Binary mlf reader
This commit is contained in:
Vadim Mazalov
Родитель
f5b481a6ab
Коммит
222efb8048
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@ -99,6 +99,7 @@
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<ClInclude Include="HTKMLFReader.h" />
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<ClInclude Include="LatticeDeserializer.h" />
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<ClInclude Include="LatticeIndexBuilder.h" />
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<ClInclude Include="MLFBinaryDeserializer.h" />
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<ClInclude Include="MLFDeserializer.h" />
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<ClInclude Include="MLFUtils.h" />
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<ClInclude Include="MLFIndexBuilder.h" />
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@ -117,6 +118,7 @@
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<ClCompile Include="HTKMLFReader.cpp" />
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<ClCompile Include="LatticeDeserializer.cpp" />
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<ClCompile Include="LatticeIndexBuilder.cpp" />
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<ClCompile Include="MLFBinaryDeserializer.cpp" />
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<ClCompile Include="MLFDeserializer.cpp" />
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<ClCompile Include="MLFUtils.cpp" />
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<ClCompile Include="MLFIndexBuilder.cpp" />
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@ -24,6 +24,9 @@
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<ClCompile Include="LatticeIndexBuilder.cpp">
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<Filter>Lattice</Filter>
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</ClCompile>
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<ClCompile Include="MLFBinaryDeserializer.cpp">
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<Filter>MLF</Filter>
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</ClCompile>
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</ItemGroup>
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<ItemGroup>
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<ClInclude Include="stdafx.h" />
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@ -75,6 +78,9 @@
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<ClInclude Include="LatticeIndexBuilder.h">
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<Filter>Lattice</Filter>
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</ClInclude>
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<ClInclude Include="MLFBinaryDeserializer.h">
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<Filter>MLF</Filter>
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</ClInclude>
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</ItemGroup>
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<ItemGroup>
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<Filter Include="Common">
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@ -0,0 +1,558 @@
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//
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// Copyright (c) Microsoft. All rights reserved.
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// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
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//
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#include "stdafx.h"
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#include <limits>
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#include "MLFBinaryDeserializer.h"
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#include "ConfigHelper.h"
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#include "SequenceData.h"
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#include "StringUtil.h"
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#include "ReaderConstants.h"
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#include "FileWrapper.h"
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#include "Index.h"
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#include "MLFIndexBuilder.h"
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namespace CNTK {
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using namespace std;
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using namespace Microsoft::MSR::CNTK;
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static float s_oneFloat = 1.0;
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static double s_oneDouble = 1.0;
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// A constant used in 1-hot vectors to identify the first frame of a phone.
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// Used only in CTC-type training.
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static float s_phoneBoundary = 2.0f;
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// Sparse labels for an utterance.
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template <class ElemType>
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struct MLFSequenceData : SparseSequenceData
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{
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vector<ElemType> m_values;
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vector<IndexType> m_indexBuffer;
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const NDShape& m_frameShape;
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MLFSequenceData(size_t numberOfSamples, const NDShape& frameShape) :
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m_values(numberOfSamples, 1), m_frameShape(frameShape)
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{
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if (numberOfSamples > numeric_limits<IndexType>::max())
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{
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RuntimeError("Number of samples in an MLFSequenceData (%zu) "
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"exceeds the maximum allowed value (%zu)\n",
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numberOfSamples, (size_t)numeric_limits<IndexType>::max());
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}
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m_indexBuffer.resize(numberOfSamples);
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m_nnzCounts.resize(numberOfSamples, static_cast<IndexType>(1));
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m_numberOfSamples = (uint32_t)numberOfSamples;
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m_totalNnzCount = static_cast<IndexType>(numberOfSamples);
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m_indices = &m_indexBuffer[0];
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}
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MLFSequenceData(size_t numberOfSamples, const vector<size_t>& phoneBoundaries, const NDShape& frameShape) :
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MLFSequenceData(numberOfSamples, frameShape)
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{
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for (auto boundary : phoneBoundaries)
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m_values[boundary] = s_phoneBoundary;
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}
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const void* GetDataBuffer() override
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{
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return m_values.data();
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}
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const NDShape& GetSampleShape() override
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{
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return m_frameShape;
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}
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};
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// Base class for chunks in frame and sequence mode.
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// The lifetime is always less than the lifetime of the parent deserializer.
