add private module to create model object for speech api

opendbm/api_lib/speech/__init__.py

@@ -0,0 +1 @@
+from opendbm.api_lib.speech.api import Speech

opendbm/api_lib/speech/_speech_features.py

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+import tempfile
+
+from opendbm.api_lib.model import OPENDBM_DATA, AudioModel
+from opendbm.dbm_lib import run_speech_feature
+
+
+class SpeechFeature(AudioModel):
+    def __init__(self):
+        super().__init__()
+        self._params = [
+            "nlp_numSentences",
+            "nlp_singPronPerAns",
+            "nlp_singPronPerSen",
+            "nlp_pastTensePerAns",
+            "nlp_pastTensePerSen",
+            "nlp_pronounsPerAns",
+            "nlp_pronounsPerSen",
+            "nlp_verbsPerAns",
+            "nlp_verbsPerSen",
+            "nlp_adjectivesPerAns",
+            "nlp_adjectivesPerSen",
+            "nlp_nounsPerAns",
+            "nlp_nounsPerSen",
+            "nlp_sentiment_mean",
+            "nlp_mattr",
+            "nlp_wordsPerMin",
+            "nlp_totalTime",
+        ]
+
+    @AudioModel.prep_func
+    def _fit_transform(self, path):
+        return run_speech_feature(
+            path, f"{tempfile.gettempdir()}/", self.r_config, OPENDBM_DATA, save=False
+        )

opendbm/api_lib/speech/_transcribe.py

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+import tempfile
+
+from opendbm.api_lib.model import OPENDBM_DATA, AudioModel
+from opendbm.dbm_lib.dbm_features.raw_features.nlp.transcribe import run_transcribe
+
+
+class Transcribe(AudioModel):
+    def __init__(self):
+        super().__init__()
+        self._params = ["nlp_transcribe", "nlp_totalTime"]
+
+    @AudioModel.prep_func
+    def _fit_transform(self, path):
+        return run_transcribe(
+            path, f"{tempfile.gettempdir()}/", self.r_config, OPENDBM_DATA, save=False
+        )