| |
| |
| """Reference output of the binding tests, from the sherpa-onnx Python package. Every binding test prints |
| the same JSON lines for the same inputs: |
| {"file": <clip>, "events": [[label, index, prob], ...]} AudioTagging, all 23 languages, largest first |
| {"pipeline_file": <recording>, "pipeline": {...}} LidPipeline JSON of the long recording |
| |
| python3 tests/bindings/python_ref.py <lid model> <vad model> <long wav> <clip.wav> ... |
| """ |
| import json |
| import os |
| import sys |
|
|
| import sherpa_onnx |
| import soundfile as sf |
|
|
|
|
| def main(): |
| model, vad, long_wav, clips = sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4:] |
| tagger = sherpa_onnx.AudioTagging(sherpa_onnx.AudioTaggingConfig( |
| model=sherpa_onnx.AudioTaggingModelConfig(lid=model, num_threads=2), top_k=23)) |
| for path in clips: |
| w, sr = sf.read(path, dtype="float32") |
| s = tagger.create_stream() |
| s.accept_waveform(sr, w) |
| ev = tagger.compute(s) |
| print(json.dumps({"file": os.path.basename(path), "events": [[e.name, e.index, e.prob] for e in ev]})) |
| pipe = sherpa_onnx.LidPipeline(sherpa_onnx.LidPipelineConfig( |
| model=sherpa_onnx.AudioTaggingModelConfig(lid=model, num_threads=2), vad_model=vad)) |
| w, sr = sf.read(long_wav, dtype="float32") |
| print(json.dumps({"pipeline_file": os.path.basename(long_wav), "pipeline": json.loads(pipe.process(w, sr))})) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
