ci: fix duplicate triggers, pin macos-14 to avoid migration warning

refactor: migrate core modules to use TTSBackendMetadata.voices via registry

.github/workflows/test_pip.yml

@@ -2,6 +2,7 @@ name: pip install
 run-name: pip install
 on:
   push:
+    branches: [main]
     paths:
       - '**.py'
       - 'pyproject.toml'
@@ -15,18 +16,18 @@ jobs:
   install-and-run:
     strategy:
       matrix:
-        os: [ubuntu-latest, macos-latest, windows-latest]
+        os: [ubuntu-latest, macos-14, windows-latest]
         python-version: ['3.12']
       fail-fast: false
-    continue-on-error: true
     runs-on: ${{ matrix.os }}
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v7
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
+          cache: pip
       - name: Install from repository
         run: python -m pip install .
       #- name: Run abogen

.github/workflows/test_publish.yml

@@ -18,7 +18,7 @@ jobs:
   build:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v7
 
     - name: Login to Github Container Registry
       # Only if we need to push an image

README.md

@@ -38,8 +38,6 @@ This method handles everything automatically - installing all dependencies inclu
 #### <b>OPTION 2: Install using uv</b>
 First, [install uv](https://docs.astral.sh/uv/getting-started/installation/) if you haven't already.
 
-The CUDA extras install both GPU-accelerated Kokoro (via PyTorch) and Supertonic (via onnxruntime-gpu).
-
 ```bash
 # For NVIDIA GPUs (CUDA 12.8) - Recommended
 uv tool install --python 3.12 abogen[cuda] --extra-index-url https://download.pytorch.org/whl/cu128 --index-strategy unsafe-best-match
@@ -67,9 +65,6 @@ venv\Scripts\activate
 # We need to use an older version of PyTorch (2.8.0) until this issue is fixed: https://github.com/pytorch/pytorch/issues/166628
 pip install torch==2.8.0+cu128 torchvision==0.23.0+cu128 torchaudio==2.8.0 --index-url https://download.pytorch.org/whl/cu128
 
-# Also install onnxruntime-gpu for Supertonic GPU acceleration:
-pip install onnxruntime-gpu
-
 # For AMD GPUs:
 # Not supported yet, because ROCm is not available on Windows. Use Linux if you have AMD GPU.
 
@@ -178,7 +173,7 @@ Abogen offers **two interfaces**, but currently they have different feature sets
 
 | Command | Interface | Features |
 |---------|-----------|----------|
-| `abogen` | PyQt6 Desktop GUI | Stable core features + **Supertonic TTS**|
+| `abogen` | PyQt6 Desktop GUI | Stable core features |
 | `abogen-web` | Flask Web UI | Core features + **Supertonic TTS**, **LLM Normalization**, **Audiobookshelf Integration** and more! |
 
 > **Note:** The Web UI is under active development. We are working to integrate these new features into the PyQt desktop app. until then, the Web UI provides the most feature-rich experience.

WINDOWS_INSTALL.bat

@@ -323,13 +323,6 @@ if /I "%IS_NVIDIA%"=="true" (
             pause
             exit /b
         )
-        echo Installing onnxruntime-gpu for Supertonic GPU acceleration...
-        %PYTHON_CONSOLE_PATH% -m uv pip install --system onnxruntime-gpu
-        if errorlevel 1 (
-            echo Failed to install onnxruntime-gpu.
-            pause
-            exit /b
-        )
     ) else (
         echo CUDA is available on NVIDIA GPU.
     )
@@ -355,13 +348,6 @@ if /I "%IS_NVIDIA%"=="true" (
             pause
             exit /b
         )
-        echo Installing onnxruntime-gpu for Supertonic GPU acceleration...
-        %PYTHON_CONSOLE_PATH% -m uv pip install --system onnxruntime-gpu
-        if errorlevel 1 (
-            echo Failed to install onnxruntime-gpu.
-            pause
-            exit /b
-        )
     )
 )
 

abogen/constants.py

@@ -29,57 +29,6 @@ LANGUAGE_DESCRIPTIONS = {
     "z": "Mandarin Chinese",
 }
 
-# Mapping from Kokoro single-letter language codes to ISO 3166-1 alpha-2 country codes
-# Used for loading flag icons
-KOKORO_LANG_TO_COUNTRY = {
-    "a": "us",  # American English -> United States
-    "b": "gb",  # British English -> United Kingdom
-    "e": "es",  # Spanish -> Spain
-    "f": "fr",  # French -> France
-    "h": "in",  # Hindi -> India
-    "i": "it",  # Italian -> Italy
-    "j": "jp",  # Japanese -> Japan
-    "p": "br",  # Brazilian Portuguese -> Brazil
-    "z": "cn",  # Mandarin Chinese -> China
-}
-
-# Mapping from Supertonic ISO 639-1 language codes to ISO 3166-1 alpha-2 country codes
-# Used for loading flag icons in the Supertonic language picker
-SUPERTONIC_LANG_TO_COUNTRY = {
-    "en": "gb",
-    "ko": "kr",
-    "ja": "jp",
-    "ar": "ae",
-    "bg": "bg",
-    "cs": "cz",
-    "da": "dk",
-    "de": "de",
-    "el": "gr",
-    "es": "es",
-    "et": "ee",
-    "fi": "fi",
-    "fr": "fr",
-    "hi": "in",
-    "hr": "hr",
-    "hu": "hu",
-    "id": "id",
-    "it": "it",
-    "lt": "lt",
-    "lv": "lv",
-    "nl": "nl",
-    "pl": "pl",
-    "pt": "pt",
-    "ro": "ro",
-    "ru": "ru",
-    "sk": "sk",
-    "sl": "si",
-    "sv": "se",
-    "tr": "tr",
-    "uk": "ua",
-    "vi": "vn",
-    "na": "na",
-}
-
 # Supported sound formats
 SUPPORTED_SOUND_FORMATS = [
     "wav",

abogen/pyqt/conversion.py

@@ -5,6 +5,7 @@ import hashlib  # For generating unique cache filenames
 from platformdirs import user_desktop_dir
 from PyQt6.QtCore import QThread, pyqtSignal, Qt, QTimer
 from PyQt6.QtWidgets import QCheckBox, QVBoxLayout, QDialog, QLabel, QDialogButtonBox
+import numpy as np
 import soundfile as sf
 from abogen.utils import (
     create_process,
@@ -20,7 +21,6 @@ from abogen.constants import (
     SUPPORTED_SOUND_FORMATS,
     SUPPORTED_SUBTITLE_FORMATS,
 )
-from abogen.tts_supertonic import SupertonicPipeline, SUPERTONIC_AVAILABLE_LANGS, DEFAULT_SUPERTONIC_VOICES
 from abogen.voice_formulas import get_new_voice
 import abogen.hf_tracker as hf_tracker
 import static_ffmpeg
@@ -260,30 +260,21 @@ class ConversionThread(QThread):
         output_folder,
         subtitle_mode,
         output_format,
-        np_module,
+        backend,
-        kpipeline_class,
         start_time,
         total_char_count,
         use_gpu=True,
         from_queue=False,
         save_base_path=None,
-        tts_provider="kokoro",
+    ):  # Add use_gpu parameter
-        supertonic_language="en",
-        supertonic_total_steps=8,
-    ):
         super().__init__()
         self._chapter_options_event = threading.Event()
         self._timestamp_response_event = threading.Event()
-        self.np = np_module
+        self.backend = backend
-        self.KPipeline = kpipeline_class
         self.file_name = file_name
         self.lang_code = lang_code
         self.speed = speed
         self.voice = voice
-        self.tts_provider = tts_provider
-        self.supertonic_language = supertonic_language
-        self.supertonic_total_steps = supertonic_total_steps
-        self.sample_rate = 44100 if tts_provider == "supertonic" else 24000
         self.save_option = save_option
         self.output_folder = output_folder
         self.subtitle_mode = subtitle_mode
@@ -435,10 +426,6 @@ class ConversionThread(QThread):
             )
             self.log_updated.emit(f"- Voice: {self.voice}")
             self.log_updated.emit(f"- Speed: {self.speed}")
-            tts_provider_label = self.tts_provider.capitalize()
-            if self.tts_provider == "supertonic":
-                tts_provider_label += f" (lang={self.supertonic_language}, steps={self.supertonic_total_steps})"
-            self.log_updated.emit(f"- TTS Engine: {tts_provider_label}")
             self.log_updated.emit(f"- Subtitle mode: {self.subtitle_mode}")
             self.log_updated.emit(f"- Output format: {self.output_format}")
             self.log_updated.emit(
@@ -502,26 +489,6 @@ class ConversionThread(QThread):
 
             self.log_updated.emit(("\nInitializing TTS pipeline...", "grey"))
 
-            # Set device based on use_gpu setting and platform
-            if self.use_gpu:
-                if platform.system() == "Darwin" and platform.processor() == "arm":
-                    device = "mps"  # Use MPS for Apple Silicon
-                else:
-                    device = "cuda"  # Use CUDA for other platforms
-            else:
-                device = "cpu"
-
-            if self.tts_provider == "supertonic":
-                tts = SupertonicPipeline(
-                    sample_rate=self.sample_rate,
-                    lang=self.supertonic_language,
-                    total_steps=self.supertonic_total_steps,
-                )
-            else:
-                tts = self.KPipeline(
-                    lang_code=self.lang_code, repo_id="hexgrad/Kokoro-82M", device=device
-                )
-
             # Check if the input is a subtitle file or timestamp text file
             is_subtitle_file = False
             is_timestamp_text = False
@@ -557,7 +524,7 @@ class ConversionThread(QThread):
 
             # Process subtitle files separately
             if is_subtitle_file or is_timestamp_text:
-                self._process_subtitle_file(tts, base_path, is_timestamp_text)
+                self._process_subtitle_file(self.backend, base_path, is_timestamp_text)
                 return
 
             if self.is_direct_text:
@@ -766,7 +733,7 @@ class ConversionThread(QThread):
                 merged_out_path = f"{base_filepath_no_ext}.{self.output_format}"
                 subtitle_entries = []
                 current_time = 0.0
-                rate = self.sample_rate
+                rate = 24000
                 subtitle_mode = self.subtitle_mode
                 self.etr_start_time = time.time()
                 self.processed_char_count = 0
@@ -782,7 +749,7 @@ class ConversionThread(QThread):
                     merged_out_file = sf.SoundFile(
                         merged_out_path,
                         "w",
-                        samplerate=self.sample_rate,
+                        samplerate=24000,
                         channels=1,
                         format=self.output_format,
                     )
@@ -804,7 +771,51 @@ class ConversionThread(QThread):
                         "-f",
                         "f32le",
                         "-ar",
-                            str(self.sample_rate),
+                        "24000",
+                        "-ac",
+                        "1",
+                        "-i",
+                        "pipe:0",
+                    ]
+                    if cover_path and os.path.exists(cover_path):
+                        cmd.extend(
+                            [
+                                "-i",
+                                cover_path,
+                                "-map",
+                                "0:a",
+                                "-map",
+                                "1",
+                                "-c:v",
+                                "copy",
+                                "-disposition:v",
+                                "attached_pic",
+                            ]
+                        )
+                    cmd.extend(
+                        [
+                            "-c:a",
+                            "aac",
+                            "-q:a",
+                            "2",
+                            "-movflags",
+                            "+faststart+use_metadata_tags",
+                        ]
+                    )
+                    cmd += metadata_options
+                    cmd.append(merged_out_path)
+                    ffmpeg_proc = create_process(cmd, stdin=subprocess.PIPE, text=False)
+                elif self.output_format == "opus":
+                    static_ffmpeg.add_paths()
+                    cmd = [
+                        "ffmpeg",
+                        "-y",
+                        "-thread_queue_size",
+                        "32768",
+                        "-f",
+                        "f32le",
+                        "-ar",
+                        "24000",
                         "-ac",
                         "1",
                         "-i",
@@ -891,7 +902,7 @@ class ConversionThread(QThread):
                 merged_out_path = None
                 subtitle_entries = []
                 current_time = 0.0
-                rate = self.sample_rate
+                rate = 24000
                 subtitle_mode = self.subtitle_mode
                 self.etr_start_time = time.time()
                 self.processed_char_count = 0
@@ -944,7 +955,7 @@ class ConversionThread(QThread):
                         chapter_out_file = sf.SoundFile(
                             chapter_out_path,
                             "w",
-                            samplerate=self.sample_rate,
+                            samplerate=24000,
                             channels=1,
                             format=separate_chapters_format,
                         )
@@ -959,7 +970,7 @@ class ConversionThread(QThread):
                             "-f",
                             "f32le",
                             "-ar",
-                            str(self.sample_rate),
+                            "24000",
                             "-ac",
                             "1",
                             "-i",
@@ -1046,7 +1057,7 @@ class ConversionThread(QThread):
                 for segment_idx, (voice_name, segment_text) in enumerate(voice_segments):
                     # Load voice for this segment (with caching)
                     try:
-                        loaded_voice = self.load_voice_cached(voice_name, tts)
+                        loaded_voice = self.load_voice_cached(voice_name, self.backend)
                         if segment_idx > 0:
                             voice_display = voice_name if len(voice_name) < 50 else voice_name[:47] + "..."
