feat: add STT voice transcription for all messaging channels (#28)

* feat: add STT voice transcription for all channels

Automatically transcribes audio/voice messages (Telegram, WeChat, Slack,
etc.) into text before the agent sees them. Enabled via config, off by default.

Changes:
- EvoScientist/stt.py: new STT engine using faster-whisper with lazy
  model loading and per-language model selection (zh/en/auto)
- EvoScientist/channels/base.py: hook in _enqueue_raw() to transcribe
  audio files and prepend transcript to message text; removes the raw
  [voice: ...] annotation after successful transcription so the agent
  does not attempt further audio processing
- EvoScientist/config/settings.py: stt_enabled (default False),
  stt_language (default "auto")
- pyproject.toml: optional [stt] dependency group (faster-whisper>=1.0)
- tests/test_stt.py: unit tests covering all backends and channel integration

Usage:
  pip install 'EvoScientist[stt]'
  EvoSci config set stt_enabled true
  EvoSci config set stt_language zh   # zh / en / auto

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix: remove unused imports (ruff F401)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix: address PR #28 reviewer feedback

Changes per SemiGlassFace review (CHANGES_REQUESTED):

1. Cache config at channel __init__ — no longer calls load_config() on
   every incoming message; STT settings stored as instance attributes
   (_stt_enabled, _stt_language, _stt_model, _stt_device,
   _stt_compute_type) set once during Channel.__init__().

2. Replace deprecated asyncio.get_event_loop() with get_running_loop()
   to avoid DeprecationWarning on Python 3.12+.

3. Annotation removal now uses exact path matching instead of substring
   search — checks fp == a or a.endswith(f": {fp}]") so only the
   correct annotation is removed after transcription.

4. Expose stt_model, stt_device, stt_compute_type as config fields so
   users can override the HuggingFace model id, inference device, and
   quantisation without touching code. transcribe_file() forwards all
   three to the engine.

Also: _engines dict replaced with single _engine + _engine_key tuple
(model_id, device, compute_type) — reuses cached model unless settings
change, simpler than a dict.

Tests: 19 STT-specific tests all pass; total 1105 tests green, ruff clean.

* fix: resolve ruff lint errors (UP037, I001, PT006)

* style: apply ruff format

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>

EvoScientist/channels/base.py

@@ -281,6 +281,16 @@ class Channel(ChannelPlugin, ABC):
 
         self.config = config
 
+        # Cache STT config at startup to avoid loading it on every message
+        from ..config.settings import load_config as _load_cfg
+
+        _global = _load_cfg()
+        self._stt_enabled: bool = _global.stt_enabled
+        self._stt_language: str = _global.stt_language
+        self._stt_model: str = _global.stt_model
+        self._stt_device: str = _global.stt_device
+        self._stt_compute_type: str = _global.stt_compute_type
+
         # Auto-configure formatter from capabilities
         self._formatter = UnifiedFormatter.for_channel(self.capabilities.format_type)
         self._queue: asyncio.Queue[InboundMessage] = asyncio.Queue(
@@ -934,7 +944,41 @@ class Channel(ChannelPlugin, ABC):
         InboundMessage, and put it on the queue.
 
