refactor raw ctc decoder into ctcdecoder

deepspeech/decoders/README.MD

-# Reference
-* [Sequence Modeling With CTC](https://distill.pub/2017/ctc/)
-* [First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs](https://arxiv.org/pdf/1408.2873.pdf)
\ No newline at end of file

deepspeech/decoders/__init__.py

-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
+from .ctcdecoder import swig_wrapper 
\ No newline at end of file

deepspeech/decoders/ctcdecoder/swig/__init__.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

deepspeech/decoders/ctcdecoder/swig/ctc_beam_search_decoder.cpp

+// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "ctc_beam_search_decoder.h"
+
+#include <algorithm>
+#include <cmath>
+#include <iostream>
+#include <limits>
+#include <map>
+#include <utility>
+
+#include "ThreadPool.h"
+#include "fst/fstlib.h"
+
+#include "decoder_utils.h"
+#include "path_trie.h"
+
+using FSTMATCH = fst::SortedMatcher<fst::StdVectorFst>;
+
+std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
+    const std::vector<std::vector<double>> &probs_seq,
+    const std::vector<std::string> &vocabulary,
+    size_t beam_size,
+    double cutoff_prob,
+    size_t cutoff_top_n,
+    Scorer *ext_scorer,
+    size_t blank_id) {
+    // dimension check
+    size_t num_time_steps = probs_seq.size();
+    for (size_t i = 0; i < num_time_steps; ++i) {
+        VALID_CHECK_EQ(probs_seq[i].size(),
+                       // vocabulary.size() + 1,
+                       vocabulary.size(),
+                       "The shape of probs_seq does not match with "
+                       "the shape of the vocabulary");
+    }
+    // assign space id
+    auto it = std::find(vocabulary.begin(), vocabulary.end(), kSPACE);
+    int space_id = it - vocabulary.begin();
+    // if no space in vocabulary
+    if ((size_t)space_id >= vocabulary.size()) {
+        space_id = -2;
+    }
+    // init prefixes' root
+    PathTrie root;
+    root.score = root.log_prob_b_prev = 0.0;
+    std::vector<PathTrie *> prefixes;
+    prefixes.push_back(&root);
+
+    if (ext_scorer != nullptr && !ext_scorer->is_character_based()) {
+        auto fst_dict =
+            static_cast<fst::StdVectorFst *>(ext_scorer->dictionary);
+        fst::StdVectorFst *dict_ptr = fst_dict->Copy(true);
+        root.set_dictionary(dict_ptr);
+        auto matcher = std::make_shared<FSTMATCH>(*dict_ptr, fst::MATCH_INPUT);
+        root.set_matcher(matcher);
+    }
+
+    // prefix search over time
+    for (size_t time_step = 0; time_step < num_time_steps; ++time_step) {
+        auto &prob = probs_seq[time_step];
+
+        float min_cutoff = -NUM_FLT_INF;
+        bool full_beam = false;
+        if (ext_scorer != nullptr) {
+            size_t num_prefixes = std::min(prefixes.size(), beam_size);
+            std::sort(prefixes.begin(),
+                      prefixes.begin() + num_prefixes,
+                      prefix_compare);
+            min_cutoff = prefixes[num_prefixes - 1]->score +
+                         std::log(prob[blank_id]) -
+                         std::max(0.0, ext_scorer->beta);
+            full_beam = (num_prefixes == beam_size);
+        }
+
+        std::vector<std::pair<size_t, float>> log_prob_idx =
+            get_pruned_log_probs(prob, cutoff_prob, cutoff_top_n);
+        // loop over chars
+        for (size_t index = 0; index < log_prob_idx.size(); index++) {
+            auto c = log_prob_idx[index].first;
+            auto log_prob_c = log_prob_idx[index].second;
+
+            for (size_t i = 0; i < prefixes.size() && i < beam_size; ++i) {
+                auto prefix = prefixes[i];
+                if (full_beam && log_prob_c + prefix->score < min_cutoff) {
+                    break;
+                }
+                // blank
+                if (c == blank_id) {
+                    prefix->log_prob_b_cur = log_sum_exp(
+                        prefix->log_prob_b_cur, log_prob_c + prefix->score);
+                    continue;
+                }
+                // repeated character
+                if (c == prefix->character) {
+                    prefix->log_prob_nb_cur =
+                        log_sum_exp(prefix->log_prob_nb_cur,
+                                    log_prob_c + prefix->log_prob_nb_prev);
+                }
+                // get new prefix
+                auto prefix_new = prefix->get_path_trie(c);
+
+                if (prefix_new != nullptr) {
+                    float log_p = -NUM_FLT_INF;
+
+                    if (c == prefix->character &&
+                        prefix->log_prob_b_prev > -NUM_FLT_INF) {
+                        log_p = log_prob_c + prefix->log_prob_b_prev;
+                    } else if (c != prefix->character) {
+                        log_p = log_prob_c + prefix->score;
+                    }
+
+                    // language model scoring