                             self.log_updated.emit((f"  → Voice: {voice_display}", "grey"))
@@ -1055,7 +1066,7 @@ class ConversionThread(QThread):
                             (f"⚠ Voice loading error for '{voice_name}', continuing with previous", "orange")
                         )
                         if segment_idx == 0:
-                            loaded_voice = self.load_voice_cached(self.voice, tts)
+                            loaded_voice = self.load_voice_cached(self.voice, self.backend)
 
                     # Determine if spaCy segmentation should be used for PRE-TTS segmentation
                     # Only non-English languages use spaCy for pre-segmentation
@@ -1141,7 +1152,7 @@ class ConversionThread(QThread):
                         print("Using split pattern: (unprintable)")
 
                     for text_segment in text_segments:
-                        for result in tts(
+                        for result in self.backend(
                             text_segment,
                             voice=loaded_voice,
                             speed=self.speed,
@@ -1341,9 +1352,9 @@ class ConversionThread(QThread):
                 # Add silence between chapters for merged output (except after the last chapter)
                 if merge_chapters_at_end and chapter_idx < total_chapters:
                     silence_samples = int(
-                        self.silence_duration * self.sample_rate
+                        self.silence_duration * 24000
                     )  # Silence duration at 24,000 Hz
-                    silence_audio = self.np.zeros(silence_samples, dtype="float32")
+                    silence_audio = np.zeros(silence_samples, dtype="float32")
                     silence_bytes = silence_audio.tobytes()
 
                     if merged_out_file:
@@ -1572,7 +1583,7 @@ class ConversionThread(QThread):
                 parent_dir, f"{sanitized_base_name}{suffix}"
             )
             merged_out_path = f"{base_filepath_no_ext}.{self.output_format}"
-            rate = self.sample_rate
+            rate = 24000
 
             # Setup audio output
             merged_out_file, ffmpeg_proc = None, None
@@ -1682,7 +1693,7 @@ class ConversionThread(QThread):
             max_end_time = max(
                 (end for _, end, _ in subtitles if end is not None), default=0
             )
-            audio_buffer = self.np.zeros(
+            audio_buffer = np.zeros(
                 int(max_end_time * rate) + rate, dtype="float32"
             )
 
@@ -1746,7 +1757,7 @@ class ConversionThread(QThread):
                 # Generate TTS audio
                 tts_results = [
                     r
-                    for r in tts(
+                    for r in self.backend(
                         processed_text,
                         voice=loaded_voice,
                         speed=self.speed,
@@ -1764,11 +1775,11 @@ class ConversionThread(QThread):
 
                 # Concatenate audio and determine duration
                 full_audio = (
-                    self.np.concatenate(
+                    np.concatenate(
                         [a.numpy() if hasattr(a, "numpy") else a for a in audio_chunks]
                     )
                     if audio_chunks
-                    else self.np.zeros(
+                    else np.zeros(
                         int((subtitle_duration or 0) * rate), dtype="float32"
                     )
                 )
@@ -1802,8 +1813,8 @@ class ConversionThread(QThread):
                         num_stages = max(
                             1,
                             int(
-                                self.np.ceil(
+                                np.ceil(
-                                    self.np.log(speed_factor) / self.np.log(2.0)
+                                    np.log(speed_factor) / np.log(2.0)
                                 )
                             ),
                         )
@@ -1836,7 +1847,7 @@ class ConversionThread(QThread):
                             stdout=subprocess.PIPE,
                             stderr=subprocess.PIPE,
                         )
-                        full_audio = self.np.frombuffer(
+                        full_audio = np.frombuffer(
                             speed_proc.communicate(input=full_audio.tobytes())[0],
                             dtype="float32",
                         )
@@ -1850,7 +1861,7 @@ class ConversionThread(QThread):
 
                         tts_results = [
                             r
-                            for r in tts(
+                            for r in self.backend(
                                 processed_text,
                                 voice=loaded_voice,
                                 speed=new_speed,
@@ -1861,14 +1872,14 @@ class ConversionThread(QThread):
                         audio_chunks = [r.audio for r in tts_results]
 
                         full_audio = (
-                            self.np.concatenate(
+                            np.concatenate(
                                 [
                                     a.numpy() if hasattr(a, "numpy") else a
                                     for a in audio_chunks
                                 ]
                             )
                             if audio_chunks
-                            else self.np.zeros(
+                            else np.zeros(
                                 int(subtitle_duration * rate), dtype="float32"
                             )
                         )
@@ -1885,10 +1896,10 @@ class ConversionThread(QThread):
                 # Pad or trim to subtitle duration
                 target_samples = int(subtitle_duration * rate)
                 if len(full_audio) < target_samples:
-                    full_audio = self.np.concatenate(
+                    full_audio = np.concatenate(
                         [
                             full_audio,
-                            self.np.zeros(
+                            np.zeros(
                                 target_samples - len(full_audio), dtype="float32"
                             ),
                         ]
@@ -1901,10 +1912,10 @@ class ConversionThread(QThread):
                 end_sample = start_sample + len(full_audio)
                 if end_sample > len(audio_buffer):
                     # Extend buffer if needed
-                    audio_buffer = self.np.concatenate(
+                    audio_buffer = np.concatenate(
                         [
                             audio_buffer,
-                            self.np.zeros(
+                            np.zeros(
                                 end_sample - len(audio_buffer), dtype="float32"
                             ),
                         ]
@@ -1946,7 +1957,7 @@ class ConversionThread(QThread):
                 self.progress_updated.emit(percent, etr_str)
 
             # Normalize audio buffer to prevent clipping from mixed overlaps
-            max_amplitude = self.np.abs(audio_buffer).max()
+            max_amplitude = np.abs(audio_buffer).max()
             if max_amplitude > 1.0:
                 self.log_updated.emit(
                     f"\n  -> Normalizing audio (peak: {max_amplitude:.2f})"
@@ -2415,28 +2426,19 @@ class VoicePreviewThread(QThread):
 
     def __init__(
         self,
-        np_module,
+        backend,
-        kpipeline_class,
         lang_code,
         voice,
         speed,
         use_gpu=False,
         parent=None,
-        tts_provider="kokoro",
-        supertonic_language="en",
-        supertonic_total_steps=8,
     ):
         super().__init__(parent)
-        self.np_module = np_module
+        self.backend = backend
-        self.kpipeline_class = kpipeline_class
         self.lang_code = lang_code
         self.voice = voice
         self.speed = speed
         self.use_gpu = use_gpu
-        self.tts_provider = tts_provider
-        self.supertonic_language = supertonic_language
-        self.supertonic_total_steps = supertonic_total_steps
-        self.sample_rate = 44100 if tts_provider == "supertonic" else 24000
 
         # Cache location for preview audio
         self.cache_dir = get_user_cache_path("preview_cache")
@@ -2446,11 +2448,6 @@ class VoicePreviewThread(QThread):
 
     def _get_cache_path(self):
         """Generate a unique filename for the voice with its parameters"""
-        if self.tts_provider == "supertonic":
-            voice_id = self.voice or "M1"
-            filename = f"st_{voice_id}_{self.supertonic_language}_steps{self.supertonic_total_steps}_{self.speed:.2f}.wav"
-            return os.path.join(self.cache_dir, filename)
-
         # For a voice formula, use a hash of the formula
         if "*" in self.voice:
             voice_id = (
@@ -2465,56 +2462,27 @@ class VoicePreviewThread(QThread):
 
     def run(self):
         print(
-            f"\nVoice: {self.voice}\nLanguage: {self.lang_code}\nSpeed: {self.speed}\nGPU: {self.use_gpu}\nTTS Provider: {self.tts_provider}\n"
+            f"\nVoice: {self.voice}\nLanguage: {self.lang_code}\nSpeed: {self.speed}\nGPU: {self.use_gpu}\n"
         )
 
         # Generate the preview and save to cache
         try:
-            if self.tts_provider == "supertonic":
-                from abogen.tts_supertonic import SupertonicPipeline
 
-                tts = SupertonicPipeline(
+            # Enable voice formula support for preview
-                    sample_rate=self.sample_rate,
-                    lang=self.supertonic_language,
-                    total_steps=self.supertonic_total_steps,
-                )
-                loaded_voice = self.voice or "M1"
-                sample_text = "Hello, this is a sample of the selected voice."
-                audio_segments = []
-                for result in tts(
-                    sample_text,
-                    voice=loaded_voice,
-                    speed=self.speed,
-                    split_pattern=None,
-                ):
-                    audio_segments.append(result.audio)
-            else:
-                # Set device based on use_gpu setting and platform
-                if self.use_gpu:
-                    if platform.system() == "Darwin" and platform.processor() == "arm":
-                        device = "mps"
-                    else:
-                        device = "cuda"
-                else:
-                    device = "cpu"
-
-                tts = self.kpipeline_class(
-                    lang_code=self.lang_code, repo_id="hexgrad/Kokoro-82M", device=device
-                )
             if "*" in self.voice:
-                    loaded_voice = get_new_voice(tts, self.voice, self.use_gpu)
+                loaded_voice = get_new_voice(self.backend, self.voice, self.use_gpu)
             else:
                 loaded_voice = self.voice
             sample_text = get_sample_voice_text(self.lang_code)
             audio_segments = []
-                for result in tts(
+            for result in self.backend(
                 sample_text, voice=loaded_voice, speed=self.speed, split_pattern=None
             ):
                 audio_segments.append(result.audio)
-
             if audio_segments:
-                audio = self.np_module.concatenate(audio_segments)
+                audio = np.concatenate(audio_segments)
-                sf.write(self.cache_path, audio, self.sample_rate)
+                # Save directly to the cache path
+                sf.write(self.cache_path, audio, 24000)
                 self.temp_wav = self.cache_path
             self.finished.emit()
         except Exception as e:

abogen/pyqt/gui.py

@@ -9,7 +9,6 @@ from abogen.pyqt.queue_manager_gui import QueueManager
 from abogen.pyqt.queued_item import QueuedItem
 import abogen.hf_tracker as hf_tracker
 import hashlib  # Added for cache path generation
-from abogen.tts_supertonic import SUPERTONIC_AVAILABLE_LANGS, DEFAULT_SUPERTONIC_VOICES
 from PyQt6.QtWidgets import (
     QApplication,
     QWidget,
@@ -84,8 +83,6 @@ from abogen.constants import (
     PROGRAM_DESCRIPTION,
     LANGUAGE_DESCRIPTIONS,
     VOICES_INTERNAL,
-    KOKORO_LANG_TO_COUNTRY,
-    SUPERTONIC_LANG_TO_COUNTRY,
     SUPPORTED_LANGUAGES_FOR_SUBTITLE_GENERATION,
     COLORS,
     SUBTITLE_FORMATS,
@@ -973,9 +970,6 @@ class abogen(QWidget):
         self.fix_nonstandard_punctuation = self.config.get(
             "fix_nonstandard_punctuation", False
         )
-        self.tts_provider_config = self.config.get("tts_provider", "kokoro")
-        self.supertonic_language_config = self.config.get("supertonic_language", "en")
-        self.supertonic_total_steps_config = self.config.get("supertonic_total_steps", 8)
         self._pending_close_event = None
         self.gpu_ok = False  # Initialize GPU availability status
 
@@ -1024,16 +1018,6 @@ class abogen(QWidget):
                 else:
                     self.mixed_voice_state = entry
                     self.selected_lang = entry[0][0] if entry and entry[0] else None
-        # Restore TTS provider and supertonic settings from config
-        provider_text = "Supertonic" if self.tts_provider_config == "supertonic" else "Kokoro"
-        idx_st = self.provider_combo.findText(provider_text)
-        if idx_st >= 0:
-            self.provider_combo.setCurrentIndex(idx_st)
-        self.st_lang_combo.setCurrentText(self.supertonic_language_config)
-        idx_steps = self.st_steps_combo.findData(self.supertonic_total_steps_config)
-        if idx_steps >= 0:
-            self.st_steps_combo.setCurrentIndex(idx_steps)
-
         if self.save_option == "Choose output folder" and self.selected_output_folder:
             self.save_path_label.setText(self.selected_output_folder)
             self.save_path_row_widget.show()
@@ -1123,53 +1107,6 @@ class abogen(QWidget):
         speed_layout.addWidget(self.speed_label)
         controls_layout.addLayout(speed_layout)
         self.speed_slider.valueChanged.connect(self.update_speed_label)
-
-        # TTS Provider selection
-        provider_layout = QHBoxLayout()
-        provider_layout.setSpacing(7)
-        provider_label = QLabel("TTS Engine:", self)
-        provider_layout.addWidget(provider_label)
-        self.provider_combo = QComboBox(self)
-        self.provider_combo.addItem("Kokoro", "kokoro")
-        self.provider_combo.addItem("Supertonic", "supertonic")
-        self.provider_combo.setStyleSheet("QComboBox { min-height: 20px; padding: 6px 12px; }")
-        self.provider_combo.currentIndexChanged.connect(self.on_provider_changed)
-        provider_layout.addWidget(self.provider_combo)
-        controls_layout.addLayout(provider_layout)
-
-        # Supertonic-specific controls (language + steps), hidden by default
-        self.supertonic_row = QWidget()
-        supertonic_row_layout = QHBoxLayout(self.supertonic_row)
-        supertonic_row_layout.setContentsMargins(0, 0, 0, 0)
-        supertonic_row_layout.setSpacing(7)
-
-        st_lang_label = QLabel("Language:", self)
-        supertonic_row_layout.addWidget(st_lang_label)
-        self.st_lang_combo = QComboBox(self)
-        for code in SUPERTONIC_AVAILABLE_LANGS:
-            country_code = SUPERTONIC_LANG_TO_COUNTRY.get(code, code)
-            flag = get_resource_path("abogen.assets.flags", f"{country_code}.png")
-            icon_st = QIcon(flag) if flag and os.path.exists(flag) else QIcon()
-            self.st_lang_combo.addItem(icon_st, code, code)
-        self.st_lang_combo.setCurrentText("en")
-        self.st_lang_combo.setStyleSheet("QComboBox { min-height: 20px; padding: 6px 12px; }")
-        self.st_lang_combo.currentTextChanged.connect(self._on_st_lang_changed)
-        supertonic_row_layout.addWidget(self.st_lang_combo)
-
-        st_steps_label = QLabel("Steps:", self)
-        supertonic_row_layout.addWidget(st_steps_label)
-        self.st_steps_combo = QComboBox(self)
-        for val in range(2, 16):
-            self.st_steps_combo.addItem(str(val), val)
-        self.st_steps_combo.setCurrentIndex(self.st_steps_combo.findData(8))
-        self.st_steps_combo.setStyleSheet("QComboBox { min-height: 20px; padding: 6px 12px; }")
-        self.st_steps_combo.currentIndexChanged.connect(self._on_st_steps_changed)
-        supertonic_row_layout.addWidget(self.st_steps_combo)
-
-        supertonic_row_layout.addStretch()
-        self.supertonic_row.hide()
-        controls_layout.addWidget(self.supertonic_row)
-
         # Voice selection
         voice_layout = QHBoxLayout()
         voice_layout.setSpacing(7)
@@ -1827,12 +1764,6 @@ class abogen(QWidget):
         Update the enabled state of subtitle options based on the selected language.
         For non-English languages, only sentence-based and line-based modes are supported.
