func main() {
if len(os.Args) != 3 {
fmt.Fprintln(os.Stderr, "Usage: classify <rnn> <sample.wav>")
os.Exit(1)
}
rnnData, err := ioutil.ReadFile(os.Args[1])
if err != nil {
die(err)
}
seqFunc, err := rnn.DeserializeBidirectional(rnnData)
if err != nil {
die(err)
}
sample, err := cubewhisper.ReadAudioFile(os.Args[2])
if err != nil {
die(err)
}
inSeq := make([]autofunc.Result, len(sample))
for i, x := range sample {
inSeq[i] = &autofunc.Variable{Vector: x}
}
res := seqFunc.BatchSeqs([][]autofunc.Result{inSeq})
classification := ctc.PrefixSearch(res.OutputSeqs()[0], PrefixThreshold)
labels := make([]cubewhisper.Label, len(classification))
for i, c := range classification {
labels[i] = cubewhisper.Label(c)
}
fmt.Println("Raw labels:", labels)
fmt.Println("Algorithm:", cubewhisper.LabelsToMoveString(labels))
}
func classifySamples() {
emitLoading("Processing audio")
sample := cubewhisper.SeqForAudioSamples(Samples, SampleRate)
inSeq := make([]autofunc.Result, len(sample))
for i, x := range sample {
inSeq[i] = &autofunc.Variable{Vector: x}
}
emitLoading("Running neural network")
res := Network.BatchSeqs([][]autofunc.Result{inSeq})
classification := ctc.PrefixSearch(res.OutputSeqs()[0], SearchBlankCutoff)
labels := make([]cubewhisper.Label, len(classification))
for i, c := range classification {
labels[i] = cubewhisper.Label(c)
}
emitMoves(cubewhisper.LabelsToMoveString(labels), fmt.Sprintf("%v", labels))
}