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enginex-mr_series-sherpa-onnx/ios-swiftui/SherpaOnnx/SherpaOnnx/Model.swift
2024-04-01 20:34:14 +08:00

76 lines
2.4 KiB
Swift

import Foundation
func getResource(_ forResource: String, _ ofType: String) -> String {
let path = Bundle.main.path(forResource: forResource, ofType: ofType)
precondition(
path != nil,
"\(forResource).\(ofType) does not exist!\n" + "Remember to change \n"
+ " Build Phases -> Copy Bundle Resources\n" + "to add it!"
)
return path!
}
/// Please refer to
/// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
/// to download pre-trained models
/// sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20 (Bilingual, Chinese + English)
/// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/zipformer-transducer-models.html
func getBilingualStreamZhEnZipformer20230220() -> SherpaOnnxOnlineModelConfig {
let encoder = getResource("encoder-epoch-99-avg-1", "onnx")
let decoder = getResource("decoder-epoch-99-avg-1", "onnx")
let joiner = getResource("joiner-epoch-99-avg-1", "onnx")
let tokens = getResource("tokens", "txt")
return sherpaOnnxOnlineModelConfig(
tokens: tokens,
transducer: sherpaOnnxOnlineTransducerModelConfig(
encoder: encoder,
decoder: decoder,
joiner: joiner),
numThreads: 2,
modelType: "zipformer"
)
}
// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/online-paraformer/index.html
func getBilingualStreamingZhEnParaformer() -> SherpaOnnxOnlineModelConfig {
let encoder = getResource("encoder.int8", "onnx")
let decoder = getResource("decoder.int8", "onnx")
let tokens = getResource("tokens", "txt")
return sherpaOnnxOnlineModelConfig(
tokens: tokens,
paraformer: sherpaOnnxOnlineParaformerModelConfig(
encoder: encoder,
decoder: decoder),
numThreads: 1,
modelType: "paraformer"
)
}
// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/whisper/tiny.en.html#tiny-en
//
func getLanguageIdentificationTiny() -> SherpaOnnxSpokenLanguageIdentificationConfig
{
let encoder = getResource("tiny-encoder.int8", "onnx")
let decoder = getResource("tiny-decoder.int8", "onnx")
let whisperConfig = sherpaOnnxSpokenLanguageIdentificationWhisperConfig(
encoder: encoder,
decoder: decoder
)
let config = sherpaOnnxSpokenLanguageIdentificationConfig(
whisper: whisperConfig,
numThreads: 1,
debug: 1,
provider: "cpu"
)
return config
}
/// Please refer to
/// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
/// to add more models if you need