         """
-        provider = self.provider_combo.currentData()
-        if provider == "supertonic":
-            self.subtitle_combo.setEnabled(False)
-            self.subtitle_format_combo.setEnabled(False)
-            return
-
         # Check if current file is a subtitle file
         is_subtitle_input = False
         if self.selected_file and self.selected_file.lower().endswith(
@@ -1892,48 +1823,6 @@ class abogen(QWidget):
         # Enable/disable subtitle options based on language
         self.update_subtitle_options_availability()
 
-    def on_provider_changed(self, index):
-        provider = self.provider_combo.itemData(index)
-        self.config["tts_provider"] = provider
-        save_config(self.config)
-        is_supertonic = provider == "supertonic"
-
-        # Show/hide Supertonic controls
-        self.supertonic_row.setVisible(is_supertonic)
-
-        # Update subtitles availability
-        self.update_subtitle_options_availability()
-
-        # Repopulate voice list
-        self.populate_profiles_in_voice_combo()
-
-        # Clear/reset mixed voice state when switching provider
-        if is_supertonic:
-            self.mixed_voice_state = None
-            self.btn_voice_formula_mixer.setEnabled(False)
-            self.voice_combo.setToolTip(
-                "Supertonic voices:\n"
-                "M1-M5 = Male voices\n"
-                "F1-F5 = Female voices"
-            )
-        else:
-            self.btn_voice_formula_mixer.setEnabled(True)
-            self.voice_combo.setToolTip(
-                "The first character represents the language:\n"
-                '"a" => American English\n"b" => British English\n"e" => Spanish\n"f" => French\n"h" => Hindi\n"i" => Italian\n"j" => Japanese\n"p" => Brazilian Portuguese\n"z" => Mandarin Chinese\nThe second character represents the gender:\n"m" => Male\n"f" => Female'
-            )
-
-    def _on_st_lang_changed(self, lang):
-        self.config["supertonic_language"] = lang
-        save_config(self.config)
-        if self.provider_combo.currentData() == "supertonic":
-            self.selected_lang = lang
-            self.update_subtitle_options_availability()
-
-    def _on_st_steps_changed(self):
-        self.config["supertonic_total_steps"] = self.st_steps_combo.currentData()
-        save_config(self.config)
-
     def on_voice_combo_changed(self, index):
         data = self.voice_combo.itemData(index)
         if isinstance(data, str) and data.startswith("profile:"):
@@ -1942,24 +1831,8 @@ class abogen(QWidget):
             from abogen.voice_profiles import load_profiles
 
             entry = load_profiles().get(pname, {})
+            # set mixed voices and language
             if isinstance(entry, dict):
-                entry_provider = str(entry.get("provider", "")).strip().lower()
-                if entry_provider == "supertonic":
-                    # Switch provider to Supertonic if not already
-                    if self.provider_combo.currentData() != "supertonic":
-                        self.provider_combo.setCurrentIndex(1)
-                    self.mixed_voice_state = None
-                    self.selected_lang = entry.get("language", self.st_lang_combo.currentText())
-                    # Sync supertonic controls from profile
-                    profile_steps = entry.get("total_steps")
-                    if profile_steps is not None:
-                        idx_steps = self.st_steps_combo.findData(int(profile_steps))
-                        if idx_steps >= 0:
-                            self.st_steps_combo.setCurrentIndex(idx_steps)
-                    profile_lang = entry.get("language")
-                    if profile_lang and profile_lang in SUPERTONIC_AVAILABLE_LANGS:
-                        self.st_lang_combo.setCurrentText(profile_lang)
-                else:
                 self.mixed_voice_state = entry.get("voices", [])
                 self.selected_lang = entry.get("language")
             else:
@@ -1974,12 +1847,7 @@ class abogen(QWidget):
         else:
             self.mixed_voice_state = None
             self.selected_profile_name = None
-            self.selected_voice = data
+            self.selected_voice, self.selected_lang = data, data[0]
-            provider = self.provider_combo.currentData()
-            if provider == "supertonic":
-                self.selected_lang = self.st_lang_combo.currentText()
-            else:
-                self.selected_lang = data[0] if data else ""
             self.config["selected_voice"] = data
             if "selected_profile_name" in self.config:
                 del self.config["selected_profile_name"]
@@ -1998,37 +1866,16 @@ class abogen(QWidget):
     def populate_profiles_in_voice_combo(self):
         # preserve current voice or profile
         current = self.voice_combo.currentData()
-        provider = self.provider_combo.currentData()
         self.voice_combo.blockSignals(True)
         self.voice_combo.clear()
-        # re-add profiles matching current provider
+        # re-add profiles
         profile_icon = QIcon(get_resource_path("abogen.assets", "profile.png"))
-        for pname, entry in load_profiles().items():
+        for pname in load_profiles().keys():
-            entry_provider = ""
-            if isinstance(entry, dict):
-                entry_provider = str(entry.get("provider", "")).strip().lower()
-            if provider == "supertonic":
-                if entry_provider == "supertonic":
-                    self.voice_combo.addItem(profile_icon, pname, f"profile:{pname}")
-            else:
-                if entry_provider != "supertonic":
             self.voice_combo.addItem(profile_icon, pname, f"profile:{pname}")
         # re-add voices
-        if provider == "supertonic":
-            for v in DEFAULT_SUPERTONIC_VOICES:
-                icon = QIcon()
-                if v.startswith("F"):
-                    icon_path = get_resource_path("abogen.assets", "female.png")
-                else:
-                    icon_path = get_resource_path("abogen.assets", "male.png")
-                if icon_path and os.path.exists(icon_path):
-                    icon = QIcon(icon_path)
-                self.voice_combo.addItem(icon, f"{v}", v)
-        else:
         for v in VOICES_INTERNAL:
             icon = QIcon()
-                country_code = KOKORO_LANG_TO_COUNTRY.get(v[0], v[0])
+            flag_path = get_resource_path("abogen.assets.flags", f"{v[0]}.png")
-                flag_path = get_resource_path("abogen.assets.flags", f"{country_code}.png")
             if flag_path and os.path.exists(flag_path):
                 icon = QIcon(flag_path)
             self.voice_combo.addItem(icon, f"{v}", v)
@@ -2222,9 +2069,6 @@ class abogen(QWidget):
             save_base_path=save_base_path,
             save_chapters_separately=getattr(self, "save_chapters_separately", None),
             merge_chapters_at_end=getattr(self, "merge_chapters_at_end", None),
-            tts_provider=self.provider_combo.currentData(),
-            supertonic_language=self.st_lang_combo.currentText(),
-            supertonic_total_steps=self.st_steps_combo.currentData(),
         )
 
         # Prevent adding duplicate items to the queue
@@ -2368,15 +2212,6 @@ class abogen(QWidget):
                 self.config["replace_numerals"] = self.replace_numerals
                 self.config["fix_nonstandard_punctuation"] = self.fix_nonstandard_punctuation
 
-                # TTS provider settings
-                tts_provider = getattr(queued_item, "tts_provider", "kokoro")
-                self.provider_combo.setCurrentText("Supertonic" if tts_provider == "supertonic" else "Kokoro")
-                self.st_lang_combo.setCurrentText(getattr(queued_item, "supertonic_language", "en"))
-                steps_val = getattr(queued_item, "supertonic_total_steps", 8)
-                idx_steps = self.st_steps_combo.findData(steps_val)
-                if idx_steps >= 0:
-                    self.st_steps_combo.setCurrentIndex(idx_steps)
-
                 # Sync Voice/Profile in config
                 self.config["selected_voice"] = self.selected_voice
                 if "selected_profile_name" in self.config:
@@ -2399,8 +2234,6 @@ class abogen(QWidget):
             self.current_queue_index = 0  # Reset for next time
 
     def get_voice_formula(self) -> str:
-        if self.provider_combo.currentData() == "supertonic":
-            return self._get_supertonic_voice()
         if self.mixed_voice_state:
             formula_components = [
                 f"{name}*{weight}" for name, weight in self.mixed_voice_state
@@ -2410,8 +2243,6 @@ class abogen(QWidget):
             return self.selected_voice
 
     def get_selected_lang(self, voice_formula) -> str:
-        if self.provider_combo.currentData() == "supertonic":
-            return self.st_lang_combo.currentText()
         if self.selected_profile_name:
             from abogen.voice_profiles import load_profiles
 
@@ -2485,9 +2316,9 @@ class abogen(QWidget):
             file_size_str = "Unknown"
 
         # pipeline_loaded_callback remains unchanged
-        def pipeline_loaded_callback(np_module, kpipeline_class, error):
+        def pipeline_loaded_callback(backend, error):
             if error:
-                self.update_log((f"Error loading numpy or KPipeline: {error}", "red"))
+                self.update_log((f"Error loading TTS backend: {error}", "red"))
                 prevent_sleep_end()
                 return
 
@@ -2501,10 +2332,6 @@ class abogen(QWidget):
             # determine selected language: use profile setting if profile selected, else voice code
             selected_lang = self.get_selected_lang(voice_formula)
 
-            tts_provider = self.provider_combo.currentData()
-            supertonic_language = self.st_lang_combo.currentText()
-            supertonic_total_steps = self.st_steps_combo.currentData()
-
             self.conversion_thread = ConversionThread(
                 self.selected_file,
                 selected_lang,
@@ -2514,17 +2341,13 @@ class abogen(QWidget):
                 self.selected_output_folder,
                 subtitle_mode=actual_subtitle_mode,
                 output_format=self.selected_format,
-                np_module=np_module,
+                backend=backend,
-                kpipeline_class=kpipeline_class,
                 start_time=self.start_time,
                 total_char_count=self.char_count,
                 use_gpu=self.gpu_ok,
                 from_queue=from_queue,
-                save_base_path=self.displayed_file_path,
+                save_base_path=self.displayed_file_path,  # Pass the save base path (original file for EPUB)
-                tts_provider=tts_provider,
+            )  # Use gpu_ok status
-                supertonic_language=supertonic_language,
-                supertonic_total_steps=supertonic_total_steps,
-            )
             # Pass the displayed file path to the log_updated signal handler in ConversionThread
             self.conversion_thread.display_path = display_path
             # Pass the file size string
@@ -2601,14 +2424,21 @@ class abogen(QWidget):
             # Store gpu_ok status to use when creating the conversion thread
             self.gpu_ok = gpu_ok
             self.update_log((gpu_msg, gpu_ok))
-            tts_provider = self.provider_combo.currentData()
-            if tts_provider == "supertonic":
-                # Supertonic doesn't need KPipeline, call callback directly
-                import numpy as np
-                pipeline_loaded_callback(np, None, None)
-            else:
             self.update_log("Loading modules...")
-                load_thread = LoadPipelineThread(pipeline_loaded_callback)
+
+            # Determine device based on GPU availability
+            if gpu_ok:
+                if platform.system() == "Darwin" and platform.processor() == "arm":
+                    device = "mps"
+                else:
+                    device = "cuda"
+            else:
+                device = "cpu"
+
+            lang_code = self.selected_lang or "a"
+            load_thread = LoadPipelineThread(
+                pipeline_loaded_callback, lang_code=lang_code, device=device
+            )
             load_thread.start()
 
         threading.Thread(target=gpu_and_load, daemon=True).start()
@@ -2922,32 +2752,9 @@ class abogen(QWidget):
                 "Open File Error", f"Could not open file:\n{e}"
             )
 
-    def _get_supertonic_voice(self) -> str:
-        """Resolve the effective Supertonic voice from the current combo selection."""
-        if self.selected_profile_name:
-            from abogen.voice_profiles import load_profiles
-            entry = load_profiles().get(self.selected_profile_name, {})
-            if isinstance(entry, dict):
-                return str(entry.get("voice", "M1"))
-            return "M1"
-        current_data = self.voice_combo.currentData()
-        if current_data and isinstance(current_data, str) and not current_data.startswith("profile:"):
-            return current_data
-        return "M1"
-
     def _get_preview_cache_path(self):
         """Generate the expected cache path for the current voice settings."""
         speed = self.speed_slider.value() / 100.0
-        provider = self.provider_combo.currentData()
-
-        if provider == "supertonic":
-            voice_to_cache = self._get_supertonic_voice()
-            lang_to_cache = self.st_lang_combo.currentText()
-            steps = self.st_steps_combo.currentData()
-            cache_dir = get_user_cache_path("preview_cache")
-            filename = f"st_{voice_to_cache}_{lang_to_cache}_steps{steps}_{speed:.2f}.wav"
-            return os.path.join(cache_dir, filename)
-
         voice_to_cache = ""
         lang_to_cache = ""
 
@@ -3052,13 +2859,6 @@ class abogen(QWidget):
         self.btn_voice_formula_mixer.setEnabled(False)  # Disable mixer button
         self.btn_start.setEnabled(False)  # Disable start button during preview
 
-        # For Supertonic, skip KPipeline loading and use SupertonicPipeline directly
-        if self.provider_combo.currentData() == "supertonic":
-            import numpy as np
-            self.loading_movie.start()
-            self._on_pipeline_loaded_for_preview(np, None, None)
-            return
-
         # Start loading animation - ensure signal connection is always active
         if hasattr(self, "loading_movie"):
             # Disconnect previous connections to avoid multiple connections
@@ -3075,18 +2875,27 @@ class abogen(QWidget):
             )
             self.loading_movie.start()
 
-        def pipeline_loaded_callback(np_module, kpipeline_class, error):
+        # Determine device based on GPU availability
-            self._on_pipeline_loaded_for_preview(np_module, kpipeline_class, error)
+        if self.gpu_ok:
+            if platform.system() == "Darwin" and platform.processor() == "arm":
+                device = "mps"
+            else:
+                device = "cuda"
+        else:
+            device = "cpu"
 
-        load_thread = LoadPipelineThread(pipeline_loaded_callback)
+        lang = self.selected_lang or "a"
+        load_thread = LoadPipelineThread(
+            self._on_pipeline_loaded_for_preview, lang_code=lang, device=device
+        )
         load_thread.start()
 
-    def _on_pipeline_loaded_for_preview(self, np_module, kpipeline_class, error):
+    def _on_pipeline_loaded_for_preview(self, backend, error):
         # stop loading animation and restore icon on error
         if error:
             self.loading_movie.stop()
             self._show_error_message_box(
-                "Loading Error", f"Error loading numpy or KPipeline: {error}"
+                "Loading Error", f"Error loading TTS backend: {error}"
             )
             self.btn_preview.setIcon(self.play_icon)
             self.btn_preview.setEnabled(True)
@@ -3117,28 +2926,14 @@ class abogen(QWidget):
                     else None
                 )
         else:
-            if self.provider_combo.currentData() == "supertonic":
+            lang = self.selected_voice[0]
-                voice = self._get_supertonic_voice()
+            voice = self.selected_voice
-            else:
-                voice = self.selected_voice or ""
-
-        tts_provider = self.provider_combo.currentData()
-        supertonic_language = self.st_lang_combo.currentText()
-        supertonic_total_steps = self.st_steps_combo.currentData()
-
-        if tts_provider == "supertonic":
-            lang = supertonic_language
-        else:
-            lang = self.selected_voice[0] if self.selected_voice else ""
 
         # use same gpu/cpu logic as in conversion
         gpu_msg, gpu_ok = get_gpu_acceleration(self.use_gpu)
 
         self.preview_thread = VoicePreviewThread(
-            np_module, kpipeline_class, lang, voice, speed, gpu_ok,
+            backend, lang, voice, speed, gpu_ok
-            tts_provider=tts_provider,
-            supertonic_language=supertonic_language,
-            supertonic_total_steps=supertonic_total_steps,
         )
         self.preview_thread.finished.connect(self._play_preview_audio)
         self.preview_thread.error.connect(self._preview_error)

abogen/pyqt/queued_item.py

@@ -26,7 +26,3 @@ class QueuedItem:
     replace_all_caps: bool = False
     replace_numerals: bool = False
     fix_nonstandard_punctuation: bool = False
-    # TTS Provider fields
-    tts_provider: str = "kokoro"
-    supertonic_language: str = "en"
-    supertonic_total_steps: int = 8

abogen/pyqt/voice_formula_gui.py

@@ -31,7 +31,6 @@ from abogen.constants import (
     VOICES_INTERNAL,
     SUPPORTED_LANGUAGES_FOR_SUBTITLE_GENERATION,
     LANGUAGE_DESCRIPTIONS,
-    KOKORO_LANG_TO_COUNTRY,
     COLORS,
 )
 import re
@@ -190,9 +189,8 @@ class VoiceMixer(QWidget):
         )  # Center the icons horizontally
 
         # Flag icon
-        country_code = KOKORO_LANG_TO_COUNTRY.get(language_code, language_code)
         flag_icon_path = get_resource_path(
-            "abogen.assets.flags", f"{country_code}.png"
+            "abogen.assets.flags", f"{language_code}.png"
         )
         gender_icon_path = get_resource_path(
             "abogen.assets", "female.png" if is_female else "male.png"
@@ -514,8 +512,7 @@ class VoiceFormulaDialog(QDialog):
         header_row.addWidget(QLabel("Language:"))
         self.language_combo = QComboBox()
         for code, desc in LANGUAGE_OPTIONS:
-            country_code = KOKORO_LANG_TO_COUNTRY.get(code, code)
+            flag = get_resource_path("abogen.assets.flags", f"{code}.png")
-            flag = get_resource_path("abogen.assets.flags", f"{country_code}.png")
             if flag and os.path.exists(flag):
                 self.language_combo.addItem(QIcon(flag), desc, code)
             else:

abogen/tts_backend.py

@@ -0,0 +1,89 @@
+"""
+TTS Backend Interface
+
+This module defines the protocol for TTS backends and the
+metadata model that describes a backend implementation.
+"""
+
+from dataclasses import dataclass
+from typing import Protocol, List, Dict, Any
+
+
+@dataclass(frozen=True)
+class TTSBackendMetadata:
+    """
+    Immutable metadata describing a TTS backend implementation.
+
+    Attributes:
+        id: Unique backend identifier (e.g. ``"kokoro"``, ``"supertonic"``).
+        name: Human-readable display name.
+        description: Short description of the backend.
+        voices: Tuple of supported voice identifiers.
+    """
+
+    id: str
+    name: str
+    description: str
+    voices: tuple[str, ...] = ()
+
+
+class TTSBackend(Protocol):
+    """
+    Protocol for TTS backends.
+
+    All TTS backends must implement this interface to be compatible
+    with the application.
+    """
+
+    @property
+    def metadata(self) -> TTSBackendMetadata:
+        ...
+
+    def __init__(self, **kwargs) -> None:
+        """
+        Initialize the TTS backend.
+
+        Args:
+            **kwargs: Backend-specific configuration parameters
+        """
+        ...
+
+    def synthesize(self, text: str, **kwargs) -> bytes:
+        """
+        Synthesize speech from text.
+
+        Args:
+            text: Text to synthesize
+            **kwargs: Additional parameters for synthesis
+
+        Returns:
+            Audio data as bytes
+        """
+        ...
+
+    def get_available_voices(self) -> List[str]:
+        """
+        Get list of available voices.
+
+        Returns:
+            List of voice identifiers
+        """
+        ...
+
+    def get_supported_formats(self) -> List[str]:
+        """
+        Get list of supported audio formats.
+
+        Returns:
+            List of supported audio formats
+        """
+        ...
+
+    def get_info(self) -> Dict[str, Any]:
+        """
+        Get backend information.
+
+        Returns:
+            Dictionary with backend information
+        """
+        ...

abogen/tts_backend_registry.py

@@ -0,0 +1,84 @@
+"""
+TTS Backend Registry
+
+Provides a global registry for TTS backend factories.
+Backends register themselves with metadata and a factory callable.
+The registry is universal and does not know about backend constructors.
+"""
+
+from typing import Callable, Any
+
+from abogen.tts_backend import TTSBackend, TTSBackendMetadata
+
+
+class TTSBackendRegistry:
+    """Registry of TTS backend factories.
+
+    Stores metadata and factory callables for registered backends.