+                    if (ext_scorer != nullptr &&
+                        (c == space_id || ext_scorer->is_character_based())) {
+                        PathTrie *prefix_to_score = nullptr;
+                        // skip scoring the space
+                        if (ext_scorer->is_character_based()) {
+                            prefix_to_score = prefix_new;
+                        } else {
+                            prefix_to_score = prefix;
+                        }
+
+                        float score = 0.0;
+                        std::vector<std::string> ngram;
+                        ngram = ext_scorer->make_ngram(prefix_to_score);
+                        score = ext_scorer->get_log_cond_prob(ngram) *
+                                ext_scorer->alpha;
+                        log_p += score;
+                        log_p += ext_scorer->beta;
+                    }
+                    prefix_new->log_prob_nb_cur =
+                        log_sum_exp(prefix_new->log_prob_nb_cur, log_p);
+                }
+            }  // end of loop over prefix
+        }      // end of loop over vocabulary
+
+
+        prefixes.clear();
+        // update log probs
+        root.iterate_to_vec(prefixes);
+
+        // only preserve top beam_size prefixes
+        if (prefixes.size() >= beam_size) {
+            std::nth_element(prefixes.begin(),
+                             prefixes.begin() + beam_size,
+                             prefixes.end(),
+                             prefix_compare);
+            for (size_t i = beam_size; i < prefixes.size(); ++i) {
+                prefixes[i]->remove();
+            }
+        }
+    }  // end of loop over time
+
+    // score the last word of each prefix that doesn't end with space
+    if (ext_scorer != nullptr && !ext_scorer->is_character_based()) {
+        for (size_t i = 0; i < beam_size && i < prefixes.size(); ++i) {
+            auto prefix = prefixes[i];
+            if (!prefix->is_empty() && prefix->character != space_id) {
+                float score = 0.0;
+                std::vector<std::string> ngram = ext_scorer->make_ngram(prefix);
+                score =
+                    ext_scorer->get_log_cond_prob(ngram) * ext_scorer->alpha;
+                score += ext_scorer->beta;
+                prefix->score += score;
+            }
+        }
+    }
+
+    size_t num_prefixes = std::min(prefixes.size(), beam_size);
+    std::sort(
+        prefixes.begin(), prefixes.begin() + num_prefixes, prefix_compare);
+
+    // compute aproximate ctc score as the return score, without affecting the
+    // return order of decoding result. To delete when decoder gets stable.
+    for (size_t i = 0; i < beam_size && i < prefixes.size(); ++i) {
+        double approx_ctc = prefixes[i]->score;
+        if (ext_scorer != nullptr) {
+            std::vector<int> output;
+            prefixes[i]->get_path_vec(output);
+            auto prefix_length = output.size();
+            auto words = ext_scorer->split_labels(output);
+            // remove word insert
+            approx_ctc = approx_ctc - prefix_length * ext_scorer->beta;
+            // remove language model weight:
+            approx_ctc -=
+                (ext_scorer->get_sent_log_prob(words)) * ext_scorer->alpha;
+        }
+        prefixes[i]->approx_ctc = approx_ctc;
+    }
+
+    return get_beam_search_result(prefixes, vocabulary, beam_size);
+}
+
+
+std::vector<std::vector<std::pair<double, std::string>>>
+ctc_beam_search_decoder_batch(
+    const std::vector<std::vector<std::vector<double>>> &probs_split,
+    const std::vector<std::string> &vocabulary,
+    size_t beam_size,
+    size_t num_processes,
+    double cutoff_prob,
+    size_t cutoff_top_n,
+    Scorer *ext_scorer,
+    size_t blank_id) {
+    VALID_CHECK_GT(num_processes, 0, "num_processes must be nonnegative!");
+    // thread pool
+    ThreadPool pool(num_processes);
+    // number of samples
+    size_t batch_size = probs_split.size();
+
+    // enqueue the tasks of decoding
+    std::vector<std::future<std::vector<std::pair<double, std::string>>>> res;
+    for (size_t i = 0; i < batch_size; ++i) {
+        res.emplace_back(pool.enqueue(ctc_beam_search_decoder,
+                                      probs_split[i],
+                                      vocabulary,
+                                      beam_size,
+                                      cutoff_prob,
+                                      cutoff_top_n,
+                                      ext_scorer,
+                                      blank_id));
+    }
+
+    // get decoding results
+    std::vector<std::vector<std::pair<double, std::string>>> batch_results;
+    for (size_t i = 0; i < batch_size; ++i) {
+        batch_results.emplace_back(res[i].get());
+    }
+    return batch_results;
+}

