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Refactor HypaProcesser instantiation to remove hardcoded model name and add models and others

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kwaroran
2024-12-07 00:58:09 +09:00
parent 998e8e6c65
commit e0f6c58540
15 changed files with 443 additions and 92 deletions
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26
src/ts/process/transformers.ts
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@@ -1,4 +1,4 @@
import {env, AutoTokenizer, pipeline, type SummarizationOutput, type TextGenerationConfig, type TextGenerationOutput, FeatureExtractionPipeline, TextToAudioPipeline, type ImageToTextOutput } from '@xenova/transformers';
import {env, AutoTokenizer, pipeline, type SummarizationOutput, type TextGenerationConfig, type TextGenerationOutput, FeatureExtractionPipeline, TextToAudioPipeline, type ImageToTextOutput } from '@huggingface/transformers';
import { unzip } from 'fflate';
import { globalFetch, loadAsset, saveAsset } from 'src/ts/globalApi.svelte';
import { selectSingleFile } from 'src/ts/util';
@@ -15,6 +15,7 @@ async function initTransformers(){
env.useBrowserCache = false
env.useFSCache = false
env.useCustomCache = true
env.allowLocalModels = true
env.customCache = {
put: async (url:URL|string, response:Response) => {
await tfCache.put(url, response)
@@ -33,10 +34,12 @@ async function initTransformers(){
console.log('transformers loaded')
}
export const runTransformers = async (baseText:string, model:string,config:TextGenerationConfig = {}) => {
export const runTransformers = async (baseText:string, model:string,config:TextGenerationConfig, device:'webgpu'|'wasm' = 'wasm') => {
await initTransformers()
let text = baseText
let generator = await pipeline('text-generation', model);
let generator = await pipeline('text-generation', model, {
device
});
let output = await generator(text, config) as TextGenerationOutput
const outputOne = output[0]
return outputOne
@@ -50,16 +53,25 @@ export const runSummarizer = async (text: string) => {
}
let extractor:FeatureExtractionPipeline = null
let lastEmbeddingModelQuery:string = ''
type EmbeddingModel = 'Xenova/all-MiniLM-L6-v2'|'nomic-ai/nomic-embed-text-v1.5'
export const runEmbedding = async (texts: string[], model:EmbeddingModel = 'Xenova/all-MiniLM-L6-v2'):Promise<Float32Array[]> => {
export const runEmbedding = async (texts: string[], model:EmbeddingModel = 'Xenova/all-MiniLM-L6-v2', device:'webgpu'|'wasm'):Promise<Float32Array[]> => {
await initTransformers()
if(!extractor){
extractor = await pipeline('feature-extraction', model);
console.log('running embedding')
let embeddingModelQuery = model + device
if(!extractor || embeddingModelQuery !== lastEmbeddingModelQuery){
extractor = await pipeline('feature-extraction', model, {
device: device,
progress_callback: (progress) => {
console.log(progress)
}
});
console.log('extractor loaded')
}
let result = await extractor(texts, { pooling: 'mean', normalize: true });
console.log(texts, result)
const data = result.data as Float32Array
console.log(data)
const lenPerText = data.length / texts.length
let res:Float32Array[] = []
for(let i = 0; i < texts.length; i++){