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class MLFBinaryDeserializer::ChunkBase : public Chunk
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{
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protected:
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vector<char> m_buffer; // Buffer for the whole chunk
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vector<bool> m_valid; // Bit mask whether the parsed sequence is valid.
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MLFUtteranceParser m_parser;
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const MLFBinaryDeserializer& m_deserializer;
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const ChunkDescriptor& m_descriptor; // Current chunk descriptor.
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ChunkBase(const MLFBinaryDeserializer& deserializer, const ChunkDescriptor& descriptor, const wstring& fileName, const StateTablePtr& states)
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: m_parser(states),
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m_descriptor(descriptor),
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m_deserializer(deserializer)
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{
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if (descriptor.NumberOfSequences() == 0 || descriptor.SizeInBytes() == 0)
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LogicError("Empty chunks are not supported.");
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auto f = FileWrapper::OpenOrDie(fileName, L"rbS");
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size_t sizeInBytes = descriptor.SizeInBytes();
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// Make sure we always have 0 at the end for buffer overrun.
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m_buffer.resize(sizeInBytes + 1);
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m_buffer[sizeInBytes] = 0;
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// Seek and read chunk into memory.
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f.SeekOrDie(descriptor.StartOffset(), SEEK_SET);
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f.ReadOrDie(m_buffer.data(), sizeInBytes, 1);
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// all sequences are valid by default.
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m_valid.resize(m_descriptor.NumberOfSequences(), true);
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}
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string KeyOf(const SequenceDescriptor& s)
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{
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return m_deserializer.m_corpus->IdToKey(s.m_key);
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}
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void CleanBuffer()
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{
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// Make sure we do not keep unnecessary memory after sequences have been parsed.
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vector<char> tmp;
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m_buffer.swap(tmp);
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}
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};
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// MLF chunk when operating in sequence mode.
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class MLFBinaryDeserializer::SequenceChunk : public MLFBinaryDeserializer::ChunkBase
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{
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vector<vector<MLFFrameRange>> m_sequences; // Each sequence is a vector of sequential frame ranges.
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public:
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SequenceChunk(const MLFBinaryDeserializer& parent, const ChunkDescriptor& descriptor, const wstring& fileName, StateTablePtr states)
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: ChunkBase(parent, descriptor, fileName, states)
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{
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m_sequences.resize(m_descriptor.Sequences().size());
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#pragma omp parallel for schedule(dynamic)
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for (int i = 0; i < descriptor.Sequences().size(); ++i)
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CacheSequence(descriptor.Sequences()[i], i);
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CleanBuffer();
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}
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void CacheSequence(const SequenceDescriptor& sequence, size_t index)
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{
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auto start = m_buffer.data() + sequence.OffsetInChunk();
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auto end = start + sequence.SizeInBytes();
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vector<MLFFrameRange> utterance;
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auto absoluteOffset = m_descriptor.StartOffset() + sequence.OffsetInChunk();
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bool parsed = m_parser.Parse(boost::make_iterator_range(start, end), utterance, absoluteOffset);
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if (!parsed) // cannot parse
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{
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fprintf(stderr, "WARNING: Cannot parse the utterance '%s'\n", KeyOf(sequence).c_str());
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m_valid[index] = false;
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return;
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}
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m_sequences[index] = move(utterance);
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}
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void GetSequence(size_t sequenceIndex, vector<SequenceDataPtr>& result) override
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{
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if (m_deserializer.m_elementType == DataType::Float)
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return GetSequence<float>(sequenceIndex, result);
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else
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{
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assert(m_deserializer.m_elementType == DataType::Double);
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return GetSequence<double>(sequenceIndex, result);
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}
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}
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template<class ElementType>
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void GetSequence(size_t sequenceIndex, vector<SequenceDataPtr>& result)
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{
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if (!m_valid[sequenceIndex])
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{
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SparseSequenceDataPtr s = make_shared<MLFSequenceData<ElementType>>(0, m_deserializer.m_streams.front().m_sampleLayout);
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s->m_isValid = false;
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result.push_back(s);
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return;
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}
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const auto& utterance = m_sequences[sequenceIndex];
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const auto& sequence = m_descriptor.Sequences()[sequenceIndex];
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// Packing labels for the utterance into sparse sequence.
