertonic_total_steps", 5),
+                    total_steps=int(supertonic_steps_override if supertonic_steps_override is not None else getattr(job, "supertonic_total_steps", 5)),
                 )
             else:
-                segment_iter = pipeline(
+                kokoro_pipeline = get_pipeline("kokoro")
+                segment_iter = kokoro_pipeline(
                     normalized,
                     voice=voice_choice,
-                    speed=job.speed,
+                    speed=float(speed_override if speed_override is not None else job.speed),
                     split_pattern=split_pattern,
                 )
 
@@ -1655,10 +1776,16 @@ def run_conversion_job(job: Job) -> None:
             if not chapter_voice_spec:
                 chapter_voice_spec = base_voice_spec
 
-            voice_choice = voice_cache.get(chapter_voice_spec)
-            if voice_choice is None:
-                voice_choice = _resolve_voice(pipeline, chapter_voice_spec, job.use_gpu)
-                voice_cache[chapter_voice_spec] = voice_choice
+            chapter_provider, chapter_voice_resolved, chapter_speed, chapter_steps = resolve_voice_target(chapter_voice_spec)
+            chapter_cache_key = f"{chapter_provider}:{chapter_voice_resolved}" if chapter_voice_resolved else chapter_provider
+            if chapter_provider == "kokoro":
+                voice_choice = voice_cache.get(chapter_cache_key)
+                if voice_choice is None:
+                    kokoro_pipeline = get_pipeline("kokoro")
+                    voice_choice = _resolve_voice(kokoro_pipeline, chapter_voice_resolved, job.use_gpu)
+                    voice_cache[chapter_cache_key] = voice_choice
+            else:
+                voice_choice = chapter_voice_resolved
 
             chapter_audio_path: Optional[Path] = None
             segments_emitted = 0
@@ -1694,6 +1821,9 @@ def run_conversion_job(job: Job) -> None:
                         voice_choice=voice_choice,
                         chapter_sink=chapter_sink,
                         preview_prefix="Book intro",
+                        tts_provider=chapter_provider,
+                        speed_override=chapter_speed,
+                        supertonic_steps_override=chapter_steps,
                     )
                     intro_emitted = True
                     if intro_segments > 0 and job.chapter_intro_delay > 0:
@@ -1710,6 +1840,9 @@ def run_conversion_job(job: Job) -> None:
                         chapter_sink=chapter_sink,
                         preview_prefix=f"Chapter {idx} title",
                         split_pattern=SPLIT_PATTERN,
+                        tts_provider=chapter_provider,
+                        speed_override=chapter_speed,
+                        supertonic_steps_override=chapter_steps,
                     )
                     segments_emitted += heading_segments
                     if heading_segments > 0 and job.chapter_intro_delay > 0:
@@ -1782,16 +1915,26 @@ def run_conversion_job(job: Job) -> None:
                         chunk_voice_spec = chapter_voice_spec or base_voice_spec
 
                     if chunk_voice_spec == chapter_voice_spec:
+                        chunk_provider = chapter_provider
+                        chunk_voice_resolved = chapter_voice_resolved
+                        chunk_speed_use = chapter_speed
+                        chunk_steps_use = chapter_steps
                         chunk_voice_choice = voice_choice
                     else:
-                        chunk_voice_choice = voice_cache.get(chunk_voice_spec)
-                        if chunk_voice_choice is None:
-                            chunk_voice_choice = _resolve_voice(
-                                pipeline,
-                                chunk_voice_spec,
-                                job.use_gpu,
-                            )
-                            voice_cache[chunk_voice_spec] = chunk_voice_choice
+                        chunk_provider, chunk_voice_resolved, chunk_speed_use, chunk_steps_use = resolve_voice_target(chunk_voice_spec)
+                        chunk_cache_key = f"{chunk_provider}:{chunk_voice_resolved}" if chunk_voice_resolved else chunk_provider
+                        if chunk_provider == "kokoro":
+                            chunk_voice_choice = voice_cache.get(chunk_cache_key)
+                            if chunk_voice_choice is None:
+                                kokoro_pipeline = get_pipeline("kokoro")
+                                chunk_voice_choice = _resolve_voice(
+                                    kokoro_pipeline,
+                                    chunk_voice_resolved,
+                                    job.use_gpu,
+                                )
+                                voice_cache[chunk_cache_key] = chunk_voice_choice
+                        else:
+                            chunk_voice_choice = chunk_voice_resolved
 
