yesno
This section describes how to use the tdnn model of the yesno dataset from icefall in sherpa-onnx.
Note
It is a non-streaming model and it can only recognize
two words in Hebrew:
yes and no.
To download the model, please use:
cd /path/to/sherpa-onnx
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-tdnn-yesno.tar.bz2
# For Chinese users, please use the following mirror
# wget https://hub.nuaa.cf/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-tdnn-yesno.tar.bz2
tar xvf sherpa-onnx-tdnn-yesno.tar.bz2
rm sherpa-onnx-tdnn-yesno.tar.bz2
Please check that the file sizes of the pre-trained models are correct. See
the file sizes of *.onnx files below.
sherpa-onnx-tdnn-yesno fangjun$ ls -lh *.onnx
-rw-r--r-- 1 fangjun staff 55K Aug 12 17:02 model-epoch-14-avg-2.int8.onnx
-rw-r--r-- 1 fangjun staff 54K Aug 12 17:02 model-epoch-14-avg-2.onnx
Decode wave files
Hint
It supports decoding only wave files of a single channel with 16-bit encoded samples, while the sampling rate does not need to be 16 kHz.
The following code shows how to use fp32 models to decode wave files.
Please replace model-epoch-14-avg-2.int8.onnx with model-epoch-14-avg-2.int8.onnx
to use the int8 quantized model.
cd /path/to/sherpa-onnx
./build/bin/sherpa-onnx-offline \
--sample-rate=8000 \
--feat-dim=23 \
--tokens=./sherpa-onnx-tdnn-yesno/tokens.txt \
--tdnn-model=./sherpa-onnx-tdnn-yesno/model-epoch-14-avg-2.onnx \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_0_1_0_0_0_1.wav \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_0_0_0_1_0.wav \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_0_0_1_1_1.wav \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_0_1_0_0_1.wav \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_1_0_0_0_1.wav \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_1_0_1_1_0.wav
The output is given below:
OfflineRecognizerConfig(feat_config=OfflineFeatureExtractorConfig(sampling_rate=8000, feature_dim=23), model_config=OfflineModelConfig(transducer=OfflineTransducerModelConfig(encoder_filename="", decoder_filename="", joiner_filename=""), paraformer=OfflineParaformerModelConfig(model=""), nemo_ctc=OfflineNemoEncDecCtcModelConfig(model=""), whisper=OfflineWhisperModelConfig(encoder="", decoder=""), tdnn=OfflineTdnnModelConfig(model="./sherpa-onnx-tdnn-yesno/model-epoch-14-avg-2.onnx"), tokens="./sherpa-onnx-tdnn-yesno/tokens.txt", num_threads=2, debug=False, provider="cpu", model_type=""), lm_config=OfflineLMConfig(model="", scale=0.5), decoding_method="greedy_search", max_active_paths=4, context_score=1.5)
Creating recognizer ...
Started
Done!
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_0_1_0_0_0_1.wav
{"text":"NNNYNNNY","timestamps":"[]","tokens":["N","N","N","Y","N","N","N","Y"]}
----
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_0_0_0_1_0.wav
{"text":"NNYNNNYN","timestamps":"[]","tokens":["N","N","Y","N","N","N","Y","N"]}
----
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_0_0_1_1_1.wav
{"text":"NNYNNYYY","timestamps":"[]","tokens":["N","N","Y","N","N","Y","Y","Y"]}
----
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_0_1_0_0_1.wav
{"text":"NNYNYNNY","timestamps":"[]","tokens":["N","N","Y","N","Y","N","N","Y"]}
----
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_1_0_0_0_1.wav
{"text":"NNYYNNNY","timestamps":"[]","tokens":["N","N","Y","Y","N","N","N","Y"]}
----
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_1_0_1_1_0.wav
{"text":"NNYYNYYN","timestamps":"[]","tokens":["N","N","Y","Y","N","Y","Y","N"]}
----
num threads: 2
decoding method: greedy_search
Elapsed seconds: 0.071 s
Real time factor (RTF): 0.071 / 38.530 = 0.002
Note
In the above output, N represents NO, while Y is YES.
So for the last wave, NNYYNYYN means NO NO YES YES NO YES YES NO.
In the filename of the last wave 0_0_1_1_0_1_1_0.wav, 0 means NO
and 1 means YES. So the ground truth of the last wave is
NO NO YES YES NO YES YES NO.