+    """
+
+    def __init__(self) -> None:
+        self._backends: dict[str, TTSBackendMetadata] = {}
+        self._factories: dict[str, Callable[..., TTSBackend]] = {}
+
+    def register(
+        self,
+        metadata: TTSBackendMetadata,
+        factory: Callable[..., TTSBackend],
+    ) -> None:
+        """Register a backend with its metadata and factory callable."""
+        self._backends[metadata.id] = metadata
+        self._factories[metadata.id] = factory
+
+    def list_backends(self) -> list[TTSBackendMetadata]:
+        """Return metadata for all registered backends."""
+        return list(self._backends.values())
+
+    def get_metadata(self, backend_id: str) -> TTSBackendMetadata:
+        """Get metadata for a specific backend.
+
+        Raises:
+            KeyError: If backend with given id is not registered.
+        """
+        if backend_id not in self._backends:
+            raise KeyError(f"Unknown backend: {backend_id}")
+        return self._backends[backend_id]
+
+    def create_backend(self, backend_id: str, **kwargs: Any) -> TTSBackend:
+        """Create a backend instance by id.
+
+        Raises:
+            KeyError: If backend with given id is not registered.
+        """
+        if backend_id not in self._factories:
+            raise KeyError(f"Unknown backend: {backend_id}")
+        return self._factories[backend_id](**kwargs)
+
+
+_registry = TTSBackendRegistry()
+
+
+def register_backend(
+    metadata: TTSBackendMetadata,
+    factory: Callable[..., TTSBackend],
+) -> None:
+    """Register a TTS backend in the global registry."""
+    _registry.register(metadata, factory)
+
+
+def get_metadata(backend_id: str) -> TTSBackendMetadata:
+    """Get metadata for a specific backend by id.
+
+    Ensures all backends are registered by importing the tts_backends
+    package on first access.
+
+    Raises:
+        KeyError: If backend with given id is not registered.
+    """
+    import abogen.tts_backends  # noqa: F401  — triggers backend registration
+    return _registry.get_metadata(backend_id)
+
+
+def create_backend(backend_id: str, **kwargs: Any) -> TTSBackend:
+    """Create a TTS backend instance by provider id."""
+    return _registry.create_backend(backend_id, **kwargs)

abogen/tts_backends/__init__.py

@@ -0,0 +1,20 @@
+"""TTS backends package.
+
+Backend modules are auto-discovered and imported here.
+Each backend module registers itself with the global registry
+when imported.
+"""
+
+import importlib
+import pkgutil
+
+
+def _discover_backends():
+    """Import all modules in this package to trigger their registration."""
+    package = __name__
+    for _importer, modname, _ispkg in pkgutil.iter_modules(path=__path__):
+        importlib.import_module(f"{package}.{modname}")
+
+
+_discover_backends()
+

abogen/tts_backends/kokoro.py

@@ -0,0 +1,121 @@
+"""
+Kokoro TTS Backend
+
+Encapsulates the Kokoro KPipeline as a TTSBackend implementation.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Dict, Iterator, List, Optional
+
+import numpy as np
+
+from abogen.constants import VOICES_INTERNAL
+from abogen.tts_backend import TTSBackendMetadata
+
+_KOKORO_METADATA = TTSBackendMetadata(
+    id="kokoro",
+    name="Kokoro",
+    description="Kokoro TTS engine",
+    voices=tuple(VOICES_INTERNAL),
+)
+
+
+def _load_kpipeline():
+    """Lazy-load Kokoro dependencies."""
+    from kokoro import KPipeline  # type: ignore[import-not-found]
+
+    return KPipeline
+
+
+class KokoroBackend:
+    """TTSBackend implementation wrapping the Kokoro KPipeline.
+
+    All interaction with KPipeline is encapsulated here.
+    The rest of the project depends only on this class.
+    """
+
+    def __init__(self, **kwargs: Any) -> None:
+        lang_code = kwargs["lang_code"]
+        repo_id = kwargs.get("repo_id", "hexgrad/Kokoro-82M")
+        device = kwargs.get("device", "cpu")
+
+        KPipeline = _load_kpipeline()
+        self._pipeline = KPipeline(
+            lang_code=lang_code,
+            repo_id=repo_id,
+            device=device,
+        )
+        self._lang_code = lang_code
+
+    @property
+    def metadata(self) -> TTSBackendMetadata:
+        return _KOKORO_METADATA
+
+    def __call__(
+        self,
+        text: str,
+        *,
+        voice: Any,
+        speed: float = 1.0,
+        split_pattern: Optional[str] = None,
+    ) -> Iterator[Any]:
+        """Delegate to KPipeline's __call__."""
+        return self._pipeline(
+            text,
+            voice=voice,
+            speed=speed,
+            split_pattern=split_pattern,
+        )
+
+    def load_single_voice(self, voice_name: str) -> Any:
+        """Load a single voice tensor. Used by voice formula system."""
+        return self._pipeline.load_single_voice(voice_name)
+
+    def synthesize(self, text: str, **kwargs: Any) -> bytes:
+        """Synthesize speech from text. Returns raw audio bytes."""
+        voice = kwargs.get("voice", "")
+        speed = kwargs.get("speed", 1.0)
+        split_pattern = kwargs.get("split_pattern", None)
+
+        audio_parts: list[np.ndarray] = []
+        for segment in self(text, voice=voice, speed=speed, split_pattern=split_pattern):
+            audio = segment.audio
+            if hasattr(audio, "numpy"):
+                audio = audio.numpy()
+            audio_parts.append(np.asarray(audio, dtype="float32"))
+
+        if not audio_parts:
+            return b""
+
+        combined = np.concatenate(audio_parts).astype("float32", copy=False)
+        return combined.tobytes()
+
+    def get_available_voices(self) -> List[str]:
+        """Return known Kokoro voice identifiers."""
+        return list(self.metadata.voices)
+
+    def get_supported_formats(self) -> List[str]:
+        """Kokoro outputs raw PCM float32 audio."""
+        return ["pcm_float32"]
+
+    def get_info(self) -> Dict[str, Any]:
+        return {
+            "id": "kokoro",
+            "name": "Kokoro",
+            "lang_code": self._lang_code,
+        }
+
+
+def create_kokoro_backend(**kwargs: Any) -> KokoroBackend:
+    """Factory callable registered with TTSBackendRegistry."""
+    return KokoroBackend(**kwargs)
+
+
+# --- Registration ---
+from abogen.tts_backend_registry import register_backend  # noqa: E402
+
+register_backend(
+    metadata=_KOKORO_METADATA,
+    factory=create_kokoro_backend,
+)

abogen/tts_backends/supertonic.py

@@ -4,9 +4,8 @@ import ast
 from dataclasses import dataclass
 import logging
 import math
-import os
 import re
-from typing import Any, Iterable, Iterator, Optional
+from typing import Any, Dict, Iterable, Iterator, List, Optional
 
 import numpy as np
 
@@ -16,12 +15,14 @@ logger = logging.getLogger(__name__)
 
 DEFAULT_SUPERTONIC_VOICES = ("M1", "M2", "M3", "M4", "M5", "F1", "F2", "F3", "F4", "F5")
 
-SUPERTONIC_AVAILABLE_LANGS = [
+from abogen.tts_backend import TTSBackendMetadata
-    "en", "ko", "ja", "ar", "bg", "cs", "da", "de", "el",
+
-    "es", "et", "fi", "fr", "hi", "hr", "hu", "id", "it",
+_SUPERTONIC_METADATA = TTSBackendMetadata(
-    "lt", "lv", "nl", "pl", "pt", "ro", "ru", "sk", "sl",
+    id="supertonic",
-    "sv", "tr", "uk", "vi", "na",
+    name="SuperTonic",
-]
+    description="SuperTonic TTS engine",
+    voices=DEFAULT_SUPERTONIC_VOICES,
+)
 
 
 @dataclass
@@ -97,7 +98,7 @@ _UNSUPPORTED_CHARS_RE = re.compile(
 
 
 def _parse_unsupported_characters(error: BaseException) -> list[str]:
-    """Best-effort extraction of unsupported characters from Supertonic errors."""
+    """Best-effort extraction of unsupported characters from SuperTonic errors."""
 
     message = " ".join(
         str(part) for part in getattr(error, "args", ()) if part is not None
@@ -163,7 +164,6 @@ def _configure_supertonic_gpu() -> None:
     except Exception as exc:
         logger.warning("Could not configure supertonic GPU providers: %s", exc)
 
-SUPERTONIC_MAX_CHUNK_LENGTH = 500
 
 class SupertonicPipeline:
     """Minimal adapter that mimics Kokoro's pipeline iteration interface."""
@@ -174,14 +174,11 @@ class SupertonicPipeline:
         sample_rate: int,
         auto_download: bool = True,
         total_steps: int = 5,
-        max_chunk_length: int = SUPERTONIC_MAX_CHUNK_LENGTH,
+        max_chunk_length: int = 300,
-        lang: str = "en",
-        intra_op_num_threads: Optional[int] = None,
     ) -> None:
         self.sample_rate = int(sample_rate)
         self.total_steps = int(total_steps)
         self.max_chunk_length = int(max_chunk_length)
-        self.lang = str(lang or "en")
 
         # Configure GPU providers before importing TTS
         _configure_supertonic_gpu()
@@ -193,8 +190,7 @@ class SupertonicPipeline:
                 "Supertonic is not installed. Install it with `pip install supertonic`."
             ) from exc
 
-        threads = intra_op_num_threads if intra_op_num_threads is not None else os.cpu_count()
+        self._tts = TTS(auto_download=auto_download)
-        self._tts = TTS(auto_download=auto_download, intra_op_num_threads=threads)
 
     def __call__(
         self,
@@ -204,14 +200,12 @@ class SupertonicPipeline:
         speed: float,
         split_pattern: Optional[str] = None,
         total_steps: Optional[int] = None,
-        lang: Optional[str] = None,
     ) -> Iterator[SupertonicSegment]:
         voice_name = (voice or "").strip() or "M1"
         steps = int(total_steps) if total_steps is not None else self.total_steps
         steps = max(2, min(15, steps))
         speed_value = float(speed) if speed is not None else 1.0
         speed_value = max(0.7, min(2.0, speed_value))
-        language = str(lang or self.lang or "en")
 
         style = self._tts.get_voice_style(voice_name=voice_name)
         chunks = _split_text(
@@ -222,13 +216,12 @@ class SupertonicPipeline:
             removed: set[str] = set()
             last_exc: Exception | None = None
 
-            # Supertonic can raise ValueError for unsupported characters; strip and retry.
+            # SuperTonic can raise ValueError for unsupported characters; strip and retry.
             for attempt in range(3):
                 try:
                     wav, duration = self._tts.synthesize(
                         text=chunk_to_speak,
                         voice_style=style,
-                        lang=language,
                         total_steps=steps,
                         speed=speed_value,
                         max_chunk_length=self.max_chunk_length,
@@ -254,14 +247,14 @@ class SupertonicPipeline:
                     chunk_to_speak = sanitized
                     if not chunk_to_speak:
                         logger.warning(
-                            "Supertonic: dropped a chunk after removing unsupported characters: %s",
+                            "SuperTonic: dropped a chunk after removing unsupported characters: %s",
                             sorted(removed),
                         )
                         break
 
                     if attempt == 0:
                         logger.warning(
-                            "Supertonic: removed unsupported characters %s and retried.",
+                            "SuperTonic: removed unsupported characters %s and retried.",
                             sorted(removed),
                         )
             else:
@@ -289,3 +282,111 @@ class SupertonicPipeline:
                 audio = _resample_linear(audio, src_rate, self.sample_rate)
 
             yield SupertonicSegment(graphemes=chunk_to_speak, audio=audio)
+
+
+class SupertonicBackend:
+    """Supertonic TTS backend implementing the TTSBackend protocol.
+
+    Encapsulates ``SupertonicPipeline`` as an internal implementation detail.
+    """
+
+    @property
+    def metadata(self) -> TTSBackendMetadata:
+        return _SUPERTONIC_METADATA
+
+    def __init__(self, **kwargs: Any) -> None:
+        self._pipeline = SupertonicPipeline(
+            sample_rate=kwargs.get("sample_rate", 24000),
+            auto_download=kwargs.get("auto_download", True),
+            total_steps=kwargs.get("total_steps", 5),
+        )
+
+    def synthesize(self, text: str, **kwargs: Any) -> bytes:
+        """Synthesize speech and return raw audio bytes (WAV).
+
+        Delegates to the internal :class:`SupertonicPipeline` and concatenates
+        all produced segments into a single byte buffer.
+        """
+        import io
+
+        import soundfile as sf
+
+        voice = kwargs.get("voice", "M1")
+        speed = float(kwargs.get("speed", 1.0))
+        split_pattern = kwargs.get("split_pattern")
+        total_steps = kwargs.get("total_steps")
+
+        segments = self._pipeline(
+            text,
+            voice=voice,
+            speed=speed,
+            split_pattern=split_pattern,
+            total_steps=total_steps,
+        )
+
+        audio_parts: list[np.ndarray] = []
+        for seg in segments:
+            audio_parts.append(seg.audio)
+
+        if not audio_parts:
+            return b""
+
+        combined = np.concatenate(audio_parts)
+        buf = io.BytesIO()
+        sf.write(buf, combined, self._pipeline.sample_rate, format="WAV")
+        return buf.getvalue()
+
+    def get_available_voices(self) -> List[str]:
+        """Return the list of built-in SuperTonic voice identifiers."""
+        return list(self.metadata.voices)
+
+    def get_supported_formats(self) -> List[str]:
+        return ["wav"]
+
+    def get_info(self) -> Dict[str, Any]:
+        return {
+            "sample_rate": self._pipeline.sample_rate,
+            "total_steps": self._pipeline.total_steps,
+            "max_chunk_length": self._pipeline.max_chunk_length,
+            "voices": list(DEFAULT_SUPERTONIC_VOICES),
+        }
+
+    def __call__(
+        self,
+        text: str,
+        *,
+        voice: str,
+        speed: float,
+        split_pattern: Optional[str] = None,
+        total_steps: Optional[int] = None,
+    ) -> Iterator[SupertonicSegment]:
+        """Backward-compatible call interface, delegates to the pipeline."""
+        return self._pipeline(
+            text,
+            voice=voice,
+            speed=speed,
+            split_pattern=split_pattern,
+            total_steps=total_steps,
+        )
+
+
+def create_supertonic_backend(**kwargs: Any) -> SupertonicBackend:
+    """Create a SuperTonic TTS backend instance.
+
+    Args:
+        sample_rate: Audio sample rate. Defaults to 24000.
+        auto_download: Auto-download models. Defaults to True.
+        total_steps: Inference steps. Defaults to 5.
+
+    Returns:
+        SupertonicBackend instance.
