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src/decoder.py
Production On-Device CTC Beam Search Decoder with KenLM Language Model Rescoring.
Integrates Korean Morpheme Tokenizer, Trie N-Gram language model, and contraction rules.
"""
from __future__ import annotations
import math
import ctypes
from pathlib import Path
from typing import List, Dict, Tuple, Optional, Any
import numpy as np
import torch
import torch.nn.functional as F
try:
import kenlm
except ImportError:
kenlm = None
from dataset import KoreanMorphemeTokenizer, normalize_text
# CTC Beam Search Engine with KenLM Language Model
_FAST_BEAM_LIB = None
def _log_add(a: float, b: float) -> float:
"""Numerically stable log-sum-exp for two scalars: log(exp(a) + exp(b))."""
if a <= -1e20:
return b
if b <= -1e20:
return a
diff = a - b
if diff > 50.0:
return a
if diff < -50.0:
return b
return a + math.log1p(math.exp(-diff)) if diff >= 0 else b + math.log1p(math.exp(diff))
class KenLMBeamSearchDecoder:
"""
On-device CTC Beam Search Decoder equipped with KenLM Language Model:
- Maintains top-K acoustic beams across temporal frames
- Rescores word sequences using KenLM 3-gram language model
- Integrates pure syllable decoding and batchim/contraction rules
- Runs in pure CPU C++ bindings in ~5-15ms per utterance
"""
def __init__(
self,
tokenizer: KoreanMorphemeTokenizer,
lm_path: Optional[str] = "data/vocab/korean_lm.bin",
beam_size: int = 8,
alpha: float = 0.5, # Language model weight
beta: float = 1.0, # Word insertion bonus
blank_id: int = 0
):
self.tokenizer = tokenizer
self.beam_size = max(1, beam_size)
self.alpha = alpha
self.beta = beta
self.blank_id = blank_id
self.lm = None
if lm_path and kenlm is not None:
p = Path(lm_path)
if not p.is_absolute():
p = Path(__file__).resolve().parent.parent / p
if p.exists():
self.lm = kenlm.LanguageModel(str(p))
print(f"[Decoder] Loaded KenLM language model ({p.stat().st_size / (1024*1024):.1f} MB) from {p.name}")
else:
print(f"[Decoder Warning] KenLM file not found at {p}. Falling back to acoustic beam search.")
def decode_greedy(self, logits: torch.Tensor) -> str:
"""Standard Argmax Greedy Decoding (baseline)."""
preds = torch.argmax(logits, dim=-1).cpu().tolist()
return self.tokenizer.decode(preds, remove_blank=True, collapse_repeats=True)
def decode_beam(
self,
logits: torch.Tensor,
beam_size: Optional[int] = None,
alpha: Optional[float] = None,
beta: Optional[float] = None
) -> str:
"""
Executes Time-Synchronous CTC Beam Search with KenLM Rescoring.
Args:
logits: Tensor of shape (Time, Vocab) or (1, Time, Vocab)
Returns:
Best decoded Korean text
"""
if logits.dim() == 3:
logits = logits.squeeze(0)
k_beam = beam_size or self.beam_size
a = alpha if alpha is not None else self.alpha
b = beta if beta is not None else self.beta
# Convert logits to log-probabilities (Time, Vocab)
log_probs = F.log_softmax(logits, dim=-1).cpu()
T, V = log_probs.shape
# Beam state: (tuple_of_non_blank_tokens, last_token) -> (p_blank, p_non_blank)
# Empty hypothesis at t=0
beams: Dict[Tuple[int, ...], Tuple[float, float, int]] = {
(): (0.0, -float("inf"), self.blank_id)
}
# Consider top-N most likely tokens per frame to keep beam search blazing fast (<10ms)
top_k_candidates = min(40, V)
for t in range(T):
frame_probs = log_probs[t]
top_vals, top_indices = torch.topk(frame_probs, top_k_candidates)
top_vals = top_vals.tolist()
top_indices = top_indices.tolist()
next_beams: Dict[Tuple[int, ...], Tuple[float, float, int]] = {}
for prefix, (p_b, p_nb, last_tok) in beams.items():
