Zipformer-CTC-based Models
Hint
Please refer to Installation to install sherpa-onnx before you read this section.
sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13 (Chinese)
Training code for this model can be found at https://github.com/k2-fsa/icefall/pull/1369. It supports only Chinese.
Please refer to https://github.com/k2-fsa/icefall/tree/master/egs/multi_zh-hans/ASR#included-training-sets for the detailed information about the training data. In total, there are 14k hours of training data.
In the following, we describe how to download it and use it with sherpa-onnx.
Download the model
Please use the following commands to download it.
cd /path/to/sherpa-onnx
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13.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-streaming-zipformer-ctc-multi-zh-hans-2023-12-13.tar.bz2
tar xvf sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13.tar.bz2
rm sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13.tar.bz2
ls -lh sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13
The output is given below:
$ ls -lh sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13
total 654136
-rw-r--r--@ 1 fangjun staff 28B Dec 13 16:19 README.md
-rw-r--r--@ 1 fangjun staff 258K Dec 13 16:19 bpe.model
-rw-r--r--@ 1 fangjun staff 68M Dec 13 16:19 ctc-epoch-20-avg-1-chunk-16-left-128.int8.onnx
-rw-r--r--@ 1 fangjun staff 252M Dec 13 16:19 ctc-epoch-20-avg-1-chunk-16-left-128.onnx
drwxr-xr-x@ 8 fangjun staff 256B Dec 13 16:19 test_wavs
-rw-r--r--@ 1 fangjun staff 18K Dec 13 16:19 tokens.txt
Decode a single wave file
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.
fp32
The following code shows how to use fp32 models to decode a wave file:
cd /path/to/sherpa-onnx
./build/bin/sherpa-onnx \
--zipformer2-ctc-model=./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/ctc-epoch-20-avg-1-chunk-16-left-128.onnx \
--tokens=./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/tokens.txt \
./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/test_wavs/DEV_T0000000000.wav
Note
Please use ./build/bin/Release/sherpa-onnx.exe for Windows.
Caution
If you use Windows and get encoding issues, please run:
CHCP 65001
in your commandline.
You should see the following output:
/Users/fangjun/open-source/sherpa-onnx/sherpa-onnx/csrc/parse-options.cc:Read:361 ./build/bin/sherpa-onnx --zipformer2-ctc-model=./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/ctc-epoch-20-avg-1-chunk-16-left-128.onnx --tokens=./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/tokens.txt ./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/test_wavs/DEV_T0000000000.wav
OnlineRecognizerConfig(feat_config=FeatureExtractorConfig(sampling_rate=16000, feature_dim=80), model_config=OnlineModelConfig(transducer=OnlineTransducerModelConfig(encoder="", decoder="", joiner=""), paraformer=OnlineParaformerModelConfig(encoder="", decoder=""), wenet_ctc=OnlineWenetCtcModelConfig(model="", chunk_size=16, num_left_chunks=4), zipformer2_ctc=OnlineZipformer2CtcModelConfig(model="./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/ctc-epoch-20-avg-1-chunk-16-left-128.onnx"), tokens="./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/tokens.txt", num_threads=1, debug=False, provider="cpu", model_type=""), lm_config=OnlineLMConfig(model="", scale=0.5), endpoint_config=EndpointConfig(rule1=EndpointRule(must_contain_nonsilence=False, min_trailing_silence=2.4, min_utterance_length=0), rule2=EndpointRule(must_contain_nonsilence=True, min_trailing_silence=1.2, min_utterance_length=0), rule3=EndpointRule(must_contain_nonsilence=False, min_trailing_silence=0, min_utterance_length=20)), enable_endpoint=True, max_active_paths=4, hotwords_score=1.5, hotwords_file="", decoding_method="greedy_search")
./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/test_wavs/DEV_T0000000000.wav
Elapsed seconds: 0.66, Real time factor (RTF): 0.12
对我做了介绍那么我想说的是大家如果对我的研究感兴趣
{"is_final":false, "segment":0, "start_time":0.00, "text": " 对我做了介绍那么我想说的是大家如果对我的研究感兴趣", "timestamps": [0.00, 0.52, 0.76, 0.84, 1.08, 1.24, 1.96, 2.04, 2.24, 2.36, 2.56, 2.68, 2.80, 3.28, 3.40, 3.60, 3.72, 3.84, 3.96, 4.04, 4.16, 4.28, 4.36, 4.60, 4.80], "tokens":[" 对", "我", "做", "了", "介", "绍", "那", "么", "我", "想", "说", "的", "是", "大", "家", "如", "果", "对", "我", "的", "研", "究", "感", "兴", "趣"]}
int8
The following code shows how to use int8 models to decode a wave file:
cd /path/to/sherpa-onnx
./build/bin/sherpa-onnx \
--zipformer2-ctc-model=./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/ctc-epoch-20-avg-1-chunk-16-left-128.int8.onnx \
--tokens=./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/tokens.txt \
./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/test_wavs/DEV_T0000000000.wav
Note
Please use ./build/bin/Release/sherpa-onnx.exe for Windows.
