mirror of
https://github.com/k2-fsa/sherpa-onnx.git
synced 2026-11-11 05:20:52 +08:00
102 lines
3.4 KiB
Markdown
102 lines
3.4 KiB
Markdown
# Orukeet with the offline WebSocket server
|
|
|
|
[Orukeet](https://huggingface.co/oruk/orukeet) is a 25-language fine-tune of
|
|
Parakeet TDT 0.6B v3. Its ONNX export uses sherpa-onnx's existing NeMo transducer
|
|
implementation. The fitted Gabor filters are stored as convolution weights;
|
|
inference requires no custom operators.
|
|
|
|
## Download
|
|
|
|
The following commands download the release's manifest and INT8 model archive
|
|
from an immutable Hugging Face revision. The manifest supplies the archive's
|
|
size, checksum, and extraction directory.
|
|
|
|
```bash
|
|
set -e
|
|
base=https://huggingface.co/oruk/orukeet/resolve/55a984d46f68323301837194ce647c702f55facc/onnx
|
|
curl -fL "$base/manifest.json" -o orukeet-manifest.json
|
|
curl -fL "$base/sherpa-onnx-orukeet-v0.1.0-int8.tar.bz2" \
|
|
-o sherpa-onnx-orukeet-v0.1.0-int8.tar.bz2
|
|
|
|
python3 - <<'PY'
|
|
import hashlib
|
|
import json
|
|
from pathlib import Path
|
|
|
|
manifest_path = Path("orukeet-manifest.json")
|
|
expected = "7e80f93f0e9b923c392424b0f85d28a717feee0a4d2a6aa9bfa723693868e727"
|
|
assert hashlib.sha256(manifest_path.read_bytes()).hexdigest() == expected
|
|
manifest = json.loads(manifest_path.read_text())
|
|
archive = Path(manifest["archive"])
|
|
assert archive.stat().st_size == manifest["archive_bytes"]
|
|
digest = hashlib.sha256()
|
|
with archive.open("rb") as stream:
|
|
for block in iter(lambda: stream.read(1024 * 1024), b""):
|
|
digest.update(block)
|
|
assert digest.hexdigest() == manifest["archive_sha256"]
|
|
print("Verified:", archive)
|
|
PY
|
|
|
|
tar xf sherpa-onnx-orukeet-v0.1.0-int8.tar.bz2
|
|
```
|
|
|
|
The archive contains `encoder.int8.onnx`, `decoder.int8.onnx`,
|
|
`joiner.int8.onnx`, `tokens.txt`, and the weight license and attribution.
|
|
Keep these files together. Downloads occupy approximately 487 MB; the extracted
|
|
model occupies approximately 672 MB. Existing model files can be reused offline.
|
|
|
|
## Serve
|
|
|
|
From the sherpa-onnx repository root, install the example's dependencies. This
|
|
example uses the legacy WebSocket server API:
|
|
|
|
```bash
|
|
python3 -m pip install sherpa-onnx numpy 'websockets<14'
|
|
```
|
|
|
|
Start the server:
|
|
|
|
```bash
|
|
model=./sherpa-onnx-orukeet-v0.1.0-int8
|
|
python3 python-api-examples/non_streaming_server.py \
|
|
--model-type nemo_transducer \
|
|
--encoder "$model/encoder.int8.onnx" \
|
|
--decoder "$model/decoder.int8.onnx" \
|
|
--joiner "$model/joiner.int8.onnx" \
|
|
--tokens "$model/tokens.txt" \
|
|
--feat-dim 128 \
|
|
--num-threads 2 \
|
|
--provider cpu \
|
|
--port 6006
|
|
```
|
|
|
|
Transcribe files with the existing client:
|
|
|
|
```bash
|
|
python3 python-api-examples/offline-websocket-client-decode-files-sequential.py \
|
|
--server-addr localhost --server-port 6006 recording.wav
|
|
```
|
|
|
|
Audio is decoded locally. The model uses a 16 kHz frontend and the existing
|
|
greedy TDT decoder. This is utterance-based recognition: the client sends each
|
|
recording for a final transcript.
|
|
|
|
For microphone input, download `silero_vad.onnx` as described in
|
|
[vad-with-non-streaming-asr.py](./vad-with-non-streaming-asr.py), then run:
|
|
|
|
```bash
|
|
python3 -m pip install sounddevice
|
|
model=./sherpa-onnx-orukeet-v0.1.0-int8
|
|
python3 python-api-examples/vad-with-non-streaming-asr.py \
|
|
--silero-vad-model ./silero_vad.onnx \
|
|
--model-type nemo_transducer \
|
|
--encoder "$model/encoder.int8.onnx" \
|
|
--decoder "$model/decoder.int8.onnx" \
|
|
--joiner "$model/joiner.int8.onnx" \
|
|
--tokens "$model/tokens.txt" \
|
|
--feature-dim 128 --num-threads 2
|
|
```
|
|
|
|
Stock Parakeet TDT v3 uses the same server options with its own model directory.
|
|
Other transducer examples retain the default `--model-type transducer`.
|