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3bf986d08d
This PR adds support for non-streaming Zipformer CTC ASR models across multiple language bindings, WebAssembly, examples, and CI workflows. - Introduces a new OfflineZipformerCtcModelConfig in C/C++, Python, Swift, Java, Kotlin, Go, Dart, Pascal, and C# APIs - Updates initialization, freeing, and recognition logic to include Zipformer CTC in WASM and Node.js - Adds example scripts and CI steps for downloading, building, and running Zipformer CTC models Model doc is available at https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/icefall/zipformer.html
57 lines
1.5 KiB
Python
Executable File
57 lines
1.5 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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This file shows how to use a non-streaming zipformer CTC model from icefall
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to decode files.
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Please download model files from
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https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
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"""
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from pathlib import Path
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import sherpa_onnx
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import soundfile as sf
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def create_recognizer():
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model = "./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx"
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tokens = "./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt"
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test_wav = "./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/test_wavs/0.wav"
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if not Path(model).is_file() or not Path(test_wav).is_file():
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raise ValueError(
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"""Please download model files from
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https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
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"""
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)
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return (
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sherpa_onnx.OfflineRecognizer.from_zipformer_ctc(
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model=model,
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tokens=tokens,
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debug=True,
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),
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test_wav,
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)
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def main():
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recognizer, wave_filename = create_recognizer()
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audio, sample_rate = sf.read(wave_filename, dtype="float32", always_2d=True)
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audio = audio[:, 0] # only use the first channel
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# audio is a 1-D float32 numpy array normalized to the range [-1, 1]
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# sample_rate does not need to be 16000 Hz
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stream = recognizer.create_stream()
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stream.accept_waveform(sample_rate, audio)
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recognizer.decode_stream(stream)
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print(wave_filename)
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print(stream.result)
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if __name__ == "__main__":
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main()
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