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SherpaOnnx.swift
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2249 lines (1952 loc) · 64.2 KB
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/// swift-api-examples/SherpaOnnx.swift
/// Copyright (c) 2023 Xiaomi Corporation
import Foundation // For NSString
/// Convert a String from swift to a `const char*` so that we can pass it to
/// the C language.
///
/// - Parameters:
/// - s: The String to convert.
/// - Returns: A pointer that can be passed to C as `const char*`
func toCPointer(_ s: String) -> UnsafePointer<Int8>! {
let cs = (s as NSString).utf8String
return UnsafePointer<Int8>(cs)
}
/// Return an instance of SherpaOnnxOnlineTransducerModelConfig.
///
/// Please refer to
/// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/online-transducer/index.html
/// to download the required `.onnx` files.
///
/// - Parameters:
/// - encoder: Path to encoder.onnx
/// - decoder: Path to decoder.onnx
/// - joiner: Path to joiner.onnx
///
/// - Returns: Return an instance of SherpaOnnxOnlineTransducerModelConfig
func sherpaOnnxOnlineTransducerModelConfig(
encoder: String = "",
decoder: String = "",
joiner: String = ""
) -> SherpaOnnxOnlineTransducerModelConfig {
return SherpaOnnxOnlineTransducerModelConfig(
encoder: toCPointer(encoder),
decoder: toCPointer(decoder),
joiner: toCPointer(joiner)
)
}
/// Return an instance of SherpaOnnxOnlineParaformerModelConfig.
///
/// Please refer to
/// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/online-paraformer/index.html
/// to download the required `.onnx` files.
///
/// - Parameters:
/// - encoder: Path to encoder.onnx
/// - decoder: Path to decoder.onnx
///
/// - Returns: Return an instance of SherpaOnnxOnlineParaformerModelConfig
func sherpaOnnxOnlineParaformerModelConfig(
encoder: String = "",
decoder: String = ""
) -> SherpaOnnxOnlineParaformerModelConfig {
return SherpaOnnxOnlineParaformerModelConfig(
encoder: toCPointer(encoder),
decoder: toCPointer(decoder)
)
}
func sherpaOnnxOnlineZipformer2CtcModelConfig(
model: String = ""
) -> SherpaOnnxOnlineZipformer2CtcModelConfig {
return SherpaOnnxOnlineZipformer2CtcModelConfig(
model: toCPointer(model)
)
}
func sherpaOnnxOnlineNemoCtcModelConfig(
model: String = ""
) -> SherpaOnnxOnlineNemoCtcModelConfig {
return SherpaOnnxOnlineNemoCtcModelConfig(
model: toCPointer(model)
)
}
func sherpaOnnxOnlineToneCtcModelConfig(
model: String = ""
) -> SherpaOnnxOnlineToneCtcModelConfig {
return SherpaOnnxOnlineToneCtcModelConfig(
model: toCPointer(model)
)
}
/// Return an instance of SherpaOnnxOnlineModelConfig.
///
/// Please refer to
/// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
/// to download the required `.onnx` files.
///
/// - Parameters:
/// - tokens: Path to tokens.txt
/// - numThreads: Number of threads to use for neural network computation.
///
/// - Returns: Return an instance of SherpaOnnxOnlineTransducerModelConfig
func sherpaOnnxOnlineModelConfig(
tokens: String,
transducer: SherpaOnnxOnlineTransducerModelConfig = sherpaOnnxOnlineTransducerModelConfig(),
paraformer: SherpaOnnxOnlineParaformerModelConfig = sherpaOnnxOnlineParaformerModelConfig(),
zipformer2Ctc: SherpaOnnxOnlineZipformer2CtcModelConfig =
sherpaOnnxOnlineZipformer2CtcModelConfig(),
numThreads: Int = 1,
provider: String = "cpu",
debug: Int = 0,
modelType: String = "",
modelingUnit: String = "cjkchar",
bpeVocab: String = "",
tokensBuf: String = "",
tokensBufSize: Int = 0,
nemoCtc: SherpaOnnxOnlineNemoCtcModelConfig = sherpaOnnxOnlineNemoCtcModelConfig(),
toneCtc: SherpaOnnxOnlineToneCtcModelConfig = sherpaOnnxOnlineToneCtcModelConfig()
) -> SherpaOnnxOnlineModelConfig {