deepspeech/decoders/ctcdecoder/swig/decoder_utils.h

+// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef DECODER_UTILS_H_
+#define DECODER_UTILS_H_
+
+#include <string>
+#include <utility>
+#include "fst/log.h"
+#include "path_trie.h"
+
+const std::string kSPACE = "<space>";
+const float NUM_FLT_INF = std::numeric_limits<float>::max();
+const float NUM_FLT_MIN = std::numeric_limits<float>::min();
+
+// inline function for validation check
+inline void check(
+    bool x, const char *expr, const char *file, int line, const char *err) {
+    if (!x) {
+        std::cout << "[" << file << ":" << line << "] ";
+        LOG(FATAL) << "\"" << expr << "\" check failed. " << err;
+    }
+}
+
+#define VALID_CHECK(x, info) \
+    check(static_cast<bool>(x), #x, __FILE__, __LINE__, info)
+#define VALID_CHECK_EQ(x, y, info) VALID_CHECK((x) == (y), info)
+#define VALID_CHECK_GT(x, y, info) VALID_CHECK((x) > (y), info)
+#define VALID_CHECK_LT(x, y, info) VALID_CHECK((x) < (y), info)
+
+
+// Function template for comparing two pairs
+template <typename T1, typename T2>
+bool pair_comp_first_rev(const std::pair<T1, T2> &a,
+                         const std::pair<T1, T2> &b) {
+    return a.first > b.first;
+}
+
+// Function template for comparing two pairs
+template <typename T1, typename T2>
+bool pair_comp_second_rev(const std::pair<T1, T2> &a,
+                          const std::pair<T1, T2> &b) {
+    return a.second > b.second;
+}
+
+// Return the sum of two probabilities in log scale
+template <typename T>
+T log_sum_exp(const T &x, const T &y) {
+    static T num_min = -std::numeric_limits<T>::max();
+    if (x <= num_min) return y;
+    if (y <= num_min) return x;
+    T xmax = std::max(x, y);
+    return std::log(std::exp(x - xmax) + std::exp(y - xmax)) + xmax;
+}
+
+// Get pruned probability vector for each time step's beam search
+std::vector<std::pair<size_t, float>> get_pruned_log_probs(
+    const std::vector<double> &prob_step,
+    double cutoff_prob,
+    size_t cutoff_top_n);
+
+// Get beam search result from prefixes in trie tree
+std::vector<std::pair<double, std::string>> get_beam_search_result(
+    const std::vector<PathTrie *> &prefixes,
+    const std::vector<std::string> &vocabulary,
+    size_t beam_size);
+
+// Functor for prefix comparsion
+bool prefix_compare(const PathTrie *x, const PathTrie *y);
+
+/* Get length of utf8 encoding string
+ * See: http://stackoverflow.com/a/4063229
+ */
+size_t get_utf8_str_len(const std::string &str);
+
+/* Split a string into a list of strings on a given string
+ * delimiter. NB: delimiters on beginning / end of string are
+ * trimmed. Eg, "FooBarFoo" split on "Foo" returns ["Bar"].
+ */
+std::vector<std::string> split_str(const std::string &s,
+                                   const std::string &delim);
+
+/* Splits string into vector of strings representing
+ * UTF-8 characters (not same as chars)
+ */
+std::vector<std::string> split_utf8_str(const std::string &str);
+
+// Add a word in index to the dicionary of fst
+void add_word_to_fst(const std::vector<int> &word,
+                     fst::StdVectorFst *dictionary);
+
+// Add a word in string to dictionary
+bool add_word_to_dictionary(
+    const std::string &word,
+    const std::unordered_map<std::string, int> &char_map,
+    bool add_space,
+    int SPACE_ID,
+    fst::StdVectorFst *dictionary);
+#endif  // DECODER_UTILS_H

