Switch to --prune 0 0 1 model and move generation code to a script

data/lm/README.rst

@@ -1,59 +1,8 @@
 
-lm.binary was generated from the LibriSpeech normalized LM training text, available `here <http://www.openslr.org/11>`_\ , following this recipe (Jupyter notebook code):
-
-.. code-block:: python
-
-   import gzip
-   import io
-   import os
-
-   from urllib import request
-   from collections import Counter
-
-   # Grab corpus.
-   url = 'http://www.openslr.org/resources/11/librispeech-lm-norm.txt.gz'
-   data_upper = '/tmp/upper.txt.gz'
-   request.urlretrieve(url, data_upper)
-
-   # Convert to lowercase and count word occurences.
-   counter = Counter()
-   data_lower = '/tmp/lower.txt.gz'
-   with io.TextIOWrapper(io.BufferedWriter(gzip.open(data_lower, 'w')), encoding='utf-8') as lower:
-       with io.TextIOWrapper(io.BufferedReader(gzip.open(data_upper)), encoding='utf8') as upper:
-           for line in upper:
-               line_lower = line.lower()
-               counter.update(line_lower.split())
-               lower.write(line_lower)
-
-   # Create vocabulary file with top 500k words
-   vocab_path = '/tmp/vocab-500k.txt'
-   with open(vocab_path, 'w') as fout:
-       fout.write('\n'.join(word for word, count in counter.most_common(500000)))
-
-   # Build pruned LM.
-   lm_path = '/tmp/lm.arpa'
-   !lmplz --order 5 \
-          --temp_prefix /tmp/ \
-          --memory 50% \
-          --text {data_lower} \
-          --arpa {lm_path} \
-          --prune 0 0 0 1
-
-   # Filter LM using vocabulary.
-   filtered_path = '/tmp/lm_filtered.arpa'
-   !filter single model:{lm_path} {filtered_path} < {vocab_path}
-
-   # Quantize and produce trie binary.
-   binary_path = '/tmp/lm.binary'
-   !build_binary -a 255 \
-                 -q 8 \
-                 trie \
-                 {filtered_path} \
-                 {binary_path} 
-   os.remove(lm_path)
+lm.binary was generated from the LibriSpeech normalized LM training text, available `here <http://www.openslr.org/11>`_\ , using the `generate_lm.py` script (will generate lm.binary in the folder it is run from). KenLM's built binaries must be in your PATH (lmplz, build_binary, filter).
 
 The trie was then generated from the vocabulary of the language model:
 
 .. code-block:: bash
 
-   ./generate_trie ../data/alphabet.txt /tmp/lm.binary /tmp/trie
+   ./generate_trie ../data/alphabet.txt lm.binary trie

data/lm/generate_lm.py

@@ -0,0 +1,59 @@
+import gzip
+import io
+import os
+import subprocess
+import tempfile
+
+from collections import Counter
+from urllib import request
+
+def main():
+  # Grab corpus.
+  url = 'http://www.openslr.org/resources/11/librispeech-lm-norm.txt.gz'
+
+  with tempfile.TemporaryDirectory() as tmp:
+    data_upper = os.path.join(tmp, 'upper.txt.gz')
+    print('Downloading {} into {}...'.format(url, data_upper))
+    request.urlretrieve(url, data_upper)
+
+    # Convert to lowercase and count word occurences.
+    counter = Counter()
+    data_lower = os.path.join(tmp, 'lower.txt.gz')
+    print('Converting to lower case and counting word frequencies...')
+    with io.TextIOWrapper(io.BufferedWriter(gzip.open(data_lower, 'w')), encoding='utf-8') as lower:
+      with io.TextIOWrapper(io.BufferedReader(gzip.open(data_upper)), encoding='utf8') as upper:
+        for line in upper:
+          line_lower = line.lower()
+          counter.update(line_lower.split())
+          lower.write(line_lower)
+
+    # Build pruned LM.
+    lm_path = os.path.join(tmp, 'lm.arpa')
+    print('Creating ARPA file...')
+    subprocess.check_call([
+      'lmplz', '--order', '5',
+               '--temp_prefix', tmp,
+               '--memory', '50%',
+               '--text', data_lower,
+               '--arpa', lm_path,
+               '--prune', '0', '0', '1'
+    ])
+
+    # Filter LM using vocabulary of top 500k words
+    filtered_path = os.path.join(tmp, 'lm_filtered.arpa')
+    vocab_str = '\n'.join(word for word, count in counter.most_common(500000))
+    print('Filtering ARPA file...')
+    subprocess.run(['filter', 'single', 'model:{}'.format(lm_path), filtered_path], input=vocab_str.encode('utf-8'), check=True)
+
+    # Quantize and produce trie binary.
+    print('Building lm.binary...')
+    subprocess.check_call([
+      'build_binary', '-a', '255',
+                      '-q', '8',
+                      'trie',
+                      filtered_path,
+                      'lm.binary'
+    ])
+
+if __name__ == '__main__':
+  main()

data/lm/lm.binary

@@ -1,3 +1,3 @@
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-size 1728766378
+oid sha256:a24953ce3f013bbf5f4a1c9f5a0e5482bc56eaa81638276de522f39e62ff3a56
+size 945699324