return SherpaOnnxOnlineModelConfig(
transducer: transducer,
paraformer: paraformer,
zipformer2_ctc: zipformer2Ctc,
tokens: toCPointer(tokens),
num_threads: Int32(numThreads),
provider: toCPointer(provider),
debug: Int32(debug),
model_type: toCPointer(modelType),
modeling_unit: toCPointer(modelingUnit),
bpe_vocab: toCPointer(bpeVocab),
tokens_buf: toCPointer(tokensBuf),
tokens_buf_size: Int32(tokensBufSize),
nemo_ctc: nemoCtc,
t_one_ctc: toneCtc
)
}
func sherpaOnnxFeatureConfig(
sampleRate: Int = 16000,
featureDim: Int = 80
) -> SherpaOnnxFeatureConfig {
return SherpaOnnxFeatureConfig(
sample_rate: Int32(sampleRate),
feature_dim: Int32(featureDim))
}
func sherpaOnnxOnlineCtcFstDecoderConfig(
graph: String = "",
maxActive: Int = 3000
) -> SherpaOnnxOnlineCtcFstDecoderConfig {
return SherpaOnnxOnlineCtcFstDecoderConfig(
graph: toCPointer(graph),
max_active: Int32(maxActive))
}
func sherpaOnnxHomophoneReplacerConfig(
dictDir: String = "",
lexicon: String = "",
ruleFsts: String = ""
) -> SherpaOnnxHomophoneReplacerConfig {
return SherpaOnnxHomophoneReplacerConfig(
dict_dir: toCPointer(dictDir),
lexicon: toCPointer(lexicon),
rule_fsts: toCPointer(ruleFsts))
}
func sherpaOnnxOnlineRecognizerConfig(
featConfig: SherpaOnnxFeatureConfig,
modelConfig: SherpaOnnxOnlineModelConfig,
enableEndpoint: Bool = false,
rule1MinTrailingSilence: Float = 2.4,
rule2MinTrailingSilence: Float = 1.2,
rule3MinUtteranceLength: Float = 30,
decodingMethod: String = "greedy_search",
maxActivePaths: Int = 4,
hotwordsFile: String = "",
hotwordsScore: Float = 1.5,
ctcFstDecoderConfig: SherpaOnnxOnlineCtcFstDecoderConfig = sherpaOnnxOnlineCtcFstDecoderConfig(),
ruleFsts: String = "",
ruleFars: String = "",
blankPenalty: Float = 0.0,
hotwordsBuf: String = "",
hotwordsBufSize: Int = 0,
hr: SherpaOnnxHomophoneReplacerConfig = sherpaOnnxHomophoneReplacerConfig()
) -> SherpaOnnxOnlineRecognizerConfig {
return SherpaOnnxOnlineRecognizerConfig(
feat_config: featConfig,
model_config: modelConfig,
decoding_method: toCPointer(decodingMethod),
max_active_paths: Int32(maxActivePaths),
enable_endpoint: enableEndpoint ? 1 : 0,
rule1_min_trailing_silence: rule1MinTrailingSilence,
rule2_min_trailing_silence: rule2MinTrailingSilence,
rule3_min_utterance_length: rule3MinUtteranceLength,
hotwords_file: toCPointer(hotwordsFile),
hotwords_score: hotwordsScore,
ctc_fst_decoder_config: ctcFstDecoderConfig,
rule_fsts: toCPointer(ruleFsts),
rule_fars: toCPointer(ruleFars),
blank_penalty: blankPenalty,
hotwords_buf: toCPointer(hotwordsBuf),
hotwords_buf_size: Int32(hotwordsBufSize),
hr: hr
)
}
/// Wrapper for recognition result.
///
/// Usage:
///
/// let result = recognizer.getResult()
/// print("text: \(result.text)")
///
class SherpaOnnxOnlineRecongitionResult {
/// A pointer to the underlying counterpart in C
private let result: UnsafePointer<SherpaOnnxOnlineRecognizerResult>
private lazy var _text: String = {
guard let cstr = result.pointee.text else { return "" }
return String(cString: cstr)
}()
private lazy var _tokens: [String] = {
guard let tokensPointer = result.pointee.tokens_arr else { return [] }
return (0..<count).compactMap { index in
guard let ptr = tokensPointer[index] else { return nil }
return String(cString: ptr)
}
}()
private lazy var _timestamps: [Float] = {
guard let timestampsPointer = result.pointee.timestamps else { return [] }
return (0..<count).map { index in timestampsPointer[index] }
}()
init(result: UnsafePointer<SherpaOnnxOnlineRecognizerResult>) {
self.result = result
}
deinit {
SherpaOnnxDestroyOnlineRecognizerResult(result)
}
/// Return the actual recognition result.
/// For English models, it contains words separated by spaces.
