refactor: split tts.py into per-backend packages

1 files changed, 322 insertions, 0 deletions

diff --git a/app/converter/clients/qwen.py b/app/converter/clients/qwen.py
new file mode 100644
index 0000000..354ee04
--- /dev/null
+++ b/app/converter/clients/qwen.py
@@ -0,0 +1,322 @@
+"""Client for the qwen-tts Gradio demo servers (CustomVoice + Base)."""
+
+import io
+import logging
+import shutil
+import sys
+import tempfile
+from pathlib import Path
+from typing import Any, Dict, Optional, Tuple
+
+from .. import config
+from ..audio import concat_audio_files
+from ..chunking import split_into_chunks
+from .base import (BaseTTSClient, ConversionCancelled, resolve_request_seed,
+                   VOICE_MODE_CLONE, VOICE_MODE_CUSTOM, VOICE_MODES)
+from .languages import normalize_language
+from .speakers import speaker_display_name
+
+logger = logging.getLogger(__name__)
+
+# Fixed model facts: both demos run the 1.7B model (the CustomVoice demo
+# takes its full HuggingFace id), and the 12Hz codec outputs 24 kHz audio.
+MODEL_SIZE = "1.7B"
+CUSTOM_VOICE_MODEL_ID = "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice"
+
+
+class QwenTTSClient(BaseTTSClient):
+    """Generates audio chunks through a Qwen3-TTS demo server."""
+
+    def __init__(self, chunks_dir: Path,
+                 voice_mode: str = "custom_voice", voice_clone_ref_audio: Optional[str] = None,
+                 voice_clone_ref_text: Optional[str] = None, skip_transcription: bool = False,
+                 language: Optional[str] = None, api_url: Optional[str] = None,
+                 quiet: bool = False):
+        super().__init__(chunks_dir, quiet=quiet)
+        if voice_mode not in VOICE_MODES:
+            raise ValueError(
+                f"Unknown voice mode: {voice_mode!r} (expected one of {VOICE_MODES})"
+            )
+        self.voice_mode = voice_mode
+        self.voice_clone_ref_audio = voice_clone_ref_audio
+        self.voice_clone_ref_text = (voice_clone_ref_text or "").strip()
+        self.skip_transcription = skip_transcription
+        # api_url overrides the configured endpoint for the active voice mode
+        # (used by the hub's "[remote]" backend entries and --api-url).
+        self.api_url = (api_url or "").strip() or None
+        # Seed sent with every request: config.SEED as-is, or (with
+        # CONSTANT_SEED and SEED < 0) one random value drawn per run and
+        # reused for every request so the voice stays consistent across
+        # chunk boundaries. Without CONSTANT_SEED, -1 is forwarded so the
+        # server re-samples the voice on every generation.
+        self._seed = resolve_request_seed()
+        if language is None:
+            language = config.LANGUAGE
+        # Validate before connecting so bad values fail fast without a server.
+        self.language = normalize_language(language)
+        self.client = None
+        self.api_info: Dict[str, Any] = {}
+        self.clone_client = None
+        self.clone_api_info: Dict[str, Any] = {}
+        self._ref_audio_filedata: Optional[Dict[str, Any]] = None
+        self._connect()
+
+    # ------------------------------------------------------------------
+    # Connection
+    # ------------------------------------------------------------------
+
+    def _connect(self) -> None:
+        api_url = self.api_url or (
+            config.CLONE_API_URL if self.voice_mode == VOICE_MODE_CLONE
+            else config.QWEN_API_URL)
+        try:
+            if self.voice_mode == VOICE_MODE_CLONE:
+                # Voice clone uses the Base-model demo, which is a separate server
+                # from the CustomVoice demo (that one only exposes /run_instruct).
+                self._init_client(api_url, clone=True)
+                self._report(f"[OK] Connected to Voice Clone API at {api_url}")
+                self._resolve_reference_text()
+            else:
+                self._init_client(api_url, clone=False)
+                self._report("[OK] Connected to Qwen API")
+        except Exception as exc:
+            raise RuntimeError(
+                f"Qwen API initialization failed at {api_url}: {exc}. "
+                "Make sure the Qwen demo server is running and reachable, and that your "
+                "installed Qwen3-TTS version matches this converter's API expectations "
+                "(voice clone requires the Base-model demo: Qwen/Qwen3-TTS-12Hz-1.7B-Base)."
+            ) from exc
+
+    def _resolve_reference_text(self) -> None:
+        """Resolve the reference transcript: explicit text, then local
+        transcription, then x-vector-only mode."""
+        if not self.voice_clone_ref_text and self.voice_clone_ref_audio:
+            if self.skip_transcription:
+                self._report("[INFO] Skipping reference audio transcription (--no-transcription).")