deepspeech/decoders/ctcdecoder/swig/scorer.cpp

+// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "scorer.h"
+
+#include <unistd.h>
+#include <iostream>
+
+#include "lm/config.hh"
+#include "lm/model.hh"
+#include "lm/state.hh"
+#include "util/string_piece.hh"
+#include "util/tokenize_piece.hh"
+
+#include "decoder_utils.h"
+
+using namespace lm::ngram;
+
+Scorer::Scorer(double alpha,
+               double beta,
+               const std::string& lm_path,
+               const std::vector<std::string>& vocab_list) {
+    this->alpha = alpha;
+    this->beta = beta;
+
+    dictionary = nullptr;
+    is_character_based_ = true;
+    language_model_ = nullptr;
+
+    max_order_ = 0;
+    dict_size_ = 0;
+    SPACE_ID_ = -1;
+
+    setup(lm_path, vocab_list);
+}
+
+Scorer::~Scorer() {
+    if (language_model_ != nullptr) {
+        delete static_cast<lm::base::Model*>(language_model_);
+    }
+    if (dictionary != nullptr) {
+        delete static_cast<fst::StdVectorFst*>(dictionary);
+    }
+}
+
+void Scorer::setup(const std::string& lm_path,
+                   const std::vector<std::string>& vocab_list) {
+    // load language model
+    load_lm(lm_path);
+    // set char map for scorer
+    set_char_map(vocab_list);
+    // fill the dictionary for FST
+    if (!is_character_based()) {
+        fill_dictionary(true);
+    }
+}
+
+void Scorer::load_lm(const std::string& lm_path) {
+    const char* filename = lm_path.c_str();
+    VALID_CHECK_EQ(access(filename, F_OK), 0, "Invalid language model path");
+
+    RetriveStrEnumerateVocab enumerate;
+    lm::ngram::Config config;
+    config.enumerate_vocab = &enumerate;
+    language_model_ = lm::ngram::LoadVirtual(filename, config);
+    max_order_ = static_cast<lm::base::Model*>(language_model_)->Order();
+    vocabulary_ = enumerate.vocabulary;
+    for (size_t i = 0; i < vocabulary_.size(); ++i) {
+        if (is_character_based_ && vocabulary_[i] != UNK_TOKEN &&
+            vocabulary_[i] != START_TOKEN && vocabulary_[i] != END_TOKEN &&
+            get_utf8_str_len(enumerate.vocabulary[i]) > 1) {
+            is_character_based_ = false;
+        }
+    }
+}
+
+double Scorer::get_log_cond_prob(const std::vector<std::string>& words) {
+    lm::base::Model* model = static_cast<lm::base::Model*>(language_model_);
+    double cond_prob;
+    lm::ngram::State state, tmp_state, out_state;
+    // avoid to inserting <s> in begin
+    model->NullContextWrite(&state);
+    for (size_t i = 0; i < words.size(); ++i) {
+        lm::WordIndex word_index = model->BaseVocabulary().Index(words[i]);
+        // encounter OOV
+        if (word_index == 0) {
+            return OOV_SCORE;
+        }
+        cond_prob = model->BaseScore(&state, word_index, &out_state);
+        tmp_state = state;
+        state = out_state;
+        out_state = tmp_state;
+    }
+    // return  log10 prob
+    return cond_prob;
+}
+
+double Scorer::get_sent_log_prob(const std::vector<std::string>& words) {
+    std::vector<std::string> sentence;
+    if (words.size() == 0) {
+        for (size_t i = 0; i < max_order_; ++i) {
+            sentence.push_back(START_TOKEN);
+        }
+    } else {
+        for (size_t i = 0; i < max_order_ - 1; ++i) {
+            sentence.push_back(START_TOKEN);
+        }
+        sentence.insert(sentence.end(), words.begin(), words.end());
+    }
+    sentence.push_back(END_TOKEN);
+    return get_log_prob(sentence);
+}
+
+double Scorer::get_log_prob(const std::vector<std::string>& words) {
+    assert(words.size() > max_order_);
+    double score = 0.0;
+    for (size_t i = 0; i < words.size() - max_order_ + 1; ++i) {
+        std::vector<std::string> ngram(words.begin() + i,