/// For Chinese models, it contains Chinese words.
var text: String { _text }
var count: Int { Int(result.pointee.count) }
var tokens: [String] { _tokens }
var timestamps: [Float] { _timestamps }
}
class SherpaOnnxRecognizer {
/// A pointer to the underlying counterpart in C
private let recognizer: OpaquePointer
private var stream: OpaquePointer
private let lock = NSLock() // for thread-safe stream replacement
/// Constructor taking a model config
init(
config: UnsafePointer<SherpaOnnxOnlineRecognizerConfig>
) {
self.recognizer = SherpaOnnxCreateOnlineRecognizer(config)
self.stream = SherpaOnnxCreateOnlineStream(recognizer)
}
deinit {
SherpaOnnxDestroyOnlineStream(stream)
SherpaOnnxDestroyOnlineRecognizer(recognizer)
}
/// Decode wave samples.
///
/// - Parameters:
/// - samples: Audio samples normalized to the range [-1, 1]
/// - sampleRate: Sample rate of the input audio samples. Must match
/// the one expected by the model.
func acceptWaveform(samples: [Float], sampleRate: Int = 16_000) {
SherpaOnnxOnlineStreamAcceptWaveform(stream, Int32(sampleRate), samples, Int32(samples.count))
}
func isReady() -> Bool {
return SherpaOnnxIsOnlineStreamReady(recognizer, stream) != 0
}
/// If there are enough number of feature frames, it invokes the neural
/// network computation and decoding. Otherwise, it is a no-op.
func decode() {
SherpaOnnxDecodeOnlineStream(recognizer, stream)
}
/// Get the decoding results so far
func getResult() -> SherpaOnnxOnlineRecongitionResult {
guard let result = SherpaOnnxGetOnlineStreamResult(recognizer, stream) else {
fatalError("SherpaOnnxGetOnlineStreamResult returned nil")
}
return SherpaOnnxOnlineRecongitionResult(result: result)
}
/// Reset the recognizer, which clears the neural network model state
/// and the state for decoding.
/// If hotwords is an empty string, it just recreates the decoding stream
/// If hotwords is not empty, it will create a new decoding stream with
/// the given hotWords appended to the default hotwords.
func reset(hotwords: String? = nil) {
guard let words = hotwords, !words.isEmpty else {
SherpaOnnxOnlineStreamReset(recognizer, stream)
return
}
words.withCString { cString in
guard let newStream = SherpaOnnxCreateOnlineStreamWithHotwords(recognizer, cString) else {
fatalError("SherpaOnnxCreateOnlineStreamWithHotwords returned nil")
}
lock.lock()
// lock while release and replace stream
SherpaOnnxDestroyOnlineStream(stream)
stream = newStream
lock.unlock()
}
}
/// Signal that no more audio samples would be available.
/// After this call, you cannot call acceptWaveform() any more.
func inputFinished() {
SherpaOnnxOnlineStreamInputFinished(stream)
}
/// Return true is an endpoint has been detected.
func isEndpoint() -> Bool {
return SherpaOnnxOnlineStreamIsEndpoint(recognizer, stream) != 0
}
}
// For offline APIs
func sherpaOnnxOfflineTransducerModelConfig(
encoder: String = "",
decoder: String = "",
joiner: String = ""
) -> SherpaOnnxOfflineTransducerModelConfig {
return SherpaOnnxOfflineTransducerModelConfig(
encoder: toCPointer(encoder),
decoder: toCPointer(decoder),
joiner: toCPointer(joiner)
)
}
func sherpaOnnxOfflineParaformerModelConfig(
model: String = ""
) -> SherpaOnnxOfflineParaformerModelConfig {
return SherpaOnnxOfflineParaformerModelConfig(
model: toCPointer(model)
)
}
func sherpaOnnxOfflineZipformerCtcModelConfig(
model: String = ""
) -> SherpaOnnxOfflineZipformerCtcModelConfig {
return SherpaOnnxOfflineZipformerCtcModelConfig(
model: toCPointer(model)
)
}
func sherpaOnnxOfflineWenetCtcModelConfig(
model: String = ""
) -> SherpaOnnxOfflineWenetCtcModelConfig {