+    """
+    return SupertonicBackend(**kwargs)
+
+
+from abogen.tts_backend_registry import register_backend  # noqa: E402
+
+register_backend(
+    metadata=_SUPERTONIC_METADATA,
+    factory=create_supertonic_backend,
+)

abogen/utils.py

@@ -529,21 +529,20 @@ def prevent_sleep_end():
                 _sleep_procs[system] = None
 
 
-def load_numpy_kpipeline():
-    import numpy as np
-    from kokoro import KPipeline  # type: ignore[import-not-found]
-
-    return np, KPipeline
-
-
 class LoadPipelineThread(Thread):
-    def __init__(self, callback):
+    def __init__(self, callback, lang_code="a", device="cpu"):
         super().__init__()
         self.callback = callback
+        self.lang_code = lang_code
+        self.device = device
 
     def run(self):
         try:
-            np_module, kpipeline_class = load_numpy_kpipeline()
+            from abogen.tts_backend_registry import create_backend
-            self.callback(np_module, kpipeline_class, None)
+
+            backend = create_backend(
+                "kokoro", lang_code=self.lang_code, device=self.device
+            )
+            self.callback(backend, None)
         except Exception as e:
-            self.callback(None, None, str(e))
+            self.callback(None, str(e))

abogen/voice_cache.py

@@ -17,7 +17,7 @@ if LocalEntryNotFoundError is None:  # pragma: no cover - fallback for tests
         pass
 
 
-from abogen.constants import VOICES_INTERNAL
+from abogen.tts_backend_registry import get_metadata
 
 _CACHE_LOCK = threading.Lock()
 _CACHED_VOICES: Set[str] = set()
@@ -26,8 +26,9 @@ _BOOTSTRAPPED = False
 
 
 def _normalize_targets(voices: Optional[Iterable[str]]) -> Set[str]:
+    kokoro_voices = get_metadata("kokoro").voices
     if not voices:
-        return set(VOICES_INTERNAL)
+        return set(kokoro_voices)
     normalized: Set[str] = set()
     for voice in voices:
         if not voice:
@@ -35,7 +36,7 @@ def _normalize_targets(voices: Optional[Iterable[str]]) -> Set[str]:
         voice_id = str(voice).strip()
         if not voice_id:
             continue
-        if voice_id in VOICES_INTERNAL:
+        if voice_id in kokoro_voices:
             normalized.add(voice_id)
     return normalized
 

abogen/voice_formulas.py

@@ -1,7 +1,7 @@
 import re
 from typing import List, Tuple
 
-from abogen.constants import VOICES_INTERNAL
+from abogen.tts_backend_registry import get_metadata
 
 
 # Calls parsing and loads the voice to gpu or cpu
@@ -22,6 +22,7 @@ def parse_formula_terms(formula: str) -> List[Tuple[str, float]]:
         raise ValueError("Empty voice formula")
 
     terms: List[Tuple[str, float]] = []
+    kokoro_voices = get_metadata("kokoro").voices
     for segment in formula.split("+"):
         part = segment.strip()
         if not part:
@@ -30,7 +31,7 @@ def parse_formula_terms(formula: str) -> List[Tuple[str, float]]:
             raise ValueError("Each component must be in the form voice*weight")
         voice_name, raw_weight = part.split("*", 1)
         voice_name = voice_name.strip()
-        if voice_name not in VOICES_INTERNAL:
+        if voice_name not in kokoro_voices:
             raise ValueError(f"Unknown voice: {voice_name}")
         try:
             weight = float(raw_weight.strip())

abogen/voice_metadata.py

@@ -0,0 +1,33 @@
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True)
+class VoiceMetadata:
+    """
+    Immutable metadata describing a voice from a TTS backend.
+
+    This model describes a voice independently of any backend implementation.
+    Backends populate these objects; the application consumes them.
+
+    The ``backend_id`` field is set by the backend itself (via
+    ``self.metadata.id``) — the application never hardcodes it.
+    This ensures renaming a backend does not require touching voice definitions.
+    """
+
+    id: str
+    """Unique voice identifier within the backend (e.g. ``"af_alloy"``, ``"M1"``)."""
+
+    display_name: str
+    """Human-readable display name (e.g. ``"Alloy"``, ``"Male 1"``)."""
+
+    language: str
+    """Language code — backend-specific format is acceptable (e.g. ``"a"``, ``"en"``)."""
+
+    gender: str
+    """Gender category: ``"female"``, ``"male"``, or ``"unknown"``."""
+
+    backend_id: str
+    """Identifier of the backend that owns this voice (e.g. ``"kokoro"``).
+
+    Set automatically by the backend — never hardcoded in voice definitions.
+    """

abogen/voice_profiles.py

@@ -2,8 +2,7 @@ import json
 import os
 from typing import Any, Dict, Iterable, List, Tuple
 
-from abogen.constants import VOICES_INTERNAL
+from abogen.tts_backend_registry import get_metadata
-from abogen.tts_supertonic import DEFAULT_SUPERTONIC_VOICES
 from abogen.utils import get_user_config_path
 
 
@@ -70,7 +69,8 @@ def serialize_profiles() -> Dict[str, Dict[str, Iterable[Tuple[str, float]]]]:
 
 def _normalize_supertonic_voice(value: Any) -> str:
     raw = str(value or "").strip().upper()
-    return raw if raw in DEFAULT_SUPERTONIC_VOICES else "M1"
+    supertonic_voices = get_metadata("supertonic").voices
+    return raw if raw in supertonic_voices else "M1"
 
 
 def _coerce_supertonic_steps(value: Any) -> int:
@@ -135,6 +135,7 @@ def normalize_profile_entry(entry: Any) -> Dict[str, Any]:
 
 def _normalize_voice_entries(entries: Iterable) -> List[Tuple[str, float]]:
     normalized: List[Tuple[str, float]] = []
+    kokoro_voices = get_metadata("kokoro").voices
     for item in entries or []:
         if isinstance(item, dict):
             voice = item.get("id") or item.get("voice")
@@ -143,7 +144,7 @@ def _normalize_voice_entries(entries: Iterable) -> List[Tuple[str, float]]:
             voice, weight = item[0], item[1]
         else:
             continue
-        if voice not in VOICES_INTERNAL:
+        if voice not in kokoro_voices:
             continue
         if weight is None:
             continue

abogen/webui/Dockerfile

@@ -27,8 +27,6 @@ RUN python3 -m venv "$VIRTUAL_ENV"
 WORKDIR /app
 
 COPY pyproject.toml README.md ./
-COPY abogen ./abogen
-
 RUN pip install --upgrade pip \
   && if [ -n "$TORCH_VERSION" ]; then \
        pip install torch=="$TORCH_VERSION" torchvision=="$TORCH_VERSION" torchaudio=="$TORCH_VERSION" --index-url "$TORCH_INDEX_URL"; \
@@ -39,6 +37,8 @@ RUN pip install --upgrade pip \
     https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.8.0/en_core_web_sm-3.8.0-py3-none-any.whl \
   && pip install --no-cache-dir "mutagen>=1.47.0"
 
+COPY abogen ./abogen
+
 # Install onnxruntime-gpu for CUDA acceleration (supertonic uses ONNX Runtime)
 # Set USE_GPU=false to skip this for CPU-only deployments
 RUN if [ "$USE_GPU" = "true" ]; then \

abogen/webui/conversion_runner.py

@@ -39,14 +39,15 @@ from abogen.utils import (
     get_user_cache_path,
     get_user_output_path,
     load_config,
-    load_numpy_kpipeline,
 )
+from abogen.tts_backend_registry import create_backend
+from abogen.tts_backend import TTSBackend
 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
+from abogen.tts_backends.supertonic import DEFAULT_SUPERTONIC_VOICES
 
 from .service import Job, JobStatus
 
@@ -59,7 +60,7 @@ def _supertonic_voice_from_spec(spec: Any, fallback: str) -> str:
     raw = str(spec or "").strip()
     fallback_raw = str(fallback or "").strip()
 
-    # Supertonic voices are discrete IDs (M1/F3/...). If we see a Kokoro mix
+    # SuperTonic voices are discrete IDs (M1/F3/...). If we see a Kokoro mix
     # formula (contains '*' or '+'), ignore it and fall back to a safe voice.
     if not raw or "*" in raw or "+" in raw:
         raw = fallback_raw
@@ -1581,10 +1582,11 @@ def run_conversion_job(job: Job) -> None:
                 return existing
 
             if provider_norm == "supertonic":
-                pipelines[provider_norm] = SupertonicPipeline(
+                pipelines[provider_norm] = create_backend(
+                    "supertonic",
                     sample_rate=SAMPLE_RATE,
                     auto_download=True,
-                    total_steps=int(getattr(job, "supertonic_total_steps", 8) or 8),
+                    total_steps=int(getattr(job, "supertonic_total_steps", 5) or 5),
                 )
                 return pipelines[provider_norm]
 
@@ -1594,16 +1596,12 @@ def run_conversion_job(job: Job) -> None:
             device = "cpu"
             if not disable_gpu:
                 device = _select_device()
-            _np, KPipeline = load_numpy_kpipeline()
+            # Create KPipeline instance directly (conforms to TTSBackend protocol)
-            # Try to initialize with the selected device; fall back to CPU if CUDA fails
+            pipelines[provider_norm] = create_backend(
-            try:
+                "kokoro",
-                pipelines[provider_norm] = KPipeline(lang_code=job.language, repo_id="hexgrad/Kokoro-82M", device=device)
+                lang_code=job.language,
-            except RuntimeError as e:
+                device=device
-                if "CUDA" in str(e) and device != "cpu":
+            )
-                    job.add_log(f"CUDA initialization failed, falling back to CPU: {e}", level="warning")
-                    pipelines[provider_norm] = KPipeline(lang_code=job.language, repo_id="hexgrad/Kokoro-82M", device="cpu")
-                else:
-                    raise
             if not kokoro_cache_ready:
                 _initialize_voice_cache(job)
                 kokoro_cache_ready = True
@@ -1618,7 +1616,7 @@ def run_conversion_job(job: Job) -> None:
                 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", 8) or 8)
+                    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)
@@ -1634,7 +1632,7 @@ def run_conversion_job(job: Job) -> None:
             """Resolve a raw voice spec into (provider, resolved_spec, choice, speed, steps).
 
             For Kokoro formulas, `choice` will be a resolved voice tensor (via `voice_formulas`).
-            For Supertonic, `choice` will be a valid Supertonic voice id.
+            For SuperTonic, `choice` will be a valid SuperTonic voice id.
             """
 
             provider, resolved, speed, steps = resolve_voice_target(raw_spec)
@@ -1644,8 +1642,8 @@ def run_conversion_job(job: Job) -> None:
                 return provider, resolved, cached, speed, steps
 
             if provider == "kokoro":
-                kokoro_pipeline = get_pipeline("kokoro")
+                kokoro_backend = get_pipeline("kokoro")
-                choice = _resolve_voice(kokoro_pipeline, resolved, job.use_gpu)
+                choice = _resolve_voice(kokoro_backend, resolved, job.use_gpu)
             else:
                 choice = resolved
 
@@ -1774,8 +1772,8 @@ def run_conversion_job(job: Job) -> None:
         voice_cache: Dict[str, Any] = {}
         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")
+            kokoro_backend = get_pipeline("kokoro")
-            voice_cache[f"kokoro:{base_voice_resolved}"] = _resolve_voice(kokoro_pipeline, base_voice_resolved, job.use_gpu)
+            voice_cache[f"kokoro:{base_voice_resolved}"] = _resolve_voice(kokoro_backend, base_voice_resolved, job.use_gpu)
         processed_chars = 0
         subtitle_index = 1
         current_time = 0.0
@@ -1857,11 +1855,11 @@ def run_conversion_job(job: Job) -> None:
                     voice=voice_name,
                     speed=float(speed_override if speed_override is not None else job.speed),
                     split_pattern=split_pattern,
-                    total_steps=int(supertonic_steps_override if supertonic_steps_override is not None else getattr(job, "supertonic_total_steps", 8)),
+                    total_steps=int(supertonic_steps_override if supertonic_steps_override is not None else getattr(job, "supertonic_total_steps", 5)),
                 )
             else:
-                kokoro_pipeline = get_pipeline("kokoro")
+                kokoro_backend = get_pipeline("kokoro")
-                segment_iter = kokoro_pipeline(
+                segment_iter = kokoro_backend(
                     normalized,
                     voice=voice_choice,
                     speed=float(speed_override if speed_override is not None else job.speed),
@@ -1950,8 +1948,8 @@ def run_conversion_job(job: Job) -> None:
             if chapter_provider == "kokoro":
                 voice_choice = voice_cache.get(chapter_cache_key)
                 if voice_choice is None:
-                    kokoro_pipeline = get_pipeline("kokoro")
+                    kokoro_backend = get_pipeline("kokoro")
-                    voice_choice = _resolve_voice(kokoro_pipeline, chapter_voice_resolved, job.use_gpu)
+                    voice_choice = _resolve_voice(kokoro_backend, chapter_voice_resolved, job.use_gpu)
                     voice_cache[chapter_cache_key] = voice_choice
             else:
                 voice_choice = chapter_voice_resolved
@@ -2095,9 +2093,9 @@ def run_conversion_job(job: Job) -> None:
                         if chunk_provider == "kokoro":
                             chunk_voice_choice = voice_cache.get(chunk_cache_key)
                             if chunk_voice_choice is None:
-                                kokoro_pipeline = get_pipeline("kokoro")
+                                kokoro_backend = get_pipeline("kokoro")
                                 chunk_voice_choice = _resolve_voice(
-                                    kokoro_pipeline,
+                                    kokoro_backend,
                                     chunk_voice_resolved,
                                     job.use_gpu,
                                 )
@@ -2445,17 +2443,17 @@ def _load_pipeline(job: Job):
     disable_gpu = not job.use_gpu or not cfg.get("use_gpu", True)
     provider = str(getattr(job, "tts_provider", "kokoro") or "kokoro").strip().lower()
     if provider == "supertonic":
-        return SupertonicPipeline(
+        return create_backend(
+            "supertonic",
             sample_rate=SAMPLE_RATE,
             auto_download=True,
-            total_steps=int(getattr(job, "supertonic_total_steps", 8) or 8),
+            total_steps=int(getattr(job, "supertonic_total_steps", 5) or 5),
         )
 
     device = "cpu"
     if not disable_gpu:
         device = _select_device()
-    _np, KPipeline = load_numpy_kpipeline()
+    return create_backend("kokoro", lang_code=job.language, device=device)
-    return KPipeline(lang_code=job.language, repo_id="hexgrad/Kokoro-82M", device=device)
 
 
 def _select_device() -> str:
@@ -2610,7 +2608,7 @@ def _build_ffmpeg_command(path: Path, fmt: str, metadata: Optional[Dict[str, str
 def _resolve_voice(pipeline, voice_spec: str, use_gpu: bool):
     if "*" in voice_spec:
         # Voice formulas are a Kokoro-only feature (they require a pipeline that can
-        # load individual Kokoro voices). When running with Supertonic (or when the
+        # load individual Kokoro voices). When running with SuperTonic (or when the
         # pipeline is otherwise unavailable), treat the spec as a plain string and
         # allow downstream provider-specific resolution to choose a safe fallback.
         if pipeline is None or not hasattr(pipeline, "load_single_voice"):

abogen/webui/debug_tts_runner.py

@@ -15,7 +15,7 @@ from abogen.normalization_settings import build_apostrophe_config
 from abogen.text_extractor import extract_from_path
 from abogen.voice_cache import ensure_voice_assets
 from abogen.webui.conversion_runner import SAMPLE_RATE, SPLIT_PATTERN, _select_device, _to_float32, _resolve_voice, _spec_to_voice_ids
-from abogen.utils import load_numpy_kpipeline
+from abogen.tts_backend_registry import create_backend
 
 
 _MARKER_RE = re.compile(re.escape(MARKER_PREFIX) + r"(?P<code>[A-Z0-9_]+)" + re.escape(MARKER_SUFFIX))
@@ -45,8 +45,7 @@ def _load_pipeline(language: str, use_gpu: bool) -> Any:
     device = "cpu"
     if use_gpu:
         device = _select_device()
-    _np, KPipeline = load_numpy_kpipeline()
+    return create_backend("kokoro", lang_code=language, device=device)
-    return KPipeline(lang_code=language, repo_id="hexgrad/Kokoro-82M", device=device)
 
 
 def _extract_cases_from_text(text: str) -> List[Tuple[str, str]]:

abogen/webui/routes/api.py

@@ -162,7 +162,7 @@ def api_voice_profiles_preview() -> ResponseReturnValue:
     formula = str(payload.get("formula") or "").strip()
     profile_name = str(payload.get("profile") or "").strip()
     provider = str(payload.get("tts_provider") or payload.get("provider") or "").strip().lower() or None
-    supertonic_total_steps = int(payload.get("supertonic_total_steps") or payload.get("total_steps") or settings.get("supertonic_total_steps") or 8)
+    supertonic_total_steps = int(payload.get("supertonic_total_steps") or payload.get("total_steps") or settings.get("supertonic_total_steps") or 5)
 
     voice_spec = ""
     resolved_provider = provider or "kokoro"
@@ -224,7 +224,7 @@ def api_speaker_preview() -> ResponseReturnValue:
     speed_value = payload.get("speed")
     speed = coerce_float(speed_value, 1.0)
     tts_provider = str(payload.get("tts_provider") or "").strip().lower()
-    supertonic_total_steps = int(payload.get("supertonic_total_steps") or 8)
+    supertonic_total_steps = int(payload.get("supertonic_total_steps") or 5)
     
     settings = load_settings()
     use_gpu = settings.get("use_gpu", False)
@@ -269,7 +269,7 @@ def api_speaker_preview() -> ResponseReturnValue:
             use_gpu=use_gpu
             ,
             tts_provider=resolved_provider,
-            supertonic_total_steps=supertonic_total_steps or int(settings.get("supertonic_total_steps") or 8),
+            supertonic_total_steps=supertonic_total_steps or int(settings.get("supertonic_total_steps") or 5),
             pronunciation_overrides=pronunciation_overrides,
             manual_overrides=manual_overrides,
             speakers=speakers,

abogen/webui/routes/settings.py

@@ -43,12 +43,8 @@ def update_settings() -> ResponseReturnValue:
     current["language"] = (form.get("language") or "en").strip()
     current["default_speaker"] = (form.get("default_speaker") or "").strip()
     current["default_voice"] = (form.get("default_voice") or "").strip()
-    provider = str(form.get("tts_provider") or "kokoro").strip().lower()
-    if provider in {"kokoro", "supertonic"}:
-        current["tts_provider"] = provider
     try:
-        total_steps = int(form.get("supertonic_total_steps", current.get("supertonic_total_steps", 8)))
+        current["supertonic_total_steps"] = max(2, min(15, int(form.get("supertonic_total_steps", current.get("supertonic_total_steps", 5)))))
-        current["supertonic_total_steps"] = max(2, min(15, total_steps))
     except (TypeError, ValueError):
         pass
     try:

abogen/webui/routes/utils/form.py

@@ -577,7 +577,7 @@ def apply_book_step_form(
     # NOTE: Do not auto-set a global TTS provider at the book level based on the
     # narrator defaults. Provider is resolved per-speaker/per-chunk from the voice
     # spec (e.g. "speaker:Name" for saved speakers, or a Kokoro mix formula).
-    # This enables mixed-provider conversions (e.g. narrator=Supertonic, characters=Kokoro).