+                                       words.begin() + i + max_order_);
+        score += get_log_cond_prob(ngram);
+    }
+    return score;
+}
+
+void Scorer::reset_params(float alpha, float beta) {
+    this->alpha = alpha;
+    this->beta = beta;
+}
+
+std::string Scorer::vec2str(const std::vector<int>& input) {
+    std::string word;
+    for (auto ind : input) {
+        word += char_list_[ind];
+    }
+    return word;
+}
+
+std::vector<std::string> Scorer::split_labels(const std::vector<int>& labels) {
+    if (labels.empty()) return {};
+
+    std::string s = vec2str(labels);
+    std::vector<std::string> words;
+    if (is_character_based_) {
+        words = split_utf8_str(s);
+    } else {
+        words = split_str(s, " ");
+    }
+    return words;
+}
+
+void Scorer::set_char_map(const std::vector<std::string>& char_list) {
+    char_list_ = char_list;
+    char_map_.clear();
+
+    // Set the char map for the FST for spelling correction
+    for (size_t i = 0; i < char_list_.size(); i++) {
+        if (char_list_[i] == kSPACE) {
+            SPACE_ID_ = i;
+        }
+        // The initial state of FST is state 0, hence the index of chars in
+        // the FST should start from 1 to avoid the conflict with the initial
+        // state, otherwise wrong decoding results would be given.
+        char_map_[char_list_[i]] = i + 1;
+    }
+}
+
+std::vector<std::string> Scorer::make_ngram(PathTrie* prefix) {
+    std::vector<std::string> ngram;
+    PathTrie* current_node = prefix;
+    PathTrie* new_node = nullptr;
+
+    for (int order = 0; order < max_order_; order++) {
+        std::vector<int> prefix_vec;
+
+        if (is_character_based_) {
+            new_node = current_node->get_path_vec(prefix_vec, SPACE_ID_, 1);
+            current_node = new_node;
+        } else {
+            new_node = current_node->get_path_vec(prefix_vec, SPACE_ID_);
+            current_node = new_node->parent;  // Skipping spaces
+        }
+
+        // reconstruct word
+        std::string word = vec2str(prefix_vec);
+        ngram.push_back(word);
+
+        if (new_node->character == -1) {
+            // No more spaces, but still need order
+            for (int i = 0; i < max_order_ - order - 1; i++) {
+                ngram.push_back(START_TOKEN);
+            }
+            break;
+        }
+    }
+    std::reverse(ngram.begin(), ngram.end());
+    return ngram;
+}
+
+void Scorer::fill_dictionary(bool add_space) {
+    fst::StdVectorFst dictionary;
+    // For each unigram convert to ints and put in trie
+    int dict_size = 0;
+    for (const auto& word : vocabulary_) {
+        bool added = add_word_to_dictionary(
+            word, char_map_, add_space, SPACE_ID_ + 1, &dictionary);
+        dict_size += added ? 1 : 0;
+    }
+
+    dict_size_ = dict_size;
+
+    /* Simplify FST
+
+     * This gets rid of "epsilon" transitions in the FST.
+     * These are transitions that don't require a string input to be taken.
+     * Getting rid of them is necessary to make the FST determinisitc, but
+     * can greatly increase the size of the FST
+     */
+    fst::RmEpsilon(&dictionary);
+    fst::StdVectorFst* new_dict = new fst::StdVectorFst;
+
+    /* This makes the FST deterministic, meaning for any string input there's
+     * only one possible state the FST could be in.  It is assumed our
+     * dictionary is deterministic when using it.
+     * (lest we'd have to check for multiple transitions at each state)
+     */
+    fst::Determinize(dictionary, new_dict);
+
+    /* Finds the simplest equivalent fst. This is unnecessary but decreases
+     * memory usage of the dictionary
+     */
+    fst::Minimize(new_dict);
+    this->dictionary = new_dict;
+}