return SherpaOnnxOfflineWenetCtcModelConfig(
model: toCPointer(model)
)
}
func sherpaOnnxOfflineOmnilingualAsrCtcModelConfig(
model: String = ""
) -> SherpaOnnxOfflineOmnilingualAsrCtcModelConfig {
return SherpaOnnxOfflineOmnilingualAsrCtcModelConfig(
model: toCPointer(model)
)
}
func sherpaOnnxOfflineMedAsrCtcModelConfig(
model: String = ""
) -> SherpaOnnxOfflineMedAsrCtcModelConfig {
return SherpaOnnxOfflineMedAsrCtcModelConfig(
model: toCPointer(model)
)
}
func sherpaOnnxOfflineFireRedAsrCtcModelConfig(
model: String = ""
) -> SherpaOnnxOfflineFireRedAsrCtcModelConfig {
return SherpaOnnxOfflineFireRedAsrCtcModelConfig(
model: toCPointer(model)
)
}
func sherpaOnnxOfflineNemoEncDecCtcModelConfig(
model: String = ""
) -> SherpaOnnxOfflineNemoEncDecCtcModelConfig {
return SherpaOnnxOfflineNemoEncDecCtcModelConfig(
model: toCPointer(model)
)
}
func sherpaOnnxOfflineDolphinModelConfig(
model: String = ""
) -> SherpaOnnxOfflineDolphinModelConfig {
return SherpaOnnxOfflineDolphinModelConfig(
model: toCPointer(model)
)
}
func sherpaOnnxOfflineWhisperModelConfig(
encoder: String = "",
decoder: String = "",
language: String = "",
task: String = "transcribe",
tailPaddings: Int = -1,
enableTokenTimestamps: Bool = false,
enableSegmentTimestamps: Bool = false
) -> SherpaOnnxOfflineWhisperModelConfig {
return SherpaOnnxOfflineWhisperModelConfig(
encoder: toCPointer(encoder),
decoder: toCPointer(decoder),
language: toCPointer(language),
task: toCPointer(task),
tail_paddings: Int32(tailPaddings),
enable_token_timestamps: enableTokenTimestamps ? 1 : 0,
enable_segment_timestamps: enableSegmentTimestamps ? 1 : 0
)
}
func sherpaOnnxOfflineCanaryModelConfig(
encoder: String = "",
decoder: String = "",
srcLang: String = "en",
tgtLang: String = "en",
usePnc: Bool = true
) -> SherpaOnnxOfflineCanaryModelConfig {
return SherpaOnnxOfflineCanaryModelConfig(
encoder: toCPointer(encoder),
decoder: toCPointer(decoder),
src_lang: toCPointer(srcLang),
tgt_lang: toCPointer(tgtLang),
use_pnc: usePnc ? 1 : 0
)
}
func sherpaOnnxOfflineCohereTranscribeModelConfig(
encoder: String = "",
decoder: String = "",
language: String = "",
usePunct: Bool = true,
useInverseTextNormalization: Bool = true
) -> SherpaOnnxOfflineCohereTranscribeModelConfig {
return SherpaOnnxOfflineCohereTranscribeModelConfig(
encoder: toCPointer(encoder),
decoder: toCPointer(decoder),
language: toCPointer(language),
use_punct: usePunct ? 1 : 0,
use_itn: useInverseTextNormalization ? 1 : 0
)
}
func sherpaOnnxOfflineFireRedAsrModelConfig(
encoder: String = "",
decoder: String = ""
) -> SherpaOnnxOfflineFireRedAsrModelConfig {
return SherpaOnnxOfflineFireRedAsrModelConfig(
encoder: toCPointer(encoder),
decoder: toCPointer(decoder)
)
}
// there are two versions of Moonshine
// For v1, you need four models: preprocessor, encoder, uncachedDecoder, cachedDecoder
// For v2, you need two models: encoder, mergedDecoder
func sherpaOnnxOfflineMoonshineModelConfig(
preprocessor: String = "",
encoder: String = "",
uncachedDecoder: String = "",
cachedDecoder: String = "",
mergedDecoder: String = ""
) -> SherpaOnnxOfflineMoonshineModelConfig {
return SherpaOnnxOfflineMoonshineModelConfig(
preprocessor: toCPointer(preprocessor),
encoder: toCPointer(encoder),
uncached_decoder: toCPointer(uncachedDecoder),
cached_decoder: toCPointer(cachedDecoder),
merged_decoder: toCPointer(mergedDecoder)
)
}
func sherpaOnnxOfflineQwen3ASRModelConfig(
convFrontend: String = "",
encoder: String = "",
decoder: String = "",
tokenizer: String = "",
maxTotalLen: Int = 512,
maxNewTokens: Int = 128,
temperature: Float = 1e-6,