+    # This enables mixed-provider conversions (e.g. narrator=SuperTonic, characters=Kokoro).
     provider_value = str(form.get("tts_provider") or "").strip().lower()
     if provider_value in {"kokoro", "supertonic"}:
         pending.tts_provider = provider_value
@@ -913,15 +913,6 @@ def build_pending_job_from_extraction(
         else:
             normalization_overrides[key] = default_val
 
-    provider_value = str(form.get("tts_provider") or "").strip().lower()
-    if provider_value not in {"kokoro", "supertonic"}:
-        provider_value = settings.get("tts_provider", "kokoro")
-    try:
-        total_steps = int(form.get("supertonic_total_steps", settings.get("supertonic_total_steps", 8)))
-        supertonic_steps = max(2, min(15, total_steps))
-    except (TypeError, ValueError):
-        supertonic_steps = int(settings.get("supertonic_total_steps", 8))
-
     pending = PendingJob(
         id=uuid.uuid4().hex,
         original_filename=original_name,
@@ -937,8 +928,6 @@ def build_pending_job_from_extraction(
         replace_single_newlines=replace_single_newlines,
         subtitle_format=subtitle_format,
         total_characters=total_chars,
-        tts_provider=provider_value,
-        supertonic_total_steps=supertonic_steps,
         save_chapters_separately=save_chapters_separately,
         merge_chapters_at_end=merge_chapters_at_end,
         separate_chapters_format=separate_chapters_format,

abogen/webui/routes/utils/preview.py

@@ -78,10 +78,9 @@ def get_preview_pipeline(language: str, device: str) -> Any:
         pipeline = _preview_pipelines.get(key)
         if pipeline is not None:
             return pipeline
-        from abogen.utils import load_numpy_kpipeline
+        from abogen.tts_backend_registry import create_backend
 
-        _, KPipeline = load_numpy_kpipeline()
+        pipeline = create_backend("kokoro", lang_code=language, device=device)
-        pipeline = KPipeline(lang_code=language, repo_id="hexgrad/Kokoro-82M", device=device)
         _preview_pipelines[key] = pipeline
         return pipeline
 
@@ -92,7 +91,7 @@ def generate_preview_audio(
     speed: float,
     use_gpu: bool,
     tts_provider: str = "kokoro",
-    supertonic_total_steps: int = 8,
+    supertonic_total_steps: int = 5,
     max_seconds: float = 8.0,
     pronunciation_overrides: Optional[Iterable[Mapping[str, Any]]] = None,
     manual_overrides: Optional[Iterable[Mapping[str, Any]]] = None,
@@ -137,9 +136,9 @@ def generate_preview_audio(
             normalized_text = source_text
 
     if provider == "supertonic":
-        from abogen.tts_supertonic import SupertonicPipeline
+        from abogen.tts_backend_registry import create_backend
 
-        pipeline = SupertonicPipeline(sample_rate=SAMPLE_RATE, auto_download=True, total_steps=supertonic_total_steps)
+        pipeline = create_backend("supertonic", sample_rate=SAMPLE_RATE, auto_download=True, total_steps=supertonic_total_steps)
         segments = pipeline(
             normalized_text,
             voice=voice_spec,
@@ -201,7 +200,7 @@ def synthesize_preview(
     speed: float,
     use_gpu: bool,
     tts_provider: str = "kokoro",
-    supertonic_total_steps: int = 8,
+    supertonic_total_steps: int = 5,
     max_seconds: float = 8.0,
     pronunciation_overrides: Optional[Iterable[Mapping[str, Any]]] = None,
     manual_overrides: Optional[Iterable[Mapping[str, Any]]] = None,

abogen/webui/routes/utils/service.py

@@ -25,7 +25,7 @@ def submit_job(pending: PendingJob) -> str:
         tts_provider=getattr(pending, "tts_provider", "kokoro"),
         voice=pending.voice,
         speed=pending.speed,
-        supertonic_total_steps=getattr(pending, "supertonic_total_steps", 8),
+        supertonic_total_steps=getattr(pending, "supertonic_total_steps", 5),
         use_gpu=pending.use_gpu,
         subtitle_mode=pending.subtitle_mode,
         output_format=pending.output_format,

abogen/webui/routes/utils/settings.py

@@ -175,8 +175,7 @@ def settings_defaults() -> Dict[str, Any]:
         "save_mode": "default_output" if has_output_override() else "save_next_to_input",
         "default_speaker": "",
         "default_voice": VOICES_INTERNAL[0] if VOICES_INTERNAL else "",
-        "tts_provider": "kokoro",
+        "supertonic_total_steps": 5,
-        "supertonic_total_steps": 8,
         "supertonic_speed": 1.0,
         "replace_single_newlines": False,
         "use_gpu": True,

abogen/webui/routes/utils/voice.py

@@ -20,7 +20,7 @@ from abogen.constants import (
     VOICES_INTERNAL,
 )
 from abogen.speaker_configs import list_configs
-from abogen.utils import load_numpy_kpipeline
+from abogen.tts_backend_registry import create_backend
 from abogen.webui.conversion_runner import _select_device, _to_float32, SAMPLE_RATE, SPLIT_PATTERN
 
 _preview_pipeline_lock = threading.RLock()
@@ -666,7 +666,7 @@ def resolve_voice_choice(
 
         # Provider-aware behavior:
         # - Kokoro profiles typically represent mixes (formula strings).
-        # - Supertonic profiles represent a discrete voice id + settings.
+        # - SuperTonic profiles represent a discrete voice id + settings.
         #   In that case, we return a speaker reference so downstream can
         #   resolve provider per-speaker and allow mixed-provider casting.
         if provider == "supertonic":
@@ -741,8 +741,7 @@ def get_preview_pipeline(language: str, device: str):
         pipeline = _preview_pipelines.get(key)
         if pipeline is not None:
             return pipeline
-        _, KPipeline = load_numpy_kpipeline()
+        pipeline = create_backend("kokoro", lang_code=language, device=device)
-        pipeline = KPipeline(lang_code=language, repo_id="hexgrad/Kokoro-82M", device=device)
         _preview_pipelines[key] = pipeline
         return pipeline
 

abogen/webui/service.py

@@ -111,7 +111,7 @@ class Job:
     subtitle_format: str
     created_at: float
     tts_provider: str = "kokoro"
-    supertonic_total_steps: int = 8
+    supertonic_total_steps: int = 5
     save_chapters_separately: bool = False
     merge_chapters_at_end: bool = True
     separate_chapters_format: str = "wav"
@@ -204,7 +204,7 @@ class Job:
             "queue_position": self.queue_position,
             "options": {
                 "tts_provider": getattr(self, "tts_provider", "kokoro"),
-                "supertonic_total_steps": getattr(self, "supertonic_total_steps", 8),
+                "supertonic_total_steps": getattr(self, "supertonic_total_steps", 5),
                 "save_chapters_separately": self.save_chapters_separately,
                 "merge_chapters_at_end": self.merge_chapters_at_end,
                 "separate_chapters_format": self.separate_chapters_format,
@@ -552,7 +552,7 @@ class PendingJob:
     normalization_overrides: Dict[str, Any]
     created_at: float
     tts_provider: str = "kokoro"
-    supertonic_total_steps: int = 8
+    supertonic_total_steps: int = 5
     cover_image_path: Optional[Path] = None
     cover_image_mime: Optional[str] = None
     chapter_intro_delay: float = 0.5
@@ -621,7 +621,7 @@ class ConversionService:
         voice: str,
         speed: float,
         tts_provider: str = "kokoro",
-        supertonic_total_steps: int = 8,
+        supertonic_total_steps: int = 5,
         use_gpu: bool,
         subtitle_mode: str,
         output_format: str,
@@ -674,7 +674,7 @@ class ConversionService:
             voice=voice,
             speed=speed,
             tts_provider=tts_provider,
-            supertonic_total_steps=int(supertonic_total_steps or 8),
+            supertonic_total_steps=int(supertonic_total_steps or 5),
             use_gpu=use_gpu,
             subtitle_mode=subtitle_mode,
             output_format=output_format,
@@ -1147,7 +1147,7 @@ class ConversionService:
             "tts_provider": getattr(job, "tts_provider", "kokoro"),
             "voice": job.voice,
             "speed": job.speed,
-            "supertonic_total_steps": getattr(job, "supertonic_total_steps", 8),
+            "supertonic_total_steps": getattr(job, "supertonic_total_steps", 5),
             "use_gpu": job.use_gpu,
             "subtitle_mode": job.subtitle_mode,
             "output_format": job.output_format,
@@ -1275,7 +1275,7 @@ class ConversionService:
             replace_single_newlines=bool(payload.get("replace_single_newlines", False)),
             subtitle_format=payload.get("subtitle_format", "srt"),
             created_at=float(payload.get("created_at", time.time())),
-            supertonic_total_steps=int(payload.get("supertonic_total_steps", 8)),
+            supertonic_total_steps=int(payload.get("supertonic_total_steps", 5)),
             save_chapters_separately=bool(payload.get("save_chapters_separately", False)),
             merge_chapters_at_end=bool(payload.get("merge_chapters_at_end", True)),
             separate_chapters_format=payload.get("separate_chapters_format", "wav"),

abogen/webui/templates/partials/new_job_step_book.html

@@ -26,26 +26,6 @@
     {% set subtitle_value = settings_dict.get('subtitle_mode', 'Disabled') %}
   {% endif %}
 {% endif %}
-{% set tts_provider_value = form_values.get('tts_provider') if form_values else None %}
-{% if not tts_provider_value %}
-  {% if pending and pending.tts_provider %}
-    {% set tts_provider_value = pending.tts_provider %}
-  {% else %}
-    {% set tts_provider_value = settings_dict.get('tts_provider', 'kokoro') %}
-  {% endif %}
-{% endif %}
-{% set supertonic_steps_value = form_values.get('supertonic_total_steps') if form_values else None %}
-{% if supertonic_steps_value is none %}
-  {% if pending and pending.supertonic_total_steps is not none %}
-    {% set supertonic_steps_value = pending.supertonic_total_steps %}
-  {% else %}
-    {% set supertonic_steps_value = settings_dict.get('supertonic_total_steps', 8) %}
-  {% endif %}
-{% endif %}
-{% if supertonic_steps_value is not none and supertonic_steps_value is string %}
-  {% set supertonic_steps_value = supertonic_steps_value|int %}
-{% endif %}
-{% set is_supertonic = tts_provider_value == 'supertonic' %}
 {% set generate_flag = form_values.get('generate_epub3') if form_values else None %}
 {% if generate_flag is not none %}
   {% set generate_epub3 = True %}
@@ -293,13 +273,6 @@
       <div class="form-section__layout form-section__layout--split">
         <div class="form-section__group">
           <div class="field">
-            <label for="tts_provider">TTS Engine</label>
-            <select id="tts_provider" name="tts_provider" data-role="tts-provider" {{ 'disabled' if readonly else '' }}>
-              <option value="kokoro" {% if tts_provider_value == 'kokoro' %}selected{% endif %}>Kokoro</option>
-              <option value="supertonic" {% if tts_provider_value == 'supertonic' %}selected{% endif %}>Supertonic</option>
-            </select>
-          </div>
-          <div class="field" data-role="voice-profile-field">
             <label for="voice_profile">Voice profile</label>
             <select id="voice_profile" name="voice_profile" data-role="voice-profile" {{ 'disabled' if readonly else '' }}>
               <option value="__standard" {% if profile_value == '__standard' %}selected{% endif %}>Standard voice</option>
@@ -307,13 +280,13 @@
               {% if options.voice_profile_options %}
               <optgroup label="Saved mixes">
                 {% for profile in options.voice_profile_options %}
-                <option value="{{ profile.name }}" data-language="{{ profile.language }}" data-formula="{{ profile.formula|e }}" data-provider="{{ profile.provider|default('kokoro')|lower }}" {% if profile_value == profile.name %}selected{% endif %}>{{ profile.name }}{% if profile.language %} ({{ profile.language|upper }}){% endif %}{% if profile.provider and profile.provider|lower != 'kokoro' %} · {{ profile.provider|capitalize }}{% endif %}</option>
+                <option value="{{ profile.name }}" data-language="{{ profile.language }}" data-formula="{{ profile.formula|e }}" {% if profile_value == profile.name %}selected{% endif %}>{{ profile.name }}{% if profile.language %} ({{ profile.language|upper }}){% endif %}</option>
                 {% endfor %}
               </optgroup>
               {% endif %}
             </select>
           </div>
-          <div class="field" data-role="voice-field" data-provider="kokoro" {% if profile_value != '__standard' or is_supertonic %}hidden aria-hidden="true"{% endif %}>
+          <div class="field" data-role="voice-field" {% if profile_value != '__standard' %}hidden aria-hidden="true"{% endif %}>
             <label for="voice">Voice</label>
             <select id="voice" name="voice" data-role="voice-select" data-default="{{ narrator_voice or settings_dict.get('default_voice', '') }}" {{ 'disabled' if readonly else '' }}>
               {% for voice in options.voices %}
@@ -321,23 +294,10 @@
               {% endfor %}
             </select>
           </div>
-          <div class="field" data-role="voice-field" data-provider="supertonic" {% if profile_value != '__standard' or not is_supertonic %}hidden aria-hidden="true"{% endif %}>
+          <div class="field" data-conditional="formula" data-role="formula-field" {% if profile_value != '__formula' %}hidden aria-hidden="true"{% endif %}>
-            <label for="voice_st">Supertonic voice</label>
-            <select id="voice_st" name="voice" data-role="voice-select" data-default="{{ narrator_voice or 'M1' }}" {{ 'disabled' if readonly else '' }}>
-              {% for voice in ['M1','M2','M3','M4','M5','F1','F2','F3','F4','F5'] %}
-              <option value="{{ voice }}" {% if narrator_voice == voice and profile_value == '__standard' %}selected{% endif %}>{{ voice }}</option>
-              {% endfor %}
-            </select>
-          </div>
-          <div class="field" data-conditional="formula" data-role="formula-field" data-provider="kokoro" {% if profile_value != '__formula' or is_supertonic %}hidden aria-hidden="true"{% endif %}>
             <label for="voice_formula">Custom voice formula</label>
             <input type="text" id="voice_formula" name="voice_formula" placeholder="af_nova*0.4+am_liam*0.6" data-role="voice-formula" value="{{ voice_formula_value }}" {{ 'disabled' if readonly else '' }}>
           </div>
-          <div class="field" data-role="supertonic-steps-field" {% if not is_supertonic %}hidden aria-hidden="true"{% endif %}>
-            <label for="supertonic_total_steps">Supertonic quality (total steps)</label>
-            <input type="number" id="supertonic_total_steps" name="supertonic_total_steps" min="2" max="15" value="{{ supertonic_steps_value }}" {{ 'disabled' if readonly else '' }}>
-            <p class="hint">2 = fastest/lowest quality, 15 = slowest/highest quality.</p>
-          </div>
         </div>
         <div class="form-section__group">
           <div class="field field--slider">
@@ -463,54 +423,3 @@
     </div>
   </footer>
 </form>
-
-<script nonce="{{ csp_nonce() if csp_nonce else '' }}">
-(function() {
-  const form = document.querySelector('[data-wizard-form="true"][data-step="book"]');
-  if (!form) return;
-  const providerSelect = form.querySelector('[data-role="tts-provider"]');
-  if (!providerSelect) return;
-
-  function filterProfilesByProvider(provider) {
-    const profileSelect = form.querySelector('[data-role="voice-profile"]');
-    if (!profileSelect) return;
-    const options = profileSelect.querySelectorAll('option[data-provider]');
-    options.forEach(function(opt) {
-      const matches = !opt.dataset.provider || opt.dataset.provider === provider;
-      opt.hidden = !matches;
-      if (opt.selected && opt.hidden) {
-        opt.selected = false;
-      }
-    });
-    if (!profileSelect.value || profileSelect.selectedOptions[0]?.hidden) {
