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SherpaNcnn.kt
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SherpaNcnn.kt
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package com.k2fsa.sherpa.ncnn
import android.content.res.AssetManager
data class FeatureExtractorConfig(
var sampleRate: Float,
var featureDim: Int,
)
data class ModelConfig(
var encoderParam: String,
var encoderBin: String,
var decoderParam: String,
var decoderBin: String,
var joinerParam: String,
var joinerBin: String,
var tokens: String,
var numThreads: Int = 1,
var useGPU: Boolean = true, // If there is a GPU and useGPU true, we will use GPU
)
data class DecoderConfig(
var method: String = "modified_beam_search", // valid values: greedy_search, modified_beam_search
var numActivePaths: Int = 4, // used only by modified_beam_search
)
data class RecognizerConfig(
var featConfig: FeatureExtractorConfig,
var modelConfig: ModelConfig,
var decoderConfig: DecoderConfig,
var enableEndpoint: Boolean = true,
var rule1MinTrailingSilence: Float = 2.4f,
var rule2MinTrailingSilence: Float = 1.0f,
var rule3MinUtteranceLength: Float = 30.0f,
var hotwordsFile: String = "",
var hotwordsScore: Float = 1.5f,
)
class SherpaNcnn(
var config: RecognizerConfig,
assetManager: AssetManager? = null,
) {
private val ptr: Long
init {
if (assetManager != null) {
ptr = newFromAsset(assetManager, config)
} else {
ptr = newFromFile(config)
}
}
protected fun finalize() {
delete(ptr)
}
fun acceptSamples(samples: FloatArray) =
acceptWaveform(ptr, samples = samples, sampleRate = config.featConfig.sampleRate)
fun isReady() = isReady(ptr)
fun decode() = decode(ptr)
fun inputFinished() = inputFinished(ptr)
fun isEndpoint(): Boolean = isEndpoint(ptr)
fun reset(recreate: Boolean = false) = reset(ptr, recreate = recreate)
val text: String
get() = getText(ptr)
private external fun newFromAsset(
assetManager: AssetManager,
config: RecognizerConfig,
): Long
private external fun newFromFile(
config: RecognizerConfig,
): Long
private external fun delete(ptr: Long)
private external fun acceptWaveform(ptr: Long, samples: FloatArray, sampleRate: Float)
private external fun inputFinished(ptr: Long)
private external fun isReady(ptr: Long): Boolean
private external fun decode(ptr: Long)
private external fun isEndpoint(ptr: Long): Boolean
private external fun reset(ptr: Long, recreate: Boolean)
private external fun getText(ptr: Long): String
companion object {
init {
System.loadLibrary("sherpa-ncnn-jni")
}
}
}
fun getFeatureExtractorConfig(
sampleRate: Float,
featureDim: Int
): FeatureExtractorConfig {
return FeatureExtractorConfig(
sampleRate = sampleRate,
featureDim = featureDim,
)
}
fun getDecoderConfig(method: String, numActivePaths: Int): DecoderConfig {
return DecoderConfig(method = method, numActivePaths = numActivePaths)
}
/*
@param type
0 - https://huggingface.co/csukuangfj/sherpa-ncnn-2022-09-30
This model supports only Chinese
1 - https://huggingface.co/csukuangfj/sherpa-ncnn-conv-emformer-transducer-2022-12-06
This model supports both English and Chinese
2 - https://huggingface.co/csukuangfj/sherpa-ncnn-streaming-zipformer-bilingual-zh-en-2023-02-13