topP: Float = 0.8,
seed: Int = 42,
hotwords: String = ""
) -> SherpaOnnxOfflineQwen3ASRModelConfig {
return SherpaOnnxOfflineQwen3ASRModelConfig(
conv_frontend: toCPointer(convFrontend),
encoder: toCPointer(encoder),
decoder: toCPointer(decoder),
tokenizer: toCPointer(tokenizer),
max_total_len: Int32(maxTotalLen),
max_new_tokens: Int32(maxNewTokens),
temperature: temperature,
top_p: topP,
seed: Int32(seed),
hotwords: toCPointer(hotwords)
)
}
func sherpaOnnxOfflineTdnnModelConfig(
model: String = ""
) -> SherpaOnnxOfflineTdnnModelConfig {
return SherpaOnnxOfflineTdnnModelConfig(
model: toCPointer(model)
)
}
func sherpaOnnxOfflineSenseVoiceModelConfig(
model: String = "",
language: String = "",
useInverseTextNormalization: Bool = false
) -> SherpaOnnxOfflineSenseVoiceModelConfig {
return SherpaOnnxOfflineSenseVoiceModelConfig(
model: toCPointer(model),
language: toCPointer(language),
use_itn: useInverseTextNormalization ? 1 : 0
)
}
func sherpaOnnxOfflineLMConfig(
model: String = "",
scale: Float = 1.0
) -> SherpaOnnxOfflineLMConfig {
return SherpaOnnxOfflineLMConfig(
model: toCPointer(model),
scale: scale
)
}
func sherpaOnnxOfflineFunASRNanoModelConfig(
encoderAdaptor: String = "",
llm: String = "",
embedding: String = "",
tokenizer: String = "",
systemPrompt: String = "You are a helpful assistant.",
userPrompt: String = "语音转写:",
maxNewTokens: Int = 512,
temperature: Float = 1e-6,
topP: Float = 0.8,
seed: Int = 42,
language: String = "",
itn: Bool = true,
hotwords: String = ""
) -> SherpaOnnxOfflineFunASRNanoModelConfig {
return SherpaOnnxOfflineFunASRNanoModelConfig(
encoder_adaptor: toCPointer(encoderAdaptor),
llm: toCPointer(llm),
embedding: toCPointer(embedding),
tokenizer: toCPointer(tokenizer),
system_prompt: toCPointer(systemPrompt),
user_prompt: toCPointer(userPrompt),
max_new_tokens: Int32(maxNewTokens),
temperature: temperature,
top_p: topP,
seed: Int32(seed),
language: toCPointer(language),
itn: itn ? 1 : 0,
hotwords: toCPointer(hotwords)
)
}
func sherpaOnnxOfflineModelConfig(
tokens: String,
transducer: SherpaOnnxOfflineTransducerModelConfig = sherpaOnnxOfflineTransducerModelConfig(),
paraformer: SherpaOnnxOfflineParaformerModelConfig = sherpaOnnxOfflineParaformerModelConfig(),
nemoCtc: SherpaOnnxOfflineNemoEncDecCtcModelConfig = sherpaOnnxOfflineNemoEncDecCtcModelConfig(),
whisper: SherpaOnnxOfflineWhisperModelConfig = sherpaOnnxOfflineWhisperModelConfig(),
tdnn: SherpaOnnxOfflineTdnnModelConfig = sherpaOnnxOfflineTdnnModelConfig(),
numThreads: Int = 1,
provider: String = "cpu",
debug: Int = 0,
modelType: String = "",
modelingUnit: String = "cjkchar",
bpeVocab: String = "",
teleSpeechCtc: String = "",
senseVoice: SherpaOnnxOfflineSenseVoiceModelConfig = sherpaOnnxOfflineSenseVoiceModelConfig(),
moonshine: SherpaOnnxOfflineMoonshineModelConfig = sherpaOnnxOfflineMoonshineModelConfig(),
fireRedAsr: SherpaOnnxOfflineFireRedAsrModelConfig = sherpaOnnxOfflineFireRedAsrModelConfig(),
dolphin: SherpaOnnxOfflineDolphinModelConfig = sherpaOnnxOfflineDolphinModelConfig(),
zipformerCtc: SherpaOnnxOfflineZipformerCtcModelConfig =
sherpaOnnxOfflineZipformerCtcModelConfig(),
canary: SherpaOnnxOfflineCanaryModelConfig = sherpaOnnxOfflineCanaryModelConfig(),
wenetCtc: SherpaOnnxOfflineWenetCtcModelConfig =
sherpaOnnxOfflineWenetCtcModelConfig(),
omnilingual: SherpaOnnxOfflineOmnilingualAsrCtcModelConfig =
sherpaOnnxOfflineOmnilingualAsrCtcModelConfig(),
medasr: SherpaOnnxOfflineMedAsrCtcModelConfig =
sherpaOnnxOfflineMedAsrCtcModelConfig(),
funasrNano: SherpaOnnxOfflineFunASRNanoModelConfig =
sherpaOnnxOfflineFunASRNanoModelConfig(),