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vector<size_t> sequencePhoneBoundaries(m_deserializer.m_withPhoneBoundaries ? utterance.size() : 0);
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if (m_deserializer.m_withPhoneBoundaries)
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{
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for (size_t i = 0; i < utterance.size(); ++i)
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sequencePhoneBoundaries[i] = utterance[i].FirstFrame();
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}
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auto s = make_shared<MLFSequenceData<ElementType>>(sequence.m_numberOfSamples, sequencePhoneBoundaries, m_deserializer.m_streams.front().m_sampleLayout);
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auto* startRange = s->m_indices;
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for (const auto& range : utterance)
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{
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if (range.ClassId() >= m_deserializer.m_dimension)
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// TODO: Possibly set m_valid to false, but currently preserving the old behavior.
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RuntimeError("Class id '%ud' exceeds the model output dimension '%d'.", range.ClassId(), (int)m_deserializer.m_dimension);
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// Filling all range of frames with the corresponding class id.
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fill(startRange, startRange + range.NumFrames(), static_cast<IndexType>(range.ClassId()));
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startRange += range.NumFrames();
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}
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result.push_back(s);
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}
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};
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// MLF chunk when operating in frame mode.
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// Implementation is different because frames of the same sequence can be accessed
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// in parallel by the randomizer, so all parsing/preprocessing should be done during
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// sequence caching, so that GetSequence only works with read only data structures.
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class MLFBinaryDeserializer::FrameChunk : public MLFBinaryDeserializer::ChunkBase
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{
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// Actual values of frames.
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vector<ClassIdType> m_classIds;
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//For each sequence this vector contains the sequence offset in samples from the beginning of the chunk.
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std::vector<uint32_t> m_sequenceOffsetInChunkInSamples;
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public:
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FrameChunk(const MLFBinaryDeserializer& parent, const ChunkDescriptor& descriptor, const wstring& fileName, StateTablePtr states)
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: ChunkBase(parent, descriptor, fileName, states)
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{
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uint32_t numSamples = static_cast<uint32_t>(m_descriptor.NumberOfSamples());
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// The current assumption is that the number of samples in a chunk fits in uint32,
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// therefore we can save 4 bytes per sequence, storing offsets in samples as uint32.
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if (numSamples != m_descriptor.NumberOfSamples())
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RuntimeError("Exceeded maximum number of samples in a chunk");
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// Preallocate a big array for filling in class ids for the whole chunk.
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m_classIds.resize(numSamples);
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m_sequenceOffsetInChunkInSamples.resize(m_descriptor.NumberOfSequences());
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uint32_t offset = 0;
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for (auto i = 0; i < m_descriptor.NumberOfSequences(); ++i)
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{
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m_sequenceOffsetInChunkInSamples[i] = offset;
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offset += descriptor[i].m_numberOfSamples;
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}
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if (numSamples != offset)
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RuntimeError("Unexpected number of samples in a FrameChunk.");
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// Parse the data on different threads to avoid locking during GetSequence calls.
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#pragma omp parallel for schedule(dynamic)
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for (auto i = 0; i < m_descriptor.NumberOfSequences(); ++i)
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CacheSequence(descriptor[i], i);
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CleanBuffer();
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}
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// Get utterance by the absolute frame index in chunk.
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// Uses the upper bound to do the binary search among sequences of the chunk.
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size_t GetUtteranceForChunkFrameIndex(size_t frameIndex) const
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{
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auto result = upper_bound(
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m_sequenceOffsetInChunkInSamples.begin(),
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m_sequenceOffsetInChunkInSamples.end(),
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frameIndex,
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[](size_t fi, const size_t& a) { return fi < a; });
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return result - 1 - m_sequenceOffsetInChunkInSamples.begin();
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}
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void GetSequence(size_t sequenceIndex, vector<SequenceDataPtr>& result) override
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{
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size_t utteranceId = GetUtteranceForChunkFrameIndex(sequenceIndex);
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if (!m_valid[utteranceId])
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{
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SparseSequenceDataPtr s = make_shared<MLFSequenceData<float>>(0, m_deserializer.m_streams.front().m_sampleLayout);
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s->m_isValid = false;
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result.push_back(s);
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return;
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}
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size_t label = m_classIds[sequenceIndex];
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assert(label < m_deserializer.m_categories.size());
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result.push_back(m_deserializer.m_categories[label]);
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}
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// Parses and caches sequence in the buffer for GetSequence fast retrieval.