                     chunk_start = current_time
                     emitted = emit_text(
@@ -1799,6 +1942,9 @@ def run_conversion_job(job: Job) -> None:
                         voice_choice=chunk_voice_choice,
                         chapter_sink=chapter_sink,
                         preview_prefix=f"Chunk {chunk_entry.get('id') or chunk_entry.get('chunk_index')}",
+                        tts_provider=chunk_provider,
+                        speed_override=chunk_speed_use,
+                        supertonic_steps_override=chunk_steps_use,
                     )
                     if emitted <= 0:
                         continue
@@ -1851,6 +1997,9 @@ def run_conversion_job(job: Job) -> None:
                         chapter_text,
                         voice_choice=voice_choice,
                         chapter_sink=chapter_sink,
+                        tts_provider=chapter_provider,
+                        speed_override=chapter_speed,
+                        supertonic_steps_override=chapter_steps,
                     )
                     if emitted > 0:
                         segments_emitted += emitted
@@ -2093,6 +2242,7 @@ def run_conversion_job(job: Job) -> None:
 
         # Explicitly release the pipeline and force garbage collection to prevent
         # memory accumulation in the worker process, which can lead to host lockups.
+        pipelines.clear()
         pipeline = None
         gc.collect()
         try:

abogen/web/routes/api.py

@@ -166,7 +166,12 @@ def api_voice_profiles_preview() -> ResponseReturnValue:
     resolved_provider = provider or "kokoro"
 
     profiles = load_profiles()
-    if profile_name:
+    if resolved_provider == "supertonic" and not profile_name:
+        voice_spec = str(payload.get("voice") or payload.get("supertonic_voice") or "M1").strip() or "M1"
+        # Allow per-speaker overrides via payload.
+        supertonic_total_steps = int(payload.get("supertonic_total_steps") or payload.get("total_steps") or supertonic_total_steps)
+        speed = coerce_float(payload.get("supertonic_speed") or payload.get("speed"), speed)
+    elif profile_name:
         entry = profiles.get(profile_name)
         normalized_entry = normalize_profile_entry(entry)
         if not normalized_entry:

abogen/web/routes/utils/preview.py

@@ -6,20 +6,47 @@ import soundfile as sf
 from flask import current_app, send_file
 from flask.typing import ResponseReturnValue
 
-from abogen.utils import load_numpy_kpipeline
-from abogen.voice_formulas import get_new_voice
-from abogen.web.conversion_runner import SPLIT_PATTERN, SAMPLE_RATE, _select_device, _to_float32
-from abogen.kokoro_text_normalization import normalize_for_pipeline
+
+SPLIT_PATTERN = r"\n+"
+SAMPLE_RATE = 24000
 
 _preview_pipelines: Dict[Tuple[str, str], Any] = {}
 _preview_pipeline_lock = threading.Lock()
 
