Merge pull request #2176 from coqui-ai/wav2vec2-decoder

Wav2vec2 decoder

.github/workflows/build-and-test.yml

@@ -274,17 +274,17 @@ jobs:
           fetch-depth: 1
       - uses: actions/setup-python@v2
         with:
-          python-version: 3.6
+          python-version: "3.7"
       - uses: actions/download-artifact@v2
         with:
-          name: "coqui_stt_ctcdecoder-Linux-3.6.whl"
+          name: "coqui_stt_ctcdecoder-Linux-3.7.whl"
       - run: |
           python --version
           pip --version
       - run: |
           pip install --upgrade pip setuptools wheel
       - run: |
-          pip install coqui_stt_ctcdecoder-*-cp36-cp36m-*_x86_64.whl
+          pip install coqui_stt_ctcdecoder-*-cp37-cp37m-*_x86_64.whl
           DS_NODECODER=y pip install --upgrade .
       - run: |
           # Easier to rename to that we can exercize the LDC93S1 importer code to
@@ -540,7 +540,7 @@ jobs:
     if: ${{ github.event_name == 'pull_request' }}
     strategy:
       matrix:
-        python-version: ["3.6", "3.7"]
+        python-version: ["3.7"]
         samplerate: ["8000", "16000"]
     env:
       CI_TMP_DIR: ${{ github.workspace }}/tmp/
@@ -700,7 +700,7 @@ jobs:
       - run: |
           python -m pip install --upgrade pip setuptools wheel jupyter
       - run: |
-          python -m pip install coqui_stt_ctcdecoder-*-cp37-cp37m-*_x86_64.whl
+          python -m pip install coqui_stt_ctcdecoder*.whl
           DS_NODECODER=y python -m pip install --upgrade .
       - name: Run python notebooks
         run: |
@@ -713,7 +713,7 @@ jobs:
     strategy:
       matrix:
         samplerate: ["8000", "16000"]
-        pyver: [3.6, 3.7]
+        pyver: ["3.7"]
     steps:
       - uses: actions/checkout@v2
       - uses: actions/setup-python@v2
@@ -779,7 +779,7 @@ jobs:
     strategy:
       matrix:
         samplerate: ["8000", "16000"]
-        pyver: [3.6, 3.7]
+        pyver: ["3.7"]
     steps:
       - uses: actions/checkout@v2
       - uses: actions/setup-python@v2
@@ -830,7 +830,7 @@ jobs:
     strategy:
       matrix:
         samplerate: ["8000", "16000"]
-        pyver: [3.6, 3.7]
+        pyver: ["3.7"]
     steps:
       - uses: actions/checkout@v2
       - uses: actions/setup-python@v2
@@ -874,7 +874,7 @@ jobs:
     strategy:
       matrix:
         samplerate: ["8000", "16000"]
-        pyver: [3.6, 3.7]
+        pyver: ["3.7"]
     steps:
       - uses: actions/checkout@v2
       - uses: actions/setup-python@v2
@@ -914,7 +914,7 @@ jobs:
     strategy:
       matrix:
         samplerate: ["8000", "16000"]
-        pyver: [3.6, 3.7]
+        pyver: ["3.7"]
     steps:
       - uses: actions/checkout@v2
       - uses: actions/setup-python@v2
@@ -950,7 +950,7 @@ jobs:
     strategy:
       matrix:
         samplerate: ["8000", "16000"]
-        pyver: [3.6, 3.7]
+        pyver: ["3.7"]
     steps:
       - uses: actions/checkout@v2
       - uses: actions/setup-python@v2
@@ -1193,6 +1193,8 @@ jobs:
   docker-publish:
     name: "Build and publish Docker training image to GHCR"
     runs-on: ubuntu-20.04
+    needs: [upload-nc-release-assets]
+    if: always()
     steps:
       - uses: actions/checkout@v2
         with:

.pre-commit-config.yaml

@@ -7,7 +7,7 @@ repos:
       - id: end-of-file-fixer
       - id: trailing-whitespace
   - repo: 'https://github.com/psf/black'
-    rev: "22.1.0"
+    rev: "22.3.0"
     hooks:
       - id: black
         language_version: python3

