Add a better split_on_silence (processing_pipeline)

Going to extend this a bit more going forwards to support the entire pipeline.

codes/scripts/audio/preparation/processing_pipeline.py

@@ -0,0 +1,68 @@
+
+
+"""
+Master script that processes all MP3 files found in an input directory. Performs the following operations, per-file:
+1. Splits the file on silence intervals, throwing out all clips that are too short or long.
+2.
+"""
+import argparse
+import functools
+import os
+from multiprocessing.pool import ThreadPool
+
+from pydub import AudioSegment
+from pydub.exceptions import CouldntDecodeError
+from pydub.silence import split_on_silence
+from tqdm import tqdm
+
+from data.util import find_audio_files
+
+
+def report_progress(progress_file, file):
+    with open(progress_file, 'a', encoding='utf-8') as f:
+        f.write(f'{file}\n')
+
+
+def process_file(file, base_path, output_path, progress_file):
+    # Hyper-parameters; feel free to adjust.
+    minimum_duration = 4
+    maximum_duration = 20
+
+    # Part 1 is to split a large file into chunks.
+    try:
+        speech = AudioSegment.from_file(file)
+    except CouldntDecodeError as e:
+        print(e)
+        report_progress(progress_file, file)
+        return
+    outdir = os.path.join(output_path, f'{os.path.relpath(file, base_path)[:-4]}').replace('.', '').strip()
+    os.makedirs(outdir, exist_ok=True)
+    chunks = split_on_silence(speech, min_silence_len=600, silence_thresh=-40, seek_step=100, keep_silence=50)
+    for i in range(0, len(chunks)):
+        if chunks[i].duration_seconds < minimum_duration or chunks[i].duration_seconds > maximum_duration:
+            continue
+        chunks[i].export(f"{outdir}/{i:05d}.mp3", format='mp3', parameters=["-ac", "1"])
+    report_progress(progress_file, file)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-path', type=str, help='Path to search for files', default='Y:\\sources\\big_podcast')
+    parser.add_argument('-progress_file', type=str, help='Place to store all files that have already been processed', default='Y:\\sources\\big_podcast\\already_processed.txt')
+    parser.add_argument('-output_path', type=str, help='Path for output files', default='Y:\\split\\big_podcast')
+    parser.add_argument('-num_threads', type=int, help='Number of concurrent workers processing files.', default=4)
+    args = parser.parse_args()
+
+    processed_files = set()
+    if os.path.exists(args.progress_file):
+        with open(args.progress_file, 'r', encoding='utf-8') as f:
+            for line in f.readlines():
+                processed_files.add(line.strip())
+
+    files = set(find_audio_files(args.path, include_nonwav=True))
+    orig_len = len(files)
+    files = files - processed_files
+    print(f"Found {len(files)} files to process. Total processing is {100*(orig_len-len(files))/orig_len}% complete.")
+
+    with ThreadPool(args.num_threads) as pool:
+        list(tqdm(pool.imap(functools.partial(process_file, output_path=args.output_path, base_path=args.path, progress_file=args.progress_file), files), total=len(files)))

codes/scripts/audio/preparation/split_on_silence.py

@@ -19,8 +19,6 @@ def main():
     maximum_duration = 20
     files = find_audio_files(args.path, include_nonwav=True)
     for e, wav_file in enumerate(tqdm(files)):
-        if e < 12593:
-            continue
         print(f"Processing {wav_file}..")
         outdir = os.path.join(args.out, f'{e}_{os.path.basename(wav_file[:-4])}').replace('.', '').strip()
         os.makedirs(outdir, exist_ok=True)
@@ -36,7 +34,7 @@ def main():
         for i in range(0, len(chunks)):
             if chunks[i].duration_seconds < minimum_duration or chunks[i].duration_seconds > maximum_duration:
                 continue
-            chunks[i].export(f"{outdir}/{i:05d}.wav", format='wav', parameters=["-ar", "22050", "-ac", "1"])
+            chunks[i].export(f"{outdir}/{i:05d}.mp3", format='mp3', parameters=["-ac", "1"])
 
 if __name__ == '__main__':
     main()