deepspeech/decoders/ctcdecoder/swig/setup.sh

+#!/usr/bin/env bash
+
+if [ ! -d kenlm ]; then
+    git clone https://github.com/kpu/kenlm.git
+    cd kenlm/
+    git checkout df2d717e95183f79a90b2fa6e4307083a351ca6a
+    cd ..
+    echo -e "\n"
+fi
+
+if [ ! -d openfst-1.6.3 ]; then
+    echo "Download and extract openfst ..."
+    wget http://www.openfst.org/twiki/pub/FST/FstDownload/openfst-1.6.3.tar.gz
+    tar -xzvf openfst-1.6.3.tar.gz
+    echo -e "\n"
+fi
+
+if [ ! -d ThreadPool ]; then
+    git clone https://github.com/progschj/ThreadPool.git
+    echo -e "\n"
+fi
+
+echo "Install decoders ..."
+python3 setup.py install --num_processes 4

deepspeech/modules/ctc.py

@@ -32,7 +32,7 @@ except Exception as e:
 __all__ = ['CTCDecoder']
 
 
-class CTCDecoder(nn.Layer):
+class CTCDecoderBase(nn.Layer):
     def __init__(self,
                  odim,
                  enc_n_units,
@@ -65,9 +65,6 @@ class CTCDecoder(nn.Layer):
             batch_average=batch_average,
             grad_norm_type=grad_norm_type)
 
-        # CTCDecoder LM Score handle
-        self._ext_scorer = None
-
     def forward(self, hs_pad, hlens, ys_pad, ys_lens):
         """Calculate CTC loss.
 
@@ -126,6 +123,13 @@ class CTCDecoder(nn.Layer):
         """
         return ctc_utils.forced_align(ctc_probs, y, blank_id)
 
+
+class CTCDecoder(CTCDecoderBase):
+    def __init__(self,*args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # CTCDecoder LM Score handle
+        self._ext_scorer = None
+
     def _decode_batch_greedy(self, probs_split, vocab_list):
         """Decode by best path for a batch of probs matrix input.
         :param probs_split: List of 2-D probability matrix, and each consists

setup.sh

@@ -69,8 +69,8 @@ fi
 # install decoders
 python3 -c "import pkg_resources; pkg_resources.require(\"swig_decoders==1.1\")"
 if [ $? != 0 ]; then
-    cd deepspeech/decoders/swig > /dev/null
-    sh setup.sh
+    cd deepspeech/decoders/ctcdecoder/swig > /dev/null
+    bash setup.sh
     cd - > /dev/null
 fi
 python3 -c "import pkg_resources; pkg_resources.require(\"swig_decoders==1.1\")"