mirror of
https://github.com/mudler/LocalAI.git
synced 2026-07-08 15:28:23 -04:00
13734ae9fa
feat(backend): Add Sherpa ONNX backend and Omnilingual ASR Adds a new Go backend wrapping sherpa-onnx via purego (no cgo). Same approach as opus/stablediffusion-ggml/whisper — a thin C shim (csrc/shim.c + shim.h → libsherpa-shim.so) wraps the bits purego can't reach directly: nested struct config writes, result-struct field reads, and the streaming TTS callback trampoline. The Go side uses opaque uintptr handles and purego.NewCallback for the TTS callback. Supports: - VAD via sherpa-onnx's Silero VAD - Offline ASR: Whisper, Paraformer, SenseVoice, Omnilingual CTC - Online/streaming ASR: zipformer transducer with endpoint detection (AudioTranscriptionStream emits delta events during decode) - Offline TTS: VITS (LJS, etc.) - Streaming TTS: sherpa-onnx's callback API → PCM chunks on a channel, prefixed by a streaming WAV header Gallery entries: omnilingual-0.3b-ctc-q8-sherpa (1600-language offline ASR), streaming-zipformer-en-sherpa (low-latency streaming ASR), silero-vad-sherpa, vits-ljs-sherpa. E2E coverage: tests/e2e-backends for offline + streaming ASR, tests/e2e for the full realtime pipeline (VAD + STT + TTS). Assisted-by: claude-opus-4-7-1M [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com>
137 lines
5.3 KiB
Bash
Executable File
137 lines
5.3 KiB
Bash
Executable File
#!/bin/bash
|
|
# Drives tests/e2e/realtime_ws_test.go against a realtime pipeline where
|
|
# VAD, STT and TTS are served by the sherpa-onnx Docker backend, and the
|
|
# LLM slot stays mocked by the in-repo mock-backend. Pre-requisites:
|
|
# - `make build-mock-backend` has produced tests/e2e/mock-backend/mock-backend
|
|
# - `make docker-build-sherpa-onnx` has produced local-ai-backend:sherpa-onnx
|
|
# - `make protogen-go` is up-to-date
|
|
# Environment overrides:
|
|
# WORK_DIR Where to stage the extracted backend + model files.
|
|
# Defaults to a mktemp'd directory (cleaned on exit).
|
|
# KEEP_WORK Non-empty to preserve WORK_DIR after the test exits (useful for
|
|
# debugging repeated runs — skips redownloads if files already present).
|
|
set -euo pipefail
|
|
|
|
ROOT=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")/../.." && pwd)
|
|
IMAGE=${BACKEND_IMAGE:-local-ai-backend:sherpa-onnx}
|
|
MODEL=${REALTIME_STT_MODEL:-omnilingual-0.3b-ctc-q8-sherpa}
|
|
VAD_MODEL=${REALTIME_VAD_MODEL:-silero-vad-sherpa}
|
|
TTS_MODEL=${REALTIME_TTS_MODEL:-vits-ljs-sherpa}
|
|
|
|
WORK_DIR=${WORK_DIR:-$(mktemp -d -t localai-sherpa-realtime.XXXXXX)}
|
|
if [[ -z "${KEEP_WORK:-}" ]]; then
|
|
trap 'rm -rf "$WORK_DIR"' EXIT
|
|
fi
|
|
echo "WORK_DIR=$WORK_DIR"
|
|
|
|
BACKENDS_DIR="$WORK_DIR/backends"
|
|
MODELS_DIR="$WORK_DIR/models"
|
|
mkdir -p "$BACKENDS_DIR/sherpa-onnx" "$MODELS_DIR"
|
|
|
|
# 1. Extract the sherpa-onnx backend image rootfs. Mirrors the pattern in
|
|
# tests/e2e-backends/backend_test.go:extractImage — docker create + export
|
|
# avoids having to pull and parse layer tarballs.
|
|
if [[ ! -x "$BACKENDS_DIR/sherpa-onnx/run.sh" ]]; then
|
|
echo "Extracting $IMAGE rootfs into $BACKENDS_DIR/sherpa-onnx ..."
|
|
CID=$(docker create --entrypoint=/run.sh "$IMAGE")
|
|
trap 'docker rm -f "$CID" >/dev/null 2>&1 || true; [[ -z "${KEEP_WORK:-}" ]] && rm -rf "$WORK_DIR"' EXIT
|
|
docker export "$CID" | tar -xC "$BACKENDS_DIR/sherpa-onnx" \
|
|
--exclude='dev/*' --exclude='proc/*' --exclude='sys/*'