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void CacheSequence(const SequenceDescriptor& sequence, size_t index)
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{
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auto start = m_buffer.data() + sequence.OffsetInChunk();
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auto end = start + sequence.SizeInBytes();
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vector<MLFFrameRange> utterance;
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auto absoluteOffset = m_descriptor.StartOffset() + sequence.OffsetInChunk();
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bool parsed = m_parser.Parse(boost::make_iterator_range(start, end), utterance, absoluteOffset);
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if (!parsed)
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{
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m_valid[index] = false;
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fprintf(stderr, "WARNING: Cannot parse the utterance %s\n", KeyOf(sequence).c_str());
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return;
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}
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auto startRange = m_classIds.begin() + m_sequenceOffsetInChunkInSamples[index];
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for(size_t i = 0; i < utterance.size(); ++i)
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{
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const auto& range = utterance[i];
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if (range.ClassId() >= m_deserializer.m_dimension)
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// TODO: Possibly set m_valid to false, but currently preserving the old behavior.
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RuntimeError("Class id '%ud' exceeds the model output dimension '%d'.", range.ClassId(), (int)m_deserializer.m_dimension);
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fill(startRange, startRange + range.NumFrames(), range.ClassId());
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startRange += range.NumFrames();
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}
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}
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};
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MLFBinaryDeserializer::MLFBinaryDeserializer(CorpusDescriptorPtr corpus, const ConfigParameters& cfg, bool primary)
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: DataDeserializerBase(primary),
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m_corpus(corpus)
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{
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if (primary)
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RuntimeError("MLFBinaryDeserializer currently does not support primary mode.");
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m_frameMode = (ConfigValue)cfg("frameMode", "true");
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wstring precision = cfg(L"precision", L"float");
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m_elementType = AreEqualIgnoreCase(precision, L"float") ? DataType::Float : DataType::Double;
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// Same behavior as for the old deserializer - keep almost all in memory,
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// because there are a lot of none aligned sets.
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m_chunkSizeBytes = cfg(L"chunkSizeInBytes", g_64MB);
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ConfigParameters input = cfg("input");
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auto inputName = input.GetMemberIds().front();
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ConfigParameters streamConfig = input(inputName);
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ConfigHelper config(streamConfig);
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m_dimension = config.GetLabelDimension();
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if (m_dimension > numeric_limits<ClassIdType>::max())
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RuntimeError("Label dimension (%zu) exceeds the maximum allowed "
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"value '%ud'\n", m_dimension, numeric_limits<ClassIdType>::max());
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m_withPhoneBoundaries = streamConfig(L"phoneBoundaries", false);
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if (m_frameMode && m_withPhoneBoundaries)
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LogicError("frameMode and phoneBoundaries are mutually exclusive options.");
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wstring labelMappingFile = streamConfig(L"labelMappingFile", L"");
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InitializeStream(inputName);
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InitializeChunkInfos(corpus, config, labelMappingFile);
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}
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// TODO: Should be removed. Currently a lot of end to end tests still use this one.
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MLFBinaryDeserializer::MLFBinaryDeserializer(CorpusDescriptorPtr corpus, const ConfigParameters& labelConfig, const wstring& name)
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: DataDeserializerBase(false)
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{
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// The frame mode is currently specified once per configuration,
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// not in the configuration of a particular deserializer, but on a higher level in the configuration.
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// Because of that we are using find method below.
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m_frameMode = labelConfig.Find("frameMode", "true");
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ConfigHelper config(labelConfig);
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config.CheckLabelType();
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m_dimension = config.GetLabelDimension();
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if (m_dimension > numeric_limits<ClassIdType>::max())
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{
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RuntimeError("Label dimension (%zu) exceeds the maximum allowed "
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"value (%zu)\n", m_dimension, (size_t)numeric_limits<ClassIdType>::max());
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}
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// Same behavior as for the old deserializer - keep almost all in memory,
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// because there are a lot of none aligned sets.
