1516 lines
55 KiB
Kotlin
1516 lines
55 KiB
Kotlin
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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val lang: String,
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val emotion: String,
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val event: String,
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// valid only for TDT models
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val durations: 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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var qnnConfig: QnnConfig = QnnConfig(),
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)
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data class OfflineParaformerModelConfig(
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var model: String = "",
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var qnnConfig: QnnConfig = QnnConfig(),
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)
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data class OfflineNemoEncDecCtcModelConfig(
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var model: String = "",
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)
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data class OfflineDolphinModelConfig(
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var model: String = "",
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)
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data class OfflineZipformerCtcModelConfig(
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var model: String = "",
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var qnnConfig: QnnConfig = QnnConfig(),
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)
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data class OfflineWenetCtcModelConfig(
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var model: String = "",
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)
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data class OfflineOmnilingualAsrCtcModelConfig(
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var model: String = "",
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)
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data class OfflineMedAsrCtcModelConfig(
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var model: String = "",
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)
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data class OfflineFireRedAsrCtcModelConfig(
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var model: String = "",
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)
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data class OfflineFunAsrNanoModelConfig(
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var encoderAdaptor: String = "",
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var llm: String = "",
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var embedding: String = "",
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var tokenizer: String = "",
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var systemPrompt: String = "You are a helpful assistant.",
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var userPrompt: String = "语音转写：",
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var maxNewTokens: Int = 512,
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var temperature: Float = 1e-6f,
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var topP: Float = 0.8f,
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var seed: Int = 42,
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var language: String = "",
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var itn: Boolean = true,
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var hotwords: String = "",
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)
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data class OfflineQwen3AsrModelConfig(
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var convFrontend: String = "",
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var encoder: String = "",
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var decoder: String = "",
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var tokenizer: String = "",
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var maxTotalLen: Int = 512,
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var maxNewTokens: Int = 128,
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var temperature: Float = 1e-6f,
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var topP: Float = 0.8f,
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var seed: Int = 42,
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var hotwords: 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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var enableTokenTimestamps: Boolean = false,
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var enableSegmentTimestamps: Boolean = false,
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)
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data class OfflineCanaryModelConfig(
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var encoder: String = "",
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var decoder: String = "",
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var srcLang: String = "en",
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var tgtLang: String = "en",
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var usePnc: Boolean = true,
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)
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data class OfflineCohereTranscribeModelConfig(
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var encoder: String = "",
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var decoder: String = "",