-      const firstVisible = profileSelect.querySelector('option:not([hidden])');
-      if (firstVisible) profileSelect.value = firstVisible.value;
-    }
-    profileSelect.dispatchEvent(new Event('change', { bubbles: true }));
-  }
-
-  function syncProviderUI(provider) {
-    var isSupertonic = provider === 'supertonic';
-    form.querySelectorAll('[data-role="voice-field"]').forEach(function(el) {
-      el.hidden = el.dataset.provider !== provider;
-      el.setAttribute('aria-hidden', el.hidden ? 'true' : 'false');
-    });
-    var formulaField = form.querySelector('[data-role="formula-field"]');
-    if (formulaField) {
-      formulaField.hidden = isSupertonic;
-      formulaField.setAttribute('aria-hidden', isSupertonic ? 'true' : 'false');
-    }
-    var stepsField = form.querySelector('[data-role="supertonic-steps-field"]');
-    if (stepsField) {
-      stepsField.hidden = !isSupertonic;
-      stepsField.setAttribute('aria-hidden', isSupertonic ? 'false' : 'true');
-    }
-    filterProfilesByProvider(provider);
-  }
-
-  providerSelect.addEventListener('change', function() {
-    syncProviderUI(providerSelect.value);
-  });
-  syncProviderUI(providerSelect.value);
-})();
-</script>

abogen/webui/templates/settings.html

@@ -61,15 +61,6 @@
               <p class="hint">Pick a saved speaker from Speaker Studio to use by default for new jobs.</p>
             </div>
 
-            <div class="field">
-              <label for="tts_provider">Default TTS Engine</label>
-              <select id="tts_provider" name="tts_provider">
-                <option value="kokoro" {% if settings.tts_provider == 'kokoro' %}selected{% endif %}>Kokoro</option>
-                <option value="supertonic" {% if settings.tts_provider == 'supertonic' %}selected{% endif %}>Supertonic</option>
-              </select>
-              <p class="hint">Select the default TTS engine for new jobs.</p>
-            </div>
-
             <div class="field field--wide">
               <p class="tag">Kokoro settings</p>
             </div>

pyproject.toml

@@ -30,7 +30,7 @@ dependencies = [
   "pip",
   "kokoro>=0.9.4",
   "misaki[zh]>=0.9.4",
-  "supertonic>=1.3.1",
+  "supertonic>=0.1.0",
   "ebooklib>=0.19",
   "beautifulsoup4>=4.13.4",
   "spacy>=3.8.7,<4.0",
@@ -111,11 +111,11 @@ filterwarnings = [
 
 [project.optional-dependencies]
 # NVIDIA GPU (Windows) (CUDA 12.6)       # uv tool install abogen[cuda126]
-cuda126 = ["torch", "onnxruntime-gpu>=1.26.0"]
+cuda126 = ["torch"]
 # NVIDIA GPU (Windows) (CUDA 12.8)       # uv tool install abogen[cuda]
-cuda = ["torch", "onnxruntime-gpu>=1.26.0"]
+cuda = ["torch"]
 # NVIDIA GPU (Windows) (CUDA 13.0)       # uv tool install abogen[cuda130]
-cuda130 = ["torch", "onnxruntime-gpu>=1.26.0"]
+cuda130 = ["torch"]
 # AMD GPU (Linux) (ROCm 6.4)             # uv tool install abogen[rocm]
 rocm = ["torch", "pytorch-triton-rocm"]
 # Development dependencies               # uv tool install abogen[dev]

tests/test_kokoro_backend.py

@@ -0,0 +1,216 @@
+"""Tests for KokoroBackend class."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Iterator, List
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+import pytest
+
+from abogen.tts_backend import TTSBackendMetadata
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+@dataclass
+class _FakeSegment:
+    graphemes: str
+    audio: Any  # np.ndarray or torch-like tensor
+
+
+class _FakePipeline:
+    """Minimal mock for kokoro.KPipeline."""
+
+    def __init__(self, *, lang_code: str, repo_id: str, device: str):
+        self.lang_code = lang_code
+        self.repo_id = repo_id
+        self.device = device
+        self._voices: dict[str, np.ndarray] = {}
+
+    def __call__(
+        self,
+        text: str,
+        *,
+        voice: Any = "",
+        speed: float = 1.0,
+        split_pattern: str | None = None,
+    ) -> Iterator[_FakeSegment]:
+        yield _FakeSegment(graphemes=text, audio=np.zeros(100, dtype="float32"))
+
+    def load_single_voice(self, name: str) -> np.ndarray:
+        if name not in self._voices:
+            self._voices[name] = np.ones((1, 256), dtype="float32") * 0.5
+        return self._voices[name]
+
+
+def _make_backend(**kwargs: Any):
+    """Create KokoroBackend with mocked KPipeline."""
+    with patch("abogen.tts_backends.kokoro._load_kpipeline") as load:
+        load.return_value = _FakePipeline
+        from abogen.tts_backends.kokoro import KokoroBackend
+
+        return KokoroBackend(**kwargs)
+
+
+# ---------------------------------------------------------------------------
+# Tests
+# ---------------------------------------------------------------------------
+
+class TestKokoroBackendMetadata:
+    def test_metadata_returns_tts_backend_metadata(self):
+        backend = _make_backend(lang_code="a")
+        meta = backend.metadata
+        assert isinstance(meta, TTSBackendMetadata)
+
+    def test_metadata_fields(self):
+        backend = _make_backend(lang_code="a")
+        meta = backend.metadata
+        assert meta.id == "kokoro"
+        assert meta.name == "Kokoro"
+        assert "Kokoro" in meta.description
+
+
+class TestKokoroBackendInit:
+    def test_stores_lang_code(self):
+        backend = _make_backend(lang_code="b")
+        assert backend._lang_code == "b"
+
+    def test_default_repo_id(self):
+        with patch("abogen.tts_backends.kokoro._load_kpipeline") as load:
+            load.return_value = _FakePipeline
+            from abogen.tts_backends.kokoro import KokoroBackend
+
+            b = KokoroBackend(lang_code="a")
+            assert b._pipeline.repo_id == "hexgrad/Kokoro-82M"
+
+    def test_custom_repo_id(self):
+        backend = _make_backend(lang_code="a", repo_id="custom/repo")
+        assert backend._pipeline.repo_id == "custom/repo"
+
+    def test_default_device(self):
+        backend = _make_backend(lang_code="a")
+        assert backend._pipeline.device == "cpu"
+
+    def test_custom_device(self):
+        backend = _make_backend(lang_code="a", device="cuda")
+        assert backend._pipeline.device == "cuda"
+
+
+class TestKokoroBackendCall:
+    def test_call_delegates_to_pipeline(self):
+        backend = _make_backend(lang_code="a")
+        results = list(backend("hello", voice="af_heart", speed=1.2, split_pattern=r"\n"))
+        assert len(results) == 1
+        assert results[0].graphemes == "hello"
+
+    def test_call_returns_iterator(self):
+        backend = _make_backend(lang_code="a")
+        result = backend("test", voice="af_heart")
+        assert hasattr(result, "__iter__")
+
+    def test_call_with_voice_tensor(self):
+        backend = _make_backend(lang_code="a")
+        voice_tensor = np.ones((1, 256), dtype="float32")
+        results = list(backend("test", voice=voice_tensor))
+        assert len(results) == 1
+
+    def test_call_default_speed(self):
+        backend = _make_backend(lang_code="a")
+        # Should not raise with default speed
+        list(backend("text", voice="af_heart"))
+
+    def test_call_default_split_pattern_is_none(self):
+        backend = _make_backend(lang_code="a")
+        # split_pattern defaults to None
+        list(backend("text", voice="af_heart"))
+
+
+class TestLoadSingleVoice:
+    def test_load_single_voice_delegates(self):
+        backend = _make_backend(lang_code="a")
+        tensor = backend.load_single_voice("af_heart")
+        assert isinstance(tensor, np.ndarray)
+        assert tensor.shape == (1, 256)
+
+    def test_load_single_voice_caches(self):
+        backend = _make_backend(lang_code="a")
+        t1 = backend.load_single_voice("af_heart")
+        t2 = backend.load_single_voice("af_heart")
+        assert t1 is t2  # same object
+
+
+class TestSynthesize:
+    def test_synthesize_returns_bytes(self):
+        backend = _make_backend(lang_code="a")
+        result = backend.synthesize("hello", voice="af_heart")
+        assert isinstance(result, bytes)
+
+    def test_synthesize_nonempty(self):
+        backend = _make_backend(lang_code="a")
+        result = backend.synthesize("hello", voice="af_heart")
+        assert len(result) > 0
+
+    def test_synthesize_with_speed(self):
+        backend = _make_backend(lang_code="a")
+        result = backend.synthesize("hello", voice="af_heart", speed=1.5)
+        assert isinstance(result, bytes)
+
+    def test_synthesize_empty_text(self):
+        backend = _make_backend(lang_code="a")
+        # Empty text produces no segments
+        result = backend.synthesize("", voice="af_heart")
+        assert isinstance(result, bytes)
+
+
+class TestProtocolMethods:
+    def test_get_available_voices(self):
+        backend = _make_backend(lang_code="a")
+        voices = backend.get_available_voices()
+        assert isinstance(voices, list)
+        assert len(voices) > 0
+        assert all(isinstance(v, str) for v in voices)
+
+    def test_get_supported_formats(self):
+        backend = _make_backend(lang_code="a")
+        formats = backend.get_supported_formats()
+        assert "pcm_float32" in formats
+
+    def test_get_info(self):
+        backend = _make_backend(lang_code="a")
+        info = backend.get_info()
+        assert info["id"] == "kokoro"
+        assert info["name"] == "Kokoro"
+        assert info["lang_code"] == "a"
+
+
+class TestRegistration:
+    def test_factory_creates_kokoro_backend(self):
+        from abogen.tts_backends.kokoro import create_kokoro_backend, KokoroBackend
+
+        with patch("abogen.tts_backends.kokoro._load_kpipeline") as load:
+            load.return_value = _FakePipeline
+            backend = create_kokoro_backend(lang_code="a")
+            assert isinstance(backend, KokoroBackend)
+
+    def test_registry_has_kokoro(self):
+        import abogen.tts_backends  # noqa: F401
+        from abogen.tts_backend_registry import _registry
+
+        meta = _registry.get_metadata("kokoro")
+        assert meta.id == "kokoro"
+        assert meta.name == "Kokoro"
+
+    def test_registry_factory_returns_kokoro_backend(self):
+        import abogen.tts_backends  # noqa: F401
+        from abogen.tts_backend_registry import _registry
+        from abogen.tts_backends.kokoro import KokoroBackend
+
+        factory = _registry._factories["kokoro"]
+        with patch("abogen.tts_backends.kokoro._load_kpipeline") as load:
+            load.return_value = _FakePipeline
+            backend = factory(lang_code="a")
+            assert isinstance(backend, KokoroBackend)

tests/test_preview_applies_manual_overrides.py

@@ -19,7 +19,7 @@ def test_preview_applies_manual_override_before_normalization(monkeypatch):
 
     # And stub the kokoro pipeline path so generate_preview_audio won't proceed.
     # We'll instead validate by calling the override logic through generate_preview_audio
-    # with provider=supertonic and stub SupertonicPipeline to capture input.
+    # with provider=supertonic and stub create_backend to capture input.
     captured = {}
 
     class DummyPipeline:
@@ -30,11 +30,16 @@ def test_preview_applies_manual_override_before_normalization(monkeypatch):
             captured["text"] = text
             return iter(())
 
-    monkeypatch.setitem(
+    from abogen import tts_backend_registry
-        __import__("sys").modules,
+
-        "abogen.tts_supertonic",
+    original_create_backend = tts_backend_registry.create_backend
-        type("M", (), {"SupertonicPipeline": DummyPipeline}),
+
-    )
+    def _mock_create_backend(backend_id, **kwargs):
+        if backend_id == "supertonic":
+            return DummyPipeline(**kwargs)
+        return original_create_backend(backend_id, **kwargs)
+
+    monkeypatch.setattr(tts_backend_registry, "create_backend", _mock_create_backend)
 
     try:
         preview.generate_preview_audio(

tests/test_tts_backend.py

@@ -0,0 +1,204 @@
+from dataclasses import dataclass
+
+from abogen.tts_backend import TTSBackendMetadata
+from abogen.tts_backend_registry import TTSBackendRegistry
+
+
+class TestTTSBackendMetadata:
+    def test_is_frozen_dataclass(self):
+        assert dataclass(TTSBackendMetadata)
+
+    def test_fields_are_present(self):
+        meta = TTSBackendMetadata(
+            id="test",
+            name="Test Backend",
+            description="A test backend",
+        )
+        assert meta.id == "test"
+        assert meta.name == "Test Backend"
+        assert meta.description == "A test backend"
+
+    def test_voices_field_default_empty(self):
+        meta = TTSBackendMetadata(
+            id="test",
+            name="Test",
+            description="Test backend",
+        )
+        assert meta.voices == ()
+
+    def test_voices_field_stored(self):
+        meta = TTSBackendMetadata(
+            id="test",
+            name="Test",
+            description="Test backend",
+            voices=("v1", "v2"),
+        )
+        assert meta.voices == ("v1", "v2")
+
+    def test_is_immutable(self):
+        import pytest
+
+        meta = TTSBackendMetadata(
+            id="kokoro",
+            name="Kokoro",
+            description="Test",
+        )
+        with pytest.raises(Exception):
+            meta.id = "changed"
+
+
+class TestTTSBackendRegistry:
+    def test_register_and_list(self):
+        registry = TTSBackendRegistry()
+        meta = TTSBackendMetadata(id="a", name="A", description="Backend A")
+        registry.register(metadata=meta, factory=lambda: None)
+
+        backends = registry.list_backends()
+        assert len(backends) == 1
+        assert backends[0].id == "a"
+
+    def test_list_multiple(self):
+        registry = TTSBackendRegistry()
+        meta_a = TTSBackendMetadata(id="a", name="A", description="A")
+        meta_b = TTSBackendMetadata(id="b", name="B", description="B")
+        registry.register(metadata=meta_a, factory=lambda: None)
+        registry.register(metadata=meta_b, factory=lambda: None)
+
+        backends = registry.list_backends()
+        ids = [b.id for b in backends]
+        assert "a" in ids
+        assert "b" in ids
+
+    def test_get_metadata(self):
+        registry = TTSBackendRegistry()
+        meta = TTSBackendMetadata(id="x", name="X", description="X backend")
+        registry.register(metadata=meta, factory=lambda: None)
+
+        result = registry.get_metadata("x")
+        assert result.id == "x"
+        assert result.name == "X"
+
+    def test_get_metadata_unknown_raises(self):
+        import pytest
+
+        registry = TTSBackendRegistry()
+        with pytest.raises(KeyError, match="Unknown backend: nope"):
+            registry.get_metadata("nope")
+
+    def test_create_backend(self):
+        registry = TTSBackendRegistry()
+        meta = TTSBackendMetadata(id="test", name="Test", description="Test backend")
+
+        def factory(**kwargs):
+            return {"created": True, "kwargs": kwargs}
+
+        registry.register(metadata=meta, factory=factory)
+        result = registry.create_backend("test", foo="bar")
+
+        assert result == {"created": True, "kwargs": {"foo": "bar"}}
+
+    def test_create_backend_unknown_raises(self):
+        import pytest
+
+        registry = TTSBackendRegistry()
+        with pytest.raises(KeyError, match="Unknown backend: missing"):
+            registry.create_backend("missing")
+
+    def test_register_overwrites(self):
+        registry = TTSBackendRegistry()
+        meta1 = TTSBackendMetadata(id="x", name="V1", description="First")
+        meta2 = TTSBackendMetadata(id="x", name="V2", description="Second")
+        registry.register(metadata=meta1, factory=lambda: "v1")
+        registry.register(metadata=meta2, factory=lambda: "v2")
+
+        result = registry.get_metadata("x")
+        assert result.name == "V2"
+        assert registry.create_backend("x") == "v2"
+
+
+class TestBackendRegistration:
+    """Tests that existing backends are auto-registered."""
