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5 Commits
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|---|---|---|---|
| ca66e8e304 | |||
| cec892563e | |||
| 91066e070f | |||
| a94b153c6d | |||
| 39537a4a8f |
18
README.md
18
README.md
@@ -3,7 +3,7 @@
|
||||
<br />
|
||||
|
||||
<!-- Logo -->
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||||
<img src="https://git.zakscode.com/repo-avatars/a90851ca730480ec37a5c0c2c4f1b4609eee5eadf806eaf16c83ac4cb7493aa9" alt="Logo" width="200" height="200">
|
||||
<img alt="Logo" width="200" height="200" src="https://git.zakscode.com/repo-avatars/a82d423674763e7a0c1c945bdbb07e249b2bb786d3c9beae76d5b196a10f5c0f">
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||||
|
||||
<!-- Title -->
|
||||
### @ztimson/ai-utils
|
||||
@@ -53,13 +53,15 @@ A TypeScript library that provides a unified interface for working with multiple
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- **Provider Abstraction**: Switch between AI providers without changing your code
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||||
|
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### Built With
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[](https://anthropic.com/)
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[](https://openai.com/)
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||||
[](https://ollama.com/)
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||||
[](https://tensorflow.org/)
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||||
[](https://tesseract-ocr.github.io/)
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||||
[](https://anthropic.com/)
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||||
[](https://github.com/ggml-org/llama.cpp)
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||||
[](https://openai.com/)
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[](https://github.com/pyannote)
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[](https://tensorflow.org/)
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[](https://tesseract-ocr.github.io/)
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[](https://huggingface.co/docs/transformers.js/en/index)
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[](https://typescriptlang.org/)
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||||
[](https://github.com/ggerganov/whisper.cpp)
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[](https://github.com/ggerganov/whisper.cpp)
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## Setup
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||||
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||||
@@ -88,6 +90,8 @@ A TypeScript library that provides a unified interface for working with multiple
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#### Prerequisites
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- [Node.js](https://nodejs.org/en/download)
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- _[Whisper.cpp](https://github.com/ggml-org/whisper.cpp/releases/tag) (ASR)_
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- _[Pyannote](https://github.com/pyannote) (ASR Diarization):_ `pip install pyannote.audio`
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#### Instructions
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1. Install the dependencies: `npm i`
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@@ -1,6 +1,6 @@
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{
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"name": "@ztimson/ai-utils",
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"version": "0.7.7",
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"version": "0.8.0",
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"description": "AI Utility library",
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"author": "Zak Timson",
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"license": "MIT",
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@@ -32,8 +32,7 @@
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"@ztimson/utils": "^0.28.13",
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"cheerio": "^1.2.0",
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"openai": "^6.22.0",
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"tesseract.js": "^7.0.0",
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"wavefile": "^11.0.0"
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"tesseract.js": "^7.0.0"
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},
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"devDependencies": {
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"@types/node": "^24.8.1",
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@@ -12,7 +12,7 @@ export type AiOptions = {
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hfToken?: string;
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/** Path to models */
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path?: string;
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/** ASR model: whisper-tiny, whisper-base */
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/** Whisper ASR model: ggml-tiny.en.bin, ggml-base.en.bin */
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asr?: string;
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/** Embedding model: all-MiniLM-L6-v2, bge-small-en-v1.5, bge-large-en-v1.5 */
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embedder?: string;
