feat: Add descriptive statistics and charts

Added descriptive tools for Speech Quality Prediction:
- Mean and Standard Deviation of the results
- BarChart and LineChart of the results
This change helps provide better assistance in Speech Quality Prediction, especially for
repeated averaging of results of the same test sample in practical Audio Testing Scenarios.
Also, output name of CSV is added just like gabrielmittag#30.

nisqa/NISQA_model.py

@@ -14,6 +14,7 @@
 import yaml
 import torch
 import torch.nn as nn
+import matplotlib.pyplot as plt
 from torch import optim
 from torch.utils.data import DataLoader
 from . import NISQA_lib as NL
@@ -50,7 +51,81 @@ def evaluate(self, mapping='first_order', do_print=True, do_plot=False):
             self._evaluate_dim(mapping=mapping, do_print=do_print, do_plot=do_plot)
         else:
             self._evaluate_mos(mapping=mapping, do_print=do_print, do_plot=do_plot)      
-
+
+    def _draw_barchart(self):
+        # settings of barchart
+        bar_width = 0.15
+        index = np.arange(len(self.ds_val.df['deg']))
+        colors = ['r', 'g', 'b', 'c', 'm']
+        fig, ax = plt.subplots(figsize=(12, 8))
+        for i, col in enumerate(['mos_pred', 'noi_pred', 'dis_pred', 'col_pred', 'loud_pred']):
+            ax.bar(index + (i * bar_width), self.ds_val.df[col], bar_width, color=colors[i], label=col)
+            for j, val in enumerate(self.ds_val.df[col]):
+                ax.annotate(str(f'{val:.2f}'), xy=(index[j] + (i * bar_width), val), xytext=(0, 3),
+                    textcoords="offset points", ha='center', va='bottom')
+        # set labels and title
+        ax.set_xlabel('WavName')
+        ax.set_ylabel('Scores')
+        title = 'BarChart for '+str(self.args['deg']) if self.args['mode'] == 'predict_file' else 'Barchart for Wavs under: '+str(self.args['data_dir'])
+        ax.set_title(title)
+        ax.set_xticks(index + (2 * bar_width))
+        ax.set_xticklabels(self.ds_val.df['deg'], rotation=90)
+        ax.set_ylim(0, 5)
+        # add legend
+        ax.legend()
+        # save plot
+        if 'plot_name' in self.args and self.args['plot_name'] != 'None':
+            save_path = self.args['output_dir'] if self.args['output_dir'][-1] == '/' else self.args['output_dir']+'/' 
+            plt.savefig(save_path+'BarChart_' + self.args['plot_name'])
+        # display the plot
+        plt.tight_layout()
+        plt.show()
+
+
+    def _draw_linechart(self):
+        # settings of line chart
+        fig, ax = plt.subplots(figsize=(12, 8))
+        index = np.arange(len(self.ds_val.df['deg']))
+        colors = ['r', 'g', 'b', 'c', 'm']
+        for i, col in enumerate(['mos_pred', 'noi_pred', 'dis_pred', 'col_pred', 'loud_pred']):
+            ax.plot(index, self.ds_val.df[col], marker='o', linestyle='-', color=colors[i], label=col)
+            for x, y in zip(index, self.ds_val.df[col]):
+                ax.text(x, y, f'{y:.2f}', ha='left', va='top')
+        # set labels and title
+        ax.set_xlabel('WavName')
+        ax.set_ylabel('Scores')
+        title = 'LineChart for ' + str(self.args['deg']) if self.args['mode'] == 'predict_file' else 'Line Chart for Wavs under: ' + str(self.args['data_dir'])
+        ax.set_title(title)
+        ax.set_xticks(index)
+        ax.set_xticklabels(self.ds_val.df['deg'], rotation=90)
+        ax.set_ylim(0, 5)
+        # add legend
+        ax.legend()
+        # save plot
+        if 'plot_name' in self.args and self.args['plot_name'] != 'None':
+            save_path = self.args['output_dir'] if self.args['output_dir'][-1] == '/' else self.args['output_dir'] + '/'
+            plt.savefig(save_path + 'LineChart_' + self.args['plot_name'])
+        # display the plot
+        plt.tight_layout()
+        plt.show()
+
+    # calculate mean and standard deviation of the results and concatenate with the initial results.
+    def _compute_mean_stdDev(self):
+        mean = self.ds_val.df.drop(['deg', 'model'], axis=1).mean()
+        std = self.ds_val.df.drop(['deg', 'model'], axis=1).std()
+        # alter NAN to 0
+        std.fillna(0, inplace=True)
+
+        stat = pd.DataFrame({'deg': ['**Mean**', '**Standard Deviation**'],
+                           'mos_pred': [mean['mos_pred'], std['mos_pred']],
+                           'noi_pred': [mean['noi_pred'], std['noi_pred']],
+                           'dis_pred': [mean['dis_pred'], std['dis_pred']],
+                           'col_pred': [mean['col_pred'], std['col_pred']],
+                           'loud_pred': [mean['loud_pred'], std['loud_pred']],
+                           'model': self.ds_val.df['model'][0]})
+        stat = pd.concat([self.ds_val.df, stat])
+        return stat
+
     def predict(self):
         print('---> Predicting ...')
         if self.args['tr_parallel']:
@@ -69,15 +144,38 @@ def predict(self):
                 self.ds_val, 
                 self.args['tr_bs_val'],
                 self.dev,
-                num_workers=self.args['tr_num_workers'])                 
-                    
+                num_workers=self.args['tr_num_workers'])                  
+
         if self.args['output_dir']:
             self.ds_val.df['model'] = self.args['name']
-            self.ds_val.df.to_csv(
-                os.path.join(self.args['output_dir'], 'NISQA_results.csv'), 
-                index=False)
-
-        print(self.ds_val.df.to_string(index=False))
+            csv_name = 'NISQA_results.csv'
+            if 'output_name' in self.args:
+                csv_name = str(self.args['output_name']) + '.csv'
+            # whether print mean and standard deviation or not 
+            if 'compute_stats' in self.args and self.args['compute_stats']:
+                # generate results with mean and deviation to self.statistics.
+                self.statistics = self._compute_mean_stdDev()
+                self.statistics.to_csv(
+                    os.path.join(self.args['output_dir'], csv_name), 
+                    index=False)
+            else:
+                self.ds_val.df.to_csv(
+                    os.path.join(self.args['output_dir'], csv_name), 
+                    index=False)    
+
+        # print either statistics or ds_val based on 'compute_stats' parameter
+        if 'compute_stats' in self.args and self.args['compute_stats']:
+            print(self.statistics.to_string(index=False))
+        else:
+            print(self.ds_val.df.to_string(index=False))        
+
+        # Visualization of the results
+        if 'plot_type' in self.args and self.args['plot_type'] == 'barchart':
+            self._draw_barchart()
+        elif 'plot_type' in self.args and self.args['plot_type'] == 'linechart':
+            self._draw_linechart()
+
+        # returned DataFrame is not changed.
         return self.ds_val.df
 