+
+    def test_import_triggers_registration(self):
+        import abogen.tts_backends  # noqa: F401
+
+        from abogen.tts_backend_registry import _registry
+
+        backends = _registry.list_backends()
+        ids = [b.id for b in backends]
+        assert "kokoro" in ids
+        assert "supertonic" in ids
+
+    def test_kokoro_metadata(self):
+        import abogen.tts_backends  # noqa: F401
+
+        from abogen.tts_backend_registry import _registry
+
+        meta = _registry.get_metadata("kokoro")
+        assert meta.id == "kokoro"
+        assert meta.name == "Kokoro"
+        assert "Kokoro" in meta.description
+
+    def test_supertonic_metadata(self):
+        import abogen.tts_backends  # noqa: F401
+
+        from abogen.tts_backend_registry import _registry
+
+        meta = _registry.get_metadata("supertonic")
+        assert meta.id == "supertonic"
+        assert meta.name == "SuperTonic"
+        assert "SuperTonic" in meta.description
+
+    def test_kokoro_metadata_has_voices(self):
+        import abogen.tts_backends  # noqa: F401
+
+        from abogen.tts_backend_registry import _registry
+
+        meta = _registry.get_metadata("kokoro")
+        assert isinstance(meta.voices, tuple)
+        assert len(meta.voices) > 0
+        assert all(isinstance(v, str) for v in meta.voices)
+
+    def test_supertonic_metadata_has_voices(self):
+        import abogen.tts_backends  # noqa: F401
+
+        from abogen.tts_backend_registry import _registry
+
+        meta = _registry.get_metadata("supertonic")
+        assert isinstance(meta.voices, tuple)
+        assert len(meta.voices) == 10
+        assert meta.voices == ("M1", "M2", "M3", "M4", "M5", "F1", "F2", "F3", "F4", "F5")
+
+    def test_kokoro_factory_callable(self):
+        import abogen.tts_backends  # noqa: F401
+
+        from abogen.tts_backend_registry import _registry
+
+        factory = _registry._factories["kokoro"]
+        assert callable(factory)
+
+    def test_supertonic_factory_callable(self):
+        import abogen.tts_backends  # noqa: F401
+
+        from abogen.tts_backend_registry import _registry
+
+        factory = _registry._factories["supertonic"]
+        assert callable(factory)
+
+    def test_kokoro_metadata_voices_match_registry(self):
+        """Ensure the metadata property on the instance shares voices with registry."""
+        from abogen.tts_backends.kokoro import _KOKORO_METADATA
+        from abogen.tts_backend_registry import _registry
+
+        registry_meta = _registry.get_metadata("kokoro")
+        assert _KOKORO_METADATA is registry_meta
+        assert _KOKORO_METADATA.voices == registry_meta.voices
+
+    def test_supertonic_metadata_voices_match_registry(self):
+        """Ensure the metadata property on the instance shares voices with registry."""
+        from abogen.tts_backends.supertonic import _SUPERTONIC_METADATA
+        from abogen.tts_backend_registry import _registry
+
+        registry_meta = _registry.get_metadata("supertonic")
+        assert _SUPERTONIC_METADATA is registry_meta
+        assert _SUPERTONIC_METADATA.voices == registry_meta.voices

tests/test_tts_supertonic_unsupported_chars.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-from abogen.tts_supertonic import SupertonicPipeline
+from abogen.tts_backends.supertonic import SupertonicBackend, SupertonicPipeline
 
 
 class _DummyTTS:
@@ -26,13 +26,23 @@ class _DummyTTS:
         return audio, 0.05
 
 
-def test_supertonic_pipeline_strips_unsupported_characters_and_retries():
+def _make_pipeline() -> SupertonicPipeline:
-    # Avoid importing/initializing real supertonic by manually constructing the pipeline.
     pipeline = SupertonicPipeline.__new__(SupertonicPipeline)
     pipeline.sample_rate = 24000
     pipeline.total_steps = 5
     pipeline.max_chunk_length = 1000
     pipeline._tts = _DummyTTS()
+    return pipeline
+
+
+def _make_backend() -> SupertonicBackend:
+    backend = SupertonicBackend.__new__(SupertonicBackend)
+    backend._pipeline = _make_pipeline()
+    return backend
+
+
+def test_supertonic_pipeline_strips_unsupported_characters_and_retries():
+    pipeline = _make_pipeline()
 
     segs = list(pipeline("Hello • world", voice="M1", speed=1.0))
     assert len(segs) == 1
@@ -43,11 +53,56 @@ def test_supertonic_pipeline_strips_unsupported_characters_and_retries():
 
 
 def test_supertonic_pipeline_drops_chunk_if_only_unsupported_characters():
-    pipeline = SupertonicPipeline.__new__(SupertonicPipeline)
+    pipeline = _make_pipeline()
-    pipeline.sample_rate = 24000
-    pipeline.total_steps = 5
-    pipeline.max_chunk_length = 1000
-    pipeline._tts = _DummyTTS()
 
     segs = list(pipeline("•", voice="M1", speed=1.0))
     assert segs == []
+
+
+# --- SupertonicBackend tests ---
+
+
+def test_backend_metadata():
+    backend = _make_backend()
+    meta = backend.metadata
+    assert meta.id == "supertonic"
+    assert meta.name == "SuperTonic"
+    assert "SuperTonic" in meta.description
+
+
+def test_backend_get_available_voices():
+    backend = _make_backend()
+    voices = backend.get_available_voices()
+    assert isinstance(voices, list)
+    assert "M1" in voices
+    assert "F1" in voices
+
+
+def test_backend_get_supported_formats():
+    backend = _make_backend()
+    formats = backend.get_supported_formats()
+    assert "wav" in formats
+
+
+def test_backend_get_info():
+    backend = _make_backend()
+    info = backend.get_info()
+    assert info["sample_rate"] == 24000
+    assert info["total_steps"] == 5
+    assert isinstance(info["voices"], list)
+
+
+def test_backend_call_delegates_to_pipeline():
+    backend = _make_backend()
+    segs = list(backend("Hello • world", voice="M1", speed=1.0))
+    assert len(segs) == 1
+    assert segs[0].audio.size > 0
+
+
+def test_backend_synthesize_returns_wav_bytes():
+    backend = _make_backend()
+    wav_bytes = backend.synthesize("Hello world", voice="M1", speed=1.0)
+    assert isinstance(wav_bytes, bytes)
+    assert len(wav_bytes) > 0
+    # WAV magic number
+    assert wav_bytes[:4] == b"RIFF"

tests/test_voice_formula_resolution.py

@@ -1,18 +1,18 @@
 from __future__ import annotations
 
 from abogen.webui.conversion_runner import _resolve_voice, _supertonic_voice_from_spec
-from abogen.tts_supertonic import DEFAULT_SUPERTONIC_VOICES
+from abogen.tts_backends.supertonic import DEFAULT_SUPERTONIC_VOICES
 
 
 def test_resolve_voice_formula_without_pipeline_does_not_crash() -> None:
     # This can happen when a previously-saved Kokoro mix formula is present
-    # but the active provider is Supertonic (no Kokoro pipeline object).
+    # but the active provider is SuperTonic (no Kokoro pipeline object).
     formula = "af_heart*0.5+af_sky*0.5"
     resolved = _resolve_voice(None, formula, use_gpu=False)
     assert resolved == formula
 
 
 def test_supertonic_voice_from_formula_falls_back_to_valid_voice() -> None:
-    # When a stale Kokoro mix formula is present, Supertonic should not receive it.
+    # When a stale Kokoro mix formula is present, SuperTonic should not receive it.
     chosen = _supertonic_voice_from_spec("af_heart*0.5+af_sky*0.5", "af_heart*1.0")
     assert chosen in DEFAULT_SUPERTONIC_VOICES

tests/test_voice_metadata.py

@@ -0,0 +1,233 @@
+import pytest
+
+from abogen.voice_metadata import VoiceMetadata
+
+
+class TestVoiceMetadataCreation:
+    def test_create_with_all_fields(self):
+        voice = VoiceMetadata(
+            id="af_alloy",
+            display_name="Alloy",
+            language="a",
+            gender="female",
+            backend_id="kokoro",
+        )
+        assert voice.id == "af_alloy"
+        assert voice.display_name == "Alloy"
+        assert voice.language == "a"
+        assert voice.gender == "female"
+        assert voice.backend_id == "kokoro"
+
+    def test_create_supertonic_voice(self):
+        voice = VoiceMetadata(
+            id="M1",
+            display_name="Male 1",
+            language="en",
+            gender="male",
+            backend_id="supertonic",
+        )
+        assert voice.id == "M1"
+        assert voice.backend_id == "supertonic"
+
+    def test_create_with_unknown_gender(self):
+        voice = VoiceMetadata(
+            id="custom_voice",
+            display_name="Custom",
+            language="en",
+            gender="unknown",
+            backend_id="custom_backend",
+        )
+        assert voice.gender == "unknown"
+
+
+class TestVoiceMetadataImmutability:
+    def test_frozen_dataclass(self):
+        voice = VoiceMetadata(
+            id="af_alloy",
+            display_name="Alloy",
+            language="a",
+            gender="female",
+            backend_id="kokoro",
+        )
+        with pytest.raises(AttributeError):
+            voice.id = "new_id"
+
+    def test_cannot_modify_display_name(self):
+        voice = VoiceMetadata(
+            id="af_alloy",
+            display_name="Alloy",
+            language="a",
+            gender="female",
+            backend_id="kokoro",
+        )
+        with pytest.raises(AttributeError):
+            voice.display_name = "New Name"
+
+    def test_cannot_modify_backend_id(self):
+        voice = VoiceMetadata(
+            id="af_alloy",
+            display_name="Alloy",
+            language="a",
+            gender="female",
+            backend_id="kokoro",
+        )
+        with pytest.raises(AttributeError):
+            voice.backend_id = "new_backend"
+
+
+class TestVoiceMetadataEquality:
+    def test_equal_voices_are_equal(self):
+        voice1 = VoiceMetadata(
+            id="af_alloy",
+            display_name="Alloy",
+            language="a",
+            gender="female",
+            backend_id="kokoro",
+        )
+        voice2 = VoiceMetadata(
+            id="af_alloy",
+            display_name="Alloy",
+            language="a",
+            gender="female",
+            backend_id="kokoro",
+        )
+        assert voice1 == voice2
+
+    def test_different_voices_are_not_equal(self):
+        voice1 = VoiceMetadata(
+            id="af_alloy",
+            display_name="Alloy",
+            language="a",
+            gender="female",
+            backend_id="kokoro",
+        )
+        voice2 = VoiceMetadata(
+            id="am_adam",
+            display_name="Adam",
+            language="a",
+            gender="male",
+            backend_id="kokoro",
+        )
+        assert voice1 != voice2
+
+    def test_different_backend_id_not_equal(self):
+        voice1 = VoiceMetadata(
+            id="custom",
+            display_name="Custom",
+            language="en",
+            gender="unknown",
+            backend_id="backend_a",
+        )
+        voice2 = VoiceMetadata(
+            id="custom",
+            display_name="Custom",
+            language="en",
+            gender="unknown",
+            backend_id="backend_b",
+        )
+        assert voice1 != voice2
+
+
+class TestVoiceMetadataHashing:
+    def test_hashable(self):
+        voice = VoiceMetadata(
+            id="af_alloy",
+            display_name="Alloy",
+            language="a",
+            gender="female",
+            backend_id="kokoro",
+        )
+        assert hash(voice) is not None
+
+    def test_equal_voices_same_hash(self):
+        voice1 = VoiceMetadata(
+            id="af_alloy",
+            display_name="Alloy",
+            language="a",
+            gender="female",
+            backend_id="kokoro",
+        )
+        voice2 = VoiceMetadata(
+            id="af_alloy",
+            display_name="Alloy",
+            language="a",
+            gender="female",
+            backend_id="kokoro",
+        )
+        assert hash(voice1) == hash(voice2)
+
+    def test_usable_in_set(self):
+        voice1 = VoiceMetadata(
+            id="af_alloy",
+            display_name="Alloy",
+            language="a",
+            gender="female",
+            backend_id="kokoro",
+        )
+        voice2 = VoiceMetadata(
+            id="af_alloy",
+            display_name="Alloy",
+            language="a",
+            gender="female",
+            backend_id="kokoro",
+        )
+        voice3 = VoiceMetadata(
+            id="am_adam",
+            display_name="Adam",
+            language="a",
+            gender="male",
+            backend_id="kokoro",
+        )
+        voice_set = {voice1, voice2, voice3}
+        assert len(voice_set) == 2
+
+
+class TestVoiceMetadataUseCases:
+    def test_backend_populates_backend_id(self):
+        """Simulate how a backend would populate backend_id automatically."""
+
+        class MockBackend:
+            def __init__(self):
+                self._backend_id = "kokoro"
+
+            def get_voices(self):
+                return [
+                    VoiceMetadata(
+                        id="af_alloy",
+                        display_name="Alloy",
+                        language="a",
+                        gender="female",
+                        backend_id=self._backend_id,
+                    ),
+                ]
+
+        backend = MockBackend()
+        voices = backend.get_voices()
+        assert voices[0].backend_id == "kokoro"
+
+    def test_filter_by_language(self):
+        voices = [
+            VoiceMetadata(id="af_alloy", display_name="Alloy", language="a", gender="female", backend_id="kokoro"),
+            VoiceMetadata(id="jf_alpha", display_name="Alpha", language="j", gender="female", backend_id="kokoro"),
+            VoiceMetadata(id="am_adam", display_name="Adam", language="a", gender="male", backend_id="kokoro"),
+        ]
+        english_voices = [v for v in voices if v.language == "a"]
+        assert len(english_voices) == 2
+
+    def test_filter_by_gender(self):
+        voices = [
+            VoiceMetadata(id="af_alloy", display_name="Alloy", language="a", gender="female", backend_id="kokoro"),
+            VoiceMetadata(id="am_adam", display_name="Adam", language="a", gender="male", backend_id="kokoro"),
+            VoiceMetadata(id="am_puck", display_name="Puck", language="a", gender="male", backend_id="kokoro"),
+        ]
+        male_voices = [v for v in voices if v.gender == "male"]
+        assert len(male_voices) == 2
+
+    def test_filter_by_backend(self):
+        voices = [
+            VoiceMetadata(id="af_alloy", display_name="Alloy", language="a", gender="female", backend_id="kokoro"),
+            VoiceMetadata(id="M1", display_name="Male 1", language="en", gender="male", backend_id="supertonic"),
+        ]
+        kokoro_voices = [v for v in voices if v.backend_id == "kokoro"]
+        assert len(kokoro_voices) == 1
+        assert kokoro_voices[0].id == "af_alloy"