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m_chunkSizeBytes = labelConfig(L"chunkSizeInBytes", g_64MB);
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wstring precision = labelConfig(L"precision", L"float");;
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m_elementType = AreEqualIgnoreCase(precision, L"float") ? DataType::Float : DataType::Double;
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m_withPhoneBoundaries = labelConfig(L"phoneBoundaries", "false");
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wstring labelMappingFile = labelConfig(L"labelMappingFile", L"");
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InitializeStream(name);
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InitializeChunkInfos(corpus, config, labelMappingFile);
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}
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static inline bool LessByFirstItem(const std::tuple<size_t, size_t, size_t>& a, const std::tuple<size_t, size_t, size_t>& b)
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{
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return std::get<0>(a) < std::get<0>(b);
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}
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void MLFBinaryDeserializer::InitializeChunkInfos(CorpusDescriptorPtr corpus, const ConfigHelper& config, const wstring& stateListPath)
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{
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// Similarly to the old reader, currently we assume all Mlfs will have same root name (key)
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// restrict MLF reader to these files--will make stuff much faster without having to use shortened input files
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vector<wstring> mlfPaths = config.GetMlfPaths();
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if (!stateListPath.empty())
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{
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m_stateTable = make_shared<StateTable>();
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m_stateTable->ReadStateList(stateListPath);
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}
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auto emptyPair = make_pair(numeric_limits<uint32_t>::max(), numeric_limits<uint32_t>::max());
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size_t totalNumSequences = 0;
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size_t totalNumFrames = 0;
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bool enableCaching = corpus->IsHashingEnabled() && config.GetCacheIndex();
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for (const auto& path : mlfPaths)
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{
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attempt(5, [this, path, enableCaching, corpus]()
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{
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MLFIndexBuilder builder(FileWrapper(path, L"rbS"), corpus);
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builder.SetChunkSize(m_chunkSizeBytes).SetCachingEnabled(enableCaching);
|
||||
m_indices.emplace_back(builder.Build());
|
||||
});
|
||||
|
||||
m_mlfFiles.push_back(path);
|
||||
|
||||
auto& index = m_indices.back();
|
||||
// Build auxiliary for GetSequenceByKey.
|
||||
for (const auto& chunk : index->Chunks())
|
||||
{
|
||||
// Preparing chunk info that will be exposed to the outside.
|
||||
auto chunkId = static_cast<ChunkIdType>(m_chunks.size());
|
||||
uint32_t offsetInSamples = 0;
|
||||
for (uint32_t i = 0; i < chunk.NumberOfSequences(); ++i)
|
||||
{
|
||||
const auto& sequence = chunk[i];
|
||||
auto sequenceIndex = m_frameMode ? offsetInSamples : i;
|
||||
offsetInSamples += sequence.m_numberOfSamples;
|
||||
m_keyToChunkLocation.push_back(std::make_tuple(sequence.m_key, chunkId, sequenceIndex));
|
||||
}
|
||||
|
||||
totalNumSequences += chunk.NumberOfSequences();
|
||||
totalNumFrames += chunk.NumberOfSamples();
|
||||
m_chunkToFileIndex.insert(make_pair(&chunk, m_mlfFiles.size() - 1));
|
||||
m_chunks.push_back(&chunk);
|
||||
if (m_chunks.size() >= numeric_limits<ChunkIdType>::max())
|
||||
RuntimeError("Number of chunks exceeded overflow limit.");
|
||||
}
|
||||
}
|
||||
|
||||
std::sort(m_keyToChunkLocation.begin(), m_keyToChunkLocation.end(), LessByFirstItem);
|
||||
|
||||
fprintf(stderr, "MLFBinaryDeserializer: '%zu' utterances with '%zu' frames\n",
|
||||
totalNumSequences,
|
||||
totalNumFrames);
|
||||
|
||||
if (m_frameMode)
|
||||
InitializeReadOnlyArrayOfLabels();
|
||||
}
|
||||
|
||||
void MLFBinaryDeserializer::InitializeReadOnlyArrayOfLabels()
|
||||
{
|
||||
m_categories.reserve(m_dimension);
|
||||
m_categoryIndices.reserve(m_dimension);
|
||||
for (size_t i = 0; i < m_dimension; ++i)
|
||||
{
|
||||
auto category = make_shared<CategorySequenceData>(m_streams.front().m_sampleLayout);
|
||||
m_categoryIndices.push_back(static_cast<IndexType>(i));
|
||||
category->m_indices = &(m_categoryIndices[i]);
|
||||
category->m_nnzCounts.resize(1);
|
||||
category->m_nnzCounts[0] = 1;
|
||||
category->m_totalNnzCount = 1;
|
||||
category->m_numberOfSamples = 1;
|
||||
if (m_elementType == DataType::Float)
|
||||
category->m_data = &s_oneFloat;
|
||||
else
|
||||
category->m_data = &s_oneDouble;
|
||||
m_categories.push_back(category);
|
||||
}
|
||||
}
|
||||
|
||||
void MLFBinaryDeserializer::InitializeStream(const wstring& name)
|
||||
{
|
||||
// Initializing stream description - a single stream of MLF data.