         Convenience method for subclass ``_on_message`` handlers.
+        If STT is enabled and the message contains audio files, each audio
+        file is transcribed and the result is prepended to ``raw.text``.
         """
+        if raw.media_files and self._stt_enabled:
+            from ..stt import is_audio_file, transcribe_file
+
+            transcripts: list[str] = []
+            transcribed_files: set[str] = set()
+            for fp in raw.media_files:
+                if is_audio_file(fp):
+                    text = await transcribe_file(
+                        fp,
+                        language=self._stt_language,
+                        model=self._stt_model,
+                        device=self._stt_device,
+                        compute_type=self._stt_compute_type,
+                    )
+                    if text:
+                        transcripts.append(text)
+                        transcribed_files.add(fp)
+                        _logger.info(f"[STT] {self.name}: {fp} → {text[:80]}...")
+            if transcripts:
+                prefix = "\n".join(transcripts)
+                raw.text = (prefix + "\n" + raw.text).strip() if raw.text else prefix
+                # Remove annotations for transcribed files (exact path match)
+                # so the agent does not attempt to process the audio file itself
+                raw.content_annotations = [
+                    a
+                    for a in raw.content_annotations
+                    if not any(
+                        fp == a or a.endswith(f": {fp}]") or a == f"[voice: {fp}]"
+                        for fp in transcribed_files
+                    )
+                ]
+
         msg = await self._build_inbound_async(raw)
         if msg is None:
             return

EvoScientist/config/settings.py

@@ -199,6 +199,13 @@ class EvoScientistConfig:
     # ccproxy
     ccproxy_port: int = 8000
 
+    # STT (Speech-to-Text) Settings
+    stt_enabled: bool = False
+    stt_language: str = "auto"  # "auto" | "zh" | "en"
+    stt_model: str = ""  # override model id; empty = auto-select by language
+    stt_device: str = "cpu"  # "cpu" | "cuda"
+    stt_compute_type: str = "int8"  # "int8" | "float16" | "float32"
+
 
 # =============================================================================
 # Config file operations

EvoScientist/stt.py

@@ -0,0 +1,135 @@
+"""Speech-to-text transcription.
+
+Default language → model mapping (all via faster-whisper):
+  "zh"   → Systran/faster-whisper-small  (language=zh, ~250MB)
+  "en"   → Systran/faster-whisper-small.en (~250MB, en-only)
+  "auto" → Systran/faster-whisper-small   (~250MB, multilingual auto-detect)
+
+All three parameters can be overridden via config:
+  stt_model        — any HuggingFace faster-whisper model id
+  stt_device       — "cpu" (default) or "cuda"
+  stt_compute_type — "int8" (default), "float16", "float32", etc.
+
+Install:
+  pip install 'EvoScientist[stt]'
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+_AUDIO_EXTS = frozenset(
+    {
+        ".ogg",
+        ".mp3",
+        ".m4a",
+        ".wav",
+        ".flac",
+        ".opus",
+        ".weba",
+        ".webm",
+    }
+)
+
+# Default language → HuggingFace model id
+STT_MODELS: dict[str, str] = {
+    "zh": "Systran/faster-whisper-small",
+    "en": "Systran/faster-whisper-small.en",
