Merge branch 'pr2801' (Fixes #2801)
This commit is contained in:
Reuben Morais
commit
40250988db
@ -1,8 +1,30 @@
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The LM binary was generated from the LibriSpeech normalized LM training text, available `here <http://www.openslr.org/11>`_.
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It is created with `KenLM <https://github.com/kpu/kenlm>`_.
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The 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` and `librispeech-vocab-500k.txt` in the folder it is run from). `KenLM <https://github.com/kpu/kenlm>`_'s built binaries must be in your PATH (lmplz, build_binary, filter).
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The scorer package was then built using the `generate_package.py` script:
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You can download the LibriSpeech corpus with the following commands:
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.. code-block:: bash
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python generate_lm.py # this will create lm.binary and librispeech-vocab-500k.txt
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python generate_package.py --alphabet ../alphabet.txt --lm lm.binary --vocab librispeech-vocab-500k.txt --default_alpha 0.75 --default_beta 1.85 --package kenlm.scorer
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wget http://www.openslr.org/resources/11/librispeech-lm-norm.txt.gz
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Then use the `generate_lm.py` script to generate `lm.binary` and `vocab-500000.txt`.
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As input you can use a plain text (e.g. `file.txt`) or gzipped (e.g. `file.txt.gz`) text file with one sentence in each line.
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If you are not using the DeepSpeech docker container, you have to build `KenLM <https://github.com/kpu/kenlm>`_ first and then pass the build directory to the script.
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.. code-block:: bash
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python3 data/lm/generate_lm.py --input_txt librispeech-lm-norm.txt.gz \
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--output_dir . --top_k 500000 --kenlm_bins path/to/kenlm/build/bin/ \
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--arpa_order 5 --max_arpa_memory "85%" --arpa_prune "0|0|1" \
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--binary_a_bits 255 --binary_q_bits 8 --binary_type trie
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Afterwards you can use `generate_package.py` to generate the scorer package using the lm.binary and vocab-500000.txt files:
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.. code-block:: bash
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python3 generate_package.py --alphabet ../alphabet.txt --lm lm.binary --vocab vocab-500000.txt \
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--package kenlm.scorer --default_alpha 0.75 --default_beta 1.85
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@ -1,63 +1,203 @@
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import argparse
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import gzip
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import io
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import os
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import subprocess
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import tempfile
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from collections import Counter
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from urllib import request
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def main():
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# Grab corpus.
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url = 'http://www.openslr.org/resources/11/librispeech-lm-norm.txt.gz'
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import progressbar
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with tempfile.TemporaryDirectory() as tmp:
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data_upper = os.path.join(tmp, 'upper.txt.gz')
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print('Downloading {} into {}...'.format(url, data_upper))
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request.urlretrieve(url, data_upper)
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# Convert to lowercase and count word occurences.
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def convert_and_filter_topk(args):
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""" Convert to lowercase, count word occurrences and save top-k words to a file """
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counter = Counter()
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data_lower = os.path.join(tmp, 'lower.txt.gz')
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print('Converting to lower case and counting word frequencies...')
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with io.TextIOWrapper(io.BufferedWriter(gzip.open(data_lower, 'w')), encoding='utf-8') as lower:
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with io.TextIOWrapper(io.BufferedReader(gzip.open(data_upper)), encoding='utf8') as upper:
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for line in upper:
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data_lower = os.path.join(args.output_dir, "lower.txt.gz")
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print("\nConverting to lowercase and counting word occurrences ...")
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with io.TextIOWrapper(
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io.BufferedWriter(gzip.open(data_lower, "w+")), encoding="utf-8"
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) as file_out:
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# Open the input file either from input.txt or input.txt.gz
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_, file_extension = os.path.splitext(args.input_txt)
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if file_extension == ".gz":
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file_in = io.TextIOWrapper(
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io.BufferedReader(gzip.open(args.input_txt)), encoding="utf-8"
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)
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else:
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file_in = open(args.input_txt, encoding="utf-8")
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for line in progressbar.progressbar(file_in):
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line_lower = line.lower()
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counter.update(line_lower.split())
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lower.write(line_lower)
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file_out.write(line_lower)
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# Build pruned LM.
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lm_path = os.path.join(tmp, 'lm.arpa')
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print('Creating ARPA file...')
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subprocess.check_call([
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'lmplz', '--order', '5',
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'--temp_prefix', tmp,
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'--memory', '50%',
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'--text', data_lower,
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'--arpa', lm_path,
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'--prune', '0', '0', '1'
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])
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file_in.close()
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vocab_str = '\n'.join(word for word, count in counter.most_common(500000))
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with open('librispeech-vocab-500k.txt', 'w') as fout:
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fout.write(vocab_str)
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# Save top-k words
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print("\nSaving top {} words ...".format(args.top_k))
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top_counter = counter.most_common(args.top_k)
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vocab_str = "\n".join(word for word, count in top_counter)
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vocab_path = "vocab-{}.txt".format(args.top_k)
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vocab_path = os.path.join(args.output_dir, vocab_path)
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with open(vocab_path, "w+") as file:
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file.write(vocab_str)
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# Filter LM using vocabulary of top 500k words
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print('Filtering ARPA file...')
