add private module to create model object for acoustics api

opendbm/api_lib/verbal_acoustics/__init__.py

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+from opendbm.api_lib.verbal_acoustics.api import VerbalAcoustics

opendbm/api_lib/verbal_acoustics/_audio_intensity.py

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+from opendbm.api_lib.model import AudioModel
+from opendbm.dbm_lib import run_intensity
+
+
+class AudioIntensity(AudioModel):
+    def __init__(self):
+        super().__init__()
+        self._params = ["aco_int"]
+
+    @AudioModel.prep_func
+    def _fit_transform(self, path, **kwargs):
+        return run_intensity(path, ".", self.r_config, save=False)

opendbm/api_lib/verbal_acoustics/_formant_frequency.py

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+import pandas as pd
+
+from opendbm.api_lib.model import AudioModel
+from opendbm.dbm_lib import run_formant
+
+
+class FormantFrequency(AudioModel):
+    def __init__(self):
+        super().__init__()
+        self._params = ["aco_fm1", "aco_fm2", "aco_fm3", "aco_fm4"]
+
+    @AudioModel.prep_func
+    def _fit_transform(self, path, **kwargs):
+        return run_formant(path, ".", self.r_config, save=False)

opendbm/api_lib/verbal_acoustics/_glottal_noise.py

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+import pandas as pd
+
+from opendbm.api_lib.model import AudioModel
+from opendbm.dbm_lib.dbm_features.raw_features.audio.gne import run_gne
+
+
+class GlottalNoiseRatio(AudioModel):
+    def __init__(self):
+        super().__init__()
+        self._params = ["aco_gne"]
+
+    @AudioModel.prep_func
+    def _fit_transform(self, path, **kwargs):
+        return run_gne(path, ".", self.r_config, save=False, ff_df=kwargs["ff_df"])

opendbm/api_lib/verbal_acoustics/_harmonic_noise.py

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+import pandas as pd
+
+from opendbm.api_lib.model import AudioModel
+from opendbm.dbm_lib.dbm_features.raw_features.audio.hnr import run_hnr
+
+
+class HarmonicsNoiseRatio(AudioModel):
+    def __init__(self):
+        super().__init__()
+        self._params = ["aco_hnr"]
+
+    @AudioModel.prep_func
+    def _fit_transform(self, path, **kwargs):
+        return run_hnr(path, ".", self.r_config, save=False)

opendbm/api_lib/verbal_acoustics/_jitter.py

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+import pandas as pd
+
+from opendbm.api_lib.model import AudioModel
+from opendbm.dbm_lib.dbm_features.raw_features.audio.jitter import run_jitter
+
+
+class Jitter(AudioModel):
+    def __init__(self):
+        super().__init__()
+        self._params = ["aco_jitter"]
+
+    @AudioModel.prep_func
+    def _fit_transform(self, path, **kwargs):
+        return run_jitter(path, ".", self.r_config, save=False, ff_df=kwargs["ff_df"])

opendbm/api_lib/verbal_acoustics/_mfcc.py

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+from opendbm.api_lib.model import AudioModel
+from opendbm.dbm_lib import run_mfcc
+
+
+class MFCC(AudioModel):
+    def __init__(self):
+        super().__init__()
+        self._params = ["aco_mfcc" + str(i) for i in range(1, 13)]
+
+    @AudioModel.prep_func
+    def _fit_transform(self, path, **kwargs):
+        return run_mfcc(path, ".", self.r_config, save=False)

opendbm/api_lib/verbal_acoustics/_pause_characteristics.py

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+import pandas as pd
+
+from opendbm.api_lib.model import AudioModel
+from opendbm.dbm_lib import run_pause_segment
+
+
+class PauseCharacteristics(AudioModel):
+    def __init__(self):
+        super().__init__()
+        self._params = [
+            "aco_totaltime",
+            "aco_speakingtime",
+            "aco_numpauses",
+            "aco_pausetime",
+            "aco_pausefrac",
+        ]
+
+    @AudioModel.prep_func
+    def _fit_transform(self, path, **kwargs):
+        return run_pause_segment(path, ".", self.r_config, save=False)

opendbm/api_lib/verbal_acoustics/_pitch_frequency.py

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+import pandas as pd
+
+from opendbm.api_lib.model import AudioModel
+from opendbm.dbm_lib.dbm_features.raw_features.audio.pitch_freq import run_pitch
+
+
+class PitchFrequency(AudioModel):
+    def __init__(self):
+        super().__init__()
+        self._params = ["aco_ff"]
+
+    @AudioModel.prep_func
+    def _fit_transform(self, path, **kwargs):
+        return run_pitch(path, ".", self.r_config, save=False)

opendbm/api_lib/verbal_acoustics/_shimmer.py

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+import pandas as pd
+
+from opendbm.api_lib.model import AudioModel
+from opendbm.dbm_lib.dbm_features.raw_features.audio.shimmer import run_shimmer
+
+
+class Shimmer(AudioModel):
+    def __init__(self):
+        super().__init__()
+        self._params = ["aco_shimmer"]
+
+    @AudioModel.prep_func
+    def _fit_transform(self, path, **kwargs):
+        return run_shimmer(path, ".", self.r_config, save=False, ff_df=kwargs["ff_df"])

opendbm/api_lib/verbal_acoustics/_voice_prevalence.py

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+import pandas as pd
+
+from opendbm.api_lib.model import AudioModel
+from opendbm.dbm_lib.dbm_features.raw_features.audio.voice_frame_score import run_vfs
+
+
+class VoicePrevalence(AudioModel):
+    def __init__(self):
+        super().__init__()
+        self._params = ["aco_voiceframe", "aco_totvoiceframe", "aco_voicepct"]
+
+    @AudioModel.prep_func
+    def _fit_transform(self, path, **kwargs):
+        return run_vfs(path, ".", self.r_config, save=False)