Dockerfile.build

@@ -146,7 +146,7 @@ RUN bazel build \
 	//native_client:libstt.so
 
 # Copy built libs to /STT/native_client
-RUN cp bazel-bin/native_client/libstt.so /STT/native_client/
+RUN cp bazel-bin/native_client/libstt.so bazel-bin/native_client/libkenlm.so /STT/native_client/
 
 # Build client.cc and install Python client and decoder bindings
 ENV TFDIR /STT/tensorflow

lm_optimizer.py

@@ -1,46 +1,16 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
-from __future__ import absolute_import, print_function
+from __future__ import absolute_import, division, print_function
 
-import sys
-
-from coqui_stt_training.train import early_training_checks
-from coqui_stt_training.util.config import (
-    Config,
-    initialize_globals_from_cli,
-    log_error,
-)
-
-from coqui_stt_training.util import lm_optimize as lm_opt
-
-
-def main():
-    initialize_globals_from_cli()
-    early_training_checks()
-
-    if not Config.scorer_path:
-        log_error(
-            "Missing --scorer_path: can't optimize scorer alpha and beta "
-            "parameters without a scorer!"
-        )
-        sys.exit(1)
-
-    if not Config.test_files:
-        log_error(
-            "You need to specify what files to use for evaluation via "
-            "the --test_files flag."
-        )
-        sys.exit(1)
-
-    results = lm_opt.compute_lm_optimization()
+if __name__ == "__main__":
     print(
-        "Best params: lm_alpha={} and lm_beta={} with WER={}".format(
-            results.get("lm_alpha"),
-            results.get("lm_beta"),
-            results.get("wer"),
-        )
+        "Using the top level lm_optimizer.py script is deprecated and will be removed "
+        "in a future release. Instead use: python -m coqui_stt_training.util.lm_optimize"
     )
+    try:
+        from coqui_stt_training.util import lm_optimize
+    except ImportError:
+        print("Training package is not installed. See training documentation.")
+        raise
 
-
-if __name__ == "__main__":
-    main()
+    lm_optimize.main()

native_client/alphabet.cc

@@ -90,8 +90,8 @@ Alphabet::SerializeText()
       << "# A line that starts with # is a comment. You can escape it with \\# if you wish\n"
       << "# to use '#' in the Alphabet.\n";
 
-  for (int idx = 0; idx < entrySize(); ++idx) {
-    out << getEntry(idx) << "\n";
+  for (const std::string& label : GetLabels()) {
+    out << label << "\n";
   }
 
   out << "# The last (non-comment) line needs to end with a newline.\n";
@@ -174,6 +174,16 @@ Alphabet::GetSize() const
   return entrySize();
 }
 
+std::vector<std::string>
+Alphabet::GetLabels() const
+{
+  std::vector<std::string> labels;
+  for (int idx = 0; idx < GetSize(); ++idx) {
+    labels.push_back(DecodeSingle(idx));
+  }
+  return labels;
+}
+
 bool
 Alphabet::CanEncodeSingle(const std::string& input) const
 {

native_client/alphabet.h

@@ -36,41 +36,42 @@ class Alphabet : public fl::lib::text::Dictionary
 
   size_t GetSize() const;
 
-  bool IsSpace(unsigned int label) const {
-    return label == space_index_;
+  bool IsSpace(unsigned int index) const {
+    return index == space_index_;
   }
 
   unsigned int GetSpaceLabel() const {
     return space_index_;
   }
 
-  // Returns true if the single character/output class has a corresponding label
+  virtual std::vector<std::string> GetLabels() const;
+
+  // Returns true if the single character/output class has a corresponding index
   // in the alphabet.
-  virtual bool CanEncodeSingle(const std::string& string) const;
+  virtual bool CanEncodeSingle(const std::string& label) const;
 
-  // Returns true if the entire string can be encoded into labels in this
-  // alphabet.
-  virtual bool CanEncode(const std::string& string) const;
+  // Returns true if the entire string can be encoded with this alphabet.
+  virtual bool CanEncode(const std::string& label) const;
 
-  // Decode a single label into a string.
-  std::string DecodeSingle(unsigned int label) const;
+  // Decode a single index into its label.
+  std::string DecodeSingle(unsigned int index) const;
 