|
||||
StreamInformation stream;
|
||||
stream.m_id = 0;
|
||||
stream.m_name = name;
|
||||
stream.m_sampleLayout = NDShape({ m_dimension });
|
||||
stream.m_storageFormat = StorageFormat::SparseCSC;
|
||||
stream.m_elementType = m_elementType;
|
||||
m_streams.push_back(stream);
|
||||
}
|
||||
|
||||
std::vector<ChunkInfo> MLFBinaryDeserializer::ChunkInfos()
|
||||
{
|
||||
std::vector<ChunkInfo> chunks;
|
||||
chunks.reserve(m_chunks.size());
|
||||
for (size_t i = 0; i < m_chunks.size(); ++i)
|
||||
{
|
||||
ChunkInfo cd;
|
||||
cd.m_id = static_cast<ChunkIdType>(i);
|
||||
if (cd.m_id != i)
|
||||
RuntimeError("ChunkIdType overflow during creation of a chunk description.");
|
||||
|
||||
cd.m_numberOfSequences = m_frameMode ? m_chunks[i]->NumberOfSamples() : m_chunks[i]->NumberOfSequences();
|
||||
cd.m_numberOfSamples = m_chunks[i]->NumberOfSamples();
|
||||
chunks.push_back(cd);
|
||||
}
|
||||
return chunks;
|
||||
}
|
||||
|
||||
void MLFBinaryDeserializer::SequenceInfosForChunk(ChunkIdType, vector<SequenceInfo>& result)
|
||||
{
|
||||
UNUSED(result);
|
||||
LogicError("MLF deserializer does not support primary mode, it cannot control chunking. "
|
||||
"Please specify HTK deserializer as the first deserializer in your config file.");
|
||||
}
|
||||
|
||||
ChunkPtr MLFBinaryDeserializer::GetChunk(ChunkIdType chunkId)
|
||||
{
|
||||
ChunkPtr result;
|
||||
attempt(5, [this, &result, chunkId]()
|
||||
{
|
||||
auto chunk = m_chunks[chunkId];
|
||||
auto& fileName = m_mlfFiles[m_chunkToFileIndex[chunk]];
|
||||
|
||||
if (m_frameMode)
|
||||
result = make_shared<FrameChunk>(*this, *chunk, fileName, m_stateTable);
|
||||
else
|
||||
result = make_shared<SequenceChunk>(*this, *chunk, fileName, m_stateTable);
|
||||
});
|
||||
|
||||
return result;
|
||||
};
|
||||
|
||||
bool MLFBinaryDeserializer::GetSequenceInfoByKey(const SequenceKey& key, SequenceInfo& result)
|
||||
{
|
||||
auto found = std::lower_bound(m_keyToChunkLocation.begin(), m_keyToChunkLocation.end(), std::make_tuple(key.m_sequence, 0, 0),
|
||||
LessByFirstItem);
|
||||
|
||||
if (found == m_keyToChunkLocation.end() || std::get<0>(*found) != key.m_sequence)
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
auto chunkId = std::get<1>(*found);
|
||||
auto sequenceIndexInChunk = std::get<2>(*found);
|
||||
|
||||
|
||||
result.m_chunkId = std::get<1>(*found);
|
||||
result.m_key = key;
|
||||
|
||||
if (m_frameMode)
|
||||
{
|
||||
// in frame mode sequenceIndexInChunk == sequence offset in chunk in samples
|
||||
result.m_indexInChunk = sequenceIndexInChunk + key.m_sample;
|
||||
result.m_numberOfSamples = 1;
|
||||
}
|
||||
else
|
||||
{
|
||||
assert(result.m_key.m_sample == 0);
|
||||
|
||||
const auto* chunk = m_chunks[chunkId];
|
||||
const auto& sequence = chunk->Sequences()[sequenceIndexInChunk];
|
||||
result.m_indexInChunk = sequenceIndexInChunk;
|
||||
result.m_numberOfSamples = sequence.m_numberOfSamples;
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,88 @@
|
|||
//
|
||||
// Copyright (c) Microsoft. All rights reserved.