|
|
docker rm -f "$CID" >/dev/null
|
|
fi
|
|
|
|
# Make sure run.sh is executable (tar usually preserves this, but belt + braces).
|
|
chmod +x "$BACKENDS_DIR/sherpa-onnx/run.sh" \
|
|
"$BACKENDS_DIR/sherpa-onnx/sherpa-onnx" 2>/dev/null || true
|
|
|
|
# 2. Download model files. URLs + sha256s match gallery/index.yaml entries.
|
|
download() {
|
|
local dst="$1" url="$2" sha="$3"
|
|
if [[ -f "$dst" ]]; then
|
|
actual=$(sha256sum "$dst" | awk '{print $1}')
|
|
if [[ "$actual" == "$sha" ]]; then
|
|
echo "cached: $dst"
|
|
return
|
|
fi
|
|
fi
|
|
mkdir -p "$(dirname "$dst")"
|
|
echo "downloading: $url -> $dst"
|
|
curl -sSfL "$url" -o "$dst"
|
|
actual=$(sha256sum "$dst" | awk '{print $1}')
|
|
if [[ "$actual" != "$sha" ]]; then
|
|
echo "sha256 mismatch for $dst: got $actual, expected $sha" >&2
|
|
exit 1
|
|
fi
|
|
}
|
|
|
|
# Silero VAD (single file)
|
|
download "$MODELS_DIR/silero-vad/silero-vad.onnx" \
|
|
"https://huggingface.co/onnx-community/silero-vad/resolve/main/onnx/model.onnx" \
|
|
"a4a068cd6cf1ea8355b84327595838ca748ec29a25bc91fc82e6c299ccdc5808"
|
|
|
|
# Omnilingual ASR (model + tokens)
|
|
download "$MODELS_DIR/omnilingual-asr/model.int8.onnx" \
|
|
"https://huggingface.co/csukuangfj/sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-int8-2025-11-12/resolve/main/model.int8.onnx" \
|
|
"e7c4e54ee4c4c47829cc6667d5d00ed8ea7bef1dcfeef0fce766f77752a2726c"
|
|
download "$MODELS_DIR/omnilingual-asr/tokens.txt" \
|
|
"https://huggingface.co/csukuangfj/sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-int8-2025-11-12/resolve/main/tokens.txt" \
|
|
"a7a044c52cb29cbe8b0dc1953e92cefd4ca16b0ed968177b6beab21f9a7d0b31"
|
|
|
|
# VITS-LJS TTS (model + tokens + lexicon)
|
|
download "$MODELS_DIR/vits-ljs/vits-ljs.onnx" \
|
|
"https://huggingface.co/csukuangfj/vits-ljs/resolve/main/vits-ljs.onnx" \
|
|
"5bbd273797a9ecf8d94bd6ec02ad16cb41cbb85f055ad98d528ced3e44c9b31a"
|
|
download "$MODELS_DIR/vits-ljs/tokens.txt" \
|
|
"https://huggingface.co/csukuangfj/vits-ljs/resolve/main/tokens.txt" \
|
|
"5fee2c6b238d712287f2ecb08f34a8a8b413bcb7390862ef6fb6fd6f0f8d3a17"
|
|
download "$MODELS_DIR/vits-ljs/lexicon.txt" \
|
|
"https://huggingface.co/csukuangfj/vits-ljs/resolve/main/lexicon.txt" \
|
|
"bdccfc6da71c45c48e2e0056fcf0aab760577c5f959f6c1b5eb3e3e916fd5a0e"
|
|
|
|
# 3. Write model config YAMLs matching the gallery entries' shape. These are
|
|
# what the realtime pipeline resolves via LoadModelConfigFileByName.
|
|
cat > "$MODELS_DIR/$VAD_MODEL.yaml" <<EOF
|
|
name: $VAD_MODEL
|
|
backend: sherpa-onnx
|
|
type: vad
|
|
parameters:
|
|
model: silero-vad/silero-vad.onnx
|
|
known_usecases:
|
|
- vad
|
|
EOF
|
|
|
|
cat > "$MODELS_DIR/$MODEL.yaml" <<EOF
|
|
name: $MODEL
|
|
backend: sherpa-onnx
|
|
type: asr
|
|
parameters:
|
|
model: omnilingual-asr/model.int8.onnx
|
|
options:
|
|
- subtype=omnilingual
|
|
known_usecases:
|
|
- transcript
|
|
EOF
|
|
|
|
cat > "$MODELS_DIR/$TTS_MODEL.yaml" <<EOF
|
|
name: $TTS_MODEL
|
|
backend: sherpa-onnx
|
|
parameters:
|
|
model: vits-ljs/vits-ljs.onnx
|
|
known_usecases:
|
|
- tts
|
|
EOF
|
|
|
|
# 4. Run the Ginkgo spec. REALTIME_TEST_MODEL=realtime-test-pipeline triggers
|
|
# the e2e suite to auto-compose a pipeline YAML from the slot env vars.
|
|
export REALTIME_TEST_MODEL=realtime-test-pipeline
|
|
export REALTIME_VAD="$VAD_MODEL"
|
|
export REALTIME_STT="$MODEL"
|
|
export REALTIME_LLM=mock-llm
|
|
export REALTIME_TTS="$TTS_MODEL"
|
|
export REALTIME_MODELS_PATH="$MODELS_DIR"
|
|
export REALTIME_BACKENDS_PATH="$BACKENDS_DIR"
|
|
|
|
cd "$ROOT"
|
|
go test -v -timeout 30m ./tests/e2e/... \
|
|
-ginkgo.focus="Manual audio commit"
|