62 lines
2.2 KiB
TypeScript
62 lines
2.2 KiB
TypeScript
import type { Pipeline, PretrainedOptions } from '@xenova/transformers';
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let pipeline: (task: string, model?: string, { quantized, progress_callback, config, cache_dir, local_files_only, revision, }?: PretrainedOptions) => Promise<Pipeline> = null
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async function loadTransformer() {
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if(!pipeline){
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const transformersLib = await import('@xenova/transformers')
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transformersLib.env.localModelPath = "https://sv.risuai.xyz/transformers/"
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pipeline = transformersLib.pipeline
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}
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}
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type TransformersBodyType = {
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max_new_tokens: number,
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do_sample: boolean,
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temperature: number,
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top_p: number,
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typical_p: number,
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repetition_penalty: number,
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encoder_repetition_penalty: number,
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top_k: number,
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min_length: number,
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no_repeat_ngram_size: number,
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num_beams: number,
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penalty_alpha: number,
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length_penalty: number,
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early_stopping: boolean,
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truncation_length: number,
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ban_eos_token: boolean,
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stopping_strings: number,
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seed: number,
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add_bos_token: boolean,
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}
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export const runTransformers = async (baseText:string, model:string,bodyTemplate:TransformersBodyType) => {
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await loadTransformer()
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let text = baseText
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let generator = await pipeline('text-generation', model);
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let output:{generated_text:string}[] = await generator(text);
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return output
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}
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export const runSummarizer = async (text: string) => {
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await loadTransformer()
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let classifier = await pipeline("summarization", "Xenova/distilbart-cnn-6-6")
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const v:{summary_text:string}[] = await classifier(text)
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return v
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}
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export const runEmbedding = async (text: string):Promise<Float32Array> => {
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await loadTransformer()
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let extractor = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
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let result = await extractor(text, { pooling: 'mean', normalize: true });
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return result?.data ?? null;
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}
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export const runTTS = async (text: string) => {
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let speaker_embeddings = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/speaker_embeddings.bin';
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let synthesizer = await pipeline('text-to-speech', 'Xenova/speecht5_tts', { local_files_only: true });
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let out = await synthesizer(text, { speaker_embeddings });
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return out
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} |