+
+def _select_device() -> str:
+    import platform
+
+    system = platform.system()
+    if system == "Darwin" and platform.processor() == "arm":
+        return "mps"
+    return "cuda"
+
+
+def _to_float32(audio_segment) -> np.ndarray:
+    if audio_segment is None:
+        return np.zeros(0, dtype="float32")
+
+    tensor = audio_segment
+    if hasattr(tensor, "detach"):
+        tensor = tensor.detach()
+    if hasattr(tensor, "cpu"):
+        try:
+            tensor = tensor.cpu()
+        except Exception:
+            pass
+    if hasattr(tensor, "numpy"):
+        return np.asarray(tensor.numpy(), dtype="float32").reshape(-1)
+    return np.asarray(tensor, dtype="float32").reshape(-1)
+
 def get_preview_pipeline(language: str, device: str) -> Any:
     key = (language, device)
     with _preview_pipeline_lock:
         pipeline = _preview_pipelines.get(key)
         if pipeline is not None:
             return pipeline
+        from abogen.utils import load_numpy_kpipeline
+
         _, KPipeline = load_numpy_kpipeline()
         pipeline = KPipeline(lang_code=language, repo_id="hexgrad/Kokoro-82M", device=device)
         _preview_pipelines[key] = pipeline
@@ -40,11 +67,15 @@ def generate_preview_audio(
 
     provider = (tts_provider or "kokoro").strip().lower()
 
-    try:
-        normalized_text = normalize_for_pipeline(text)
-    except Exception:
-        current_app.logger.exception("Preview normalization failed; using raw text")
-        normalized_text = text
+    normalized_text = text
+    if provider != "supertonic":
+        try:
+            from abogen.kokoro_text_normalization import normalize_for_pipeline
+
+            normalized_text = normalize_for_pipeline(text)
+        except Exception:
+            current_app.logger.exception("Preview normalization failed; using raw text")
+            normalized_text = text
 
     if provider == "supertonic":
         from abogen.tts_supertonic import SupertonicPipeline
@@ -72,6 +103,8 @@ def generate_preview_audio(
 
         voice_choice: Any = voice_spec
         if voice_spec and "*" in voice_spec:
+            from abogen.voice_formulas import get_new_voice
+
             voice_choice = get_new_voice(pipeline, voice_spec, use_gpu)
 
         segments = pipeline(

abogen/web/static/voices.js

@@ -24,6 +24,7 @@ const setupVoiceMixer = () => {
   const mixTotalEl = app.querySelector('[data-role="mix-total"]');
   const nameInput = document.getElementById("profile-name");
   const languageSelect = document.getElementById("profile-language");
+  const languageField = app.querySelector(".voice-editor__language");
   const providerSelect = document.getElementById("profile-provider");
   const kokoroMixerEl = app.querySelector('[data-role="kokoro-mixer"]');
   const supertonicPanelEl = app.querySelector('[data-role="supertonic-panel"]');
@@ -40,6 +41,13 @@ const setupVoiceMixer = () => {
   const voiceFilterSelect = app.querySelector('[data-role="voice-filter"]');
   const genderFilterEl = app.querySelector('[data-role="gender-filter"]');
 
+  const providerPickerModal = document.querySelector('[data-role="provider-picker-modal"]');
+  const providerPickerOverlay = document.querySelector('[data-role="provider-picker-overlay"]');
+  const providerPickerClose = document.querySelector('[data-role="provider-picker-close"]');
+  const providerPickerCancel = document.querySelector('[data-role="provider-picker-cancel"]');
+  const providerPickerConfirm = document.querySelector('[data-role="provider-picker-confirm"]');
+  const providerPickerOptions = document.querySelector('[data-role="provider-picker-options"]');
+
   if (previewBtn && !previewBtn.dataset.label) {
     previewBtn.dataset.label = previewBtn.textContent.trim();
   }
@@ -143,12 +151,123 @@ const setupVoiceMixer = () => {
     return candidate === "supertonic" ? "supertonic" : "kokoro";
   };
 