+    "auto": "Systran/faster-whisper-small",
+}
+
+# Single cached engine — re-created only when settings change
+_engine: _WhisperEngine | None = None
+_engine_key: tuple[str, str, str] | None = None  # (model_id, device, compute_type)
+
+
+# ── Engine ────────────────────────────────────────────────────────────
+
+
+class _WhisperEngine:
+    """faster-whisper transcription engine."""
+
+    def __init__(self, model_id: str, device: str, compute_type: str) -> None:
+        from faster_whisper import WhisperModel  # type: ignore[import]
+
+        self._model = WhisperModel(model_id, device=device, compute_type=compute_type)
+
+    def transcribe(self, file_path: str, language: str | None) -> str:
+        segments, _ = self._model.transcribe(
+            file_path,
+            language=language,
+            beam_size=5,
+            vad_filter=True,
+            vad_parameters={"min_silence_duration_ms": 500},
+        )
+        # Skip segments where the model is not confident there is real speech
+        parts = [s.text.strip() for s in segments if s.no_speech_prob < 0.6]
+        return " ".join(parts).strip()
+
+
+def _get_engine(
+    model_id: str,
+    device: str = "cpu",
+    compute_type: str = "int8",
+) -> _WhisperEngine:
+    global _engine, _engine_key
+    key = (model_id, device, compute_type)
+    if _engine is None or _engine_key != key:
+        logger.info(
+            f"[STT] Loading model '{model_id}' device={device} compute={compute_type}"
+        )
+        _engine = _WhisperEngine(model_id, device, compute_type)
+        _engine_key = key
+    return _engine
+
+
+# ── Public API ────────────────────────────────────────────────────────
+
+
+def is_audio_file(file_path: str) -> bool:
+    """Return True if *file_path* has an audio extension."""
+    return Path(file_path).suffix.lower() in _AUDIO_EXTS
+
+
+async def transcribe_file(
+    file_path: str,
+    language: str = "auto",
+    model: str = "",
+    device: str = "cpu",
+    compute_type: str = "int8",
+) -> str | None:
+    """Transcribe an audio file asynchronously.
+
+    Args:
+        file_path:    Path to the audio file.
+        language:     Language hint — ``"zh"``, ``"en"``, or ``"auto"`` (default).
+        model:        Override the HuggingFace model id. Empty = use STT_MODELS mapping.
+        device:       Inference device — ``"cpu"`` (default) or ``"cuda"``.
+        compute_type: Quantisation — ``"int8"`` (default), ``"float16"``, etc.
+
+    Returns the transcript string, or ``None`` on error / silence.
+    """
+    if not is_audio_file(file_path):
+        return None
+    try:
+        model_id = model or STT_MODELS.get(language, STT_MODELS["auto"])
+        lang: str | None = None if language == "auto" else language
+        engine = _get_engine(model_id, device, compute_type)
+        loop = asyncio.get_running_loop()
+        result: str = await loop.run_in_executor(
+            None, engine.transcribe, file_path, lang
+        )
+        return result or None
+    except ImportError as e:
+        logger.warning(
+            f"[STT] Missing dependency: {e}. "
+            "Install with: pip install 'EvoScientist[stt]'"
+        )
+        return None
+    except Exception as e:
+        logger.error(f"[STT] Transcription failed for {file_path}: {e}")
+        return None

