add possibility to use metadata prompt column name instead of data

run_stable_speech_training.py

@@ -723,6 +723,7 @@ def load_multiple_datasets(
             
             metadata_dataset_name = dataset_dict["metadata_dataset_name"]
             if metadata_dataset_name is not None:
+                logger.info(f'Merging {dataset_dict["name"]} - {dataset_dict["split"]} with {metadata_dataset_name} - {dataset_dict["split"]}')
                 metadata_dataset = load_dataset(
                     metadata_dataset_name,
                     dataset_dict["config"],
@@ -732,7 +733,7 @@ def load_multiple_datasets(
                 )
                 
                 # TODO(YL): I forgot to create unique ids for MLS english.
-                # To iterate faster, I bypass the original id check and do another one. - Done once 
+                # To iterate faster, I bypass the original id check and do another one. - Done once because assuming it won't change next time
                 # if dataset_dict["name"] == "stable-speech/mls_eng_10k":
                 #     def concat_ids(book_id, speaker_id, begin_time):
                 #         return {"id": f"{book_id}_{speaker_id}_{str(begin_time).replace('.', '_')}"}
@@ -760,7 +761,7 @@ def load_multiple_datasets(
                     # We might have applied some transformations to the prompts (e.g  punctuation restoration)
                     # so we make sure to remove it from the original dataset
                     if prompt_column_name in dataset.column_names:
-                        print(f"REMOVE {prompt_column_name} from dataset {dataset_dict['name']} - dataset_dict['split']")
+                        logger.info(f"REMOVE {prompt_column_name} from dataset {dataset_dict['name']} - dataset_dict['split']")
                         dataset.remove_columns(prompt_column_name)
 
                 metadata_columns_to_remove = set(metadata_dataset.column_names).intersection(set(dataset.column_names))
@@ -772,10 +773,6 @@ def load_multiple_datasets(
                     if len(dataset.filter(lambda id1, id2: id1!=id2, input_columns=[id_column_name, f"metadata_{id_column_name}"])) != 0:
                         raise ValueError(f"Concatenate didn't work. Some ids don't correspond on dataset {dataset_dict['name']}")
 
-                # TODO: remove
-                print("dataset", dataset)
-                print(dataset[0][prompt_column_name])
-
                 dataset_features = dataset.features.keys()
 
             if columns_to_keep is not None:
@@ -996,6 +993,9 @@ def main():
                 num_proc=data_args.preprocessing_num_workers,
                 id_column_name=data_args.id_column_name,
                 columns_to_keep=columns_to_keep.values(),
+                prompt_column_name=data_args.prompt_column_name,
+                audio_column_name=data_args.target_audio_column_name,
+                sampling_rate=sampling_rate,
                 # streaming=data_args.streaming, TODO(SG): optionally enable streaming mode
             )