     def _train_mos(self):

run_predict.py

@@ -16,6 +16,17 @@
 parser.add_argument('--num_workers', type=int, default=0, help='number of workers for pytorchs dataloader')
 parser.add_argument('--bs', type=int, default=1, help='batch size for predicting')
 parser.add_argument('--ms_channel', type=int, help='audio channel in case of stereo file')
+### Add 
+# 1) the name of result csv,
+# 2) statistics(mean and standard deviation),
+# 3) type of visualizations
+# 4) whether to save the plot
+### to the parameters
+parser.add_argument('--output_name', type=str, help='name of the csv result file')
+parser.add_argument('--compute_stats', action='store_true', help='whether to calculate the mean and the standard deviation of the results')
+parser.add_argument('--plot_type', type=str, default='None', help='Visualization of the results. Either barchart, linechart or None')
+parser.add_argument('--plot_name', type=str, default='None', help='name of the plot file if saving plot is needed')
+###
 
 args = parser.parse_args()
 args = vars(args)
@@ -35,6 +46,12 @@
         args['data_dir'] = ''
 else:
         raise NotImplementedError('--mode given not available')
+
+# `plot_name` can only be set when `plot_type` is not `None``
+if 'plot_name' in args and args['plot_name'] != 'None':
+    if 'plot_type' in args and args['plot_type'] == 'None':
+        raise ValueError('--plot_name argument can only be set when `plot_type` is either `barchart` or `linechart`')
+
 args['tr_bs_val'] = args['bs']
 args['tr_num_workers'] = args['num_workers']