|
||||
// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
|
||||
//
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <boost/noncopyable.hpp>
|
||||
#include "HTKDeserializer.h"
|
||||
#include "CorpusDescriptor.h"
|
||||
#include "MLFUtils.h"
|
||||
#include "Index.h"
|
||||
|
||||
namespace CNTK {
|
||||
|
||||
// Class represents an MLF deserializer.
|
||||
// Provides a set of chunks/sequences to the upper layers.
|
||||
class MLFBinaryDeserializer : public DataDeserializerBase, boost::noncopyable
|
||||
{
|
||||
public:
|
||||
// Expects new configuration.
|
||||
MLFBinaryDeserializer(CorpusDescriptorPtr corpus, const ConfigParameters& config, bool primary);
|
||||
|
||||
// TODO: Should be removed, when all readers go away, expects configuration in a legacy mode.
|
||||
MLFBinaryDeserializer(CorpusDescriptorPtr corpus, const ConfigParameters& config, const std::wstring& streamName);
|
||||
|
||||
// Retrieves sequence description by its key. Used for deserializers that are not in "primary"/"driving" mode.
|
||||
bool GetSequenceInfoByKey(const SequenceKey& key, SequenceInfo& s) override;
|
||||
|
||||
// Gets description of all chunks.
|
||||
virtual std::vector<ChunkInfo> ChunkInfos() override;
|
||||
|
||||
// Get sequence descriptions of a particular chunk.
|
||||
virtual void SequenceInfosForChunk(ChunkIdType chunkId, std::vector<SequenceInfo>& s) override;
|
||||
|
||||
// Retrieves a chunk with data.
|
||||
virtual ChunkPtr GetChunk(ChunkIdType) override;
|
||||
|
||||
private:
|
||||
class ChunkBase;
|
||||
class SequenceChunk;
|
||||
class FrameChunk;
|
||||
|
||||
// Initializes chunk descriptions.
|
||||
void InitializeChunkInfos(CorpusDescriptorPtr corpus, const ConfigHelper& config, const std::wstring& stateListPath);
|
||||
|
||||
// Initializes a single stream this deserializer exposes.
|
||||
void InitializeStream(const std::wstring& name);
|
||||
|
||||
// In frame mode initializes data for all categories/labels in order to
|
||||
// avoid memory copy.
|
||||
void InitializeReadOnlyArrayOfLabels();
|
||||
|
||||
// Sorted vector that maps SequenceKey.m_sequence into an utterance ID (or type max() if the key is not assigned).
|
||||
std::vector<std::tuple<size_t, ChunkIdType, uint32_t>> m_keyToChunkLocation;
|
||||
|
||||
// Type of the data this serializer provides.
|
||||
DataType m_elementType;
|
||||
|
||||
// Array of available categories.
|
||||
// We do no allocate data for all input sequences, only returning a pointer to existing category.
|
||||
std::vector<SparseSequenceDataPtr> m_categories;
|
||||
|
||||
// A list of category indices
|
||||
// (a list of numbers from 0 to N, where N = (number of categories -1))
|
||||
std::vector<IndexType> m_categoryIndices;
|
||||
|
||||
// Flag that indicates whether a single speech frames should be exposed as a sequence.
|
||||
bool m_frameMode;
|
||||
|
||||
CorpusDescriptorPtr m_corpus;
|
||||
|
||||
std::vector<const ChunkDescriptor*> m_chunks;
|
||||
std::map<const ChunkDescriptor*, size_t> m_chunkToFileIndex;
|
||||
|
||||
size_t m_dimension;
|
||||
size_t m_chunkSizeBytes;
|
||||
|
||||
// Track phone boundaries
|
||||
bool m_withPhoneBoundaries;
|
||||
|
||||
StateTablePtr m_stateTable;
|
||||
|
||||
std::vector<std::shared_ptr<Index>> m_indices;
|
||||
std::vector<std::wstring> m_mlfFiles;
|
||||
};
|
||||
|
||||
}
|
||||
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