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@@ -22,6 +22,8 @@ export type AiOptions = {
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}
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/** OCR model: eng, eng_best, eng_fast */
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ocr?: string;
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/** Whisper binary */
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whisper?: string;
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}
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export class Ai {
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137
src/asr.ts
137
src/asr.ts
@@ -1,137 +0,0 @@
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import { pipeline } from '@xenova/transformers';
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import { parentPort } from 'worker_threads';
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import { spawn } from 'node:child_process';
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import { execSync } from 'node:child_process';
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import { mkdtempSync, rmSync, readFileSync } from 'node:fs';
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import { join } from 'node:path';
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import { tmpdir } from 'node:os';
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import wavefile from 'wavefile';
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let whisperPipeline: any;
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export async function canDiarization(): Promise<string | null> {
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const checkPython = (cmd: string) => {
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return new Promise<boolean>((resolve) => {
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const proc = spawn(cmd, ['-c', 'import pyannote.audio']);
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proc.on('close', (code: number) => resolve(code === 0));
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proc.on('error', () => resolve(false));
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||||
});
|
||||
};
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if(await checkPython('python3')) return 'python3';
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if(await checkPython('python')) return 'python';
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return null;
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}
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async function runDiarization(binary: string, audioPath: string, dir: string, token: string): Promise<any[]> {
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const script = `
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import sys
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import json
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import os
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from pyannote.audio import Pipeline
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os.environ['TORCH_HOME'] = r"${dir}"
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pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1", token="${token}")
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output = pipeline(sys.argv[1])
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segments = []
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for turn, speaker in output.speaker_diarization:
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segments.append({"start": turn.start, "end": turn.end, "speaker": speaker})
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print(json.dumps(segments))
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`;
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return new Promise((resolve, reject) => {
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let output = '';
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const proc = spawn(binary, ['-c', script, audioPath]);
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proc.stdout.on('data', (data: Buffer) => output += data.toString());
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proc.stderr.on('data', (data: Buffer) => console.error(data.toString()));
|
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proc.on('close', (code: number) => {
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if(code === 0) {
|
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try {
|
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resolve(JSON.parse(output));
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} catch (err) {
|
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reject(new Error('Failed to parse diarization output'));
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}
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} else {
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reject(new Error(`Python process exited with code ${code}`));
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}
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});
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proc.on('error', reject);
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});
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}
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function combineSpeakerTranscript(chunks: any[], speakers: any[]): string {
|
