SpeechRecognitionFSTs.jl

# SPDX-License-Identifier: CECILL-2.1

module SpeechRecognitionFSTs

using ..TensorFSTs
using ..TensorFSTs.LinearFSTs

export EmissionMapping, lexiconfst, tokenfst

"""
    struct EmissionMapping <: AbstractMatrix{Int}
        numstates::Int
        numtokens::Int
    end

Default emission mapping which assumes the same number of emissions per token.
"""
struct EmissionMapping <: AbstractMatrix{Int}
    numpdfs::Int
    numtokens::Int
end

Base.size(x::EmissionMapping) = (x.numpdfs, x.numtokens)
Base.getindex(x::EmissionMapping, i, j) = (j - 1) * x.numpdfs + i
Base.minimum(x::EmissionMapping) = 1
Base.maximum(x::EmissionMapping) = (x.numsnumpdfstates * x.num_emission)



"""
    tokenfst(S, topo, initweights, finalweights, tokens, mapping)

Create a FST composed of a set of smaller FSTs. Each token share the same FST
topology specified with `topo = [(src, dest, weight), ...]`. `S` is a
`Semiring` of output FST, `tokens` is a list of token IDs, and
`mapping[tokens[i]]` is the output symbol .
"""
function tokenfst(
    S,
    topo,
    initweights,
    finalweights,
    tokens,
    mapping = EmissionMapping(length(topo), length(tokens))
)
    print(mapping[1,2])
    emission_count = 1
    states = Set(Int[])
    arcs, init, final = [], [], []
    
    for (i, token) in enumerate(tokens)
        offset = length(states)
        
        values = [topo_arc[2] for topo_arc in topo]

        for (j,  topo_arc) in enumerate(topo)

            if j>1
                if topo_arc[2] != values[j-1]
                    emission_count += 1
                end
            end

            src, dest, weight = offset + topo_arc[1], offset + topo_arc[2], topo_arc[3]

            arc = Arc(
                src = src,
                isym = mapping[emission_count],
                osym = j == 1 ? token : 0,
                dest = dest,
                weight = S(weight)
            )

            push!(states, src)
            push!(states, dest)
            push!(arcs, arc)
        end

        for (state, weight) in initweights
            state = offset + state
            push!(states, state)
            push!(init, state => S(weight))
        end

        for (state, weight) in finalweights
            state = offset + state
            push!(states, state)
            push!(final, state => S(weight))
        end
    end

    TensorFST(arcs, init, final)
end

include("trie.jl")
include("lexicon.jl")

end