WeLe Agentic AI: LangGraph multi-agent CRM assistant with voice

2026-08-28 02:16:03 +05:30
commit 105e58e02a
69 changed files with 11501 additions and 0 deletions
@@ -0,0 +1,82 @@
+"""Voice service configuration.
+
+Deliberately small: this process does one job — turn audio into text and text
+into audio. Everything about *what to say* lives in the Node agentic service.
+"""
+from __future__ import annotations
+
+import os
+
+import torch
+
+
+def _int(name: str, default: int) -> int:
+    try:
+        return int(os.environ.get(name, default))
+    except (TypeError, ValueError):
+        return default
+
+
+def _flag(name: str, default: bool) -> bool:
+    return os.environ.get(name, str(default)).lower() in {"1", "true", "yes"}
+
+
+class Settings:
+    host: str = os.environ.get("VOICE_HOST", "127.0.0.1")
+    port: int = _int("VOICE_PORT", 4100)
+
+    # ── Models ───────────────────────────────────────────────────────────────
+    # IndicConformer is a hybrid CTC + RNNT model. CTC decoding is used because
+    # it is a single forward pass — RNNT is more accurate but decodes
+    # autoregressively, and in a voice loop the latency costs more than the
+    # accuracy buys.
+    stt_model: str = os.environ.get("STT_MODEL", "ai4bharat/indic-conformer-600m-multilingual")
+    stt_decoding: str = os.environ.get("STT_DECODING", "ctc")  # ctc | rnnt
+
+    # English is not one of IndicConformer's 22 codes, and it cannot identify
+    # languages. Whisper covers both — multilingual, not the .en checkpoint,
+    # because language ID is what makes "auto" work.
+    english_model: str = os.environ.get("ENGLISH_MODEL", "openai/whisper-small")
+    preload_english: bool = _flag("PRELOAD_ENGLISH", True)
+
+    # Used when auto-detection is not confident enough to overrule the user.
+    default_lang: str = os.environ.get("VOICE_DEFAULT_LANG", "ta")
+
+    tts_model: str = os.environ.get("TTS_MODEL", "ai4bharat/indic-parler-tts")
+
+    # ── Device / precision ───────────────────────────────────────────────────
+    # float16 on CUDA: both models together are ~3 GB in half precision, which
+    # fits the 6 GB card with room for activations. float32 would not.
+    device: str = os.environ.get("VOICE_DEVICE", "cuda" if torch.cuda.is_available() else "cpu")
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return torch.float16 if self.device == "cuda" else torch.float32
+
+    # ── Audio ────────────────────────────────────────────────────────────────
+    sample_rate_in: int = 16000    # what the browser worklet sends
+    # Indic Parler-TTS emits 44.1 kHz — verified from model.config.sampling_rate,
+    # not the 24 kHz the upstream Parler-TTS Mini uses. This is only a fallback;
+    # the real rate is read from the loaded model and sent to the browser, which
+    # configures its playback worklet from it.
+    sample_rate_out: int = _int("TTS_SAMPLE_RATE", 44100)
+
+    # ── Endpointing (Silero VAD) ─────────────────────────────────────────────
+    # Silero operates on fixed 512-sample frames at 16 kHz (32 ms).
+    vad_frame: int = 512
+    vad_threshold: float = float(os.environ.get("VAD_THRESHOLD", "0.5"))
+    # How much trailing silence ends a turn. Too short truncates people who
+    # pause mid-sentence; too long makes the assistant feel sluggish.
+    vad_silence_ms: int = _int("VAD_SILENCE_MS", 700)
+    # Ignore blips so a cough or a door does not open a turn.
+    vad_min_speech_ms: int = _int("VAD_MIN_SPEECH_MS", 250)
+    # Audio kept from *before* detected speech, so word onsets are not clipped.
+    vad_prefix_ms: int = _int("VAD_PREFIX_MS", 300)
+    vad_max_utterance_ms: int = _int("VAD_MAX_UTTERANCE_MS", 30000)
+
+    # Warm the models at startup rather than on the first user turn — a cold
+    # CUDA graph on the first utterance costs several seconds.
+    warmup: bool = _flag("VOICE_WARMUP", True)
+
+
+settings = Settings()