+  const getProviderCatalog = () => {
+    if (!providerSelect) {
+      return [
+        { id: "kokoro", label: "Kokoro" },
+        { id: "supertonic", label: "Supertonic" },
+      ];
+    }
+    return Array.from(providerSelect.options || []).map((option) => ({
+      id: normalizeProvider(option.value),
+      label: option.textContent?.trim() || option.value,
+    }));
+  };
+
+  const providerDescription = (providerId) => {
+    const provider = normalizeProvider(providerId);
+    if (provider === "supertonic") {
+      return "Voice selection + quality/speed per speaker.";
+    }
+    return "Voice mixing supported via the Kokoro mixer.";
+  };
+
+  const openProviderPicker = (defaultProvider = "kokoro") => {
+    if (!providerPickerModal || !providerPickerOptions || !providerPickerConfirm) {
+      return Promise.resolve(normalizeProvider(defaultProvider));
+    }
+
+    providerPickerOptions.innerHTML = "";
+    const catalog = getProviderCatalog();
+    const normalizedDefault = normalizeProvider(defaultProvider);
+
+    catalog.forEach((item) => {
+      const label = document.createElement("label");
+      label.className = "toggle-pill";
+
+      const input = document.createElement("input");
+      input.type = "radio";
+      input.name = "provider-picker";
+      input.value = item.id;
+      input.checked = item.id === normalizedDefault;
+
+      const span = document.createElement("span");
+      const title = document.createElement("strong");
+      title.textContent = item.label;
+      const detail = document.createElement("span");
+      detail.className = "muted";
+      detail.textContent = ` — ${providerDescription(item.id)}`;
+      span.appendChild(title);
+      span.appendChild(detail);
+
+      label.appendChild(input);
+      label.appendChild(span);
+      providerPickerOptions.appendChild(label);
+    });
+
+    const selectedRadio = providerPickerOptions.querySelector('input[name="provider-picker"]:checked');
+    providerPickerConfirm.disabled = !selectedRadio;
+
+    return new Promise((resolve) => {
+      let resolved = false;
+      const teardown = () => {
+        providerPickerModal.hidden = true;
+        document.removeEventListener("keydown", onKeydown);
+        providerPickerOptions.removeEventListener("change", onChange);
+        providerPickerOverlay?.removeEventListener("click", onCancel);
+        providerPickerClose?.removeEventListener("click", onCancel);
+        providerPickerCancel?.removeEventListener("click", onCancel);
+        providerPickerConfirm?.removeEventListener("click", onConfirm);
+      };
+
+      const finish = (value) => {
+        if (resolved) return;
+        resolved = true;
+        teardown();
+        resolve(value);
+      };
+
+      const onCancel = () => finish(null);
+      const onConfirm = () => {
+        const selected = providerPickerOptions.querySelector('input[name="provider-picker"]:checked');
+        finish(selected ? normalizeProvider(selected.value) : null);
+      };
+      const onChange = () => {
+        const selected = providerPickerOptions.querySelector('input[name="provider-picker"]:checked');
+        providerPickerConfirm.disabled = !selected;
+      };
+      const onKeydown = (event) => {
+        if (event.key === "Escape") {
+          event.preventDefault();
+          onCancel();
+        }
+      };
+
+      providerPickerModal.hidden = false;
+      document.addEventListener("keydown", onKeydown);
+      providerPickerOptions.addEventListener("change", onChange);
+      providerPickerOverlay?.addEventListener("click", onCancel);
+      providerPickerClose?.addEventListener("click", onCancel);
+      providerPickerCancel?.addEventListener("click", onCancel);
+      providerPickerConfirm?.addEventListener("click", onConfirm);
+
+      const focusTarget = providerPickerOptions.querySelector('input[name="provider-picker"]:checked')
+        || providerPickerOptions.querySelector('input[name="provider-picker"]');
+      if (focusTarget instanceof HTMLElement) {
+        focusTarget.focus();
+      }
+    });
+  };
+
   const applyProviderToUI = () => {
     const provider = normalizeProvider(state.draft.provider);
     const isSupertonic = provider === "supertonic";
     if (providerSelect) {
       providerSelect.value = provider;
     }
+    if (languageField) {
+      languageField.hidden = isSupertonic;
+    }
     if (kokoroMixerEl) {
       kokoroMixerEl.hidden = isSupertonic;
     }
@@ -588,13 +707,12 @@ const setupVoiceMixer = () => {
     setStatus(`Loaded speaker “${name}”.`, "info", 2500);
   };
 
