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* feat(voice-recognition): add /v1/voice/{verify,analyze,embed} + speaker-recognition backend
Audio analog to face recognition. Adds three gRPC RPCs
(VoiceVerify / VoiceAnalyze / VoiceEmbed), their Go service and HTTP
layers, a new FLAG_SPEAKER_RECOGNITION capability flag, and a Python
backend scaffold under backend/python/speaker-recognition/ wrapping
SpeechBrain ECAPA-TDNN with a parallel OnnxDirectEngine for
WeSpeaker / 3D-Speaker ONNX exports.
The kokoros Rust backend gets matching unimplemented trait stubs —
tonic's async_trait has no defaults, so adding an RPC without Rust
stubs breaks the build (same regression fixed by eb01c772 for face).
Swagger, /api/instructions, and the auth RouteFeatureRegistry /
APIFeatures list are updated so the endpoints surface everywhere a
client or admin UI looks.
Assisted-by: Claude:claude-opus-4-7
* feat(voice-recognition): add 1:N identify + register/forget endpoints
Mirrors the face-recognition register/identify/forget surface. New
package core/services/voicerecognition/ carries a Registry interface
and a local-store-backed implementation (same in-memory vector-store
plumbing facerecognition uses, separate instance so the embedding
spaces stay isolated).
Handlers under /v1/voice/{register,identify,forget} reuse
backend.VoiceEmbed to compute the probe vector, then delegate the
nearest-neighbour search to the registry. Default cosine-distance
threshold is tuned for ECAPA-TDNN on VoxCeleb (0.25, EER ~1.9%).
As with the face registry, the current backing is in-memory only — a
pgvector implementation is a future constructor-level swap.
Assisted-by: Claude:claude-opus-4-7
* feat(voice-recognition): gallery, docs, CI and e2e coverage
- backend/index.yaml: speaker-recognition backend entry + CPU and
CUDA-12 image variants (plus matching development variants).
- gallery/index.yaml: speechbrain-ecapa-tdnn (default) and
wespeaker-resnet34 model entries. The WeSpeaker SHA-256 is a
deliberate placeholder — the HF URI must be curl'd and its hash
filled in before the entry installs.
- docs/content/features/voice-recognition.md: API reference + quickstart,
mirrors the face-recognition docs.
- React UI: CAP_SPEAKER_RECOGNITION flag export (consumers follow face's
precedent — no dedicated tab yet).
- tests/e2e-backends: voice_embed / voice_verify / voice_analyze specs.
Helper resolveFaceFixture is reused as-is — the only thing face/voice
share is "download a file into workDir", so no need for a new helper.
- Makefile: docker-build-speaker-recognition + test-extra-backend-
speaker-recognition-{ecapa,all} targets. Audio fixtures default to
VCTK p225/p226 samples from HuggingFace.
- CI: test-extra.yml grows a tests-speaker-recognition-grpc job
mirroring insightface. backend.yml matrix gains CPU + CUDA-12 image
build entries — scripts/changed-backends.js auto-picks these up.
Assisted-by: Claude:claude-opus-4-7
* feat(voice-recognition): wire a working /v1/voice/analyze head
Adds AnalysisHead: a lazy-loading age / gender / emotion inference
wrapper that plugs into both SpeechBrainEngine and OnnxDirectEngine.
Defaults to two open-licence HuggingFace checkpoints:
- audeering/wav2vec2-large-robust-24-ft-age-gender (Apache 2.0) —
age regression + 3-way gender (female / male / child).
- superb/wav2vec2-base-superb-er (Apache 2.0) — 4-way emotion.
Both are optional and degrade gracefully when transformers or the
model can't be loaded — the engine raises NotImplementedError so the
gRPC layer returns 501 instead of a generic 500.
Emotion classes pass through from the model (neutral/happy/angry/sad
on the default checkpoint); the e2e test now accepts any non-empty
dominant gender so custom age_gender_model overrides don't fail it.
Adds transformers to the backend's CPU and CUDA-12 requirements.
Assisted-by: Claude:claude-opus-4-7
* fix(voice-recognition): pin real WeSpeaker ResNet34 ONNX SHA-256
Replaces the placeholder hash in gallery/index.yaml with the actual
SHA-256 (7bb2f06e…) of the upstream
Wespeaker/wespeaker-voxceleb-resnet34-LM ONNX at ~25MB. `local-ai
models install wespeaker-resnet34` now succeeds.
