package com.k2fsa.sherpa.onnx import android.content.res.AssetManager data class OfflineRecognizerResult( val text: String, val tokens: Array, val timestamps: FloatArray, val lang: String, val emotion: String, val event: String, // valid only for TDT models val durations: FloatArray, ) data class OfflineTransducerModelConfig( var encoder: String = "", var decoder: String = "", var joiner: String = "", var qnnConfig: QnnConfig = QnnConfig(), ) data class OfflineParaformerModelConfig( var model: String = "", var qnnConfig: QnnConfig = QnnConfig(), ) data class OfflineNemoEncDecCtcModelConfig( var model: String = "", ) data class OfflineDolphinModelConfig( var model: String = "", ) data class OfflineZipformerCtcModelConfig( var model: String = "", var qnnConfig: QnnConfig = QnnConfig(), ) data class OfflineWenetCtcModelConfig( var model: String = "", ) data class OfflineOmnilingualAsrCtcModelConfig( var model: String = "", ) data class OfflineMedAsrCtcModelConfig( var model: String = "", ) data class OfflineFireRedAsrCtcModelConfig( var model: String = "", ) data class OfflineFunAsrNanoModelConfig( var encoderAdaptor: String = "", var llm: String = "", var embedding: String = "", var tokenizer: String = "", var systemPrompt: String = "You are a helpful assistant.", var userPrompt: String = "语音转写：", var maxNewTokens: Int = 512, var temperature: Float = 1e-6f, var topP: Float = 0.8f, var seed: Int = 42, var language: String = "", var itn: Boolean = true, var hotwords: String = "", ) data class OfflineQwen3AsrModelConfig( var convFrontend: String = "", var encoder: String = "", var decoder: String = "", var tokenizer: String = "", var maxTotalLen: Int = 512, var maxNewTokens: Int = 128, var temperature: Float = 1e-6f, var topP: Float = 0.8f, var seed: Int = 42, var hotwords: String = "", ) data class OfflineWhisperModelConfig( var encoder: String = "", var decoder: String = "", var language: String = "en", // Used with multilingual model var task: String = "transcribe", // transcribe or translate var tailPaddings: Int = 1000, // Padding added at the end of the samples var enableTokenTimestamps: Boolean = false, var enableSegmentTimestamps: Boolean = false, ) data class OfflineCanaryModelConfig( var encoder: String = "", var decoder: String = "", var srcLang: String = "en", var tgtLang: String = "en", var usePnc: Boolean = true, ) data class OfflineCohereTranscribeModelConfig( var encoder: String = "", var decoder: String = "", var language: String = "", var usePunct: Boolean = true, var useItn: Boolean = true, ) data class OfflineFireRedAsrModelConfig( var encoder: String = "", var decoder: String = "", ) // For moonshine v1, you need four models. // For moonshine v2, you need two models. // - v1: preprocessor, encoder, uncachedDecoder, cachedDecoder // - v2: encoder, mergedDecoder data class OfflineMoonshineModelConfig( var preprocessor: String = "", var encoder: String = "", var uncachedDecoder: String = "", var cachedDecoder: String = "", var mergedDecoder: String = "", ) data class OfflineSenseVoiceModelConfig( var model: String = "", var language: String = "", var useInverseTextNormalization: Boolean = true, var qnnConfig: QnnConfig = QnnConfig(), ) data class OfflineModelConfig( var transducer: OfflineTransducerModelConfig = OfflineTransducerModelConfig(), var paraformer: OfflineParaformerModelConfig = OfflineParaformerModelConfig(), var whisper: OfflineWhisperModelConfig = OfflineWhisperModelConfig(), var fireRedAsr: OfflineFireRedAsrModelConfig = OfflineFireRedAsrModelConfig(), var moonshine: OfflineMoonshineModelConfig = OfflineMoonshineModelConfig(), var nemo: OfflineNemoEncDecCtcModelConfig = OfflineNemoEncDecCtcModelConfig(), var senseVoice: OfflineSenseVoiceModelConfig = OfflineSenseVoiceModelConfig(), var dolphin: OfflineDolphinModelConfig = OfflineDolphinModelConfig(), var zipformerCtc: OfflineZipformerCtcModelConfig = OfflineZipformerCtcModelConfig(), var wenetCtc: OfflineWenetCtcModelConfig = OfflineWenetCtcModelConfig(), var omnilingual: OfflineOmnilingualAsrCtcModelConfig = OfflineOmnilingualAsrCtcModelConfig(), var medasr: OfflineMedAsrCtcModelConfig = OfflineMedAsrCtcModelConfig(), var funasrNano: OfflineFunAsrNanoModelConfig = OfflineFunAsrNanoModelConfig(), var qwen3Asr: OfflineQwen3AsrModelConfig = OfflineQwen3AsrModelConfig(), var fireRedAsrCtc: OfflineFireRedAsrCtcModelConfig = OfflineFireRedAsrCtcModelConfig(), var canary: OfflineCanaryModelConfig = OfflineCanaryModelConfig(), var cohereTranscribe: OfflineCohereTranscribeModelConfig = OfflineCohereTranscribeModelConfig(), var teleSpeech: String = "", var numThreads: Int = 1, var debug: Boolean = false, var provider: String = "cpu", var modelType: String = "", var tokens: String = "", var modelingUnit: String = "", var bpeVocab: String = "", ) data class OfflineRecognizerConfig( var featConfig: FeatureConfig = FeatureConfig(), var modelConfig: OfflineModelConfig = OfflineModelConfig(), // var lmConfig: OfflineLMConfig(), // TODO(fangjun): enable it var hr: HomophoneReplacerConfig = HomophoneReplacerConfig(), var decodingMethod: String = "greedy_search", var maxActivePaths: Int = 4, var hotwordsFile: String = "", var hotwordsScore: Float = 1.5f, var ruleFsts: String = "", var ruleFars: String = "", var blankPenalty: Float = 0.0f, ) class OfflineRecognizer( assetManager: AssetManager? = null, val config: OfflineRecognizerConfig, ) { private var ptr: Long init { ptr = if (assetManager != null) { newFromAsset(assetManager, config) } else { newFromFile(config) } require(ptr != 0L) { "Invalid OfflineRecognizerConfig: failed to create native OfflineRecognizer" } } protected fun finalize() { if (ptr != 0L) { delete(ptr) ptr = 0 } } fun release() = finalize() fun createStream(): OfflineStream { val p = createStream(ptr) return OfflineStream(p) } fun createStream(hotwords: String): OfflineStream { val p = createStreamWithHotwords(ptr, hotwords) return OfflineStream(p) } fun getResult(stream: OfflineStream): OfflineRecognizerResult { return getResult(stream.ptr) } fun decode(stream: OfflineStream) = decode(ptr, stream.ptr) fun setConfig(config: OfflineRecognizerConfig) = setConfig(ptr, config) private external fun delete(ptr: Long) private external fun createStream(ptr: Long): Long private external fun createStreamWithHotwords(ptr: Long, hotwords: String): Long private external fun setConfig(ptr: Long, config: OfflineRecognizerConfig) private external fun newFromAsset( assetManager: AssetManager, config: OfflineRecognizerConfig, ): Long private external fun newFromFile( config: OfflineRecognizerConfig, ): Long private external fun decode(ptr: Long, streamPtr: Long) private external fun