dsh-speech-to-text-gigaam
用于 DeepSeek Harness 语音输入的本地 GigaAM (Sber) 俄语语音识别提供商
插件会安装到这里；不确定时保持 web。
npx -y @deepseek-ai/dsh plugin --profile web add github:kelvmg/dsh-speech-to-text-gigaam#3ac79d1b4361e53250d7f3ddf57f791c9c54b034Speech To Text Gigaam 以 dsh-speech-to-text-gigaam 发布，当前版本为 0.4.0。Plugin Hub 会校验它的 manifest，并保存精确安装来源，便于复现安装结果。
Local Russian speech recognition for DeepSeek Harness, powered by Sber GigaAM v3 E2E RNNT. Punctuation and capitalisation are included. Audio and transcripts stay on the host.
https://github.com/kelvmg/dsh-speech-to-text-gigaam. Enable the bundle.ru or auto.Automatic setup in 0.4.0 supports macOS on Apple Silicon (arm64, macOS 13+).
No system Python, Homebrew, git or separately installed ffmpeg is required.
Other platforms fail with an explicit explanation before downloading anything;
advanced users can provide a prepared interpreter with pythonPath.
Intel Mac is not supported by automatic setup: its last official PyTorch build
conflicts with the current GigaAM NumPy requirements.
Requires DSH with @deepseek-ai/dsh-experimental-voice-input-bundle and Node 20.3+
(the DSH host supplies Node). Reserve 4 GB disk, approximately 2 GB RAM;
initial setup normally takes 3–20 minutes, depending on the network and machine.
Activation only checks local resources: no background downloads or installers.
The existing check → model → verify → load preparation contract is retained.
--require-hashes. PyTorch and torchaudio are both 2.8.0. GigaAM source is pinned
to commit 7447938d791c4f3e643386ee22c33777004293a5 and SHA-256 verified.
ffmpeg comes from imageio-ffmpeg==0.6.0, not a system package manager.v3_e2e_rnnt.ckpt (448,929,252 bytes) and tokenizer
(255,336 bytes) from Sber's CDN. This is the only guided model.Files persist under ~/.dsh/speech-to-text/gigaam:
runtime/ private installer, Python, venv, package cache and readiness marker
models/ recognition checkpoint and tokenizer
An interrupted installation never creates a readiness marker. Retry reuses valid archives, downloaded Python and package cache. Completed models are reused too; a partially downloaded model starts over (HTTP Range resume is not implemented). Preparation has a heartbeat lock to prevent overlapping sessions. After a host crash, a stale lock expires within two minutes; retry then.
Upgrade from 0.3: existing models/ are reused; the old manually created
venv/ is left untouched. The default moves to runtime/venv/, so click
Download and prepare once after updating. To retain a manually prepared
interpreter instead, explicitly set its absolute pythonPath.
Setup contacts GitHub (uv, pinned standalone Python and GigaAM source),
pypi.org/files.pythonhosted.org (Python distributions) and
cdn.chatwm.opensmodel.sberdevices.ru (recognition model). The source selector in
voice settings applies only to model files, not runtime dependencies.
Runtime archives, source and locked Python distributions are verified against
pinned hashes. uv verifies standalone Python using its pinned download catalog.
Python build tooling still uses isolated build environments and the HTTPS PyPI
index; this is not a completely hermetic/offline build. Setup does not alter
system Python, shell profiles or global PATH. Ambient pip/uv configuration and
IMAGEIO_FFMPEG_EXE do not override managed setup; normal certificate/proxy
settings remain usable.
Downloads have deadlines and exact byte caps; corrupt or partial files are not
published as final assets. Cancel kills the process group and waits for process
exit before deleting temporary audio or starting another job. Recordings are
transcribed locally and temporary WAV/transcript files are removed afterwards.
Errors write diagnostic traces to dsh-gigaam-stt.log in the system temp directory.
No audio, transcript or telemetry is uploaded.
Defaults are in cordis.patch.yml; advanced options on speech-to-text-gigaam:
| Option | Default / purpose |
|---|---|
dataRoot | ~/.dsh/speech-to-text/gigaam |
pythonPath | dataRoot/runtime/venv/bin/python; an explicit path disables managed installs |
downloadRoot | dataRoot/models |
model | v3_e2e_rnnt; other models require an externally prepared environment and cache |
device | cpu; auto may choose MPS; only CPU is smoke-tested |
maxChunkSeconds | 20; clamped to 3…24.5; every sample is retained |
timeoutMs | 900000 per recognition/warmup job |
setupTimeoutMs | 1800000 per installer command/download |
downloadTimeoutMs | 900000 per model download |
downloadSources | Sber CDN origin; additional trusted model mirrors are optional |
loadModel | true; disable only for diagnostics/tests |
Unsupported guided models fail before any installation. An explicit custom
pythonPath must already contain all dependencies and ffmpeg; the plugin never
installs packages into a user-specified interpreter.
npm ci
npm test # model-free Node regressions
# Optional terminal equivalent of the explicit UI prepare button:
npm run setup:runtime
npm run test:smoke # real model: 7 recognition/integration tests
# Python-only regressions, using the prepared runtime:
~/.dsh/speech-to-text/gigaam/runtime/venv/bin/python -B -m unittest discover -s test -p python_worker.py -v
GIGAAM_DATA_ROOT=/absolute/test-directory selects an independent data root for
headless preparation and smoke tests. python/setup.sh is a compatibility wrapper
for the same Node-based preparation; it no longer calls system Python or git.
The dependency lockfile can be regenerated using pinned uv with
uv pip compile --generate-hashes --no-header --no-annotate from
python/requirements-macos-arm64.in; update the lock digest in the manifest too.
MIT © 2026 Evgeny Klimov. GigaAM is MIT-licensed by SberDevices. uv, PyTorch and ffmpeg retain their own upstream licenses. Runtime binaries are downloaded on request, not embedded in the plugin package.