fireRedAsrCtc: SherpaOnnxOfflineFireRedAsrCtcModelConfig =
sherpaOnnxOfflineFireRedAsrCtcModelConfig(),
qwen3Asr: SherpaOnnxOfflineQwen3ASRModelConfig =
sherpaOnnxOfflineQwen3ASRModelConfig(),
cohereTranscribe: SherpaOnnxOfflineCohereTranscribeModelConfig =
sherpaOnnxOfflineCohereTranscribeModelConfig()
) -> SherpaOnnxOfflineModelConfig {
return SherpaOnnxOfflineModelConfig(
transducer: transducer,
paraformer: paraformer,
nemo_ctc: nemoCtc,
whisper: whisper,
tdnn: tdnn,
tokens: toCPointer(tokens),
num_threads: Int32(numThreads),
debug: Int32(debug),
provider: toCPointer(provider),
model_type: toCPointer(modelType),
modeling_unit: toCPointer(modelingUnit),
bpe_vocab: toCPointer(bpeVocab),
telespeech_ctc: toCPointer(teleSpeechCtc),
sense_voice: senseVoice,
moonshine: moonshine,
fire_red_asr: fireRedAsr,
dolphin: dolphin,
zipformer_ctc: zipformerCtc,
canary: canary,
wenet_ctc: wenetCtc,
omnilingual: omnilingual,
medasr: medasr,
funasr_nano: funasrNano,
fire_red_asr_ctc: fireRedAsrCtc,
qwen3_asr: qwen3Asr,
cohere_transcribe: cohereTranscribe
)
}
func sherpaOnnxOfflineRecognizerConfig(
featConfig: SherpaOnnxFeatureConfig,
modelConfig: SherpaOnnxOfflineModelConfig,
lmConfig: SherpaOnnxOfflineLMConfig = sherpaOnnxOfflineLMConfig(),
decodingMethod: String = "greedy_search",
maxActivePaths: Int = 4,
hotwordsFile: String = "",
hotwordsScore: Float = 1.5,
ruleFsts: String = "",
ruleFars: String = "",
blankPenalty: Float = 0.0,
hr: SherpaOnnxHomophoneReplacerConfig = sherpaOnnxHomophoneReplacerConfig()
) -> SherpaOnnxOfflineRecognizerConfig {
return SherpaOnnxOfflineRecognizerConfig(
feat_config: featConfig,
model_config: modelConfig,
lm_config: lmConfig,
decoding_method: toCPointer(decodingMethod),
max_active_paths: Int32(maxActivePaths),
hotwords_file: toCPointer(hotwordsFile),
hotwords_score: hotwordsScore,
rule_fsts: toCPointer(ruleFsts),
rule_fars: toCPointer(ruleFars),
blank_penalty: blankPenalty,
hr: hr
)
}
class SherpaOnnxOfflineRecongitionResult {
/// A pointer to the underlying counterpart in C
let result: UnsafePointer<SherpaOnnxOfflineRecognizerResult>
private lazy var _text: String = {
guard let cstr = result.pointee.text else { return "" }
return String(cString: cstr)
}()
private lazy var _timestamps: [Float] = {
guard let p = result.pointee.timestamps else { return [] }
return (0..<result.pointee.count).map { p[Int($0)] }
}()
private lazy var _durations: [Float] = {
guard let p = result.pointee.durations else { return [] }
return (0..<result.pointee.count).map { p[Int($0)] }
}()
private lazy var _lang: String = {
guard let cstr = result.pointee.lang else { return "" }
return String(cString: cstr)
}()
private lazy var _emotion: String = {
guard let cstr = result.pointee.emotion else { return "" }
return String(cString: cstr)
}()
private lazy var _event: String = {
guard let cstr = result.pointee.event else { return "" }
return String(cString: cstr)
}()
private lazy var _segmentTimestamps: [Float] = {
guard let p = result.pointee.segment_timestamps else { return [] }
return (0..<result.pointee.segment_count).map { p[Int($0)] }
}()
private lazy var _segmentDurations: [Float] = {
guard let p = result.pointee.segment_durations else { return [] }
return (0..<result.pointee.segment_count).map { p[Int($0)] }
}()
private lazy var _segmentTexts: [String] = {
guard let arr = result.pointee.segment_texts_arr else { return [] }
return (0..<result.pointee.segment_count).compactMap { idx -> String? in
guard let ptr = arr[Int(idx)] else { return nil }
return String(cString: ptr)
}
}()
/// Return the actual recognition result.