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var language: String = "",
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var usePunct: Boolean = true,
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var useItn: Boolean = true,
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)
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data class OfflineFireRedAsrModelConfig(
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var encoder: String = "",
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var decoder: String = "",
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)
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// For moonshine v1, you need four models.
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// For moonshine v2, you need two models.
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// - v1: preprocessor, encoder, uncachedDecoder, cachedDecoder
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// - v2: encoder, mergedDecoder
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data class OfflineMoonshineModelConfig(
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var preprocessor: String = "",
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var encoder: String = "",
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var uncachedDecoder: String = "",
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var cachedDecoder: String = "",
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var mergedDecoder: String = "",
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)
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data class OfflineSenseVoiceModelConfig(
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var model: String = "",
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var language: String = "",
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var useInverseTextNormalization: Boolean = true,
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var qnnConfig: QnnConfig = QnnConfig(),
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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 fireRedAsr: OfflineFireRedAsrModelConfig = OfflineFireRedAsrModelConfig(),
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var moonshine: OfflineMoonshineModelConfig = OfflineMoonshineModelConfig(),
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var nemo: OfflineNemoEncDecCtcModelConfig = OfflineNemoEncDecCtcModelConfig(),
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var senseVoice: OfflineSenseVoiceModelConfig = OfflineSenseVoiceModelConfig(),
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var dolphin: OfflineDolphinModelConfig = OfflineDolphinModelConfig(),
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var zipformerCtc: OfflineZipformerCtcModelConfig = OfflineZipformerCtcModelConfig(),
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var wenetCtc: OfflineWenetCtcModelConfig = OfflineWenetCtcModelConfig(),
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var omnilingual: OfflineOmnilingualAsrCtcModelConfig = OfflineOmnilingualAsrCtcModelConfig(),
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var medasr: OfflineMedAsrCtcModelConfig = OfflineMedAsrCtcModelConfig(),
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var funasrNano: OfflineFunAsrNanoModelConfig = OfflineFunAsrNanoModelConfig(),
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var qwen3Asr: OfflineQwen3AsrModelConfig = OfflineQwen3AsrModelConfig(),
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var fireRedAsrCtc: OfflineFireRedAsrCtcModelConfig = OfflineFireRedAsrCtcModelConfig(),
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var canary: OfflineCanaryModelConfig = OfflineCanaryModelConfig(),
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var cohereTranscribe: OfflineCohereTranscribeModelConfig =
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OfflineCohereTranscribeModelConfig(),
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var teleSpeech: String = "",
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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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var modelingUnit: String = "",
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var bpeVocab: 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 = OfflineModelConfig(),
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// var lmConfig: OfflineLMConfig(), // TODO(fangjun): enable it
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var hr: HomophoneReplacerConfig = HomophoneReplacerConfig(),
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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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var ruleFsts: String = "",
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var ruleFars: String = "",
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var blankPenalty: Float = 0.0f,
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)
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class OfflineRecognizer(
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assetManager: AssetManager? = null,
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val config: OfflineRecognizerConfig,
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) {
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private var 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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require(ptr != 0L) {
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"Invalid OfflineRecognizerConfig: failed to create native OfflineRecognizer"
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}
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}
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protected fun finalize() {
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if (ptr != 0L) {
