Audio Classification
Transformers
ONNX
NeMo
Safetensors
anuvani_lid
audio
speech
language-identification
spoken-language-id
lid
indic
conformer
custom_code
Instructions to use CIIL/Anuvani-LID with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIIL/Anuvani-LID with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="CIIL/Anuvani-LID", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForAudioClassification model = AutoModelForAudioClassification.from_pretrained("CIIL/Anuvani-LID", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,633 Bytes
bd48fd1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | #!/bin/sh
# Copyright (c) 2026, CIIL Anuvani-LID. Apache-2.0.
# Python reference, C, Kotlin and Java binding tests, inside anuvani-lid-mobile-sherpa-linux:1 with the
# repository at /work, the Linux build at /work/build/linux-x86_64/install and the gate audio at $GATE.
set -e
OUT=${OUT:-/work/results/tmp/bindings}
MODEL=${MODEL:-/work/models/anuvani_lid.int8.onnx}
VAD=${VAD:-/work/models/silero_vad_6.2.1.onnx}
LONG=${LONG:-/work/results/tmp/long/mixed_0.wav}
LIB=/work/build/linux-x86_64/install/lib
SRC=/work/third_party/sherpa-onnx
CLIPS=$(ls $GATE/golden/*.wav)
mkdir -p $OUT /tmp/bt && export LD_LIBRARY_PATH=$LIB
pip install --user --no-deps -q /work/build/wheels/sherpa_onnx-*.whl 2>/dev/null
python3 /work/tests/bindings/python_ref.py $MODEL $VAD $LONG $CLIPS > $OUT/python.jsonl
gcc -O2 -o /tmp/bt/c_test /work/tests/bindings/c/main.c -I$SRC -L$LIB -lsherpa-onnx-c-api
/tmp/bt/c_test $MODEL $VAD $LONG $CLIPS > $OUT/c.jsonl
K=$SRC/sherpa-onnx/kotlin-api
kotlinc-jvm -include-runtime -d /tmp/bt/kotlin.jar /work/tests/bindings/kotlin/Main.kt \
$K/AudioTagging.kt $K/OfflineStream.kt $K/LidPipeline.kt $K/WaveReader.kt \
$SRC/kotlin-api-examples/faked-asset-manager.kt $SRC/kotlin-api-examples/faked-log.kt 2>/dev/null
java -Djava.library.path=$LIB -cp /tmp/bt/kotlin.jar com.k2fsa.sherpa.onnx.MainKt $MODEL $VAD $LONG $CLIPS > $OUT/kotlin.jsonl
mkdir -p /tmp/bt/java
javac -d /tmp/bt/java $(find $SRC/sherpa-onnx/java-api/src -name "*.java") /work/tests/bindings/java/Main.java
java -Djava.library.path=$LIB -cp /tmp/bt/java Main $MODEL $VAD $LONG $CLIPS > $OUT/java.jsonl
echo "python, c, kotlin, java: done"
|