0.91.3

CHANGELOG.md

@@ -1,6 +1,10 @@
 Changelog
 =========
 
+Version 0.91.3
+--------------
+* some additions for robustness
+
 Version 0.91.2
 --------------
 * making lint work by excluding constants from check

nkululeko/autopredict/ap_sid.py

@@ -0,0 +1,43 @@
+""""
+A predictor for sid - Speaker ID.
+"""
+
+from pyannote.audio import Pipeline
+
+
+import numpy as np
+
+import nkululeko.glob_conf as glob_conf
+from nkululeko.feature_extractor import FeatureExtractor
+from nkululeko.utils.util import Util
+
+
+class SIDPredictor:
+    """SIDPredictor.
+
+    predicting speaker id.
+    """
+
+    def __init__(self, df):
+        self.df = df
+        self.util = Util("sidPredictor")
+        self.pipeline = Pipeline.from_pretrained(
+            "pyannote/speaker-diarization-3.1",
+            use_auth_token="HUGGINGFACE_ACCESS_TOKEN_GOES_HERE",
+        )
+
+    def predict(self, split_selection):
+        self.util.debug(f"estimating PESQ for {split_selection} samples")
+        return_df = self.df.copy()
+        feats_name = "_".join(ast.literal_eval(glob_conf.config["DATA"]["databases"]))
+        self.feature_extractor = FeatureExtractor(
+            self.df, ["squim"], feats_name, split_selection
+        )
+        result_df = self.feature_extractor.extract()
+        # replace missing values by 0
+        result_df = result_df.fillna(0)
+        result_df = result_df.replace(np.nan, 0)
+        result_df.replace([np.inf, -np.inf], 0, inplace=True)
+        pred_vals = result_df.pesq * 100
+        return_df["pesq_pred"] = pred_vals.astype("int") / 100
+        return return_df

nkululeko/constants.py

@@ -1,2 +1,2 @@
-VERSION="0.91.2"
+VERSION="0.91.3"
 SAMPLING_RATE = 16000

nkululeko/data/dataset_csv.py

@@ -39,6 +39,8 @@ def load(self):
             df = audformat.utils.read_csv(data_file)
         if isinstance(df, pd.Series):
             df = df.to_frame()
+        elif isinstance(df, pd.Index):
+            df = pd.DataFrame(index=df)
         rename_cols = self.util.config_val_data(self.name, "colnames", False)
         if rename_cols:
             col_dict = ast.literal_eval(rename_cols)
@@ -78,7 +80,11 @@ def load(self):
 
         self.df = df
         self.db = None
-        self.got_target = True
+        target = self.util.config_val("DATA", "target", None)
+        if target is not None:
+            self.got_target = True
+        else:
+            self.got_target = False
         self.is_labeled = self.got_target
         self.start_fresh = eval(self.util.config_val("DATA", "no_reuse", "False"))
         is_index = False

nkululeko/experiment.py

@@ -125,7 +125,15 @@ def _import_csv(self, storage):
         # df = pd.read_csv(storage, header=0, index_col=[0,1,2])
         # df.index.set_levels(pd.to_timedelta(df.index.levels[1]), level=1)
         # df.index.set_levels(pd.to_timedelta(df.index.levels[2]), level=2)
-        df = audformat.utils.read_csv(storage)
+        try:
+            df = audformat.utils.read_csv(storage)
+        except ValueError:
+            # split might be empty
+            return pd.DataFrame()
+        if isinstance(df, pd.Series):
+            df = df.to_frame()
+        elif isinstance(df, pd.Index):
+            df = pd.DataFrame(index=df)
         df.is_labeled = True if self.target in df else False
         # print(df.head())
         return df

nkululeko/plots.py

@@ -305,6 +305,9 @@ def plot_durations(self, df, filename, sample_selection, caption=""):
         except AttributeError as ae:
             self.util.warn(ae)
             ax = sns.histplot(df, x="duration", kde=True)
+        except ValueError as error:
+            self.util.warn(error)
+            ax = sns.histplot(df, x="duration", kde=True)
         min = self.util.to_3_digits(df.duration.min())
         max = self.util.to_3_digits(df.duration.max())
         title = f"Duration distr. for {sample_selection} {df.shape[0]}. min={min}, max={max}"

nkululeko/segment.py

@@ -110,7 +110,7 @@ def calc_dur(x):
     )
     print("")
     # remove encoded labels
-    target = util.config_val("DATA", "target", "emotion")
+    target = util.config_val("DATA", "target", None)
     if "class_label" in df_seg.columns:
         df_seg = df_seg.drop(columns=[target])
         df_seg = df_seg.rename(columns={"class_label": target})