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delete(ptr)
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ptr = 0
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}
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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 createStream(hotwords: String): OfflineStream {
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val p = createStreamWithHotwords(ptr, hotwords)
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return OfflineStream(p)
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}
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fun getResult(stream: OfflineStream): OfflineRecognizerResult {
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return getResult(stream.ptr)
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}
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fun decode(stream: OfflineStream) = decode(ptr, stream.ptr)
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fun setConfig(config: OfflineRecognizerConfig) = setConfig(ptr, config)
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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 createStreamWithHotwords(ptr: Long, hotwords: String): Long
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private external fun setConfig(ptr: Long, config: OfflineRecognizerConfig)
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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): OfflineRecognizerResult
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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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@JvmStatic
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external fun prependAdspLibraryPath(newPath: String) // for qnn
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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-2023-09-14 (Chinese)
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https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-paraformer/paraformer-models.html#csukuangfj-sherpa-onnx-paraformer-zh-2023-09-14-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, modelPrefix: String = ""): OfflineModelConfig? {
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when (type) {
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0 -> {
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val modelDir = "sherpa-onnx-paraformer-zh-2023-09-14"
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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 = "transducer",
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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 = "transducer",
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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 = "transducer",
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)
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}
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6 -> {
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val modelDir = "sherpa-onnx-nemo-ctc-en-citrinet-512"
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return OfflineModelConfig(
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nemo = OfflineNemoEncDecCtcModelConfig(
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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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)
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}
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7 -> {
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val modelDir = "sherpa-onnx-nemo-fast-conformer-ctc-be-de-en-es-fr-hr-it-pl-ru-uk-20k"
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return OfflineModelConfig(
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nemo = OfflineNemoEncDecCtcModelConfig(
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model = "$modelDir/model.onnx",
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),
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tokens = "$modelDir/tokens.txt",
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)
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}
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8 -> {
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val modelDir = "sherpa-onnx-nemo-fast-conformer-ctc-en-24500"
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return OfflineModelConfig(
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nemo = OfflineNemoEncDecCtcModelConfig(
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model = "$modelDir/model.onnx",
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),
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tokens = "$modelDir/tokens.txt",
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)
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}
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9 -> {
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val modelDir = "sherpa-onnx-nemo-fast-conformer-ctc-en-de-es-fr-14288"
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return OfflineModelConfig(
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nemo = OfflineNemoEncDecCtcModelConfig(
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model = "$modelDir/model.onnx",
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),
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tokens = "$modelDir/tokens.txt",
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)
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}
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10 -> {
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val modelDir = "sherpa-onnx-nemo-fast-conformer-ctc-es-1424"
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return OfflineModelConfig(
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nemo = OfflineNemoEncDecCtcModelConfig(
