135 lines
5.0 KiB
Swift
135 lines
5.0 KiB
Swift
import Foundation
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func getResource(_ forResource: String, _ ofType: String) -> String {
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let path = Bundle.main.path(forResource: forResource, ofType: ofType)
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precondition(
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path != nil,
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"\(forResource).\(ofType) does not exist!\n" + "Remember to change \n"
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+ " Build Phases -> Copy Bundle Resources\n" + "to add it!"
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)
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return path!
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}
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/// Please refer to
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/// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
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/// to download pre-trained models
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/// sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20 (Bilingual, Chinese + English)
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/// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/zipformer-transducer-models.html
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func getBilingualStreamingZhEnZipformer20230220() -> SherpaOnnxOnlineModelConfig {
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let encoder = getResource("encoder-epoch-99-avg-1.int8", "onnx")
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let decoder = getResource("decoder-epoch-99-avg-1", "onnx")
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let joiner = getResource("joiner-epoch-99-avg-1.int8", "onnx")
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let tokens = getResource("tokens", "txt")
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return sherpaOnnxOnlineModelConfig(
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tokens: tokens,
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transducer: sherpaOnnxOnlineTransducerModelConfig(
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encoder: encoder,
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decoder: decoder,
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joiner: joiner),
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numThreads: 1,
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modelType: "zipformer"
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)
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}
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/// csukuangfj/sherpa-onnx-streaming-zipformer-zh-14M-2023-02-23 (Chinese)
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/// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/online-transducer/zipformer-transducer-models.html#csukuangfj-sherpa-onnx-streaming-zipformer-zh-14m-2023-02-23-chinese
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func getStreamingZh14MZipformer20230223() -> SherpaOnnxOnlineModelConfig {
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let encoder = getResource("encoder-epoch-99-avg-1.int8", "onnx")
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let decoder = getResource("decoder-epoch-99-avg-1", "onnx")
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let joiner = getResource("joiner-epoch-99-avg-1.int8", "onnx")
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let tokens = getResource("tokens", "txt")
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return sherpaOnnxOnlineModelConfig(
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tokens: tokens,
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transducer: sherpaOnnxOnlineTransducerModelConfig(
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encoder: encoder,
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decoder: decoder,
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joiner: joiner),
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numThreads: 1,
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modelType: "zipformer"
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)
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}
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/// csukuangfj/sherpa-onnx-streaming-zipformer-en-20M-2023-02-17 (English)
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/// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/online-transducer/zipformer-transducer-models.html#csukuangfj-sherpa-onnx-streaming-zipformer-en-20m-2023-02-17-english
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func getStreamingEn20MZipformer20230217() -> SherpaOnnxOnlineModelConfig {
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let encoder = getResource("encoder-epoch-99-avg-1.int8", "onnx")
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let decoder = getResource("decoder-epoch-99-avg-1", "onnx")
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let joiner = getResource("joiner-epoch-99-avg-1", "onnx")
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let tokens = getResource("tokens", "txt")
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return sherpaOnnxOnlineModelConfig(
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tokens: tokens,
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transducer: sherpaOnnxOnlineTransducerModelConfig(
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encoder: encoder,
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decoder: decoder,
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joiner: joiner),
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numThreads: 1,
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modelType: "zipformer"
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)
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}
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/// ========================================
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/// Non-streaming models
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/// ========================================
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/// 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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func getNonStreamingZhParaformer20230914() -> SherpaOnnxOfflineModelConfig {
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let model = getResource("model.int8", "onnx")
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let tokens = getResource("paraformer-tokens", "txt")
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return sherpaOnnxOfflineModelConfig(
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tokens: tokens,
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paraformer: sherpaOnnxOfflineParaformerModelConfig(
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model: model),
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numThreads: 1,
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modelType: "paraformer"
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)
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}
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// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/whisper/tiny.en.html#tiny-en
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// English, int8 encoder and decoder
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func getNonStreamingWhisperTinyEn() -> SherpaOnnxOfflineModelConfig {
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let encoder = getResource("tiny.en-encoder.int8", "onnx")
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let decoder = getResource("tiny.en-decoder.int8", "onnx")
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let tokens = getResource("tiny.en-tokens", "txt")
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return sherpaOnnxOfflineModelConfig(
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tokens: tokens,
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whisper: sherpaOnnxOfflineWhisperModelConfig(
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encoder: encoder,
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decoder: decoder
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),
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numThreads: 1,
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modelType: "whisper"
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)
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}
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// 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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func getNonStreamingEnZipformer20230504() -> SherpaOnnxOfflineModelConfig {
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let encoder = getResource("encoder-epoch-30-avg-4.int8", "onnx")
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let decoder = getResource("decoder-epoch-30-avg-4", "onnx")
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let joiner = getResource("joiner-epoch-30-avg-4", "onnx")
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let tokens = getResource("non-streaming-zipformer-tokens", "txt")
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return sherpaOnnxOfflineModelConfig(
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tokens: tokens,
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transducer: sherpaOnnxOfflineTransducerModelConfig(
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encoder: encoder,
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decoder: decoder,
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joiner: joiner),
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numThreads: 1,
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modelType: "zipformer"
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)
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}
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/// Please refer to
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/// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
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/// to add more models if you need
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