Adds a per-layer K/V ring (kv-cache.h) backed by a dedicated backend
buffer, sized at init for max_seq_len positions. The talker holds a
4096 position cache (896 MB f32) for the LM context, the code
predictor a 16 position cache (~80 KB) reset every frame.
talker_forward splits into prefill (resets the cache and writes T
positions in one shot) and decode (appends one position, reads the
[0, cur_len+1) window). code_predictor_step does the same with a T=2
prefill plus 14 single token decodes.
Bit identical audio output, validated by sha256 against the pre KV
cache run on a 64 frame F32 reference seed=42. Walltime drops ~10%
on a single utterance ; the win scales with sequence length and
unlocks frame by frame streaming.
mel-spk and mel-mag dumps in speaker-encoder-extract.h applied an extra
ggml_transpose plus cont before write. Raw ggml ne=(C, T) already
streams as numpy [T, C], so the transpose was inverting axes vs the
python upstream. Removed it.
MelMag 0.04 -> 0.999, MelSpk 0.92 -> 0.998
spk_conv1d_same passed ggml_im2col a kernel ne=(K, 1, IC, 1) and an
input ne=(T_pad, 1, IC, 1) with IC in ne[2]. But the im2col impl reads
IC = b->ne[1] when is_2D=false, so it saw IC=1, wrote OW*K floats into
a buffer declared for OW*IC*K floats, and mul_mat consumed 99% garbage.
Moved IC into ne[1] for both kernel and input, which makes the impl
read the real IC and writes a buffer coherent with the declared ne. The
permute and the retranspose after pad become unnecessary, dropped both.
SpkFrontend 0.74 -> 0.994, SpeakerEmb 0.86 -> 0.996
Adds ECAPA bisection infrastructure : 4 stage out params in
speaker_encoder_forward (frontend, block3, mfa, asp), codec encoder
intermediate dumps in pipeline-codec.cpp (seanet-out, enc-transformer
out, codec-pre-fsq), matching pytorch hooks in debug-clone-cossim.py.
Pascal