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model = "$modelDir/model.onnx",
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),
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tokens = "$modelDir/tokens.txt",
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)
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}
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11 -> {
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val modelDir = "sherpa-onnx-telespeech-ctc-int8-zh-2024-06-04"
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return OfflineModelConfig(
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teleSpeech = "$modelDir/model.int8.onnx",
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tokens = "$modelDir/tokens.txt",
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modelType = "telespeech_ctc",
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)
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}
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12 -> {
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val modelDir = "sherpa-onnx-zipformer-thai-2024-06-20"
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return OfflineModelConfig(
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transducer = OfflineTransducerModelConfig(
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encoder = "$modelDir/encoder-epoch-12-avg-5.int8.onnx",
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decoder = "$modelDir/decoder-epoch-12-avg-5.onnx",
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joiner = "$modelDir/joiner-epoch-12-avg-5.int8.onnx",
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),
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tokens = "$modelDir/tokens.txt",
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modelType = "transducer",
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)
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}
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13 -> {
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val modelDir = "sherpa-onnx-zipformer-korean-2024-06-24"
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return OfflineModelConfig(
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transducer = OfflineTransducerModelConfig(
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encoder = "$modelDir/encoder-epoch-99-avg-1.int8.onnx",
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decoder = "$modelDir/decoder-epoch-99-avg-1.onnx",
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joiner = "$modelDir/joiner-epoch-99-avg-1.int8.onnx",
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),
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tokens = "$modelDir/tokens.txt",
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modelType = "transducer",
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)
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}
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14 -> {
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val modelDir = "sherpa-onnx-paraformer-zh-small-2024-03-09"
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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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15 -> {
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val modelDir = "sherpa-onnx-sense-voice-zh-en-ja-ko-yue-int8-2024-07-17"
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return OfflineModelConfig(
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senseVoice = OfflineSenseVoiceModelConfig(
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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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)
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}
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16 -> {
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val modelDir = "sherpa-onnx-zipformer-ja-reazonspeech-2024-08-01"
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return OfflineModelConfig(
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transducer = OfflineTransducerModelConfig(
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encoder = "$modelDir/encoder-epoch-99-avg-1.int8.onnx",
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decoder = "$modelDir/decoder-epoch-99-avg-1.onnx",
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joiner = "$modelDir/joiner-epoch-99-avg-1.int8.onnx",
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),
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tokens = "$modelDir/tokens.txt",
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modelType = "transducer",
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)
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}
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17 -> {
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val modelDir = "sherpa-onnx-zipformer-ru-2024-09-18"
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return OfflineModelConfig(
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transducer = OfflineTransducerModelConfig(
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encoder = "$modelDir/encoder.int8.onnx",
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decoder = "$modelDir/decoder.onnx",
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joiner = "$modelDir/joiner.int8.onnx",
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),
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tokens = "$modelDir/tokens.txt",
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modelType = "transducer",
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)
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}
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18 -> {
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val modelDir = "sherpa-onnx-small-zipformer-ru-2024-09-18"