/// For English models, it contains words separated by spaces.
/// For Chinese models, it contains Chinese words.
var text: String { _text }
var count: Int { Int(result.pointee.count) }
var timestamps: [Float] { _timestamps }
// Non-empty for TDT models. Empty for all other non-TDT models
var durations: [Float] { _durations }
// For SenseVoice models, it can be zh, en, ja, yue, ko
// where zh is for Chinese
// en is for English
// ja is for Japanese
// yue is for Cantonese
// ko is for Korean
var lang: String { _lang }
// for SenseVoice models
var emotion: String { _emotion }
// for SenseVoice models
var event: String { _event }
// Segment-level timestamps (for Whisper with segment timestamps enabled)
var segmentCount: Int { Int(result.pointee.segment_count) }
var segmentTimestamps: [Float] { _segmentTimestamps }
var segmentDurations: [Float] { _segmentDurations }
var segmentTexts: [String] { _segmentTexts }
init(result: UnsafePointer<SherpaOnnxOfflineRecognizerResult>) {
self.result = result
}
deinit {
SherpaOnnxDestroyOfflineRecognizerResult(result)
}
}
class SherpaOnnxOfflineRecognizer {
/// A pointer to the underlying counterpart in C
private let recognizer: OpaquePointer
init(
config: UnsafePointer<SherpaOnnxOfflineRecognizerConfig>
) {
guard let ptr = SherpaOnnxCreateOfflineRecognizer(config) else {
fatalError("Failed to create SherpaOnnxOfflineRecognizer")
}
self.recognizer = ptr
}
deinit {
SherpaOnnxDestroyOfflineRecognizer(recognizer)
}
/// Decode wave samples.
///
/// - Parameters:
/// - samples: Audio samples normalized to the range [-1, 1]
/// - sampleRate: Sample rate of the input audio samples. Must match
/// the one expected by the model.
func decode(samples: [Float], sampleRate: Int = 16_000) -> SherpaOnnxOfflineRecongitionResult {
let stream = createStream()
stream.acceptWaveform(samples: samples, sampleRate: sampleRate)
decode(stream: stream)
return getResult(stream: stream)
}
func setConfig(config: UnsafePointer<SherpaOnnxOfflineRecognizerConfig>) {
SherpaOnnxOfflineRecognizerSetConfig(recognizer, config)
}
func createStream() -> SherpaOnnxOfflineStreamWrapper {
guard let stream = SherpaOnnxCreateOfflineStream(recognizer) else {
fatalError("Failed to create offline stream")
}
return SherpaOnnxOfflineStreamWrapper(stream: stream)
}
func decode(stream: SherpaOnnxOfflineStreamWrapper) {
SherpaOnnxDecodeOfflineStream(recognizer, stream.stream)
}
func getResult(stream: SherpaOnnxOfflineStreamWrapper) -> SherpaOnnxOfflineRecongitionResult {
guard let resultPtr = SherpaOnnxGetOfflineStreamResult(stream.stream) else {
fatalError("Failed to get offline recognition result")
}
return SherpaOnnxOfflineRecongitionResult(result: resultPtr)
}
}
class SherpaOnnxOfflineStreamWrapper {
let stream: OpaquePointer
init(stream: OpaquePointer) {
self.stream = stream
}
deinit {
SherpaOnnxDestroyOfflineStream(stream)
}
func setOption(key: String, value: String) {
SherpaOnnxOfflineStreamSetOption(stream, toCPointer(key), toCPointer(value))
}
func acceptWaveform(samples: [Float], sampleRate: Int = 16_000) {
SherpaOnnxAcceptWaveformOffline(stream, Int32(sampleRate), samples, Int32(samples.count))
}
}
func sherpaOnnxSileroVadModelConfig(
model: String = "",
threshold: Float = 0.5,
minSilenceDuration: Float = 0.25,
minSpeechDuration: Float = 0.5,
windowSize: Int = 512,
maxSpeechDuration: Float = 5.0
) -> SherpaOnnxSileroVadModelConfig {
return SherpaOnnxSileroVadModelConfig(
model: toCPointer(model),
threshold: threshold,