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const speakerMap = new Map();
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let speakerCount = 0;
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speakers.forEach((seg: any) => {
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if(!speakerMap.has(seg.speaker)) speakerMap.set(seg.speaker, ++speakerCount);
|
||||
});
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||||
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||||
const lines: string[] = [];
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let currentSpeaker = -1;
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let currentText = '';
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chunks.forEach((chunk: any) => {
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const time = chunk.timestamp[0];
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const speaker = speakers.find((s: any) => time >= s.start && time <= s.end);
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const speakerNum = speaker ? speakerMap.get(speaker.speaker) : 1;
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if (speakerNum !== currentSpeaker) {
|
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if(currentText) lines.push(`[Speaker ${currentSpeaker}]: ${currentText.trim()}`);
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currentSpeaker = speakerNum;
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currentText = chunk.text;
|
||||
} else {
|
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currentText += chunk.text;
|
||||
}
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||||
});
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if(currentText) lines.push(`[Speaker ${currentSpeaker}]: ${currentText.trim()}`);
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return lines.join('\n');
|
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}
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||||
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||||
function prepareAudioBuffer(file: string): [string, Float32Array] {
|
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let wav: any, tmp;
|
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try {
|
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wav = new wavefile.WaveFile(readFileSync(file));
|
||||
} catch(err) {
|
||||
tmp = join(mkdtempSync(join(tmpdir(), 'audio-')), 'converted.wav');
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execSync(`ffmpeg -i "${file}" -ar 16000 -ac 1 -f wav "${tmp}"`, { stdio: 'ignore' });
|
||||
wav = new wavefile.WaveFile(readFileSync(tmp));
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||||
} finally {
|
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wav.toBitDepth('32f');
|
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wav.toSampleRate(16000);
|
||||
const samples = wav.getSamples();
|
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if(Array.isArray(samples)) {
|
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const left = samples[0];
|
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const right = samples[1];
|
||||
const buffer = new Float32Array(left.length);
|
||||
for (let i = 0; i < left.length; i++) buffer[i] = (left[i] + right[i]) / 2;
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||||
return [tmp || file, buffer];
|
||||
}
|
||||
return [tmp || file, samples];
|
||||
}
|
||||
}
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||||
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||||
parentPort?.on('message', async ({ file, speaker, model, modelDir, token }) => {
|
||||
let tempFile = null;
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||||
try {
|
||||
if(!whisperPipeline) whisperPipeline = await pipeline('automatic-speech-recognition', `Xenova/${model}`, {cache_dir: modelDir, quantized: true});
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||||
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||||
const [f, buffer] = prepareAudioBuffer(file);
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||||
tempFile = f !== file ? f : null;
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const hasDiarization = await canDiarization();
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const [transcript, speakers] = await Promise.all([
|
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whisperPipeline(buffer, {return_timestamps: speaker ? 'word' : false}),
|
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(!speaker || !token || !hasDiarization) ? Promise.resolve(): runDiarization(hasDiarization, f, modelDir, token),
|
||||
]);
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||||
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||||
const text = transcript.text?.trim() || null;
|
||||
if(!speaker) return parentPort?.postMessage({ text });
|
||||
if(!token) return parentPort?.postMessage({ text, error: 'HuggingFace token required' });
|
||||
if(!hasDiarization) return parentPort?.postMessage({ text, error: 'Speaker diarization unavailable' });
|
||||
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||||
const combined = combineSpeakerTranscript(transcript.chunks || [], speakers || []);
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||||
parentPort?.postMessage({ text: combined });
|
||||
} catch (err: any) {
|
||||
parentPort?.postMessage({ error: err.stack || err.message });
|
||||
} finally {
|
||||
if(tempFile) rmSync(tempFile, { recursive: true, force: true });
|
||||
}
