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 +1,95 @@
+"""Honest latency benchmark: warm up first, then time repeated runs.
+
+The first CUDA generation pays for kernel autotuning and cache allocation, so a
+single cold measurement makes any model look far worse than it is in service.
+"""
+import logging
+import time
+from threading import Thread
+
+import numpy as np
+import torch
+
+logging.basicConfig(level=logging.INFO, format="%(message)s")
+log = logging.getLogger("bench")
+DEV = "cuda" if torch.cuda.is_available() else "cpu"
+
+log.info("device=%s  gpu=%s", DEV, torch.cuda.get_device_name(0) if DEV == "cuda" else "-")
+
+# ── STT ──────────────────────────────────────────────────────────────────────
+from transformers import AutoModel
+
+stt = AutoModel.from_pretrained("ai4bharat/indic-conformer-600m-multilingual", trust_remote_code=True)
+stt = stt.to(DEV).eval()
+
+# Where does it actually run? A model wrapping ONNX ignores .to(cuda).
+params = list(stt.parameters())
+log.info("STT param device: %s  (%d tensors)", params[0].device if params else "NO TORCH PARAMS", len(params))
+log.info("STT type: %s", type(stt).__name__)
+
+wav = torch.from_numpy((np.random.randn(16000 * 4) * 0.02).astype(np.float32)).unsqueeze(0).to(DEV)
+with torch.inference_mode():
+    stt(wav, "ta", "ctc")                      # warmup
+times = []
+for _ in range(3):
+    t0 = time.perf_counter()
+    with torch.inference_mode():
+        stt(wav, "ta", "ctc")
+    times.append((time.perf_counter() - t0) * 1000)
+log.info("STT  4000 ms audio → %.0f / %.0f / %.0f ms   (RTF %.2fx)",
+         *times, (sum(times) / len(times)) / 4000)
+
+# ── TTS ──────────────────────────────────────────────────────────────────────
+from parler_tts import ParlerTTSForConditionalGeneration, ParlerTTSStreamer
+from transformers import AutoTokenizer
+
+dtype = torch.float16 if DEV == "cuda" else torch.float32
+tts = ParlerTTSForConditionalGeneration.from_pretrained("ai4bharat/indic-parler-tts", torch_dtype=dtype).to(DEV).eval()
+tok = AutoTokenizer.from_pretrained("ai4bharat/indic-parler-tts")
+dtok = AutoTokenizer.from_pretrained(tts.config.text_encoder._name_or_path)
+SR = tts.config.sampling_rate
+log.info("TTS sampling_rate=%d  frame_rate=%s", SR, getattr(tts.audio_encoder.config, "frame_rate", "?"))
+
+desc = "Jaya speaks in a warm, clear, professional tone at a natural pace. The recording is very high quality with no background noise."
+d = dtok(desc, return_tensors="pt").to(DEV)
+
+
+def run(text, stream=True):
+    p = tok(text, return_tensors="pt").to(DEV)
+    kw = dict(input_ids=d.input_ids, attention_mask=d.attention_mask,
+              prompt_input_ids=p.input_ids, prompt_attention_mask=p.attention_mask)
+    t0 = time.perf_counter()
+    if stream:
+        fr = int(getattr(tts.audio_encoder.config, "frame_rate", 86) / 2)
+        s = ParlerTTSStreamer(tts, device=DEV, play_steps=fr)
+        Thread(target=tts.generate, kwargs={**kw, "streamer": s}, daemon=True).start()
+        first, n = None, 0
+        for c in s:
+            if c is None or len(c) == 0:
+                continue
+            if first is None:
+                first = (time.perf_counter() - t0) * 1000
+            n += len(c)
+    else:
+        with torch.inference_mode():
+            g = tts.generate(**kw)
+        n = g.shape[-1]
+        first = None
+    total = (time.perf_counter() - t0) * 1000
+    return first, total, 1000 * n / SR
+
+
+SHORT = "மூவாயிரம் நானூறு லீட்கள் உள்ளன."
+LONG = "புதிய லீட்கள் மூவாயிரம் நானூற்று இருபத்தேழு. இதில் எழுபத்தாறு சதவீதம் இன்னும் தொடர்பு கொள்ளப்படவில்லை."
+
+run(SHORT)   # warmup
+log.info("")
+for label, text in (("short", SHORT), ("long", LONG)):
+    first, total, audio = run(text)
+    log.info("TTS %-5s %2d chars → first %.0f ms | total %.0f ms | audio %.0f ms | RTF %.2fx",
+             label, len(text), first or -1, total, audio, total / max(audio, 1))
+
+if DEV == "cuda":
+    log.info("\nVRAM peak reserved: %.2f GB of %.1f GB",
+             torch.cuda.max_memory_reserved() / 1e9,
+             torch.cuda.get_device_properties(0).total_memory / 1e9)