min_silence_duration: minSilenceDuration,
min_speech_duration: minSpeechDuration,
window_size: Int32(windowSize),
max_speech_duration: maxSpeechDuration
)
}
func sherpaOnnxTenVadModelConfig(
model: String = "",
threshold: Float = 0.5,
minSilenceDuration: Float = 0.25,
minSpeechDuration: Float = 0.5,
windowSize: Int = 256,
maxSpeechDuration: Float = 5.0
) -> SherpaOnnxTenVadModelConfig {
return SherpaOnnxTenVadModelConfig(
model: toCPointer(model),
threshold: threshold,
min_silence_duration: minSilenceDuration,
min_speech_duration: minSpeechDuration,
window_size: Int32(windowSize),
max_speech_duration: maxSpeechDuration
)
}
func sherpaOnnxVadModelConfig(
sileroVad: SherpaOnnxSileroVadModelConfig = sherpaOnnxSileroVadModelConfig(),
sampleRate: Int32 = 16000,
numThreads: Int = 1,
provider: String = "cpu",
debug: Int = 0,
tenVad: SherpaOnnxTenVadModelConfig = sherpaOnnxTenVadModelConfig()
) -> SherpaOnnxVadModelConfig {
return SherpaOnnxVadModelConfig(
silero_vad: sileroVad,
sample_rate: sampleRate,
num_threads: Int32(numThreads),
provider: toCPointer(provider),
debug: Int32(debug),
ten_vad: tenVad
)
}
class SherpaOnnxCircularBufferWrapper {
private let buffer: OpaquePointer
init(capacity: Int) {
guard let ptr = SherpaOnnxCreateCircularBuffer(Int32(capacity)) else {
fatalError("Failed to create SherpaOnnxCircularBuffer")
}
self.buffer = ptr
}
deinit {
SherpaOnnxDestroyCircularBuffer(buffer)
}
func push(samples: [Float]) {
guard !samples.isEmpty else { return }
SherpaOnnxCircularBufferPush(buffer, samples, Int32(samples.count))
}
func get(startIndex: Int, n: Int) -> [Float] {
guard startIndex >= 0 else { return [] }
guard n > 0 else { return [] }
guard let ptr = SherpaOnnxCircularBufferGet(buffer, Int32(startIndex), Int32(n)) else {
return []
}
defer { SherpaOnnxCircularBufferFree(ptr) }
return Array(UnsafeBufferPointer(start: ptr, count: n))
}
func pop(n: Int) {
guard n > 0 else { return }
SherpaOnnxCircularBufferPop(buffer, Int32(n))
}
func size() -> Int {
return Int(SherpaOnnxCircularBufferSize(buffer))
}
func reset() {
SherpaOnnxCircularBufferReset(buffer)
}
}
class SherpaOnnxSpeechSegmentWrapper {
private let p: UnsafePointer<SherpaOnnxSpeechSegment>
init(p: UnsafePointer<SherpaOnnxSpeechSegment>) {
self.p = p
}
deinit {
SherpaOnnxDestroySpeechSegment(p)
}
var start: Int {
Int(p.pointee.start)
}
var n: Int {
Int(p.pointee.n)
}
lazy var samples: [Float] = {
Array(UnsafeBufferPointer(start: p.pointee.samples, count: n))
}()
}
class SherpaOnnxVoiceActivityDetectorWrapper {
/// A pointer to the underlying counterpart in C
private let vad: OpaquePointer
init(config: UnsafePointer<SherpaOnnxVadModelConfig>, buffer_size_in_seconds: Float) {
guard let vad = SherpaOnnxCreateVoiceActivityDetector(config, buffer_size_in_seconds) else {
fatalError("SherpaOnnxCreateVoiceActivityDetector returned nil")
}
self.vad = vad
}
deinit {
SherpaOnnxDestroyVoiceActivityDetector(vad)
}
func acceptWaveform(samples: [Float]) {
SherpaOnnxVoiceActivityDetectorAcceptWaveform(vad, samples, Int32(samples.count))
}
func isEmpty() -> Bool {
return SherpaOnnxVoiceActivityDetectorEmpty(vad) == 1
}
func isSpeechDetected() -> Bool {
return SherpaOnnxVoiceActivityDetectorDetected(vad) == 1
}
func pop() {
SherpaOnnxVoiceActivityDetectorPop(vad)
}
func clear() {
SherpaOnnxVoiceActivityDetectorClear(vad)
}
func front() -> SherpaOnnxSpeechSegmentWrapper {
guard let p = SherpaOnnxVoiceActivityDetectorFront(vad) else {
fatalError("SherpaOnnxVoiceActivityDetectorFront returned nil")