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return OfflineModelConfig(
|
|
transducer = OfflineTransducerModelConfig(
|
|
encoder = "$modelDir/encoder.int8.onnx",
|
|
decoder = "$modelDir/decoder.onnx",
|
|
joiner = "$modelDir/joiner.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
modelType = "transducer",
|
|
)
|
|
}
|
|
|
|
19 -> {
|
|
val modelDir = "sherpa-onnx-nemo-ctc-giga-am-russian-2024-10-24"
|
|
return OfflineModelConfig(
|
|
nemo = OfflineNemoEncDecCtcModelConfig(
|
|
model = "$modelDir/model.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
20 -> {
|
|
val modelDir = "sherpa-onnx-nemo-transducer-giga-am-russian-2024-10-24"
|
|
return OfflineModelConfig(
|
|
transducer = OfflineTransducerModelConfig(
|
|
encoder = "$modelDir/encoder.int8.onnx",
|
|
decoder = "$modelDir/decoder.onnx",
|
|
joiner = "$modelDir/joiner.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
modelType = "nemo_transducer",
|
|
)
|
|
}
|
|
|
|
21 -> {
|
|
val modelDir = "sherpa-onnx-moonshine-tiny-en-int8"
|
|
return OfflineModelConfig(
|
|
moonshine = OfflineMoonshineModelConfig(
|
|
preprocessor = "$modelDir/preprocess.onnx",
|
|
encoder = "$modelDir/encode.int8.onnx",
|
|
uncachedDecoder = "$modelDir/uncached_decode.int8.onnx",
|
|
cachedDecoder = "$modelDir/cached_decode.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
22 -> {
|
|
val modelDir = "sherpa-onnx-moonshine-base-en-int8"
|
|
return OfflineModelConfig(
|
|
moonshine = OfflineMoonshineModelConfig(
|
|
preprocessor = "$modelDir/preprocess.onnx",
|
|
encoder = "$modelDir/encode.int8.onnx",
|
|
uncachedDecoder = "$modelDir/uncached_decode.int8.onnx",
|
|
cachedDecoder = "$modelDir/cached_decode.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
23 -> {
|
|
val modelDir = "sherpa-onnx-zipformer-zh-en-2023-11-22"
|
|
return OfflineModelConfig(
|
|
transducer = OfflineTransducerModelConfig(
|
|
encoder = "$modelDir/encoder-epoch-34-avg-19.int8.onnx",
|
|
decoder = "$modelDir/decoder-epoch-34-avg-19.onnx",
|
|
joiner = "$modelDir/joiner-epoch-34-avg-19.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
modelType = "transducer",
|
|
)
|
|
}
|
|
|
|
24 -> {
|
|
val modelDir = "sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16"
|
|
return OfflineModelConfig(
|
|
fireRedAsr = OfflineFireRedAsrModelConfig(
|
|
encoder = "$modelDir/encoder.int8.onnx",
|
|
decoder = "$modelDir/decoder.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
25 -> {
|
|
val modelDir = "sherpa-onnx-dolphin-base-ctc-multi-lang-int8-2025-04-02"
|
|
return OfflineModelConfig(
|
|
dolphin = OfflineDolphinModelConfig(
|
|
model = "$modelDir/model.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
26 -> {
|
|
val modelDir = "sherpa-onnx-zipformer-vi-int8-2025-04-20"
|
|
return OfflineModelConfig(
|
|
transducer = OfflineTransducerModelConfig(
|
|
encoder = "$modelDir/encoder-epoch-12-avg-8.int8.onnx",
|
|
decoder = "$modelDir/decoder-epoch-12-avg-8.onnx",
|
|
joiner = "$modelDir/joiner-epoch-12-avg-8.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
modelType = "transducer",
|
|
)
|
|
}
|
|
|
|
27 -> {
|
|
val modelDir = "sherpa-onnx-nemo-ctc-giga-am-v2-russian-2025-04-19"
|
|
return OfflineModelConfig(
|
|
nemo = OfflineNemoEncDecCtcModelConfig(
|
|
model = "$modelDir/model.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
28 -> {
|
|
val modelDir = "sherpa-onnx-nemo-transducer-giga-am-v2-russian-2025-04-19"
|
|
return OfflineModelConfig(
|
|
transducer = OfflineTransducerModelConfig(
|
|
encoder = "$modelDir/encoder.int8.onnx",
|
|
decoder = "$modelDir/decoder.onnx",
|
|
joiner = "$modelDir/joiner.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
modelType = "nemo_transducer",
|
|
)
|
|
}
|
|
|
|
29 -> {
|
|
val modelDir = "sherpa-onnx-zipformer-ru-int8-2025-04-20"
|
|
return OfflineModelConfig(
|
|
transducer = OfflineTransducerModelConfig(
|
|
encoder = "$modelDir/encoder.int8.onnx",
|
|
decoder = "$modelDir/decoder.onnx",
|
|
joiner = "$modelDir/joiner.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
modelType = "transducer",
|
|
)
|
|
}
|
|
|
|
30 -> {
|
|
val modelDir = "sherpa-onnx-nemo-parakeet-tdt-0.6b-v2-int8"
|
|
return OfflineModelConfig(
|
|
transducer = OfflineTransducerModelConfig(
|
|
encoder = "$modelDir/encoder.int8.onnx",
|
|
decoder = "$modelDir/decoder.int8.onnx",
|
|
joiner = "$modelDir/joiner.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
modelType = "nemo_transducer",
|
|
)
|
|
}
|
|
|
|
31 -> {
|
|
val modelDir = "sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03"
|
|
return OfflineModelConfig(
|
|
zipformerCtc = OfflineZipformerCtcModelConfig(
|
|
model = "$modelDir/model.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
32 -> {
|
|
val modelDir = "sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8"
|
|
return OfflineModelConfig(
|
|
canary = OfflineCanaryModelConfig(
|
|
encoder = "$modelDir/encoder.int8.onnx",
|
|
decoder = "$modelDir/decoder.int8.onnx",
|
|
srcLang = "en",
|
|
tgtLang = "en",
|
|
usePnc = true,
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
33 -> {
|
|
val modelDir = "sherpa-onnx-nemo-parakeet_tdt_ctc_110m-en-36000-int8"
|
|
return OfflineModelConfig(
|
|
nemo = OfflineNemoEncDecCtcModelConfig(
|
|
model = "$modelDir/model.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
34 -> {
|
|
val modelDir = "sherpa-onnx-nemo-parakeet-tdt_ctc-0.6b-ja-35000-int8"
|
|
return OfflineModelConfig(
|
|
nemo = OfflineNemoEncDecCtcModelConfig(
|
|
model = "$modelDir/model.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
35 -> {
|
|
val modelDir = "sherpa-onnx-nemo-transducer-stt_pt_fastconformer_hybrid_large_pc-int8"
|
|
return OfflineModelConfig(
|
|
transducer = OfflineTransducerModelConfig(
|
|
encoder = "$modelDir/encoder.int8.onnx",
|
|
decoder = "$modelDir/decoder.int8.onnx",
|
|
joiner = "$modelDir/joiner.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
