131 lines
4.0 KiB
Swift
131 lines
4.0 KiB
Swift
import AVFoundation
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extension AudioBuffer {
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func array() -> [Float] {
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return Array(UnsafeBufferPointer(self))
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}
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}
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extension AVAudioPCMBuffer {
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func array() -> [Float] {
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return self.audioBufferList.pointee.mBuffers.array()
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}
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}
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func run() {
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var recognizer: SherpaOnnxOfflineRecognizer
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var modelConfig: SherpaOnnxOfflineModelConfig
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var modelType = "whisper"
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// modelType = "paraformer"
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// modelType = "sense_voice"
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// modelType = "moonshine"
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if modelType == "whisper" {
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let encoder = "./sherpa-onnx-whisper-tiny.en/tiny.en-encoder.int8.onnx"
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let decoder = "./sherpa-onnx-whisper-tiny.en/tiny.en-decoder.int8.onnx"
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let tokens = "./sherpa-onnx-whisper-tiny.en/tiny.en-tokens.txt"
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let whisperConfig = sherpaOnnxOfflineWhisperModelConfig(
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encoder: encoder,
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decoder: decoder
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)
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modelConfig = sherpaOnnxOfflineModelConfig(
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tokens: tokens,
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whisper: whisperConfig,
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debug: 0,
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modelType: "whisper"
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)
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} else if modelType == "paraformer" {
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let model = "./sherpa-onnx-paraformer-zh-2023-09-14/model.int8.onnx"
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let tokens = "./sherpa-onnx-paraformer-zh-2023-09-14/tokens.txt"
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let paraformerConfig = sherpaOnnxOfflineParaformerModelConfig(
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model: model
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)
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modelConfig = sherpaOnnxOfflineModelConfig(
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tokens: tokens,
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paraformer: paraformerConfig,
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debug: 0,
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modelType: "paraformer"
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)
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} else if modelType == "sense_voice" {
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let model = "./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/model.int8.onnx"
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let tokens = "./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/tokens.txt"
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let senseVoiceConfig = sherpaOnnxOfflineSenseVoiceModelConfig(
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model: model,
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useInverseTextNormalization: true
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)
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modelConfig = sherpaOnnxOfflineModelConfig(
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tokens: tokens,
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debug: 0,
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senseVoice: senseVoiceConfig
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)
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} else if modelType == "moonshine" {
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let preprocessor = "./sherpa-onnx-moonshine-tiny-en-int8/preprocess.onnx"
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let encoder = "./sherpa-onnx-moonshine-tiny-en-int8/encode.int8.onnx"
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let uncachedDecoder = "./sherpa-onnx-moonshine-tiny-en-int8/uncached_decode.int8.onnx"
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let cachedDecoder = "./sherpa-onnx-moonshine-tiny-en-int8/cached_decode.int8.onnx"
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let tokens = "./sherpa-onnx-moonshine-tiny-en-int8/tokens.txt"
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let moonshine = sherpaOnnxOfflineMoonshineModelConfig(
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preprocessor: preprocessor,
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encoder: encoder,
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uncachedDecoder: uncachedDecoder,
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cachedDecoder: cachedDecoder
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)
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modelConfig = sherpaOnnxOfflineModelConfig(
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tokens: tokens,
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debug: 0,
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moonshine: moonshine
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)
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} else {
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print("Please specify a supported modelType \(modelType)")
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return
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}
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let featConfig = sherpaOnnxFeatureConfig(
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sampleRate: 16000,
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featureDim: 80
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)
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var config = sherpaOnnxOfflineRecognizerConfig(
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featConfig: featConfig,
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modelConfig: modelConfig
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)
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recognizer = SherpaOnnxOfflineRecognizer(config: &config)
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var filePath = "./sherpa-onnx-whisper-tiny.en/test_wavs/0.wav"
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if modelType == "sense_voice" {
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filePath = "./sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/test_wavs/zh.wav"
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} else if modelType == "moonshine" {
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filePath = "./sherpa-onnx-moonshine-tiny-en-int8/test_wavs/0.wav"
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}
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let fileURL: NSURL = NSURL(fileURLWithPath: filePath)
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let audioFile = try! AVAudioFile(forReading: fileURL as URL)
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let audioFormat = audioFile.processingFormat
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assert(audioFormat.channelCount == 1)
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assert(audioFormat.commonFormat == AVAudioCommonFormat.pcmFormatFloat32)
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let audioFrameCount = UInt32(audioFile.length)
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let audioFileBuffer = AVAudioPCMBuffer(pcmFormat: audioFormat, frameCapacity: audioFrameCount)
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try! audioFile.read(into: audioFileBuffer!)
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let array: [Float]! = audioFileBuffer?.array()
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let result = recognizer.decode(samples: array, sampleRate: Int(audioFormat.sampleRate))
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print("\nresult is:\n\(result.text)")
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if result.timestamps.count != 0 {
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print("\ntimestamps is:\n\(result.timestamps)")
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}
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}
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@main
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struct App {
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static func main() {
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run()
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}
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}
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