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revert(chatterbox-fast): drop context-priming (§1.6) — discard-cut leaks context

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Revert the priming feature from d707439. Live A/B caught an audible artifact: the
context-priming discard-cut left part of the throwaway prefix in the output, so a
clause ("...without a trace of sarcasm,") was spoken an extra time.

Root cause is structural: generate() returns one finished waveform with no marker
for where the prefix ends, and the model renders the same prefix with different
timing when followed by content than when generated solo — so the duration-estimate
+ energy-minimum cut is a guess and can leave a sliver (or a whole clause) of prefix
in. A reliable cut would need token-level access (the abandoned native-streaming
arc) or a per-chunk ASR/alignment pass (heavy, still imperfect, eats the latency
budget). Fails the agreed bar: "keep only if it closes the gap without a seam."

Kept from d707439: the .gitignore (build artifacts). NOT re-applied: the bundled
margin_first fix — wiring it would shrink chunk 1 (more joins = worse coherence),
against the operator's priority, and margin=0.8 there is already starvation-safe.

Coherence loss at joins stays an accepted limitation; cold streaming was judged
"really good". Phase 1 + Phase 2 parity/perf untouched. Next: Phase 3 deploy.
This commit is contained in:
vh
2026-06-01 23:26:56 -07:00
parent d707439041
commit 090e70aed5
4 changed files with 22 additions and 190 deletions
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stacks/chatterbox-fast/app.py
+7 -70
View File
@@ -164,8 +164,9 @@ class Engine:
if DEVICE.startswith("cuda"):
torch.cuda.synchronize()
def _raw_wav(self, text: str, knobs: "TTSRequest") -> torch.Tensor:
"""One generate pass → wav tensor [1,T], CUDA-synced for honest timing."""
def generate(self, text: str, knobs: "TTSRequest") -> tuple[torch.Tensor, float]:
"""Synthesize ``text`` → (wav tensor [1,T], audio_seconds). CUDA-synced
so the caller's clock delta is honest gen time."""
with torch.inference_mode():
wav = self.model.generate(
text,
@@ -176,30 +177,8 @@ class Engine:
)
if DEVICE.startswith("cuda"):
torch.cuda.synchronize()
return wav
def generate(self, text: str, knobs: "TTSRequest") -> tuple[torch.Tensor, float]:
"""Synthesize ``text`` → (wav [1,T], audio_seconds)."""
wav = self._raw_wav(text, knobs)
return wav, wav.shape[-1] / self.sr
def generate_primed(
self, content: str, context: str, knobs: "TTSRequest"
) -> tuple[torch.Tensor, float]:
"""Context-primed generate (plan §1.6): render ``context + content``
together so ``content``'s prosody knows what preceded it, then discard the
context audio. The cut snaps to the inter-sentence pause (energy minimum)
near the context's solo duration, with a short fade-in to kill any click.
Costs two passes (context-solo to locate the cut, then the joint) — the
scheduler budgets for this via prime_cost_factor."""
ctx_wav = self._raw_wav(context, knobs)
d_ctx = ctx_wav.shape[-1] / self.sr
joint = self._raw_wav(f"{context} {content}", knobs)
cut = _cut_at_pause(joint, self.sr, d_ctx)
kept = joint[..., cut:].clone()
_fade_in(kept, self.sr)
return kept, kept.shape[-1] / self.sr
audio_sec = wav.shape[-1] / self.sr
return wav, audio_sec
engine = Engine()
@@ -227,50 +206,12 @@ class TTSRequest(BaseModel):
top_p: float = 0.95
top_k: int = 1000
repetition_penalty: float = 1.2
# Context-priming at joins (plan §1.6) — opt-in for A/B.
prime: bool = False
prime_first_n: int = 2 # how many early joins to prime when prime=True
# Scheduler overrides (None ⇒ ChunkConfig defaults).
margin: float | None = Field(default=None)
margin_first: float | None = Field(default=None)
rtf_prior: float | None = Field(default=None)
def _cut_at_pause(
joint: torch.Tensor, sr: int, approx_sec: float,
window_sec: float = 0.4, frame_sec: float = 0.02,
) -> int:
"""Sample index to cut the discarded context off ``joint``. Searches ±window
around the context's solo duration for the lowest-energy 20 ms frame — the
inter-sentence pause — so the seam lands in silence, not mid-phone."""
audio = joint.reshape(-1)
n = audio.shape[0]
center = int(approx_sec * sr)
w = int(window_sec * sr)
frame = max(1, int(frame_sec * sr))
lo = max(0, center - w)
hi = min(n - frame, center + w)
if hi <= lo:
return min(center, n)
best_i, best_e = center, float("inf")
i = lo
while i < hi:
e = float((audio[i:i + frame] ** 2).mean())
if e < best_e:
best_e, best_i = e, i
i += frame
return best_i
def _fade_in(wav: torch.Tensor, sr: int, fade_sec: float = 0.005) -> None:
"""In-place short fade-in to remove any click at the cut seam."""
fade = int(fade_sec * sr)
if wav.shape[-1] > fade > 0:
ramp = torch.linspace(0.0, 1.0, fade, device=wav.device, dtype=wav.dtype)
wav[..., :fade] *= ramp
def _pcm16(wav: torch.Tensor) -> bytes:
a = wav.detach().to(torch.float32).clamp_(-1.0, 1.0).cpu().numpy().reshape(-1)
return (a * 32767.0).astype("<i2").tobytes()
@@ -293,8 +234,6 @@ def _chunk_config(req: TTSRequest) -> ChunkConfig:
cfg.margin_first = req.margin_first
if req.rtf_prior is not None:
cfg.rtf_prior = req.rtf_prior
if req.prime:
cfg.prime_first_n = req.prime_first_n
return cfg
@@ -350,9 +289,7 @@ def tts(req: TTSRequest) -> StreamingResponse:
yield _pcm16(wav)
return
def _gen(text: str, context: str | None) -> tuple[torch.Tensor, float]:
if context:
return engine.generate_primed(text, context, req)
def _gen(text: str) -> tuple[torch.Tensor, float]:
return engine.generate(text, req)
total_audio = 0.0
@@ -368,7 +305,7 @@ def tts(req: TTSRequest) -> StreamingResponse:
def _log_chunk(r: ChunkResult, ttfa_ms: float) -> None:
tag = (" PRIMED" if r.primed else "") + (" STARVED" if r.starved else "")
tag = " STARVED" if r.starved else ""
if r.index == 0:
log.info("chunk 0: ttfa=%.0fms gen=%.0fms audio=%.2fs rtf=%.2f%s",
ttfa_ms, r.gen_time * 1000, r.audio_sec, r.rtf, tag)