+            else:
+                self._report("[INFO] Transcribing reference audio for voice cloning...")
+                self.voice_clone_ref_text = self.transcribe_audio(self.voice_clone_ref_audio) or ""
+        if not self.voice_clone_ref_text:
+            self._report("[WARNING] No reference text available; using "
+                         "x-vector-only clone mode (lower quality).")
+            self._report('          Pass --transcription "..." for higher-quality in-context cloning.')
+        else:
+            self._report(f"[OK] Reference text:\n{self.voice_clone_ref_text}")
+
+    def _init_client(self, url: str, clone: bool = False) -> None:
+        """Initialize a Gradio client and store its API metadata.
+
+        gradio_client prints its usage info directly to stdout while the
+        client is created and its API metadata loaded, so stdout is swapped
+        for a buffer for the whole process; the captured text is re-emitted
+        at DEBUG level for troubleshooting.
+        """
+        from gradio_client import Client
+
+        logger.info("Connecting to Qwen API at %s...", url)
+        old_stdout = sys.stdout
+        captured = io.StringIO()
+        sys.stdout = captured
+        try:
+            try:
+                client = Client(url, httpx_kwargs={"timeout": config.API_TIMEOUT})
+            except TypeError:
+                # Older gradio_client versions don't support httpx_kwargs.
+                client = Client(url)
+            if clone:
+                self.clone_client = client
+                self.clone_api_info = self._load_api_info(client)
+            else:
+                self.client = client
+                self.api_info = self._load_api_info(client)
+        finally:
+            sys.stdout = old_stdout
+        usage_info = captured.getvalue().strip()
+        if usage_info:
+            logger.debug("Gradio client output for %s:\n%s", url, usage_info)
+        logger.info("Connected to Qwen API")
+
+    @staticmethod
+    def _load_api_info(client) -> Dict[str, Any]:
+        """Load available API metadata from the Gradio app."""
+        try:
+            return client.view_api(return_format="dict")
+        except Exception as exc:
+            logger.warning("Unable to read API metadata: %s", exc)
+            return {}
+
+    def _resolve_api_name(self, *candidates: str, api_info: Optional[Dict[str, Any]] = None) -> str:
+        """Return the first available api_name from candidate list."""
+        info = api_info if api_info is not None else self.api_info
+        named_endpoints = info.get("named_endpoints", {})
+        for candidate in candidates:
+            if candidate in named_endpoints:
+                return candidate
+        return candidates[0]
+
+    def _endpoint_accepts_param(self, api_name: str, param_name: str,
+                                api_info: Optional[Dict[str, Any]] = None) -> bool:
+        """Check whether endpoint input schema includes the given parameter."""
+        info = api_info if api_info is not None else self.api_info
+        endpoint = info.get("named_endpoints", {}).get(api_name, {})
+        parameters = endpoint.get("parameters", [])
+        return any(parameter.get("parameter_name") == param_name for parameter in parameters)
+
+    # ------------------------------------------------------------------
+    # Reference audio transcription (voice clone)
+    # ------------------------------------------------------------------
+
+    def transcribe_audio(self, audio_path: str) -> Optional[str]:
+        """Transcribe reference audio locally using an optional Whisper backend."""
+        from .transcribe import transcribe_reference_audio
+        return transcribe_reference_audio(audio_path)
+
+    # ------------------------------------------------------------------
+    # Chunk generation
+    # ------------------------------------------------------------------
+
+    def generate_chunk(self, text: str, chunk_num: int) -> Optional[str]:
+        """Generate one audio chunk; returns its path in the chunks folder.
+
+        The text is split into sub-requests of at most
+        ``config.CHUNK_SIZE`` words each (the book-level chunker
+        normally guarantees this already; the split is defense in depth
+        against pathological input such as a punctuation-free run of
+        text), and the audio files returned for the sub-requests are
+        concatenated into one chunk file.
+        """
+        try:
+            sub_texts = split_into_chunks(text, max_words=config.CHUNK_SIZE)
+            if not sub_texts:
+                raise RuntimeError("No text to synthesize")
+
+            output_path: Optional[Path] = None
+            with tempfile.TemporaryDirectory(prefix="tts_parts_") as parts_dir, \
+                    self._chunk_heartbeat(chunk_num):
+                part_paths = [
+                    self._generate_sub_request(sub_text, parts_dir, sub_num,
+                                                len(sub_texts), chunk_num)
+                    for sub_num, sub_text in enumerate(sub_texts, 1)
+                ]
+                if len(part_paths) == 1:
+                    suffix = part_paths[0].suffix or ".wav"
+                    output_path = self._chunk_path(chunk_num, suffix)
+                    shutil.copy2(part_paths[0], output_path)
+                else:
+                    output_path = self._chunk_path(chunk_num, ".wav")
+                    concat_audio_files(part_paths, output_path)
+
+            logger.debug("Chunk %d generated successfully (%d sub-request(s))",
+                         chunk_num, len(sub_texts))
+            return str(output_path)
+
+        except ConversionCancelled:
+            raise
+        except Exception as exc:
+            logger.error("Qwen chunk processing failed for chunk %d: %s", chunk_num, exc)
+            return None
+
+    def _generate_sub_request(self, text: str, parts_dir: str, sub_num: int,
+                              sub_total: int, chunk_num: int) -> Path:
+        """Run one API generation for ``text``; returns the downloaded audio."""