pyproject.toml

@@ -63,6 +63,7 @@ discord = ["discord.py>=2.3"]
 slack = ["slack-sdk>=3.27", "aiohttp>=3.9"]
 wechat = ["pycryptodome>=3.20"]
 qq = ["qq-botpy>=1.0"]
+stt = ["faster-whisper>=1.0"]
 oauth = ["ccproxy-api>=0.2.4"]
 all-channels = [
     "python-telegram-bot>=21.0",

tests/test_stt.py

@@ -0,0 +1,284 @@
+"""Tests for STT (speech-to-text) module and channel integration.
+
+Unit tests mock the heavy ML backends so they run without GPU/models.
+See bottom of file for manual integration test instructions.
+"""
+
+from __future__ import annotations
+
+from datetime import datetime
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from EvoScientist.stt import STT_MODELS, is_audio_file, transcribe_file
+from tests.conftest import run_async
+
+# ── is_audio_file ─────────────────────────────────────────────────────
+
+
+@pytest.mark.parametrize(
+    ("path", "expected"),
+    [
+        ("voice.ogg", True),
+        ("audio.mp3", True),
+        ("audio.wav", True),
+        ("audio.m4a", True),
+        ("audio.opus", True),
+        ("image.jpg", False),
+        ("video.mp4", False),
+        ("doc.pdf", False),
+        ("noext", False),
+    ],
+)
+def test_is_audio_file(path, expected):
+    assert is_audio_file(path) is expected
+
+
+# ── STT_MODELS mapping ────────────────────────────────────────────────
+
+
+def test_stt_models_keys():
+    assert "zh" in STT_MODELS
+    assert "en" in STT_MODELS
+    assert "auto" in STT_MODELS
+    assert "faster-whisper" in STT_MODELS["zh"].lower()
+    assert "faster-whisper" in STT_MODELS["en"].lower()
+    assert "faster-whisper" in STT_MODELS["auto"].lower()
+
+
+# ── transcribe_file: non-audio path ──────────────────────────────────
+
+
+def test_transcribe_non_audio_returns_none():
+    result = run_async(transcribe_file("photo.jpg", language="auto"))
+    assert result is None
+
+
+# ── transcribe_file: whisper backend ─────────────────────────────────
+
+
+def _make_whisper_mock(text: str):
+    seg = MagicMock()
+    seg.text = text
+    seg.no_speech_prob = 0.0  # confident speech
+    model = MagicMock()
+    model.transcribe.return_value = ([seg], MagicMock())
+    return model
+
+
+def _patch_whisper(whisper_model):
+    return patch.dict(
+        "sys.modules",
+        {
+            "faster_whisper": MagicMock(
+                WhisperModel=MagicMock(return_value=whisper_model)
+            )
+        },
+    )
+
+
+def test_transcribe_en_uses_whisper():
+    import EvoScientist.stt as stt_mod
+
+    stt_mod._engine = None
+    with _patch_whisper(_make_whisper_mock("Hello world")):
+        result = run_async(transcribe_file("voice.mp3", language="en"))
+    stt_mod._engine = None
+    assert result == "Hello world"
+
+
+def test_transcribe_auto_uses_whisper():
+    import EvoScientist.stt as stt_mod
+
+    stt_mod._engine = None
+    with _patch_whisper(_make_whisper_mock("Bonjour monde")):
+        result = run_async(transcribe_file("voice.ogg", language="auto"))
+    stt_mod._engine = None
+    assert result == "Bonjour monde"
+
+
+def test_transcribe_zh_uses_whisper():
+    import EvoScientist.stt as stt_mod
+
+    stt_mod._engine = None
+    with _patch_whisper(_make_whisper_mock("你好世界")):
+        result = run_async(transcribe_file("voice.ogg", language="zh"))
+    stt_mod._engine = None
+    assert result == "你好世界"
+
+
+def test_transcribe_custom_model_override():
+    """stt_model config overrides the default model mapping."""
+    import EvoScientist.stt as stt_mod
+
+    stt_mod._engine = None
+    captured_model_id = []
+
+    def patched_init(self, model_id, device, compute_type):
+        captured_model_id.append(model_id)
+        # don't actually load the model
+        self._model = _make_whisper_mock("test")
+
+    with patch.object(stt_mod._WhisperEngine, "__init__", patched_init):
+        run_async(
+            transcribe_file(
+                "voice.ogg", language="auto", model="openai/whisper-large-v3"
+            )
+        )
+    stt_mod._engine = None
+    assert captured_model_id == ["openai/whisper-large-v3"]
+
+
+# ── transcribe_file: missing dependency ──────────────────────────────
+
+
+def test_transcribe_missing_dep_returns_none():
+    import sys
+
+    import EvoScientist.stt as stt_mod
+
+    stt_mod._engine = None
+    saved = sys.modules.pop("faster_whisper", None)
+    try:
+        with patch.dict("sys.modules", {"faster_whisper": None}):
+            result = run_async(transcribe_file("voice.mp3", language="auto"))