-  // Encode a single character/output class into a label. Character must be in
+  // Encode a single character/output class into its index. Character must be in
   // the alphabet, this method will assert that. Use `CanEncodeSingle` to test.
-  unsigned int EncodeSingle(const std::string& string) const;
+  unsigned int EncodeSingle(const std::string& label) const;
 
-  // Decode a sequence of labels into a string.
-  std::string Decode(const std::vector<unsigned int>& input) const;
+  // Decode a sequence of indices into a string.
+  std::string Decode(const std::vector<unsigned int>& indices) const;
 
   // We provide a C-style overload for accepting NumPy arrays as input, since
   // the NumPy library does not have built-in typemaps for std::vector<T>.
-  std::string Decode(const unsigned int* input, int length) const;
+  std::string Decode(const unsigned int* indices, int length) const;
 
-  // Encode a sequence of character/output classes into a sequence of labels.
+  // Encode a sequence of character/output classes into a sequence of indices.
   // Characters are assumed to always take a single Unicode codepoint.
   // Characters must be in the alphabet, this method will assert that. Use
   // `CanEncode` and `CanEncodeSingle` to test.
-  virtual std::vector<unsigned int> Encode(const std::string& input) const;
+  virtual std::vector<unsigned int> Encode(const std::string& labels) const;
 
 protected:
   unsigned int space_index_;
@@ -93,9 +94,9 @@ class UTF8Alphabet : public Alphabet
     return 0;
   }
 
-  bool CanEncodeSingle(const std::string& string) const override;
-  bool CanEncode(const std::string& string) const override;
-  std::vector<unsigned int> Encode(const std::string& input) const override;
+  bool CanEncodeSingle(const std::string& label) const override;
+  bool CanEncode(const std::string& label) const override;
+  std::vector<unsigned int> Encode(const std::string& label) const override;
 };
 
 #endif //ALPHABET_H

native_client/ctcdecode/__init__.py

@@ -178,6 +178,69 @@ def ctc_beam_search_decoder(
     return beam_results
 
 
+def ctc_beam_search_decoder_for_wav2vec2am(
+    probs_seq,
+    alphabet,
+    beam_size,
+    cutoff_prob=1.0,
+    cutoff_top_n=40,
+    blank_id=-1,
+    ignored_symbols=frozenset(),
+    scorer=None,
+    hot_words=dict(),
+    num_results=1,
+):
+    """Wrapper for the CTC Beam Search Decoder.
+
+    :param probs_seq: 2-D list of probability distributions over each time
+                      step, with each element being a list of normalized
+                      probabilities over alphabet and blank.
+    :type probs_seq: 2-D list
+    :param alphabet: Alphabet
+    :param beam_size: Width for beam search.
+    :type beam_size: int
+    :param cutoff_prob: Cutoff probability in pruning,
+                        default 1.0, no pruning.
+    :type cutoff_prob: float
+    :param cutoff_top_n: Cutoff number in pruning, only top cutoff_top_n
+                         characters with highest probs in alphabet will be
+                         used in beam search, default 40.
+    :type cutoff_top_n: int
+    :param scorer: External scorer for partially decoded sentence, e.g. word
+                   count or language model.
+    :type scorer: Scorer
+    :param hot_words: Map of words (keys) to their assigned boosts (values)
+    :type hot_words: dict[string, float]
+    :param num_results: Number of beams to return.
+    :type num_results: int
+    :return: List of tuples of confidence and sentence as decoding
+             results, in descending order of the confidence.
+    :rtype: list
+    """
+    beam_results = swigwrapper.ctc_beam_search_decoder_for_wav2vec2am(
+        probs_seq,
+        alphabet,
+        beam_size,
+        cutoff_prob,
+        cutoff_top_n,
+        blank_id,
+        ignored_symbols,
+        scorer,
+        hot_words,
+        num_results,
+    )
+    beam_results = [
+        DecodeResult(
+            res.confidence,
+            alphabet.Decode(res.tokens),
+            [int(t) for t in res.tokens],
+            [int(t) for t in res.timesteps],
+        )
+        for res in beam_results
+    ]
+    return beam_results
+
+
 def ctc_beam_search_decoder_batch(
     probs_seq,
     seq_lengths,