Datasets:
Languages:
Yue Chinese
License:
File size: 2,479 Bytes
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import os
import pandas as pd
import random
from shutil import copyfile
# Combine all TSV files into one
tsvs_directory = "transcript/yue/raw"
combined_tsv_path = "combined.tsv"
# List all TSV files in the transcript directory
tsv_files = [f for f in os.listdir(tsvs_directory) if f.endswith(".tsv")]
# Read each TSV and concatenate into one DataFrame
dfs = []
for tsv_file in tsv_files:
tsv_path = os.path.join(tsvs_directory, tsv_file)
df = pd.read_csv(tsv_path, sep='\t')
dfs.append(df)
combined_df = pd.concat(dfs, ignore_index=True)
# Rename 'text' column to 'sentence'
combined_df = combined_df.rename(columns={'text': 'sentence'})
# Remove rows with sentences less than 5 characters
combined_df = combined_df[combined_df['sentence'].apply(lambda x: len(str(x)) >= 5)]
# Drop timestamp_start and timestamp_end columns
combined_df = combined_df.drop(['timestamp_start', 'timestamp_end'], axis=1)
# Reorder columns
combined_df = combined_df[['path', 'sentence']]
# Save the combined TSV
combined_df.to_csv(combined_tsv_path, sep='\t', index=False)
# Split into train and test (90:10 ratio)
train_ratio = 0.9
total_rows = combined_df.shape[0]
train_rows = int(train_ratio * total_rows)
# Randomly shuffle the rows
shuffled_df = combined_df.sample(frac=1, random_state=42)
# Split into train and test DataFrames
train_df = shuffled_df[:train_rows]
test_df = shuffled_df[train_rows:]
# Save train and test TSVs
train_tsv_path = "train.tsv"
test_tsv_path = "test.tsv"
train_df.to_csv(train_tsv_path, sep='\t', index=False)
test_df.to_csv(test_tsv_path, sep='\t', index=False)
# Move corresponding audio files to train and test directories
audio_directory = "audio/"
train_audio_directory = "audio/train/"
test_audio_directory = "audio/test/"
# Create directories if they don't exist
os.makedirs(train_audio_directory, exist_ok=True)
os.makedirs(test_audio_directory, exist_ok=True)
# Move audio files to train or test directories based on the split
for index, row in train_df.iterrows():
audio_path = os.path.join(audio_directory, row['path'])
destination_path = os.path.join(train_audio_directory, os.path.basename(audio_path))
copyfile(audio_path, destination_path)
for index, row in test_df.iterrows():
audio_path = os.path.join(audio_directory, row['path'])
destination_path = os.path.join(test_audio_directory, os.path.basename(audio_path))
copyfile(audio_path, destination_path)
print("Data preprocessing completed.")
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