Refactor the JNI interface to make it more modular and maintainable (#802)
This commit is contained in:
Fangjun Kuang
221
sherpa-onnx/kotlin-api/OfflineRecognizer.kt
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221
sherpa-onnx/kotlin-api/OfflineRecognizer.kt
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package com.k2fsa.sherpa.onnx
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import android.content.res.AssetManager
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data class OfflineRecognizerResult(
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val text: String,
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val tokens: Array<String>,
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val timestamps: FloatArray,
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)
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data class OfflineTransducerModelConfig(
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var encoder: String = "",
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var decoder: String = "",
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var joiner: String = "",
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)
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data class OfflineParaformerModelConfig(
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var model: String = "",
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)
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data class OfflineWhisperModelConfig(
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var encoder: String = "",
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var decoder: String = "",
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var language: String = "en", // Used with multilingual model
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var task: String = "transcribe", // transcribe or translate
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var tailPaddings: Int = 1000, // Padding added at the end of the samples
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)
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data class OfflineModelConfig(
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var transducer: OfflineTransducerModelConfig = OfflineTransducerModelConfig(),
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var paraformer: OfflineParaformerModelConfig = OfflineParaformerModelConfig(),
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var whisper: OfflineWhisperModelConfig = OfflineWhisperModelConfig(),
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var numThreads: Int = 1,
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var debug: Boolean = false,
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var provider: String = "cpu",
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var modelType: String = "",
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var tokens: String,
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)
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data class OfflineRecognizerConfig(
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var featConfig: FeatureConfig = FeatureConfig(),
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var modelConfig: OfflineModelConfig,
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// var lmConfig: OfflineLMConfig(), // TODO(fangjun): enable it
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var decodingMethod: String = "greedy_search",
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var maxActivePaths: Int = 4,
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var hotwordsFile: String = "",
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var hotwordsScore: Float = 1.5f,
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)
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class OfflineRecognizer(
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assetManager: AssetManager? = null,
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config: OfflineRecognizerConfig,
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) {
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private val ptr: Long
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init {
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ptr = if (assetManager != null) {
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newFromAsset(assetManager, config)
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} else {
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newFromFile(config)
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}
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}
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protected fun finalize() {
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delete(ptr)
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}
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fun release() = finalize()
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fun createStream(): OfflineStream {
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val p = createStream(ptr)
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return OfflineStream(p)
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}
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fun getResult(stream: OfflineStream): OfflineRecognizerResult {
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val objArray = getResult(stream.ptr)
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val text = objArray[0] as String
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val tokens = objArray[1] as Array<String>
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val timestamps = objArray[2] as FloatArray
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return OfflineRecognizerResult(text = text, tokens = tokens, timestamps = timestamps)
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}
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fun decode(stream: OfflineStream) = decode(ptr, stream.ptr)
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private external fun delete(ptr: Long)
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private external fun createStream(ptr: Long): Long
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private external fun newFromAsset(
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assetManager: AssetManager,
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config: OfflineRecognizerConfig,
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): Long
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private external fun newFromFile(
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config: OfflineRecognizerConfig,
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): Long
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private external fun decode(ptr: Long, streamPtr: Long)
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private external fun getResult(streamPtr: Long): Array<Any>
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companion object {
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init {
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System.loadLibrary("sherpa-onnx-jni")
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}
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}
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}
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/*
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Please see
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https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
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for a list of pre-trained models.
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We only add a few here. Please change the following code
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to add your own. (It should be straightforward to add a new model
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by following the code)
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@param type
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0 - csukuangfj/sherpa-onnx-paraformer-zh-2023-03-28 (Chinese)
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https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-paraformer/paraformer-models.html#csukuangfj-sherpa-onnx-paraformer-zh-2023-03-28-chinese
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int8
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1 - icefall-asr-multidataset-pruned_transducer_stateless7-2023-05-04 (English)
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https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-transducer/zipformer-transducer-models.html#icefall-asr-multidataset-pruned-transducer-stateless7-2023-05-04-english
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encoder int8, decoder/joiner float32
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2 - sherpa-onnx-whisper-tiny.en
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https://k2-fsa.github.io/sherpa/onnx/pretrained_models/whisper/tiny.en.html#tiny-en
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encoder int8, decoder int8
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3 - sherpa-onnx-whisper-base.en
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https://k2-fsa.github.io/sherpa/onnx/pretrained_models/whisper/tiny.en.html#tiny-en
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encoder int8, decoder int8
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4 - pkufool/icefall-asr-zipformer-wenetspeech-20230615 (Chinese)
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https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-transducer/zipformer-transducer-models.html#pkufool-icefall-asr-zipformer-wenetspeech-20230615-chinese
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encoder/joiner int8, decoder fp32
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*/
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fun getOfflineModelConfig(type: Int): OfflineModelConfig? {
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when (type) {
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0 -> {
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val modelDir = "sherpa-onnx-paraformer-zh-2023-03-28"
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return OfflineModelConfig(
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paraformer = OfflineParaformerModelConfig(
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model = "$modelDir/model.int8.onnx",
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),
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tokens = "$modelDir/tokens.txt",
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modelType = "paraformer",
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)
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}
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1 -> {
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val modelDir = "icefall-asr-multidataset-pruned_transducer_stateless7-2023-05-04"
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return OfflineModelConfig(
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transducer = OfflineTransducerModelConfig(
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encoder = "$modelDir/encoder-epoch-30-avg-4.int8.onnx",
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decoder = "$modelDir/decoder-epoch-30-avg-4.onnx",
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joiner = "$modelDir/joiner-epoch-30-avg-4.onnx",
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),
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tokens = "$modelDir/tokens.txt",
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modelType = "zipformer",
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)
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}
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2 -> {
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val modelDir = "sherpa-onnx-whisper-tiny.en"
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return OfflineModelConfig(
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whisper = OfflineWhisperModelConfig(
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encoder = "$modelDir/tiny.en-encoder.int8.onnx",
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decoder = "$modelDir/tiny.en-decoder.int8.onnx",
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),
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tokens = "$modelDir/tiny.en-tokens.txt",
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modelType = "whisper",
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)
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}
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3 -> {
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val modelDir = "sherpa-onnx-whisper-base.en"
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return OfflineModelConfig(
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whisper = OfflineWhisperModelConfig(
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encoder = "$modelDir/base.en-encoder.int8.onnx",
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decoder = "$modelDir/base.en-decoder.int8.onnx",
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),
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tokens = "$modelDir/base.en-tokens.txt",
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modelType = "whisper",
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)
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}
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4 -> {
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val modelDir = "icefall-asr-zipformer-wenetspeech-20230615"
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return OfflineModelConfig(
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transducer = OfflineTransducerModelConfig(
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encoder = "$modelDir/encoder-epoch-12-avg-4.int8.onnx",
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decoder = "$modelDir/decoder-epoch-12-avg-4.onnx",
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joiner = "$modelDir/joiner-epoch-12-avg-4.int8.onnx",
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),
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tokens = "$modelDir/tokens.txt",
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modelType = "zipformer",
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)
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}
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5 -> {
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val modelDir = "sherpa-onnx-zipformer-multi-zh-hans-2023-9-2"
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return OfflineModelConfig(
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transducer = OfflineTransducerModelConfig(
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encoder = "$modelDir/encoder-epoch-20-avg-1.int8.onnx",
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decoder = "$modelDir/decoder-epoch-20-avg-1.onnx",
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joiner = "$modelDir/joiner-epoch-20-avg-1.int8.onnx",
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),
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tokens = "$modelDir/tokens.txt",
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modelType = "zipformer2",
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)
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}
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}
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return null
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}
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