getResult(streamPtr: Long): OfflineRecognizerResult companion object { init { System.loadLibrary("sherpa-onnx-jni") } @JvmStatic external fun prependAdspLibraryPath(newPath: String) // for qnn } } /* Please see https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html for a list of pre-trained models. We only add a few here. Please change the following code to add your own. (It should be straightforward to add a new model by following the code) @param type 0 - csukuangfj/sherpa-onnx-paraformer-zh-2023-09-14 (Chinese) https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-paraformer/paraformer-models.html#csukuangfj-sherpa-onnx-paraformer-zh-2023-09-14-chinese int8 1 - icefall-asr-multidataset-pruned_transducer_stateless7-2023-05-04 (English) 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 encoder int8, decoder/joiner float32 2 - sherpa-onnx-whisper-tiny.en https://k2-fsa.github.io/sherpa/onnx/pretrained_models/whisper/tiny.en.html#tiny-en encoder int8, decoder int8 3 - sherpa-onnx-whisper-base.en https://k2-fsa.github.io/sherpa/onnx/pretrained_models/whisper/tiny.en.html#tiny-en encoder int8, decoder int8 4 - pkufool/icefall-asr-zipformer-wenetspeech-20230615 (Chinese) https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-transducer/zipformer-transducer-models.html#pkufool-icefall-asr-zipformer-wenetspeech-20230615-chinese encoder/joiner int8, decoder fp32 */ fun getOfflineModelConfig(type: Int, modelPrefix: String = ""): OfflineModelConfig? { when (type) { 0 -> { val modelDir = "sherpa-onnx-paraformer-zh-2023-09-14" return OfflineModelConfig( paraformer = OfflineParaformerModelConfig( model = "$modelDir/model.int8.onnx", ), tokens = "$modelDir/tokens.txt", modelType = "paraformer", ) } 1 -> { val modelDir = "icefall-asr-multidataset-pruned_transducer_stateless7-2023-05-04" return OfflineModelConfig( transducer = OfflineTransducerModelConfig( encoder = "$modelDir/encoder-epoch-30-avg-4.int8.onnx", decoder = "$modelDir/decoder-epoch-30-avg-4.onnx", joiner = "$modelDir/joiner-epoch-30-avg-4.onnx", ), tokens = "$modelDir/tokens.txt", modelType = "transducer", ) } 2 -> { val modelDir = "sherpa-onnx-whisper-tiny.en" return OfflineModelConfig( whisper = OfflineWhisperModelConfig( encoder = "$modelDir/tiny.en-encoder.int8.onnx", decoder = "$modelDir/tiny.en-decoder.int8.onnx", ), tokens = "$modelDir/tiny.en-tokens.txt", modelType = "whisper", ) } 3 -> { val modelDir = "sherpa-onnx-whisper-base.en" return OfflineModelConfig( whisper = OfflineWhisperModelConfig( encoder = "$modelDir/base.en-encoder.int8.onnx", decoder = "$modelDir/base.en-decoder.int8.onnx", ), tokens = "$modelDir/base.en-tokens.txt", modelType = "whisper", ) } 4 -> { val modelDir = "icefall-asr-zipformer-wenetspeech-20230615" return OfflineModelConfig( transducer = OfflineTransducerModelConfig( encoder = "$modelDir/encoder-epoch-12-avg-4.int8.onnx", decoder = "$modelDir/decoder-epoch-12-avg-4.onnx", joiner = "$modelDir/joiner-epoch-12-avg-4.int8.onnx", ), tokens = "$modelDir/tokens.txt", modelType = "transducer", ) } 5 -> { val modelDir = "sherpa-onnx-zipformer-multi-zh-hans-2023-9-2" return OfflineModelConfig( transducer = OfflineTransducerModelConfig( encoder = "$modelDir/encoder-epoch-20-avg-1.int8.onnx", decoder = "$modelDir/decoder-epoch-20-avg-1.onnx", joiner = "$modelDir/joiner-epoch-20-avg-1.int8.onnx", ), tokens = "$modelDir/tokens.txt", modelType = "transducer", ) } 6 -> { val modelDir = "sherpa-onnx-nemo-ctc-en-citrinet-512" return OfflineModelConfig( nemo = OfflineNemoEncDecCtcModelConfig( model = "$modelDir/model.int8.onnx", ), tokens = "$modelDir/tokens.txt", ) } 7 -> { val modelDir = "sherpa-onnx-nemo-fast-conformer-ctc-be-de-en-es-fr-hr-it-pl-ru-uk-20k" return OfflineModelConfig( nemo = OfflineNemoEncDecCtcModelConfig( model = "$modelDir/model.onnx", ), tokens = "$modelDir/tokens.txt", ) } 8 -> { val modelDir = "sherpa-onnx-nemo-fast-conformer-ctc-en-24500" return OfflineModelConfig( nemo = OfflineNemoEncDecCtcModelConfig( model = "$modelDir/model.onnx", ), tokens = "$modelDir/tokens.txt", ) } 9 -> { val modelDir = "sherpa-onnx-nemo-fast-conformer-ctc-en-de-es-fr-14288" return OfflineModelConfig( nemo = OfflineNemoEncDecCtcModelConfig( model = "$modelDir/model.onnx", ), tokens = "$modelDir/tokens.txt", ) } 10 -> { val modelDir = "sherpa-onnx-nemo-fast-conformer-ctc-es-1424" return OfflineModelConfig( nemo = OfflineNemoEncDecCtcModelConfig( model = "$modelDir/model.onnx", ), tokens = "$modelDir/tokens.txt", ) } 11 -> { val modelDir = "sherpa-onnx-telespeech-ctc-int8-zh-2024-06-04" return OfflineModelConfig( teleSpeech = "$modelDir/model.int8.onnx", tokens = "$modelDir/tokens.txt", modelType = "telespeech_ctc", ) } 12 -> { val modelDir = "sherpa-onnx-zipformer-thai-2024-06-20" return OfflineModelConfig( transducer = OfflineTransducerModelConfig( encoder = "$modelDir/encoder-epoch-12-avg-5.int8.onnx", decoder = "$modelDir/decoder-epoch-12-avg-5.onnx", joiner = "$modelDir/joiner-epoch-12-avg-5.int8.onnx", ), tokens = "$modelDir/tokens.txt", modelType = "transducer", ) } 13 -> { val modelDir = "sherpa-onnx-zipformer-korean-2024-06-24" return OfflineModelConfig( transducer = OfflineTransducerModelConfig( encoder = "$modelDir/encoder-epoch-99-avg-1.int8.onnx", decoder = "$modelDir/decoder-epoch-99-avg-1.onnx", joiner = "$modelDir/joiner-epoch-99-avg-1.int8.onnx", ), tokens = "$modelDir/tokens.txt", modelType = "transducer", ) } 14 -> { val modelDir = "sherpa-onnx-paraformer-zh-small-2024-03-09" return OfflineModelConfig( paraformer = OfflineParaformerModelConfig( model = "$modelDir/model.int8.onnx", ), tokens = "$modelDir/tokens.txt", modelType = "paraformer", ) } 15 -> { val modelDir = "sherpa-onnx-sense-voice-zh-en-ja-ko-yue-int8-2024-07-17" return OfflineModelConfig( senseVoice = OfflineSenseVoiceModelConfig( model = "$modelDir/model.int8.onnx", ), tokens = "$modelDir/tokens.txt", ) } 16 -> { val modelDir = "sherpa-onnx-zipformer-ja-reazonspeech-2024-08-01" return OfflineModelConfig( transducer = OfflineTransducerModelConfig( encoder = "$modelDir/encoder-epoch-99-avg-1.int8.onnx", decoder = "$modelDir/decoder-epoch-99-avg-1.onnx", joiner = "$modelDir/joiner-epoch-99-avg-1.int8.onnx", ), tokens = "$modelDir/tokens.txt", modelType = "transducer", ) } 17 -> { val modelDir = "sherpa-onnx-zipformer-ru-2024-09-18" return OfflineModelConfig( transducer = OfflineTransducerModelConfig( encoder = "$modelDir/encoder.int8.onnx", decoder = "$modelDir/decoder.onnx", joiner = "$modelDir/joiner.int8.onnx", ), tokens = "$modelDir/tokens.txt", modelType = "transducer", ) } 18 -> { val modelDir = "sherpa-onnx-small-zipformer-ru-2024-09-18" 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 }