modelType = "nemo_transducer",
|
|
)
|
|
}
|
|
|
|
36 -> {
|
|
val modelDir = "sherpa-onnx-nemo-stt_pt_fastconformer_hybrid_large_pc-int8"
|
|
return OfflineModelConfig(
|
|
nemo = OfflineNemoEncDecCtcModelConfig(
|
|
model = "$modelDir/model.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
37 -> {
|
|
val modelDir = "sherpa-onnx-nemo-transducer-stt_de_fastconformer_hybrid_large_pc-int8"
|
|
return OfflineModelConfig(
|
|
transducer = OfflineTransducerModelConfig(
|
|
encoder = "$modelDir/encoder.int8.onnx",
|
|
decoder = "$modelDir/decoder.int8.onnx",
|
|
joiner = "$modelDir/joiner.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
modelType = "nemo_transducer",
|
|
)
|
|
}
|
|
|
|
38 -> {
|
|
val modelDir = "sherpa-onnx-nemo-stt_de_fastconformer_hybrid_large_pc-int8"
|
|
return OfflineModelConfig(
|
|
nemo = OfflineNemoEncDecCtcModelConfig(
|
|
model = "$modelDir/model.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
39 -> {
|
|
val modelDir = "sherpa-onnx-zipformer-ctc-small-zh-int8-2025-07-16"
|
|
return OfflineModelConfig(
|
|
zipformerCtc = OfflineZipformerCtcModelConfig(
|
|
model = "$modelDir/model.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
40 -> {
|
|
val modelDir = "sherpa-onnx-nemo-parakeet-tdt-0.6b-v3-int8"
|
|
return OfflineModelConfig(
|
|
transducer = OfflineTransducerModelConfig(
|
|
encoder = "$modelDir/encoder.int8.onnx",
|
|
decoder = "$modelDir/decoder.int8.onnx",
|
|
joiner = "$modelDir/joiner.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
modelType = "nemo_transducer",
|
|
)
|
|
}
|
|
|
|
41 -> {
|
|
val modelDir = "sherpa-onnx-sense-voice-zh-en-ja-ko-yue-int8-2025-09-09"
|
|
return OfflineModelConfig(
|
|
senseVoice = OfflineSenseVoiceModelConfig(
|
|
model = "$modelDir/model.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
42 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-wenetspeech-yue-u2pp-conformer-ctc-zh-en-cantonese-int8-2025-09-10"
|
|
return OfflineModelConfig(
|
|
wenetCtc = OfflineWenetCtcModelConfig(
|
|
model = "$modelDir/model.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
43 -> {
|
|
val modelDir = "sherpa-onnx-paraformer-zh-int8-2025-10-07"
|
|
return OfflineModelConfig(
|
|
paraformer = OfflineParaformerModelConfig(
|
|
model = "$modelDir/model.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
modelType = "paraformer",
|
|
)
|
|
}
|
|
|
|
44 -> {
|
|
val modelDir = "sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-int8-2025-11-12"
|
|
return OfflineModelConfig(
|
|
omnilingual = OfflineOmnilingualAsrCtcModelConfig(
|
|
model = "$modelDir/model.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
45 -> {
|
|
val modelDir = "sherpa-onnx-medasr-ctc-en-int8-2025-12-25"
|
|
return OfflineModelConfig(
|
|
medasr = OfflineMedAsrCtcModelConfig(
|
|
model = "$modelDir/model.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
46 -> {
|
|
val modelDir = modelPrefix + "sherpa-onnx-funasr-nano-int8-2025-12-30"
|
|
return OfflineModelConfig(
|
|
funasrNano = OfflineFunAsrNanoModelConfig(
|
|
encoderAdaptor = "$modelDir/encoder_adaptor.int8.onnx",
|
|
llm = "$modelDir/llm.int8.onnx",
|
|
embedding = "$modelDir/embedding.int8.onnx",
|
|
tokenizer = "$modelDir/Qwen3-0.6B",
|
|
),
|
|
tokens = "",
|
|
// 定稿提速:LLM 解码 4 线程(≈快一倍)。线程预算:nemotron 2 +
|
|
// nano 4 + 声纹 v2 2 + 滚动 base 1;若机身发烫/第一遍变卡,
|
|
// 退回 2 并改上"积压保险"方案
|
|
numThreads = 4,
|
|
)
|
|
}
|
|
|
|
47 -> {
|
|
val modelDir = "sherpa-onnx-wenetspeech-wu-u2pp-conformer-ctc-zh-int8-2026-02-03"
|
|
return OfflineModelConfig(
|
|
wenetCtc = OfflineWenetCtcModelConfig(
|
|
model = "$modelDir/model.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
48 -> {
|
|
val modelDir = "sherpa-onnx-wenetspeech-wu-u2pp-conformer-ctc-zh-2026-02-03"
|
|
return OfflineModelConfig(
|
|
wenetCtc = OfflineWenetCtcModelConfig(
|
|
model = "$modelDir/model.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
49 -> {
|
|
val modelDir = "sherpa-onnx-zipformer-vi-30M-int8-2026-02-09"
|
|
return OfflineModelConfig(
|
|
transducer = OfflineTransducerModelConfig(
|
|
encoder = "$modelDir/encoder.int8.onnx",
|
|
decoder = "$modelDir/decoder.onnx",
|
|
joiner = "$modelDir/joiner.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
modelType = "transducer",
|
|
)
|
|
}
|
|
|
|
50 -> {
|
|
val modelDir = "sherpa-onnx-fire-red-asr2-ctc-zh_en-int8-2026-02-25"
|
|
return OfflineModelConfig(
|
|
fireRedAsrCtc = OfflineFireRedAsrCtcModelConfig(
|
|
model = "$modelDir/model.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
51 -> {
|
|
val modelDir = "sherpa-onnx-moonshine-tiny-ko-quantized-2026-02-27"
|
|
return OfflineModelConfig(
|
|
moonshine = OfflineMoonshineModelConfig(
|
|
encoder = "$modelDir/encoder_model.ort",
|
|
mergedDecoder = "$modelDir/decoder_model_merged.ort",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
52 -> {
|
|
val modelDir = "sherpa-onnx-moonshine-tiny-ja-quantized-2026-02-27"
|
|
return OfflineModelConfig(
|
|
moonshine = OfflineMoonshineModelConfig(
|
|
encoder = "$modelDir/encoder_model.ort",
|
|
mergedDecoder = "$modelDir/decoder_model_merged.ort",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
53 -> {
|
|
val modelDir = "sherpa-onnx-moonshine-tiny-en-quantized-2026-02-27"
|
|
return OfflineModelConfig(
|
|
moonshine = OfflineMoonshineModelConfig(
|
|
encoder = "$modelDir/encoder_model.ort",
|
|
mergedDecoder = "$modelDir/decoder_model_merged.ort",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
54 -> {
|
|
val modelDir = "sherpa-onnx-moonshine-base-zh-quantized-2026-02-27"
|
|
return OfflineModelConfig(
|
|
moonshine = OfflineMoonshineModelConfig(
|
|
encoder = "$modelDir/encoder_model.ort",
|
|
mergedDecoder = "$modelDir/decoder_model_merged.ort",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
55 -> {
|
|
val modelDir = "sherpa-onnx-moonshine-base-vi-quantized-2026-02-27"
|
|
return OfflineModelConfig(
|
|