-  const createNewProfile = () => {
-    const isSupertonic = window.confirm("Create a Supertonic speaker?\n\nOK = Supertonic\nCancel = Kokoro");
+  const startNewProfile = (provider = "kokoro") => {
     state.selectedProfile = null;
     state.originalName = null;
     state.draft = {
       name: "",
-      provider: isSupertonic ? "supertonic" : "kokoro",
+      provider: normalizeProvider(provider),
       language: languageSelect ? languageSelect.value || "a" : "a",
       voices: new Map(),
       supertonic: {
@@ -608,6 +726,15 @@ const setupVoiceMixer = () => {
     loadSampleText();
   };
 
+  const requestNewProfile = async () => {
+    const chosen = await openProviderPicker(normalizeProvider(state.draft.provider));
+    if (!chosen) {
+      return;
+    }
+    startNewProfile(chosen);
+    setStatus("New speaker ready.", "info");
+  };
+
   const refreshProfiles = (nextProfiles, selectedName = null) => {
     profiles = nextProfiles || {};
     renderProfileList();
@@ -620,7 +747,7 @@ const setupVoiceMixer = () => {
       if (names.length) {
         selectProfile(names[0]);
       } else {
-        createNewProfile();
+        startNewProfile("kokoro");
       }
     }
     updateActionButtons();
@@ -657,7 +784,7 @@ const setupVoiceMixer = () => {
       name,
       originalName: state.originalName,
       provider: normalizeProvider(state.draft.provider),
-      language: languageSelect ? languageSelect.value : "a",
+      language: normalizeProvider(state.draft.provider) === "kokoro" ? (languageSelect ? languageSelect.value : "a") : "a",
       voices: normalizeProvider(state.draft.provider) === "kokoro" ? buildProfilePayload() : [],
       voice: state.draft.supertonic?.voice,
       total_steps: state.draft.supertonic?.total_steps,
@@ -964,8 +1091,7 @@ const setupVoiceMixer = () => {
       const action = target.dataset.action;
       if (!action) return;
       if (action === "new-profile") {
-        createNewProfile();
-        setStatus("New profile ready.", "info");
+        requestNewProfile();
       } else if (action === "import-profiles") {
         importInput?.click();
       } else if (action === "export-profiles") {
@@ -1023,7 +1149,7 @@ const setupVoiceMixer = () => {
 
   renderAvailableVoices();
   renderProfileList();
-  createNewProfile();
+  startNewProfile("kokoro");
 
   if (Object.keys(profiles).length) {
     const first = Object.keys(profiles).sort((a, b) => a.localeCompare(b))[0];

abogen/web/templates/voices.html

@@ -146,6 +146,25 @@
     </section>
   </div>
   <input id="voice-import-input" type="file" accept="application/json" hidden>
+
+  <div class="modal" data-role="provider-picker-modal" hidden>
+    <div class="modal__overlay" data-role="provider-picker-overlay" tabindex="-1"></div>
+    <div class="modal__content card card--modal" role="dialog" aria-modal="true" aria-labelledby="provider-picker-title">
+      <header class="modal__header">
+        <p class="modal__eyebrow">Speaker Studio</p>
+        <h2 class="modal__title" id="provider-picker-title">Choose a TTS provider</h2>
+        <button type="button" class="button button--ghost button--small" data-role="provider-picker-close" aria-label="Close provider picker">Close</button>
+      </header>
+      <div class="modal__body">
+        <p class="hint">Select which text-to-speech provider this speaker will use. You can create separate speakers per provider.</p>
+        <div class="field field--choices" data-role="provider-picker-options"></div>
+      </div>
+      <footer class="modal__footer">
+        <button type="button" class="button button--ghost" data-role="provider-picker-cancel">Cancel</button>
+        <button type="button" class="button" data-role="provider-picker-confirm">Continue</button>
+      </footer>
+    </div>
+  </div>
 </section>
 {% endblock %}