+    finally:
+        if saved is not None:
+            sys.modules["faster_whisper"] = saved
+        stt_mod._engine = None
+
+    assert result is None
+
+
+# ── Channel _enqueue_raw STT integration ─────────────────────────────
+
+
+def _make_channel():
+    from EvoScientist.channels.telegram.channel import TelegramChannel, TelegramConfig
+
+    cfg = TelegramConfig(bot_token="dummy")
+    ch = TelegramChannel(cfg)
+    captured: list = []
+
+    async def _fake_build(raw):
+        captured.append(raw)
+        return None
+
+    ch._build_inbound_async = _fake_build  # type: ignore[method-assign]
+    return ch, captured
+
+
+def test_enqueue_raw_stt_prepends_transcript():
+    """_enqueue_raw prepends STT transcript to raw.text when stt_enabled."""
+    from EvoScientist.channels.base import RawIncoming
+
+    ch, captured = _make_channel()
+    ch._stt_enabled = True
+    ch._stt_language = "zh"
+    ch._stt_model = ""
+    ch._stt_device = "cpu"
+    ch._stt_compute_type = "int8"
+
+    raw = RawIncoming(
+        sender_id="123",
+        chat_id="456",
+        text="",
+        media_files=["/tmp/voice.ogg"],
+        content_annotations=["[voice: /tmp/voice.ogg]"],
+        timestamp=datetime.now(),
+    )
+
+    async def _run():
+        with (
+            patch(
+                "EvoScientist.stt.transcribe_file", new=AsyncMock(return_value="你好")
+            ),
+            patch("EvoScientist.stt.is_audio_file", return_value=True),
+        ):
+            await ch._enqueue_raw(raw)
+
+    run_async(_run())
+    assert captured[0].text == "你好"
+    # annotation should be removed after transcription
+    assert captured[0].content_annotations == []
+
+
+def test_enqueue_raw_stt_disabled_skips_transcription():
+    """When stt_enabled=False, transcription is not called."""
+    from EvoScientist.channels.base import RawIncoming
+
+    ch, captured = _make_channel()
+    ch._stt_enabled = False
+
+    raw = RawIncoming(
+        sender_id="123",
+        chat_id="456",
+        text="",
+        media_files=["voice.ogg"],
+        timestamp=datetime.now(),
+    )
+
+    mock_transcribe = AsyncMock()
+
+    async def _run():
+        with patch("EvoScientist.stt.transcribe_file", mock_transcribe):
+            await ch._enqueue_raw(raw)
+
+    run_async(_run())
+    mock_transcribe.assert_not_called()
+    assert captured[0].text == ""
+
+
+def test_enqueue_raw_stt_appends_to_existing_text():
+    """Transcript is prepended before any existing caption text."""
+    from EvoScientist.channels.base import RawIncoming
+
+    ch, captured = _make_channel()
+    ch._stt_enabled = True
+    ch._stt_language = "auto"
+    ch._stt_model = ""
+    ch._stt_device = "cpu"
+    ch._stt_compute_type = "int8"
+
+    raw = RawIncoming(
+        sender_id="123",
+        chat_id="456",
+        text="caption text",
+        media_files=["voice.ogg"],
+        timestamp=datetime.now(),
+    )
+
+    async def _run():
+        with (
+            patch(
+                "EvoScientist.stt.transcribe_file",
+                new=AsyncMock(return_value="hello world"),
+            ),
+            patch("EvoScientist.stt.is_audio_file", return_value=True),
+        ):
+            await ch._enqueue_raw(raw)
+
+    run_async(_run())
+    assert captured[0].text == "hello world\ncaption text"
+
+
+# ── Manual integration test (run by hand) ────────────────────────────
+#
+# 1. Install deps:
+#      uv pip install 'EvoScientist[stt]'
+#
+# 2. Enable STT:
+#      EvoSci config set stt_enabled true
+#      EvoSci config set stt_language zh   # zh / en / auto
+#
+# 3. Transcribe a local audio file directly:
+#      python -c "
+#      import asyncio
+#      from EvoScientist.stt import transcribe_file
+#      print(asyncio.run(transcribe_file('sample.ogg', language='zh')))
+#      "
+#
+# 4. End-to-end via Telegram:
+#      EvoSci serve
+#      → send a voice message → bot receives transcribed text

uv.lock

@@ -1,5 +1,5 @@
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@@ -867,6 +928,9 @@ slack = [
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 ]
+stt = [
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+]
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 ]
@@ -893,6 +957,7 @@ requires-dist = [
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+provides-extras = ["dev", "telegram", "discord", "slack", "wechat", "qq", "stt", "oauth", "all-channels"]
 
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