chore: to worker.ts

packages/stt-realtime-webgpu/src/libs/worker.js → packages/stt-realtime-webgpu/src/libs/worker.ts

@@ -1,17 +1,32 @@
-import { AutoProcessor, AutoTokenizer, full, TextStreamer, WhisperForConditionalGeneration } from '@huggingface/transformers'
+import type {
+  ModelOutput,
+  PreTrainedModel,
+  PreTrainedTokenizer,
+  Processor,
+  ProgressCallback,
+  Tensor,
+} from '@huggingface/transformers'
+
+import {
+  AutoProcessor,
+  AutoTokenizer,
+  full,
+  TextStreamer,
+  WhisperForConditionalGeneration,
+} from '@huggingface/transformers'
 
 const MAX_NEW_TOKENS = 64
 
 /**
  * This class uses the Singleton pattern to ensure that only one instance of the model is loaded.
  */
 class AutomaticSpeechRecognitionPipeline {
-  static model_id = null
-  static tokenizer = null
-  static processor = null
-  static model = null
+  static model_id: string | null = null
+  static tokenizer: Promise<PreTrainedTokenizer>
+  static processor: Promise<Processor>
+  static model: Promise<PreTrainedModel>
 
-  static async getInstance(progress_callback = null) {
+  static async getInstance(progress_callback?: ProgressCallback) {
     this.model_id = 'onnx-community/whisper-base'
 
     this.tokenizer ??= AutoTokenizer.from_pretrained(this.model_id, {
@@ -36,7 +51,7 @@ class AutomaticSpeechRecognitionPipeline {
 }
 
 let processing = false
-async function generate({ audio, language }) {
+async function generate({ audio, language }: { audio: ArrayBuffer, language: string }) {
   if (processing)
     return
   processing = true
@@ -49,13 +64,14 @@ async function generate({ audio, language }) {
 
   let startTime
   let numTokens = 0
-  const callback_function = (output) => {
+  const callback_function = (output: ModelOutput | Tensor) => {
     startTime ??= performance.now()
 
     let tps
     if (numTokens++ > 0) {
       tps = numTokens / (performance.now() - startTime) * 1000
     }
+
     globalThis.postMessage({
       status: 'update',
       output,
@@ -66,7 +82,9 @@ async function generate({ audio, language }) {
 
   const streamer = new TextStreamer(tokenizer, {
     skip_prompt: true,
-    skip_special_tokens: true,
+    decode_kwargs: {
+      skip_special_tokens: true,
+    },
     callback_function,
   })
 
@@ -79,7 +97,7 @@ async function generate({ audio, language }) {
     streamer,
   })
 
-  const outputText = tokenizer.batch_decode(outputs, { skip_special_tokens: true })
+  const outputText = tokenizer.batch_decode(outputs as Tensor, { skip_special_tokens: true })
 
   // Send the output back to the main thread
   globalThis.postMessage({
@@ -96,7 +114,7 @@ async function load() {
   })
 
   // Load the pipeline and save it for future use.
-  // eslint-disable-next-line no-unused-vars, unused-imports/no-unused-vars
+  // eslint-disable-next-line unused-imports/no-unused-vars
   const [tokenizer, processor, model] = await AutomaticSpeechRecognitionPipeline.getInstance((x) => {
     // We also add a progress callback to the pipeline so that we can
     // track model loading.