moonshine = OfflineMoonshineModelConfig(
|
|
encoder = "$modelDir/encoder_model.ort",
|
|
mergedDecoder = "$modelDir/decoder_model_merged.ort",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
56 -> {
|
|
val modelDir = "sherpa-onnx-moonshine-base-uk-quantized-2026-02-27"
|
|
return OfflineModelConfig(
|
|
moonshine = OfflineMoonshineModelConfig(
|
|
encoder = "$modelDir/encoder_model.ort",
|
|
mergedDecoder = "$modelDir/decoder_model_merged.ort",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
57 -> {
|
|
val modelDir = "sherpa-onnx-moonshine-base-ja-quantized-2026-02-27"
|
|
return OfflineModelConfig(
|
|
moonshine = OfflineMoonshineModelConfig(
|
|
encoder = "$modelDir/encoder_model.ort",
|
|
mergedDecoder = "$modelDir/decoder_model_merged.ort",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
58 -> {
|
|
val modelDir = "sherpa-onnx-moonshine-base-es-quantized-2026-02-27"
|
|
return OfflineModelConfig(
|
|
moonshine = OfflineMoonshineModelConfig(
|
|
encoder = "$modelDir/encoder_model.ort",
|
|
mergedDecoder = "$modelDir/decoder_model_merged.ort",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
59 -> {
|
|
val modelDir = "sherpa-onnx-moonshine-base-en-quantized-2026-02-27"
|
|
return OfflineModelConfig(
|
|
moonshine = OfflineMoonshineModelConfig(
|
|
encoder = "$modelDir/encoder_model.ort",
|
|
mergedDecoder = "$modelDir/decoder_model_merged.ort",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
60 -> {
|
|
val modelDir = "sherpa-onnx-moonshine-base-ar-quantized-2026-02-27"
|
|
return OfflineModelConfig(
|
|
moonshine = OfflineMoonshineModelConfig(
|
|
encoder = "$modelDir/encoder_model.ort",
|
|
mergedDecoder = "$modelDir/decoder_model_merged.ort",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
61 -> {
|
|
val modelDir = "sherpa-onnx-qwen3-asr-0.6B-int8-2026-03-25"
|
|
return OfflineModelConfig(
|
|
qwen3Asr = OfflineQwen3AsrModelConfig(
|
|
convFrontend = "$modelDir/conv_frontend.onnx",
|
|
encoder = "$modelDir/encoder.int8.onnx",
|
|
decoder = "$modelDir/decoder.int8.onnx",
|
|
tokenizer = "$modelDir/tokenizer",
|
|
),
|
|
tokens = "",
|
|
numThreads=3,
|
|
)
|
|
}
|
|
|
|
62 -> {
|
|
val modelDir = "sherpa-onnx-nemo-parakeet-unified-en-0.6b-int8-non-streaming"
|
|
return OfflineModelConfig(
|
|
transducer = OfflineTransducerModelConfig(
|
|
encoder = "$modelDir/encoder.int8.onnx",
|
|
decoder = "$modelDir/decoder.int8.onnx",
|
|
joiner = "$modelDir/joiner.int8.onnx",
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
modelType = "nemo_transducer",
|
|
)
|
|
}
|
|
|
|
9000 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-5-seconds-sense-voice-zh-en-ja-ko-yue-2024-07-17-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
senseVoice = OfflineSenseVoiceModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
// Please copy libQnnHtp.so and libQnnSystem.so to jniLibs/arm64-v8a by yourself
|
|
//
|
|
// model.bin is created in the first run and is used from the second run
|
|
// to speed up the initialization
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
debug = true,
|
|
)
|
|
}
|
|
|
|
9001 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-8-seconds-sense-voice-zh-en-ja-ko-yue-2024-07-17-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
senseVoice = OfflineSenseVoiceModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
debug = true,
|
|
)
|
|
}
|
|
|
|
9002 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-10-seconds-sense-voice-zh-en-ja-ko-yue-2024-07-17-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
senseVoice = OfflineSenseVoiceModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
debug = true,
|
|
)
|
|
}
|
|
|
|
9003 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-13-seconds-sense-voice-zh-en-ja-ko-yue-2024-07-17-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
senseVoice = OfflineSenseVoiceModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
9004 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-15-seconds-sense-voice-zh-en-ja-ko-yue-2024-07-17-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
senseVoice = OfflineSenseVoiceModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
9005 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-18-seconds-sense-voice-zh-en-ja-ko-yue-2024-07-17-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
senseVoice = OfflineSenseVoiceModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
9006 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-20-seconds-sense-voice-zh-en-ja-ko-yue-2024-07-17-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
senseVoice = OfflineSenseVoiceModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
9007 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-23-seconds-sense-voice-zh-en-ja-ko-yue-2024-07-17-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
senseVoice = OfflineSenseVoiceModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
9008 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-25-seconds-sense-voice-zh-en-ja-ko-yue-2024-07-17-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
senseVoice = OfflineSenseVoiceModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
9009 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-28-seconds-sense-voice-zh-en-ja-ko-yue-2024-07-17-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
senseVoice = OfflineSenseVoiceModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
9010 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-30-seconds-sense-voice-zh-en-ja-ko-yue-2024-07-17-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
senseVoice = OfflineSenseVoiceModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
9011 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-5-seconds-zipformer-ctc-zh-2025-07-03-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
zipformerCtc = OfflineZipformerCtcModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
debug = true,
|
|
)
