Upload script/gigaspeech.py with huggingface_hub

script/gigaspeech.py

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+import pandas as pd
+from process_from_parquet import read_parquet_file, process_parquet_df, save_to_csv
+
+def process_partition(partition, process_row_with_params):
+    """
+    Process the partition after first row processing. 
+    Covert the series result to dataframe to further processing for audio partition.
+    
+    """
+    result = partition.apply(process_row_with_params, axis=1)
+    field_name = ["path", "url" ,"type", "duration", "language", "transcript", "tag", "split", "license"]
+    return pd.DataFrame(result.tolist(), columns=field_name)  # Convert Series to DataFrame
+
+def _get_split(parquet_file):
+    if "train" in parquet_file:
+        return "train"
+    elif "test" in parquet_file:
+        return "test"
+    elif "validation" in parquet_file:
+        return "validation"
+    else:
+        return "train"
+
+def process_row(row, parquet_file_name):
+    """
+    The function to process each row from dataframe.
+    Return the metadata as dictionary. 
+    
+    """
+    metadata = {}
+    metadata["path"] = f"{row["segment_id"]}.wav"
+    metadata["url"] = f"https://huggingface.co/datasets/meetween/mumospee_gigaspeech/resolve/main/gigaspeech-parquet/{parquet_file_name}"
+    metadata["type"] = "audio"
+    try:
+        metadata["duration"] = str(round(float(row['end_time']) - float(row['begin_time']), 2))
+    except Exception as e:
+        metadata["duration"] = "n/a"
+    metadata["language"] = "en"
+    metadata["transcript"] = row["text_transformed"]
+    metadata["tag"] = "GigaSpeech"
+    metadata["split"] = _get_split(parquet_file_name)
+    metadata["license"] = "apache-2.0"
+
+    return metadata
+
+def main(config):
+    parquet_df, file_name = read_parquet_file(config["parquet_file_path"], npartitions=config["npartitions"], top=config["top"])
+
+    processed_df = process_parquet_df(parquet_df=parquet_df,
+                                     file_name=file_name,
+                                     process_row_func=process_row,
+                                     process_partition=process_partition)
+
+    save_to_csv(processed_df, final_path=config["final_path"])
+
+