+        if sub_total > 1:
+            logger.info("Chunk %d: oversized input split into %d requests "
+                        "(sub-request %d/%d)", chunk_num, sub_total, sub_num, sub_total)
+        if self.voice_mode == VOICE_MODE_CUSTOM:
+            result = self._generate_custom_voice(text)
+        elif self.voice_mode == VOICE_MODE_CLONE:
+            result = self._generate_voice_clone(text)
+        else:
+            raise ValueError(f"Unknown voice mode: {self.voice_mode}")
+
+        if not isinstance(result, (tuple, list)) or not result:
+            raise RuntimeError("Qwen API returned an invalid result")
+
+        audio_path = result[0]  # First element is the audio file path
+        if not isinstance(audio_path, (str, Path)) or not audio_path:
+            raise RuntimeError("Qwen API did not return an audio file path")
+
+        source = Path(audio_path)
+        if not source.exists():
+            raise RuntimeError(f"Generated audio file not found: {audio_path}")
+
+        destination = Path(parts_dir) / f"part_{sub_num:02d}{source.suffix or '.wav'}"
+        shutil.copy2(source, destination)
+
+        return destination
+
+    # ------------------------------------------------------------------
+    # API payloads
+    # ------------------------------------------------------------------
+
+    def _generate_custom_voice(self, text: str) -> Tuple:
+        """Generate audio using CustomVoice mode."""
+        custom_api = self._resolve_api_name("/run_instruct", "/run_custom_voice", "/generate_custom_voice")
+        if custom_api == "/run_instruct":
+            payload = dict(
+                text=text,
+                lang_disp=self.language,
+                spk_disp=speaker_display_name(),
+                instruct=config.INSTRUCT,
+            )
+        else:
+            payload = dict(
+                text=text,
+                language=self.language,
+                speaker=config.SPEAKER,
+                instruct=config.INSTRUCT,
+            )
+            if self._endpoint_accepts_param(custom_api, "model_id_cv"):
+                payload["model_id_cv"] = CUSTOM_VOICE_MODEL_ID
+            elif self._endpoint_accepts_param(custom_api, "model_size"):
+                payload["model_size"] = MODEL_SIZE
+
+            if self._endpoint_accepts_param(custom_api, "seed"):
+                payload["seed"] = self._seed
+
+        return self.client.predict(**payload, api_name=custom_api)
+
+    def _ref_audio_payload(self) -> Dict[str, Any]:
+        """Gradio file payload for the reference audio (built once, reused)."""
+        if self._ref_audio_filedata is None:
+            from gradio_client import handle_file
+            self._ref_audio_filedata = handle_file(self.voice_clone_ref_audio)
+        return self._ref_audio_filedata
+
+    def _generate_voice_clone(self, text: str) -> Tuple:
+        """Generate audio using Voice Clone mode."""
+        if not Path(self.voice_clone_ref_audio).exists():
+            raise FileNotFoundError(f"Reference audio not found: {self.voice_clone_ref_audio}")
+
+        if self.clone_client is None:
+            raise RuntimeError("Voice Clone client is not initialized. Is the Base-model demo running?")
+
+        clone_api = self._resolve_api_name("/run_voice_clone", "/generate_voice_clone",
+                                           api_info=self.clone_api_info)
+        use_xvector = config.XVECTOR_ONLY or not self.voice_clone_ref_text
+
+        if clone_api == "/run_voice_clone":
+            payload = dict(
+                ref_aud=self._ref_audio_payload(),
+                ref_txt=self.voice_clone_ref_text,
+                use_xvec=use_xvector,
+                text=text,
+                lang_disp=self.language,
+            )
+        else:
+            payload = dict(
+                ref_audio=self._ref_audio_payload(),
+                ref_text=self.voice_clone_ref_text,
+                target_text=text,
+                language=self.language,
+                use_xvector_only=use_xvector,
+            )
+            optional_params = {
+                "model_size": MODEL_SIZE,
+                "seed": self._seed,
+            }
+            for name, value in optional_params.items():
+                if self._endpoint_accepts_param(clone_api, name, api_info=self.clone_api_info):
+                    payload[name] = value
+
+        return self.clone_client.predict(**payload, api_name=clone_api)