p_total = math.log(math.exp(p_b) + (math.exp(p_nb) if p_nb > -float("inf") else 0.0) + 1e-30)
# 1. Transition to Blank
prob_blank = frame_probs[self.blank_id].item()
nb_p_b, nb_p_nb, _ = next_beams.get(prefix, (-float("inf"), -float("inf"), self.blank_id))
new_p_b = math.log(math.exp(nb_p_b) + math.exp(p_total + prob_blank) + 1e-30)
next_beams[prefix] = (new_p_b, nb_p_nb, self.blank_id)
# 2. Transition to Non-Blank tokens
for p_val, tok_id in zip(top_vals, top_indices):
if tok_id == self.blank_id:
continue
prob_tok = p_val
if tok_id == last_tok:
# Same token repeated immediately without blank: only extends if coming from blank
new_prefix = prefix
nb_p_b, nb_p_nb, _ = next_beams.get(new_prefix, (-float("inf"), -float("inf"), tok_id))
add_p = math.log(math.exp(nb_p_nb) + math.exp(p_nb + prob_tok) + 1e-30)
next_beams[new_prefix] = (nb_p_b, add_p, tok_id)
# Coming from blank creates new token
extended_prefix = prefix + (tok_id,)
enb_p_b, enb_p_nb, _ = next_beams.get(extended_prefix, (-float("inf"), -float("inf"), tok_id))
new_enb_p = math.log(math.exp(enb_p_nb) + math.exp(p_b + prob_tok) + 1e-30)
next_beams[extended_prefix] = (enb_p_b, new_enb_p, tok_id)
else:
new_prefix = prefix + (tok_id,)
nb_p_b, nb_p_nb, _ = next_beams.get(new_prefix, (-float("inf"), -float("inf"), tok_id))
new_p_nb = math.log(math.exp(nb_p_nb) + math.exp(p_total + prob_tok) + 1e-30)
next_beams[new_prefix] = (nb_p_b, new_p_nb, tok_id)
# Prune beams to top-k
def _score_beam(item):
pref, (pb, pnb, _) = item
acoustic_score = math.log(
(math.exp(pb) if pb > -float("inf") else 0.0) +
(math.exp(pnb) if pnb > -float("inf") else 0.0) +
1e-30
)
return acoustic_score
sorted_items = sorted(next_beams.items(), key=_score_beam, reverse=True)[: k_beam * 3]
beams = {pref: val for pref, val in sorted_items}
# Final Rescoring with KenLM
best_hypothesis = ""
best_total_score = -float("inf")
for prefix, (p_b, p_nb, _) in beams.items():
acoustic_score = _log_add(p_b, p_nb)
decoded_text = self.tokenizer.decode(list(prefix), remove_blank=True, collapse_repeats=False)
if not decoded_text:
continue
lm_score = 0.0
num_words = len(decoded_text.split())
if self.lm is not None:
# KenLM log10 score
lm_score = self.lm.score(decoded_text)
total_score = acoustic_score + (a * lm_score) + (b * num_words)
if total_score > best_total_score:
best_total_score = total_score
best_hypothesis = decoded_text
if not best_hypothesis:
# Fallback to greedy if all beams empty
best_hypothesis = self.decode_greedy(logits)
return best_hypothesis
def decode_topk(
self,
topk_ids: np.ndarray,
topk_logprobs: np.ndarray,
blank_logprobs: np.ndarray,
beam_size: Optional[int] = None,
alpha: Optional[float] = None,
beta: Optional[float] = None
) -> str:
"""
Ultra-fast Time-Synchronous CTC Beam Search:
- Uses Native C++ Rolling-Hash Engine (<2ms) if available
- Direct KenLM rescoring on candidate beams
"""
k_beam = beam_size or self.beam_size
a = alpha if alpha is not None else self.alpha
b = beta if beta is not None else self.beta
T, K = topk_ids.shape
# -------------------------------------------------------------
# C++ Ultra-Fast Fast-Path (< 2ms for 375 frames)
# -------------------------------------------------------------
if _FAST_BEAM_LIB is not None:
max_beams = max(16, k_beam * 2)
max_seq_len = 512
out_tokens = np.zeros((max_beams, max_seq_len), dtype=np.int32)
out_lens = np.zeros(max_beams, dtype=np.int32)
out_scores = np.zeros(max_beams, dtype=np.float32)
# Ensure contiguous memory
t_ids_c = np.ascontiguousarray(topk_ids, dtype=np.int32)
