3bf986d08d
This PR adds support for non-streaming Zipformer CTC ASR models across multiple language bindings, WebAssembly, examples, and CI workflows. - Introduces a new OfflineZipformerCtcModelConfig in C/C++, Python, Swift, Java, Kotlin, Go, Dart, Pascal, and C# APIs - Updates initialization, freeing, and recognition logic to include Zipformer CTC in WASM and Node.js - Adds example scripts and CI steps for downloading, building, and running Zipformer CTC models Model doc is available at https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/icefall/zipformer.html
580 lines
20 KiB
Kotlin
580 lines
20 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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)
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data class OfflineTransducerModelConfig(
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var encoder: String = "",
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var decoder: String = "",
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var joiner: String = "",
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)
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data class OfflineParaformerModelConfig(
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var model: String = "",
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)
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data class 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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)
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data class OfflineWhisperModelConfig(
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var encoder: String = "",
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var decoder: String = "",
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var language: String = "en", // Used with multilingual model
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var task: String = "transcribe", // transcribe or translate
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var tailPaddings: Int = 1000, // Padding added at the end of the samples
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)
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data class OfflineFireRedAsrModelConfig(
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var encoder: String = "",
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var decoder: String = "",
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)
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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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)
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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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)
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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 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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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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}
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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 getResult(stream: OfflineStream): OfflineRecognizerResult {
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val objArray = getResult(stream.ptr)
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val text = objArray[0] as String
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val tokens = objArray[1] as Array<String>
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val timestamps = objArray[2] as FloatArray
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val lang = objArray[3] as String
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val emotion = objArray[4] as String
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val event = objArray[5] as String
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return OfflineRecognizerResult(
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text = text,
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tokens = tokens,
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timestamps = timestamps,
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lang = lang,
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emotion = emotion,
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event = event
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)
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}
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fun decode(stream: OfflineStream) = decode(ptr, stream.ptr)
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private external fun delete(ptr: Long)
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private external fun createStream(ptr: Long): Long
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private external fun newFromAsset(
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assetManager: AssetManager,
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config: OfflineRecognizerConfig,
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): Long
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private external fun newFromFile(
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config: OfflineRecognizerConfig,
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): Long
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private external fun decode(ptr: Long, streamPtr: Long)
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private external fun getResult(streamPtr: Long): Array<Any>
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companion object {
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init {
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System.loadLibrary("sherpa-onnx-jni")
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}
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}
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}
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/*
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Please see
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https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
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for a list of pre-trained models.
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We only add a few here. Please change the following code
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to add your own. (It should be straightforward to add a new model
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by following the code)
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@param type
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0 - csukuangfj/sherpa-onnx-paraformer-zh-2023-09-14 (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): 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-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(
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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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19 -> {
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val modelDir = "sherpa-onnx-nemo-ctc-giga-am-russian-2024-10-24"
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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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20 -> {
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val modelDir = "sherpa-onnx-nemo-transducer-giga-am-russian-2024-10-24"
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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 = "$modelDir/joiner.onnx",
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),
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tokens = "$modelDir/tokens.txt",
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modelType = "nemo_transducer",
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)
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}
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21 -> {
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val modelDir = "sherpa-onnx-moonshine-tiny-en-int8"
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return OfflineModelConfig(
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moonshine = OfflineMoonshineModelConfig(
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preprocessor = "$modelDir/preprocess.onnx",
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encoder = "$modelDir/encode.int8.onnx",
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uncachedDecoder = "$modelDir/uncached_decode.int8.onnx",
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cachedDecoder = "$modelDir/cached_decode.int8.onnx",
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),
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tokens = "$modelDir/tokens.txt",
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)
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}
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22 -> {
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val modelDir = "sherpa-onnx-moonshine-base-en-int8"
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return OfflineModelConfig(
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moonshine = OfflineMoonshineModelConfig(
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preprocessor = "$modelDir/preprocess.onnx",
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encoder = "$modelDir/encode.int8.onnx",
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uncachedDecoder = "$modelDir/uncached_decode.int8.onnx",
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cachedDecoder = "$modelDir/cached_decode.int8.onnx",
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),
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tokens = "$modelDir/tokens.txt",
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)
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}
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23 -> {
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val modelDir = "sherpa-onnx-zipformer-zh-en-2023-11-22"
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return OfflineModelConfig(
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transducer = OfflineTransducerModelConfig(
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encoder = "$modelDir/encoder-epoch-34-avg-19.int8.onnx",
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decoder = "$modelDir/decoder-epoch-34-avg-19.onnx",
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joiner = "$modelDir/joiner-epoch-34-avg-19.int8.onnx",
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),
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tokens = "$modelDir/tokens.txt",
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modelType = "transducer",
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)
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}
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24 -> {
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val modelDir = "sherpa-onnx-fire-red-asr-large-zh_en-2025-02-16"
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return OfflineModelConfig(
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fireRedAsr = OfflineFireRedAsrModelConfig(
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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",
|
|
)
|
|
}
|
|
}
|
|
return null
|
|
}
|