This model supports both English and Chinese
3 - https://huggingface.co/csukuangfj/sherpa-ncnn-streaming-zipformer-en-2023-02-13
This model supports only English
4 - https://huggingface.co/shaojieli/sherpa-ncnn-streaming-zipformer-fr-2023-04-14
This model supports only French
Please follow
https://k2-fsa.github.io/sherpa/ncnn/pretrained_models/index.html
to add more pre-trained models
*/
fun getModelConfig(type: Int, useGPU: Boolean): ModelConfig? {
when (type) {
0 -> {
val modelDir = "sherpa-ncnn-2022-09-30"
return ModelConfig(
encoderParam = "$modelDir/encoder_jit_trace-pnnx.ncnn.param",
encoderBin = "$modelDir/encoder_jit_trace-pnnx.ncnn.bin",
decoderParam = "$modelDir/decoder_jit_trace-pnnx.ncnn.param",
decoderBin = "$modelDir/decoder_jit_trace-pnnx.ncnn.bin",
joinerParam = "$modelDir/joiner_jit_trace-pnnx.ncnn.param",
joinerBin = "$modelDir/joiner_jit_trace-pnnx.ncnn.bin",
tokens = "$modelDir/tokens.txt",
numThreads = 1,
useGPU = useGPU,
)
}
1 -> {
val modelDir = "sherpa-ncnn-conv-emformer-transducer-2022-12-06"
return ModelConfig(
encoderParam = "$modelDir/encoder_jit_trace-pnnx.ncnn.int8.param",
encoderBin = "$modelDir/encoder_jit_trace-pnnx.ncnn.int8.bin",
decoderParam = "$modelDir/decoder_jit_trace-pnnx.ncnn.param",
decoderBin = "$modelDir/decoder_jit_trace-pnnx.ncnn.bin",
joinerParam = "$modelDir/joiner_jit_trace-pnnx.ncnn.int8.param",
joinerBin = "$modelDir/joiner_jit_trace-pnnx.ncnn.int8.bin",
tokens = "$modelDir/tokens.txt",
numThreads = 1,
useGPU = useGPU,
)
}
2 -> {
val modelDir = "sherpa-ncnn-streaming-zipformer-bilingual-zh-en-2023-02-13"
return ModelConfig(
encoderParam = "$modelDir/encoder_jit_trace-pnnx.ncnn.param",
encoderBin = "$modelDir/encoder_jit_trace-pnnx.ncnn.bin",
decoderParam = "$modelDir/decoder_jit_trace-pnnx.ncnn.param",
decoderBin = "$modelDir/decoder_jit_trace-pnnx.ncnn.bin",
joinerParam = "$modelDir/joiner_jit_trace-pnnx.ncnn.param",
joinerBin = "$modelDir/joiner_jit_trace-pnnx.ncnn.bin",
tokens = "$modelDir/tokens.txt",
numThreads = 1,
useGPU = useGPU,
)
}
3 -> {
val modelDir = "sherpa-ncnn-streaming-zipformer-en-2023-02-13"
return ModelConfig(
encoderParam = "$modelDir/encoder_jit_trace-pnnx.ncnn.param",
encoderBin = "$modelDir/encoder_jit_trace-pnnx.ncnn.bin",
decoderParam = "$modelDir/decoder_jit_trace-pnnx.ncnn.param",
decoderBin = "$modelDir/decoder_jit_trace-pnnx.ncnn.bin",
joinerParam = "$modelDir/joiner_jit_trace-pnnx.ncnn.param",
joinerBin = "$modelDir/joiner_jit_trace-pnnx.ncnn.bin",
tokens = "$modelDir/tokens.txt",
numThreads = 1,
useGPU = useGPU,
)
}
4 -> {
val modelDir = "sherpa-ncnn-streaming-zipformer-fr-2023-04-14"
return ModelConfig(
encoderParam = "$modelDir/encoder_jit_trace-pnnx.ncnn.param",
encoderBin = "$modelDir/encoder_jit_trace-pnnx.ncnn.bin",
decoderParam = "$modelDir/decoder_jit_trace-pnnx.ncnn.param",
decoderBin = "$modelDir/decoder_jit_trace-pnnx.ncnn.bin",
joinerParam = "$modelDir/joiner_jit_trace-pnnx.ncnn.param",
joinerBin = "$modelDir/joiner_jit_trace-pnnx.ncnn.bin",
tokens = "$modelDir/tokens.txt",
numThreads = 1,
useGPU = useGPU,
)
}
}
return null
}