Caution
If you use Windows and get encoding issues, please run:
CHCP 65001
in your commandline.
You should see the following output:
/Users/fangjun/open-source/sherpa-onnx/sherpa-onnx/csrc/parse-options.cc:Read:361 ./build/bin/sherpa-onnx --zipformer2-ctc-model=./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/ctc-epoch-20-avg-1-chunk-16-left-128.int8.onnx --tokens=./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/tokens.txt ./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/test_wavs/DEV_T0000000000.wav
OnlineRecognizerConfig(feat_config=FeatureExtractorConfig(sampling_rate=16000, feature_dim=80), model_config=OnlineModelConfig(transducer=OnlineTransducerModelConfig(encoder="", decoder="", joiner=""), paraformer=OnlineParaformerModelConfig(encoder="", decoder=""), wenet_ctc=OnlineWenetCtcModelConfig(model="", chunk_size=16, num_left_chunks=4), zipformer2_ctc=OnlineZipformer2CtcModelConfig(model="./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/ctc-epoch-20-avg-1-chunk-16-left-128.int8.onnx"), tokens="./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/tokens.txt", num_threads=1, debug=False, provider="cpu", model_type=""), lm_config=OnlineLMConfig(model="", scale=0.5), endpoint_config=EndpointConfig(rule1=EndpointRule(must_contain_nonsilence=False, min_trailing_silence=2.4, min_utterance_length=0), rule2=EndpointRule(must_contain_nonsilence=True, min_trailing_silence=1.2, min_utterance_length=0), rule3=EndpointRule(must_contain_nonsilence=False, min_trailing_silence=0, min_utterance_length=20)), enable_endpoint=True, max_active_paths=4, hotwords_score=1.5, hotwords_file="", decoding_method="greedy_search")
./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/test_wavs/DEV_T0000000000.wav
Elapsed seconds: 0.44, Real time factor (RTF): 0.078
对我做了介绍那么我想说的是大家如果对我的研究感兴趣
{"is_final":false, "segment":0, "start_time":0.00, "text": " 对我做了介绍那么我想说的是大家如果对我的研究感兴趣", "timestamps": [0.00, 0.52, 0.76, 0.84, 1.04, 1.24, 1.96, 2.04, 2.24, 2.36, 2.56, 2.68, 2.80, 3.28, 3.40, 3.60, 3.72, 3.84, 3.96, 4.04, 4.16, 4.28, 4.36, 4.60, 4.84], "tokens":[" 对", "我", "做", "了", "介", "绍", "那", "么", "我", "想", "说", "的", "是", "大", "家", "如", "果", "对", "我", "的", "研", "究", "感", "兴", "趣"]}
Real-time speech recognition from a microphone
cd /path/to/sherpa-onnx
./build/bin/sherpa-onnx-microphone \
--zipformer2-ctc-model=./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/ctc-epoch-20-avg-1-chunk-16-left-128.onnx \
--tokens=./sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13/tokens.txt
Hint
If your system is Linux (including embedded Linux), you can also use
sherpa-onnx-alsa to do real-time speech recognition with your
microphone if sherpa-onnx-microphone does not work for you.