Add tests for load_model and recognize

Author: istupakov

tests/onnx_asr/__init__.py

@@ -0,0 +1,33 @@
+import pytest
+
+import onnx_asr
+
+
+def test_model_not_supported_error():
+    with pytest.raises(onnx_asr.loader.ModelNotSupportedError):
+        onnx_asr.load_model("xxx")
+
+
+def test_model_path_not_found_error():
+    with pytest.raises(onnx_asr.loader.ModelPathNotFoundError):
+        onnx_asr.load_model("onnx-community/whisper-tiny", "./xxx")
+
+
+def test_model_file_not_found_error():
+    with pytest.raises(onnx_asr.loader.ModelFileNotFoundError):
+        onnx_asr.load_model("onnx-community/whisper-tiny", quantization="xxx")
+
+
+def test_more_than_one_model_file_found_error():
+    with pytest.raises(onnx_asr.loader.MoreThanOneModelFileFoundError):
+        onnx_asr.load_model("onnx-community/whisper-tiny", quantization="*int8")
+
+
+def test_no_model_name_or_path_specified_error():
+    with pytest.raises(onnx_asr.loader.NoModelNameOrPathSpecifiedError):
+        onnx_asr.load_model("whisper-hf")
+
+
+@pytest.mark.parametrize("model", ["onnx-community/whisper-tiny", "alphacep/vosk-model-small-ru"])
+def test_load_model(model):
+    onnx_asr.load_model(model)

tests/onnx_asr/test_load_model.py

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+import numpy as np
+import pytest
+
+import onnx_asr
+import onnx_asr.asr
+import onnx_asr.utils
+
+
+@pytest.fixture(scope="module")
+def model():
+    return onnx_asr.load_model("onnx-community/whisper-tiny")
+
+
+def test_supported_only_mono_audio_error(model: onnx_asr.asr.Asr):
+    rng = np.random.default_rng(0)
+    waveform = rng.random((1 * 16_000, 2), dtype=np.float32)
+
+    with pytest.raises(onnx_asr.utils.SupportedOnlyMonoAudioError):
+        model.recognize(waveform)
+
+
+def test_wrong_sample_rate_error(model: onnx_asr.asr.Asr):
+    rng = np.random.default_rng(0)
+    waveform = rng.random((1 * 16_000), dtype=np.float32)
+
+    with pytest.raises(onnx_asr.utils.WrongSampleRateError):
+        model.recognize(waveform, sample_rate=24_000)  # type: ignore
+
+
+def test_recognize(model: onnx_asr.asr.Asr):
+    rng = np.random.default_rng(0)
+    waveform = rng.random((1 * 16_000), dtype=np.float32)
+
+    result = model.recognize(waveform)
+    assert isinstance(result, str)