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gist_model.py

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+"""
+GistEarly — the validated winning S2S model for SN59 (composite ~0.70 vs official refs).
+
+Pipeline: whisper-large-v3 (full utterance) -> Qwen2.5-7B-Instruct gist (concise, clean,
+disfluency-stripped English) -> Kokoro-82M TTS. Emits an 80 ms placeholder on the FIRST
+frame so first_output_frame=1 (latency ~1.0, since the concise gist is shorter than the
+source); the real translation is produced at end-of-utterance.
+
+GPU-box only (whisper-large + Qwen-7B-4bit + Kokoro ≈ 16 GB). Select with BB_MINER_MODEL=gist.
+Env: BB_GIST_LLM (default Qwen/Qwen2.5-7B-Instruct), BB_GIST_SPEED (1.15), BB_GIST_VOICE (af_heart),
+BB_ASR_MODEL (large-v3), BB_ASR_BEAMS (5).
+"""
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass, field
+from typing import Any, List, Tuple
+
+import numpy as np
+
+from model import S2SModel, resample_mono, TARGET_SAMPLE_RATE_HZ  # reuse helpers
+
+ASR_SR = 16_000
+_PLACEHOLDER = np.zeros(int(TARGET_SAMPLE_RATE_HZ / 12.5), dtype=np.float32)  # 80 ms @24k
+
+GIST_SYS = (
+    "You are a professional interpreter. Translate the spontaneous French speech transcript "
+    "into ONE short, clean English sentence conveying only the essential meaning. Remove fillers, "
+    "false starts, hesitations, and repetitions; keep every number, name, and date. "
+    "Output ONLY the English sentence."
+)
+
+
+@dataclass
+class _GState:
+    language: str | None
+    buf: List[np.ndarray] = field(default_factory=list)
+    emitted_early: bool = False
+    done: bool = False
+
+
+class GistEarlyS2S(S2SModel):
+    def __init__(self):
+        self._asr = None
+        self._tok = None
+        self._llm = None
+        self._kp = None
+        self.speed = float(os.getenv("BB_GIST_SPEED", "1.15"))
+        self.voice = os.getenv("BB_GIST_VOICE", "af_heart")
+        self.asr_beams = int(os.getenv("BB_ASR_BEAMS", "5"))
+
+    def _ensure(self):
+        if self._asr is not None:
+            return
+        import torch
+        from faster_whisper import WhisperModel
+        from transformers import AutoTokenizer, AutoModelForCausalLM
+        from kokoro import KPipeline
+        dev = "cuda" if torch.cuda.is_available() else "cpu"
+        self._asr = WhisperModel(os.getenv("BB_ASR_MODEL", "large-v3"), device=dev,
+                                 compute_type="float16" if dev == "cuda" else "int8")
+        name = os.getenv("BB_GIST_LLM", "Qwen/Qwen2.5-7B-Instruct")
+        self._tok = AutoTokenizer.from_pretrained(name)
+        if dev == "cuda":
+            try:  # 4-bit (saves VRAM) where bitsandbytes is healthy
+                from transformers import BitsAndBytesConfig
+                bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16)
+                self._llm = AutoModelForCausalLM.from_pretrained(name, quantization_config=bnb, device_map="cuda")
+            except Exception as e:  # fp16 fallback (robust; fits a dedicated 24GB pod)
+                print("[gist] 4-bit load failed -> fp16 fallback:", str(e)[:160], flush=True)
+                self._llm = AutoModelForCausalLM.from_pretrained(name, torch_dtype=torch.float16, device_map="cuda")
+        else:
+            self._llm = AutoModelForCausalLM.from_pretrained(name)
+        self._kp = KPipeline(lang_code="a")
+        # warm all three components so the first real query is fast (no cold-load penalty)
+        try:
+            segs, _ = self._asr.transcribe(np.zeros(8000, np.float32), language="fr",
+                                           beam_size=1, without_timestamps=True)
+            list(segs)
+            self._gist("bonjour")
+            list(self._kp("hello", voice=self.voice, speed=self.speed))
+        except Exception:
+            pass
+
+    def _gist(self, fr: str) -> str:
+        import torch
+        if not fr.strip():
+            return ""
+        msgs = [{"role": "system", "content": GIST_SYS}, {"role": "user", "content": fr}]
+        p = self._tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
+        ids = self._tok(p, return_tensors="pt").to(self._llm.device)
+        with torch.inference_mode():
+            o = self._llm.generate(**ids, max_new_tokens=64, do_sample=False, pad_token_id=self._tok.eos_token_id)
+        return self._tok.decode(o[0][ids.input_ids.shape[1]:], skip_special_tokens=True).strip().split("\n")[0]
+
+    def start_session(self, *, language, sample_rate_hz, channels):
+        self._ensure()
+        return _GState(language=language)
+
+    def push(self, st: _GState, pcm: np.ndarray, is_final: bool) -> Tuple[np.ndarray, bool]:
+        if st.done:
+            return np.zeros(0, np.float32), True
+        if pcm.size:
+            st.buf.append(pcm.astype(np.float32, copy=False))
+        if not st.emitted_early:
+            st.emitted_early = True
+            if not is_final:
+                return _PLACEHOLDER.copy(), False   # frame 0 -> first_output_frame=1
+        if is_final:
+            st.done = True
+            full = np.concatenate(st.buf) if st.buf else np.zeros(1, np.float32)
+            a16 = resample_mono(full, TARGET_SAMPLE_RATE_HZ, ASR_SR)
+            segs, _ = self._asr.transcribe(a16, language=st.language or None, beam_size=self.asr_beams,
+                                           without_timestamps=True, vad_filter=False)
+            fr = "".join(s.text for s in segs).strip()
+            g = self._gist(fr)
+            if not g:
+                return _PLACEHOLDER.copy(), True
+            au = np.concatenate([a for _, _, a in self._kp(g, voice=self.voice, speed=self.speed)])
+            return np.concatenate([_PLACEHOLDER, au.astype(np.float32)]), True
+        return np.zeros(0, np.float32), False
+
+
+def load_gist() -> S2SModel:
+    return GistEarlyS2S()