Add two-pass speech recognition Android/iOS demo (#304)

ios-swiftui/SherpaOnnx2Pass/SherpaOnnx2Pass/Model.swift

@@ -0,0 +1,134 @@
+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 getBilingualStreamingZhEnZipformer20230220() -> SherpaOnnxOnlineModelConfig {
+  let encoder = getResource("encoder-epoch-99-avg-1.int8", "onnx")
+  let decoder = getResource("decoder-epoch-99-avg-1", "onnx")
+  let joiner = getResource("joiner-epoch-99-avg-1.int8", "onnx")
+  let tokens = getResource("tokens", "txt")
+
+  return sherpaOnnxOnlineModelConfig(
+    tokens: tokens,
+    transducer: sherpaOnnxOnlineTransducerModelConfig(
+      encoder: encoder,
+      decoder: decoder,
+      joiner: joiner),
+    numThreads: 1,
+    modelType: "zipformer"
+  )
+}
+
+/// csukuangfj/sherpa-onnx-streaming-zipformer-zh-14M-2023-02-23 (Chinese)
+/// 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
+
+func getStreamingZh14MZipformer20230223() -> SherpaOnnxOnlineModelConfig {
+  let encoder = getResource("encoder-epoch-99-avg-1.int8", "onnx")
+  let decoder = getResource("decoder-epoch-99-avg-1", "onnx")
+  let joiner = getResource("joiner-epoch-99-avg-1.int8", "onnx")
+  let tokens = getResource("tokens", "txt")
+
+  return sherpaOnnxOnlineModelConfig(
+    tokens: tokens,
+    transducer: sherpaOnnxOnlineTransducerModelConfig(
+      encoder: encoder,
+      decoder: decoder,
+      joiner: joiner),
+    numThreads: 1,
+    modelType: "zipformer"
+  )
+}
+
+/// csukuangfj/sherpa-onnx-streaming-zipformer-en-20M-2023-02-17 (English)
+/// 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
+
+func getStreamingEn20MZipformer20230217() -> SherpaOnnxOnlineModelConfig {
+  let encoder = getResource("encoder-epoch-99-avg-1.int8", "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: 1,
+    modelType: "zipformer"
+  )
+}
+
+/// ========================================
+///   Non-streaming models
+/// ========================================
+
+/// csukuangfj/sherpa-onnx-paraformer-zh-2023-03-28 (Chinese)
+/// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-paraformer/paraformer-models.html#csukuangfj-sherpa-onnx-paraformer-zh-2023-03-28-chinese
+func getNonStreamingZhParaformer20230328() -> SherpaOnnxOfflineModelConfig {
+  let model = getResource("model.int8", "onnx")
+  let tokens = getResource("paraformer-tokens", "txt")
+
+  return sherpaOnnxOfflineModelConfig(
+    tokens: tokens,
+    paraformer: sherpaOnnxOfflineParaformerModelConfig(
+      model: model),
+    numThreads: 1,
+    modelType: "paraformer"
+  )
+}
+
+// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/whisper/tiny.en.html#tiny-en
+// English, int8 encoder and decoder
+func getNonStreamingWhisperTinyEn() -> SherpaOnnxOfflineModelConfig {
+  let encoder = getResource("tiny.en-encoder.int8", "onnx")
+  let decoder = getResource("tiny.en-decoder.int8", "onnx")
+  let tokens = getResource("tiny.en-tokens", "txt")
+
+  return sherpaOnnxOfflineModelConfig(
+    tokens: tokens,
+    whisper: sherpaOnnxOfflineWhisperModelConfig(
+      encoder: encoder,
+      decoder: decoder
+    ),
+    numThreads: 1,
+    modelType: "whisper"
+  )
+}
+
+// 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
+
+func getNonStreamingEnZipformer20230504() -> SherpaOnnxOfflineModelConfig {
+  let encoder = getResource("encoder-epoch-30-avg-4.int8", "onnx")
+  let decoder = getResource("decoder-epoch-30-avg-4", "onnx")
+  let joiner = getResource("joiner-epoch-30-avg-4", "onnx")
+  let tokens = getResource("non-streaming-zipformer-tokens", "txt")
+
+  return sherpaOnnxOfflineModelConfig(
+    tokens: tokens,
+    transducer: sherpaOnnxOfflineTransducerModelConfig(
+      encoder: encoder,
+      decoder: decoder,
+      joiner: joiner),
+    numThreads: 1,
+    modelType: "zipformer"
+  )
+}
+
+/// Please refer to
+/// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
+/// to add more models if you need