Add more arguments. Rename file variables.

data/lm/README.rst

@@ -1,7 +1,12 @@
 | Generate vocab-500000.txt and lm.binary files
-| Add '--download_librispeech' to download the librispeech text corpus (will be downloaded to '--input_txt')
-| Optional change the path of the kenlm binaries with '--kenlm_bins path/to/bins/'
-| Optional change the number of most frequent words with  '--top_k 300000'
+|
+| Optional Parameters:
+
+* '--download_librispeech': Download the librispeech text corpus (will be downloaded to '--input_txt')
+* '--kenlm_bins path/to/bins/':  Change the path of the kenlm binaries (defaults to directory in docker container)
+* '--top_k 300000': Change the number of most frequent words
+* '--arpa_order 3': Change order of k-grams in arpa-file generation
+* '--max_arpa_memory 90%': Set maximum allowed memory usage in arpa-file generation
 
 
 .. code-block:: bash

data/lm/generate_lm.py

@@ -1,12 +1,10 @@
 import argparse
-import gzip
-import io
 import os
 import subprocess
 from collections import Counter
 from urllib import request
 
-import tqdm
+import progressbar
 
 
 # ======================================================================================================================
@@ -15,16 +13,15 @@ def convert_and_filter_topk(args):
     """ Convert to lowercase, count word occurrences and save top-k words to a file """
 
     counter = Counter()
-    data_lower = os.path.join(args.output_dir, 'lower.txt.gz')
+    data_lower = os.path.join(args.output_dir, 'lower.txt')
 
-    # Convert and count words
     print('\nConverting to lowercase and counting word occurrences ...')
-    with io.TextIOWrapper(io.BufferedWriter(gzip.open(data_lower, 'w+')), encoding='utf-8') as lower:
-        with open(args.input_txt, encoding='utf8') as upper:
-            for line in tqdm.tqdm(upper):
+    with open(data_lower, 'w+', encoding='utf8') as file_out:
+        with open(args.input_txt, encoding='utf8') as file_in:
+            for line in progressbar.progressbar(file_in):
                 line_lower = line.lower()
                 counter.update(line_lower.split())
-                lower.write(line_lower)
+                file_out.write(line_lower)
 
     # Save top-k words
     print('\nSaving top {} words'.format(args.top_k))
@@ -40,32 +37,34 @@ def convert_and_filter_topk(args):
 # ======================================================================================================================
 
 def build_lm(args, data_lower, vocab_str):
-    """ Create the lm.binary file """
-
-    # Calculate n-grams for the lm.arpa file
     print('\nCreating ARPA file ...')
     lm_path = os.path.join(args.output_dir, 'lm.arpa')
     subprocess.check_call([
-        args.kenlm_bins + 'lmplz', '--order', '5',
+        args.kenlm_bins + 'lmplz',
+        '--order', str(args.arpa_order),
         '--temp_prefix', args.output_dir,
-        '--memory', '75%',
+        '--memory', args.max_arpa_memory,
         '--text', data_lower,
         '--arpa', lm_path,
         '--prune', '0', '0', '1'
     ])
 
-    # Filter lm.arpa using vocabulary of top-k words
+    # Filter LM using vocabulary of top 500k words
     print('\nFiltering ARPA file using vocabulary of top-k words ...')
     filtered_path = os.path.join(args.output_dir, 'lm_filtered.arpa')
-    subprocess.run([args.kenlm_bins + 'filter', 'single', 'model:{}'.format(lm_path), filtered_path],
-                   input=vocab_str.encode('utf-8'),
-                   check=True)
+    subprocess.run([
+        args.kenlm_bins + 'filter',
+        'single',
+        'model:{}'.format(lm_path),
+        filtered_path
+    ], input=vocab_str.encode('utf-8'), check=True)
 
-    # Quantize, produce trie and save to lm.binary
+    # Quantize and produce trie binary.
     print('\nBuilding lm.binary ...')
     binary_path = os.path.join(args.output_dir, 'lm.binary')
     subprocess.check_call([
-        args.kenlm_bins + 'build_binary', '-a', '255',
+        args.kenlm_bins + 'build_binary',
+        '-a', '255',
         '-q', '8',
         '-v',
         'trie',
@@ -108,6 +107,18 @@ def main():
         type=str,
         default='/DeepSpeech/native_client/kenlm/build/bin/'
     )
+    parser.add_argument(
+        '--arpa_order',
+        help='Order of k-grams in arpa-file generation',
+        type=int,
+        default=5
+    )
+    parser.add_argument(
+        '--max_arpa_memory',
+        help='Maximum allowed memory usage in arpa-file generation',
+        type=str,
+        default='75%'
+    )
     args = parser.parse_args()
 
     if args.download_librispeech:
@@ -122,9 +133,7 @@ def main():
     build_lm(args, data_lower, vocab_str)
 
     # Delete intermediate files
-    if args.download_librispeech:
-        os.remove(args.input_txt)
-    os.remove(os.path.join(args.output_dir, 'lower.txt.gz'))
+    os.remove(os.path.join(args.output_dir, 'lower.txt'))
     os.remove(os.path.join(args.output_dir, 'lm.arpa'))
     os.remove(os.path.join(args.output_dir, 'lm_filtered.arpa'))