[test] Add recognize_whisper test cases instead of using actual models

.github/workflows/unittests.yml

@@ -31,7 +31,7 @@ jobs:
       - name: Install Python dependencies
         run: |
           python -m pip install 'pocketsphinx<5'
-          python -m pip install git+https://github.com/openai/whisper.git soundfile
+          python -m pip install openai-whisper soundfile
           python -m pip install openai
           python -m pip install .
       - name: Test with unittest

README.rst

@@ -169,7 +169,7 @@ Whisper (for Whisper users)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 Whisper is **required if and only if you want to use whisper** (``recognizer_instance.recognize_whisper``).
 
-You can install it with ``python3 -m pip install git+https://github.com/openai/whisper.git soundfile``.
+You can install it with ``python3 -m pip install openai-whisper soundfile``.
 
 Whisper API (for Whisper API users) 
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

tests/test_recognition.py

@@ -14,7 +14,6 @@ def setUp(self):
         self.AUDIO_FILE_EN = os.path.join(os.path.dirname(os.path.realpath(__file__)), "english.wav")
         self.AUDIO_FILE_FR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "french.aiff")
         self.AUDIO_FILE_ZH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "chinese.flac")
-        self.WHISPER_CONFIG = {"temperature": 0}
 
     def test_recognizer_attributes(self):
         r = sr.Recognizer()
@@ -81,21 +80,6 @@ def test_ibm_chinese(self):
         with sr.AudioFile(self.AUDIO_FILE_ZH) as source: audio = r.record(source)
         self.assertEqual(r.recognize_ibm(audio, username=os.environ["IBM_USERNAME"], password=os.environ["IBM_PASSWORD"], language="zh-CN"), u"砸 自己 的 脚 ")
 
-    def test_whisper_english(self):
-        r = sr.Recognizer()
-        with sr.AudioFile(self.AUDIO_FILE_EN) as source: audio = r.record(source)
-        self.assertEqual(r.recognize_whisper(audio, language="english", **self.WHISPER_CONFIG), " 1, 2, 3")
-
-    def test_whisper_french(self):
-        r = sr.Recognizer()
-        with sr.AudioFile(self.AUDIO_FILE_FR) as source: audio = r.record(source)
-        self.assertEqual(r.recognize_whisper(audio, language="french", **self.WHISPER_CONFIG), " et c'est la dictée numéro 1.")
-
-    def test_whisper_chinese(self):
-        r = sr.Recognizer()
-        with sr.AudioFile(self.AUDIO_FILE_ZH) as source: audio = r.record(source)
-        self.assertEqual(r.recognize_whisper(audio, model="small", language="chinese", **self.WHISPER_CONFIG), u"砸自己的腳")
-
 
 if __name__ == "__main__":
     unittest.main()

tests/test_whisper_recognition.py

@@ -0,0 +1,78 @@
+from unittest import TestCase
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+
+from speech_recognition import AudioData, Recognizer
+
+
+@patch("speech_recognition.io.BytesIO")
+@patch("soundfile.read")
+@patch("torch.cuda.is_available")
+@patch("whisper.load_model")
+class RecognizeWhisperTestCase(TestCase):
+    def test_default_parameters(
+        self, load_model, is_available, sf_read, BytesIO
+    ):
+        whisper_model = load_model.return_value
+        transcript = whisper_model.transcribe.return_value
+        audio_array = MagicMock()
+        dummy_sampling_rate = 99_999
+        sf_read.return_value = (audio_array, dummy_sampling_rate)
+
+        recognizer = Recognizer()
+        audio_data = MagicMock(spec=AudioData)
+        actual = recognizer.recognize_whisper(audio_data)
+
+        self.assertEqual(actual, transcript.__getitem__.return_value)
+        load_model.assert_called_once_with("base")
+        audio_data.get_wav_data.assert_called_once_with(convert_rate=16000)
+        BytesIO.assert_called_once_with(audio_data.get_wav_data.return_value)
+        sf_read.assert_called_once_with(BytesIO.return_value)
+        audio_array.astype.assert_called_once_with(np.float32)
+        whisper_model.transcribe.assert_called_once_with(
+            audio_array.astype.return_value,
+            language=None,
+            task=None,
+            fp16=is_available.return_value,
+        )
+        transcript.__getitem__.assert_called_once_with("text")
+
+    def test_return_as_dict(self, load_model, is_available, sf_read, BytesIO):
+        whisper_model = load_model.return_value
+        audio_array = MagicMock()
+        dummy_sampling_rate = 99_999
+        sf_read.return_value = (audio_array, dummy_sampling_rate)
+
+        recognizer = Recognizer()
+        audio_data = MagicMock(spec=AudioData)
+        actual = recognizer.recognize_whisper(audio_data, show_dict=True)
+
+        self.assertEqual(actual, whisper_model.transcribe.return_value)
+
+    def test_pass_parameters(self, load_model, is_available, sf_read, BytesIO):
+        whisper_model = load_model.return_value
+        transcript = whisper_model.transcribe.return_value
+        audio_array = MagicMock()
+        dummy_sampling_rate = 99_999
+        sf_read.return_value = (audio_array, dummy_sampling_rate)
+
+        recognizer = Recognizer()
+        audio_data = MagicMock(spec=AudioData)
+        actual = recognizer.recognize_whisper(
+            audio_data,
+            model="small",
+            language="english",
+            translate=True,
+            temperature=0,
+        )
+
+        self.assertEqual(actual, transcript.__getitem__.return_value)
+        load_model.assert_called_once_with("small")
+        whisper_model.transcribe.assert_called_once_with(
+            audio_array.astype.return_value,
+            language="english",
+            task="translate",
+            fp16=is_available.return_value,
+            temperature=0,
+        )