add test cases

tests/speech/test_api_speech.py

@@ -0,0 +1,76 @@
+import numpy as np
+from pytest import mark
+
+
+@mark.non_docker
+@mark.speech
+class SpeechTest:
+    def test_dummy_3(self):
+        assert True
+
+    def test_get_transcribe(self, processing_speech_mp4, processing_speech_wav):
+        actual_totaltime = 87.978685
+        len_words_count = 57
+
+        res_mp4 = processing_speech_mp4.get_transcribe().to_dataframe()
+        audio_duration_mp4 = res_mp4["nlp_totalTime"].item()
+        transcribed_text_mp4 = res_mp4["nlp_transcribe"].item()
+
+        res_wav = processing_speech_wav.get_transcribe().to_dataframe()
+        audio_duration_wav = res_wav["nlp_totalTime"].item()
+        transcribed_text_wav = res_wav["nlp_transcribe"].item()
+
+        # test if duration is matched
+        assert np.isclose(audio_duration_mp4, actual_totaltime, rtol=0.1, atol=1e-8)
+        assert np.isclose(audio_duration_wav, actual_totaltime, rtol=0.1, atol=1e-8)
+
+        # test if there is transcribed text or not
+        assert type(transcribed_text_mp4) == str
+        assert type(transcribed_text_wav) == str
+
+        # test the length of the text
+        assert np.isclose(
+            len(transcribed_text_mp4.split(" ")), len_words_count, rtol=0.5, atol=1e-8
+        )
+        assert np.isclose(
+            len(transcribed_text_wav.split(" ")), len_words_count, rtol=0.5, atol=1e-8
+        )
+
+    def test_get_speech_features(self, processing_speech_mp4, processing_speech_wav):
+        # actual = [
+        #     1.0,
+        #     2.0,
+        #     2.0,
+        #     1.0,
+        #     1.0,
+        #     6.0,
+        #     6.0,
+        #     11.0,
+        #     11.0,
+        #     5.0,
+        #     5.0,
+        #     15.0,
+        #     15.0,
+        #     -0.8256,
+        #     0.08860759493670886,
+        #     38.873052120437336,
+        #     87.97868480725624,
+        # ]
+
+        res_mp4 = (
+            processing_speech_mp4.get_speech_features()
+            .to_dataframe()
+            .drop(columns="dbm_master_url")
+        )
+        res_wav = (
+            processing_speech_wav.get_speech_features()
+            .to_dataframe()
+            .drop(columns="dbm_master_url")
+        )
+        desired_mp4 = np.array((res_mp4.iloc[0]))
+        desired_wav = np.array((res_wav.iloc[0]))
+
+        # check if there is any zero value or not
+        for v1, v2 in zip(desired_mp4, desired_wav):
+            assert bool(v1)
+            assert bool(v2)