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filtered_path = os.path.join(tmp, 'lm_filtered.arpa')
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subprocess.run(['filter', 'single', 'model:{}'.format(lm_path), filtered_path], input=vocab_str.encode('utf-8'), check=True)
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print("\nCalculating word statistics ...")
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total_words = sum(counter.values())
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print(" Your text file has {} words in total".format(total_words))
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print(" It has {} unique words".format(len(counter)))
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top_words_sum = sum(count for word, count in top_counter)
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word_fraction = (top_words_sum / total_words) * 100
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print(
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" Your top-{} words are {:.4f} percent of all words".format(
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args.top_k, word_fraction
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)
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)
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print(' Your most common word "{}" occurred {} times'.format(*top_counter[0]))
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last_word, last_count = top_counter[-1]
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print(
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' The least common word in your top-k is "{}" with {} times'.format(
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last_word, last_count
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)
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)
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for i, (w, c) in enumerate(reversed(top_counter)):
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if c > last_count:
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print(
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' The first word with {} occurrences is "{}" at place {}'.format(
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c, w, len(top_counter) - 1 - i
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)
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)
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break
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return data_lower, vocab_str
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def build_lm(args, data_lower, vocab_str):
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print("\nCreating ARPA file ...")
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lm_path = os.path.join(args.output_dir, "lm.arpa")
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subprocess.check_call(
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[
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os.path.join(args.kenlm_bins, "lmplz"),
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"--order",
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str(args.arpa_order),
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"--temp_prefix",
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args.output_dir,
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"--memory",
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args.max_arpa_memory,
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"--text",
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data_lower,
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"--arpa",
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lm_path,
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"--prune",
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*args.arpa_prune.split("|"),
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]
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)
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# Filter LM using vocabulary of top-k words
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print("\nFiltering ARPA file using vocabulary of top-k words ...")
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filtered_path = os.path.join(args.output_dir, "lm_filtered.arpa")
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subprocess.run(
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[
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os.path.join(args.kenlm_bins, "filter"),
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"single",
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"model:{}".format(lm_path),
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filtered_path,
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],
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input=vocab_str.encode("utf-8"),
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check=True,
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)
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# Quantize and produce trie binary.
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print('Building lm.binary...')
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subprocess.check_call([
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'build_binary', '-a', '255',
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'-q', '8',
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'-v',
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'trie',
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print("\nBuilding lm.binary ...")
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binary_path = os.path.join(args.output_dir, "lm.binary")
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subprocess.check_call(
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[
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os.path.join(args.kenlm_bins, "build_binary"),
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"-a",
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str(args.binary_a_bits),
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"-q",
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str(args.binary_q_bits),
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"-v",
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args.binary_type,
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filtered_path,
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'lm.binary'
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])
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binary_path,
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]
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)
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if __name__ == '__main__':
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def main():
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parser = argparse.ArgumentParser(
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description="Generate lm.binary and top-k vocab for DeepSpeech."
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)
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parser.add_argument(
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"--input_txt",
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help="Path to a file.txt or file.txt.gz with sample sentences",
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type=str,
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required=True,
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)
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parser.add_argument(
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"--output_dir", help="Directory path for the output", type=str, required=True
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)
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parser.add_argument(
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"--top_k",
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help="Use top_k most frequent words for the vocab.txt file. These will be used to filter the ARPA file.",
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type=int,
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required=True,
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)
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parser.add_argument(
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"--kenlm_bins",
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help="File path to the KENLM binaries lmplz, filter and build_binary",
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type=str,
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required=True,
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)
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parser.add_argument(
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"--arpa_order",
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help="Order of k-grams in ARPA-file generation",
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type=int,
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required=True,
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)
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parser.add_argument(
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"--max_arpa_memory",
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help="Maximum allowed memory usage for ARPA-file generation",
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type=str,
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required=True,
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)
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parser.add_argument(
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"--arpa_prune",
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help="ARPA pruning parameters. Separate values with '|'",
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type=str,
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required=True,
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)
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parser.add_argument(
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"--binary_a_bits",
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help="Build binary quantization value a in bits",
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type=int,
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required=True,
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)
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parser.add_argument(
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"--binary_q_bits",
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help="Build binary quantization value q in bits",
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type=int,
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required=True,
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)
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parser.add_argument(
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"--binary_type",
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help="Build binary data structure type",
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type=str,
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required=True,
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)
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args = parser.parse_args()
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data_lower, vocab_str = convert_and_filter_topk(args)
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build_lm(args, data_lower, vocab_str)
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# Delete intermediate files
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os.remove(os.path.join(args.output_dir, "lower.txt.gz"))
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os.remove(os.path.join(args.output_dir, "lm.arpa"))
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os.remove(os.path.join(args.output_dir, "lm_filtered.arpa"))
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if __name__ == "__main__":
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main()
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@ -125,7 +125,7 @@ def main():
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parser.add_argument(
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"--force_utf8",
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default="",
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help="Boolean flag, force set or unset UTF-8 mode in the scorer package. If not set, infers from the vocabulary.",
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help="Boolean flag, force set or unset UTF-8 mode in the scorer package. If not set, infers from the vocabulary. See <https://github.com/mozilla/DeepSpeech/blob/master/doc/Decoder.rst#utf-8-mode> for further explanation",
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)
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args = parser.parse_args()
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