Assisted-by: Claude:claude-opus-4-7
* fix(voice-recognition): soundfile loader + honest analyze default
Two issues surfaced on first end-to-end smoke with the actual backend
image:
1. torchaudio.load in torchaudio 2.8+ requires the torchcodec package
for audio decoding. Switch SpeechBrainEngine._load_waveform to the
already-present soundfile (listed in requirements.txt) plus a numpy
linear resample to 16kHz. Drops a heavy ffmpeg-linked dep and the
codepath we never exercise (torchaudio's ffmpeg backend).
2. The AnalysisHead was defaulting to audeering/wav2vec2-large-robust-
24-ft-age-gender, but AutoModelForAudioClassification silently
mangles that checkpoint — it reports the age head weights as
UNEXPECTED and re-initialises the classifier head with random
values, so the "gender" output is noise and there is no age output
at all. Make age/gender opt-in instead (empty default; users wire
a cleanly-loadable Wav2Vec2ForSequenceClassification checkpoint via
age_gender_model: option). Emotion keeps its working Superb default.
Also broaden _infer_age_gender's tensor-shape handling and catch
runtime exceptions so a dodgy age/gender head never takes down the
whole analyze call.
Docs and README updated to match the new policy.
Verified with the branch-scoped gallery on localhost:
- voice/embed → 192-d ECAPA-TDNN vector
- voice/verify → same-clip dist≈6e-08 verified=true; cross-speaker
dist 0.76–0.99 verified=false (as expected)
- voice/register/identify/forget → round-trip works, 404 on unknown id
- voice/analyze → emotion populated, age/gender omitted (opt-in)
Assisted-by: Claude:claude-opus-4-7
* fix(voice-recognition): real CI audio fixtures + fixture-agnostic verify spec
Two issues surfaced after CI actually ran the speaker-recognition e2e
target (I'd curl-tested against a running server but hadn't run the
make target locally):
1. The default BACKEND_TEST_VOICE_AUDIO_* URLs pointed at
huggingface.co/datasets/CSTR-Edinburgh/vctk paths that return 404
(the dataset is gated). Swap them for the speechbrain test samples
served from github.com/speechbrain/speechbrain/raw/develop/ —
public, no auth, correct 16kHz mono format.
2. The VoiceVerify spec required d(file1,file2) < 0.4, assuming
file1/file2 were same-speaker. The speechbrain samples are three
different speakers (example1/2/5), and there is no easy un-gated
source of true same-speaker audio pairs (VoxCeleb/VCTK/LibriSpeech
are all license- or size-gated for CI use). Replace the ceiling
check with a relative-ordering assertion: d(pair) > d(same-clip)
for both file2 and file3 — that's enough to prove the embeddings
encode speaker info, and it works with any three non-identical
clips. Actual speaker ordering d(1,2) vs d(1,3) is logged but not
asserted.
Local run: 4/4 voice specs pass (Health, LoadModel, VoiceEmbed,
VoiceVerify) on the built backend image. 12 non-voice specs skipped
as expected.
Assisted-by: Claude:claude-opus-4-7
* fix(ci): checkout with submodules in the reusable backend_build workflow
The kokoros Rust backend build fails with
failed to read .../sources/Kokoros/kokoros/Cargo.toml: No such file
because the reusable backend_build.yml workflow's actions/checkout
step was missing `submodules: true`. Dockerfile.rust does `COPY .
/LocalAI`, and without the submodule files the subsequent `cargo
build` can't find the vendored Kokoros crate.
The bug pre-dates this PR — scripts/changed-backends.js only triggers
the kokoros image job when something under backend/rust/kokoros or
the shared proto changes, so master had been coasting past it. The
voice-recognition proto addition re-broke it.
Other checkouts in backend.yml (llama-cpp-darwin) and test-extra.yml
(insightface, kokoros, speaker-recognition) already pass
`submodules: true`; this brings the shared backend image builder in
line.
Assisted-by: Claude:claude-opus-4-7
20 lines
632 B
Bash
Executable File
20 lines
632 B
Bash
Executable File
#!/bin/bash
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set -e
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backend_dir=$(dirname $0)
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if [ -d $backend_dir/common ]; then
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source $backend_dir/common/libbackend.sh
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else
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source $backend_dir/../common/libbackend.sh
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fi
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installRequirements
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# No pre-baked model weights. Weights flow through LocalAI's gallery
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# `files:` mechanism — see gallery entries for speechbrain-ecapa-tdnn
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# and WeSpeaker / 3D-Speaker ONNX packs. SpeechBrain's
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# EncoderClassifier.from_hparams also knows how to auto-download from
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# HuggingFace into the configured savedir (we point it at ModelPath),
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# so the first LoadModel call bootstraps the checkpoint if the gallery
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# flow wasn't used.
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