add logic to create metadata-valid.csv file

create_json.py

@@ -1,31 +1,92 @@
-"""Create a JSON file mapping of available audio files to the TTS models that generated them.
-
-The output JSON has the following structure:
-{
-    "path1.mp3": [
-        "model1",
-        "model2",
-        ...
-    ],
-    "path2.mp3": [
-        "model1",
-        ...
-    ],
-    ...
-}
-"""
 from collections import defaultdict
 import json
 import os
 import pandas as pd
 
-df = pd.read_csv("metadata-balanced.csv")
-models = ["commonvoice", "metavoice", "playht", "stylettsv2", "xttsv2"]
-ds = defaultdict(list)
-for path in df.path:
+
+def create_json(df, models, output_json):
+    """Create a dictionary file mapping of available audio files to the TTS models that generated them.
+
+    The output JSON has the following structure:
+    {
+        "path1.mp3": [
+            "model1",
+            "model2",
+            ...
+        ],
+        "path2.mp3": [
+            "model1",
+            ...
+        ],
+        ...
+    }
+    """
+
+    data = defaultdict(list)
+
+    # For each path, add the model if the file exists
+    for path in df.path:
+        for model in models:
+            if os.path.exists(os.path.join(model, path)):
+                data[path].append(model)
+
+    # Save to JSON file (Currently used in gradio app, keep it for now)
+    with open(output_json, "w") as json_file:
+        json.dump(data, json_file, indent=4)
+    return data
+
+
+if __name__ == "__main__":
+
+    output_json = "files.json"
+    output_csv = "metadata-valid.csv"
+    metadata_csv = "metadata-balanced.csv"
+    models = ["commonvoice", "metavoice", "playht", "stylettsv2", "xttsv2"]
+
+    # Load the metadata
+    df = pd.read_csv(metadata_csv)
+
+    # Create the JSON file
+    data = create_json(df, models, output_json)
+
+    # Get paths that are only available for all models
+    valid_paths = [path for path in data if len(data[path]) == len(models)]
+
+    # Filter dataframe to only include valid paths
+    valid_df = df[df.path.isin(valid_paths)]
+
+    # Create an entry for each model in csv
+    all_dfs = []
     for model in models:
-        if os.path.exists(os.path.join(model, path)):
-            ds[path].append(model)
+        valid_df_model = valid_df.copy()
+        valid_df_model["source"] = model
+        all_dfs.append(valid_df_model)
+
+        # Add is_cloned_voice column
+        is_cloned_voice = model != "commonvoice"
+        valid_df_model["is_cloned_voice"] = is_cloned_voice
+
+        # Add fname column
+        valid_df_model["fname"] = valid_df_model["path"]
+
+        # Add path column
+        valid_df_model["path"] = valid_df_model["path"].apply(
+            lambda path: os.path.join(model, path)
+        )
+
+    all_df = pd.concat(all_dfs, ignore_index=True)
+    all_df.to_csv(output_csv, index=False)
+
+    print("Statistics:")
+    print("Number of original voices: ", len(all_df[all_df.is_cloned_voice == False]))
+    print("Number of cloned voices: ", len(all_df[all_df.is_cloned_voice == True]))
+    print("Number of TOTAL voices: ", len(all_df))
+    print()
+
+    print("Gender distribution (total):")
+    print(all_df.gender.value_counts())
+    print()
 
-with open("files.json", "w") as json_file:
-    json.dump(ds, json_file, indent=4)
+    print("Gender distribution (not cloned):")
+    print(all_df[all_df.is_cloned_voice == False].gender.value_counts())
+    print()