Upload generate_training_data.py

generate_training_data.py

@@ -0,0 +1,62 @@
+import os
+import sys
+from tqdm import tqdm
+from ipatok import tokenise
+from glob import glob
+from lhotse import CutSet
+from lhotse.shar.writers import SharWriter
+from pathlib import Path
+import logging
+
+
+logging.basicConfig(
+    level=logging.DEBUG,
+    format="[%(asctime)s] %(levelname)s [%(name)s.%(funcName)s:%(lineno)d] %(message)s",
+    datefmt="%Y/%b/%d %H:%M:%S",
+    stream=sys.stdout)
+
+
+
+inpath = sys.argv[1]
+outpath = sys.argv[2]
+
+filelist = glob(inpath+'/**/*.jsonl.gz',recursive=True)
+
+
+datasets = [file.replace(inpath,'') for file in filelist]
+datasets = [file.replace(os.path.basename(file),'') for file in datasets]
+datasets = list(set(datasets))
+datasets = [file for file in datasets if 'dev' not in file and 'test' not in file and 'doreco' not in file]
+print(datasets)
+logging.info("%s speech train data files found!"%len(datasets))
+
+logging.info("Beginning processing dataset")
+
+
+data_dir = Path(outpath)
+data_dir.mkdir(parents=True, exist_ok=True)
+with SharWriter(data_dir, fields={"recording": "flac"}, shard_size=20000) as writer:
+    
+    for i,dataset in enumerate(datasets):
+
+        data_path = inpath+dataset
+        logging.info("Processing %s"%data_path)
+
+        supervision = sorted(glob(os.path.join(data_path,'cuts*')))
+        recording =  sorted(glob(os.path.join(data_path,'recording*')))
+        assert len(supervision)==len(recording)
+
+        logging.info("%s shards found"%len(supervision))
+
+        cuts = CutSet.from_shar(
+                    {
+                        "cuts": supervision,
+                        "recording": recording
+                    }
+                )
+
+
+        for cut in tqdm(cuts):
+            writer.write(cut)
+
+        logging.info("Processing done! %s datasets remaining."%(len(datasets)-i-1))