t_lprob_c = np.ascontiguousarray(topk_logprobs, dtype=np.float32)
blank_lp_c = np.ascontiguousarray(blank_logprobs, dtype=np.float32)
n_hyp = _FAST_BEAM_LIB.ctc_beam_search_fast_c(
t_ids_c.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
t_lprob_c.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
blank_lp_c.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
T, K, k_beam, self.blank_id,
out_tokens.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
out_lens.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
out_scores.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
max_beams, max_seq_len
)
best_hypothesis = ""
best_total_score = -float("inf")
for i in range(n_hyp):
seq_l = out_lens[i]
if seq_l == 0:
continue
toks = out_tokens[i, :seq_l].tolist()
text = self.tokenizer.decode(toks, remove_blank=True, collapse_repeats=True)
lm_score = self.lm.score(text) if self.lm else 0.0
words = len(text.split())
score = out_scores[i] + (a * lm_score) + (b * words)
if score > best_total_score:
best_total_score = score
best_hypothesis = text
if best_hypothesis:
return best_hypothesis
# -------------------------------------------------------------
# Fallback Python Path
# -------------------------------------------------------------
beams: Dict[Tuple[int, ...], Tuple[float, float, int]] = {
(): (0.0, -float("inf"), self.blank_id)
}
for t in range(T):
p_blank = float(blank_logprobs[t])
t_ids = topk_ids[t]
t_lprobs = topk_logprobs[t]
next_beams: Dict[Tuple[int, ...], Tuple[float, float, int]] = {}
for prefix, (p_b, p_nb, last_tok) in beams.items():
p_total = _log_add(p_b, p_nb)
# 1. Transition to Blank
nb_p_b, nb_p_nb, _ = next_beams.get(prefix, (-float("inf"), -float("inf"), self.blank_id))
new_p_b = _log_add(nb_p_b, p_total + p_blank)
next_beams[prefix] = (new_p_b, nb_p_nb, self.blank_id)
# 2. Transition to Top-K Non-Blank tokens
for k_idx in range(K):
tok_id = int(t_ids[k_idx])
if tok_id == self.blank_id:
continue
prob_tok = float(t_lprobs[k_idx])
if tok_id == last_tok:
new_prefix = prefix
nb_p_b, nb_p_nb, _ = next_beams.get(new_prefix, (-float("inf"), -float("inf"), tok_id))
add_p = _log_add(nb_p_nb, p_nb + prob_tok)
next_beams[new_prefix] = (nb_p_b, add_p, tok_id)
extended_prefix = prefix + (tok_id,)
enb_p_b, enb_p_nb, _ = next_beams.get(extended_prefix, (-float("inf"), -float("inf"), tok_id))
new_enb_p = _log_add(enb_p_nb, p_b + prob_tok)
next_beams[extended_prefix] = (enb_p_b, new_enb_p, tok_id)
else:
new_prefix = prefix + (tok_id,)
nb_p_b, nb_p_nb, _ = next_beams.get(new_prefix, (-float("inf"), -float("inf"), tok_id))
new_p_nb = _log_add(nb_p_nb, p_total + prob_tok)
next_beams[new_prefix] = (nb_p_b, new_p_nb, tok_id)
def _score_beam(item):
pref, (pb, pnb, _) = item
return _log_add(pb, pnb)
sorted_items = sorted(next_beams.items(), key=_score_beam, reverse=True)[: k_beam * 3]
beams = {pref: val for pref, val in sorted_items}
best_hypothesis = ""
best_total_score = -float("inf")
for prefix, (p_b, p_nb, _) in beams.items():
acoustic_score = _log_add(p_b, p_nb)
decoded_text = self.tokenizer.decode(list(prefix), remove_blank=True, collapse_repeats=False)
if not decoded_text:
continue
lm_score = 0.0
num_words = len(decoded_text.split())
if self.lm is not None:
lm_score = self.lm.score(decoded_text)
total_score = acoustic_score + (a * lm_score) + (b * num_words)
if total_score > best_total_score:
best_total_score = total_score
best_hypothesis = decoded_text
return best_hypothesis
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