|
|
}
|
|
|
|
9012 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-8-seconds-zipformer-ctc-zh-2025-07-03-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
zipformerCtc = OfflineZipformerCtcModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
debug = true,
|
|
)
|
|
}
|
|
|
|
9013 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-10-seconds-zipformer-ctc-zh-2025-07-03-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
zipformerCtc = OfflineZipformerCtcModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
debug = true,
|
|
)
|
|
}
|
|
|
|
9014 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-13-seconds-zipformer-ctc-zh-2025-07-03-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
zipformerCtc = OfflineZipformerCtcModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
9015 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-15-seconds-zipformer-ctc-zh-2025-07-03-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
zipformerCtc = OfflineZipformerCtcModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
9016 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-18-seconds-zipformer-ctc-zh-2025-07-03-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
zipformerCtc = OfflineZipformerCtcModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
9017 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-20-seconds-zipformer-ctc-zh-2025-07-03-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
zipformerCtc = OfflineZipformerCtcModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
9018 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-23-seconds-zipformer-ctc-zh-2025-07-03-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
zipformerCtc = OfflineZipformerCtcModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
9019 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-25-seconds-zipformer-ctc-zh-2025-07-03-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
zipformerCtc = OfflineZipformerCtcModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
9020 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-28-seconds-zipformer-ctc-zh-2025-07-03-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
zipformerCtc = OfflineZipformerCtcModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
9021 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-30-seconds-zipformer-ctc-zh-2025-07-03-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
zipformerCtc = OfflineZipformerCtcModelConfig(
|
|
model = "$modelDir/libmodel.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
)
|
|
}
|
|
|
|
9022 -> {
|
|
// for Xiaomi 17 Pro
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-SM8850-binary-10-seconds-sense-voice-zh-en-ja-ko-yue-2024-07-17-int8"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
senseVoice = OfflineSenseVoiceModelConfig(
|
|
qnnConfig = QnnConfig(
|
|
// Please copy libQnnHtp.so and libQnnSystem.so to jniLibs/arm64-v8a by yourself
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/model.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
debug = true,
|
|
)
|
|
}
|
|
|
|
9023 -> {
|
|
val modelDir = "sherpa-onnx-qnn-5-seconds-paraformer-zh-2023-03-28-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
paraformer = OfflineParaformerModelConfig(
|
|
model = "$modelDir/libencoder.so,$modelDir/libpredictor.so,$modelDir/libdecoder.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
// The following three *.bin files are generated during the first run
|
|
// and are used to replace the corresponding *.so files in later runs
|
|
contextBinary = "$modelDir/encoder.bin,$modelDir/predictor.bin,$modelDir/decoder.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
debug = true,
|
|
)
|
|
}
|
|
|
|
9024 -> {
|
|
val modelDir = "sherpa-onnx-qnn-5-seconds-paraformer-zh-2025-10-07-int8-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
paraformer = OfflineParaformerModelConfig(
|
|
model = "$modelDir/libencoder.so,$modelDir/libpredictor.so,$modelDir/libdecoder.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
// The following three *.bin files are generated during the first run
|
|
// and are used to replace the corresponding *.so files in later runs
|
|
contextBinary = "$modelDir/encoder.bin,$modelDir/predictor.bin,$modelDir/decoder.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
debug = true,
|
|
)
|
|
}
|
|
|
|
9025 -> {
|
|
// for Xiaomi 17 Pro
|
|
val modelDir = "sherpa-onnx-qnn-SM8850-binary-5-seconds-paraformer-zh-2023-03-28-int8"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
paraformer = OfflineParaformerModelConfig(
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/encoder.bin,$modelDir/predictor.bin,$modelDir/decoder.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
debug = true,
|
|
)
|
|
}
|
|
|
|
9026 -> {
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-reazonspeech-zipformer-transducer-ja-5s-2024-08-01-android-aarch64"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
transducer = OfflineTransducerModelConfig(
|
|
encoder = "$modelDir/libencoder.so",
|
|
decoder = "$modelDir/libdecoder.so",
|
|
joiner = "$modelDir/libjoiner.so",
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/encoder.bin,$modelDir/decoder.bin,$modelDir/joiner.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
modelType = "transducer",
|
|
debug = true,
|
|
)
|
|
}
|
|
|
|
9027 -> {
|
|
// for Xiaomi 17 Pro
|
|
val modelDir =
|
|
"sherpa-onnx-qnn-SM8850-binary-reazonspeech-zipformer-transducer-ja-5s-2024-08-01"
|
|
return OfflineModelConfig(
|
|
provider = "qnn",
|
|
transducer = OfflineTransducerModelConfig(
|
|
qnnConfig = QnnConfig(
|
|
backendLib = "libQnnHtp.so",
|
|
systemLib = "libQnnSystem.so",
|
|
contextBinary = "$modelDir/encoder.bin,$modelDir/decoder.bin,$modelDir/joiner.bin",
|
|
),
|
|
),
|
|
tokens = "$modelDir/tokens.txt",
|
|
modelType = "transducer",
|
|
debug = true,
|
|
)
|
|
}
|
|
}
|
|
return null
|
|
}
|