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||||
});
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263
src/audio.ts
263
src/audio.ts
@@ -1,82 +1,229 @@
|
||||
import {fileURLToPath} from 'url';
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import {Worker} from 'worker_threads';
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||||
import {execSync, spawn} from 'node:child_process';
|
||||
import {mkdtempSync} from 'node:fs';
|
||||
import fs from 'node:fs/promises';
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||||
import {tmpdir} from 'node:os';
|
||||
import * as path from 'node:path';
|
||||
import Path, {join} from 'node:path';
|
||||
import {AbortablePromise, Ai} from './ai.ts';
|
||||
import {canDiarization} from './asr.ts';
|
||||
import {dirname, join} from 'path';
|
||||
|
||||
export class Audio {
|
||||
private busy = false;
|
||||
private currentJob: any;
|
||||
private queue: Array<{file: string, model: string, speaker: boolean | 'id', modelDir: string, token: string, resolve: any, reject: any}> = [];
|
||||
private worker: Worker | null = null;
|
||||
private downloads: {[key: string]: Promise<string>} = {};
|
||||
private pyannote!: string;
|
||||
private whisperModel!: string;
|
||||
|
||||
constructor(private ai: Ai) {}
|
||||
|
||||
private processQueue() {
|
||||
if(this.busy || !this.queue.length) return;
|
||||
|
||||
this.busy = true;
|
||||
const job = this.queue.shift()!;
|
||||
if(!this.worker) {
|
||||
this.worker = new Worker(join(dirname(fileURLToPath(import.meta.url)), 'asr.js'));
|
||||
this.worker.on('message', this.handleMessage.bind(this));
|
||||
this.worker.on('error', this.handleError.bind(this));
|
||||
constructor(private ai: Ai) {
|
||||
if(ai.options.whisper) {
|
||||
this.whisperModel = ai.options.asr || 'ggml-base.en.bin';
|
||||
this.downloadAsrModel();
|
||||
}
|
||||
|
||||
this.currentJob = job;
|
||||
this.worker.postMessage({file: job.file, model: job.model, speaker: job.speaker, modelDir: job.modelDir, token: job.token});
|
||||
this.pyannote = `
|
||||
import sys
|
||||
import json
|
||||
import os
|
||||
from pyannote.audio import Pipeline
|
||||
|
||||
os.environ['TORCH_HOME'] = r"${ai.options.path}"
|
||||
pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1", token="${ai.options.hfToken}")
|
||||
output = pipeline(sys.argv[1])
|
||||
|
||||
segments = []
|
||||
for turn, speaker in output.speaker_diarization:
|
||||
segments.append({"start": turn.start, "end": turn.end, "speaker": speaker})
|
||||
|
||||
print(json.dumps(segments))
|
||||
`;
|
||||
}
|
||||
|
||||
private handleMessage({text, warning, error}: any) {
|
||||
const job = this.currentJob!;
|
||||
this.busy = false;
|
||||
if(error) job.reject(new Error(error));
|
||||
else {
|
||||
if(warning) console.warn(warning);
|
||||
job.resolve(text);
|
||||
private runAsr(file: string, opts: {model?: string, diarization?: boolean} = {}): AbortablePromise<any> {
|
||||
let proc: any;
|
||||
const p = new Promise<any>((resolve, reject) => {
|
||||
this.downloadAsrModel(opts.model).then(m => {
|
||||
if(opts.diarization) {
|
||||
let output = path.join(path.dirname(file), 'transcript');
|
||||
proc = spawn(<string>this.ai.options.whisper,
|
||||
['-m', m, '-f', file, '-np', '-ml', '1', '-oj', '-of', output],
|
||||
{stdio: ['ignore', 'ignore', 'pipe']}
|
||||
);
|
||||
proc.on('error', (err: Error) => reject(err));
|
||||
proc.on('close', async (code: number) => {
|
||||
if(code === 0) {
|
||||
output = await fs.readFile(output + '.json', 'utf-8');
|
||||
fs.rm(output + '.json').catch(() => { });
|
||||
try { resolve(JSON.parse(output)); }
|
||||
catch(e) { reject(new Error('Failed to parse whisper JSON')); }
|
||||
} else {
|
||||
reject(new Error(`Exit code ${code}`));
|
||||
}
|
||||
this.processQueue();
|
||||
}
|
||||
|
||||
private handleError(err: Error) {
|
||||
if(this.currentJob) {
|
||||
this.currentJob.reject(err);
|
||||
this.busy = false;
|
||||
this.processQueue();
|
||||
}
|
||||
}
|
||||
|
||||
asr(file: string, options: { model?: string; speaker?: boolean | 'id' } = {}): AbortablePromise<string | null> {
|
||||
const { model = this.ai.options.asr || 'whisper-base', speaker = false } = options;
|
||||
let aborted = false;
|
||||
const abort = () => { aborted = true; };
|
||||
let p = new Promise<string | null>((resolve, reject) => {
|
||||
this.queue.push({file, model, speaker, modelDir: <string>this.ai.options.path, token: <string>this.ai.options.hfToken,
|
||||
resolve: (text: string | null) => !aborted && resolve(text),
|
||||
reject: (err: Error) => !aborted && reject(err)
|
||||
});
|
||||
this.processQueue();
|
||||
} else {
|
||||
let output = '';
|
||||
proc = spawn(<string>this.ai.options.whisper, ['-m', m, '-f', file, '-np', '-nt']);
|
||||
proc.on('error', (err: Error) => reject(err));
|
||||
proc.stdout.on('data', (data: Buffer) => output += data.toString());
|
||||
proc.on('close', async (code: number) => {
|
||||
if(code === 0) {
|
||||
resolve(output.trim() || null);
|
||||
} else {
|
||||
reject(new Error(`Exit code ${code}`));
|
||||
}
|
||||
});
|
||||
}
|
||||
});
|
||||
});
|
||||
return <any>Object.assign(p, {abort: () => proc?.kill('SIGTERM')});
|
||||
}
|
||||
|
||||
private runDiarization(file: string): AbortablePromise<any> {
|
||||
let aborted = false, abort = () => { aborted = true; };
|
||||
const checkPython = (cmd: string) => {
|
||||
return new Promise<boolean>((resolve) => {
|
||||
const proc = spawn(cmd, ['-W', 'ignore', '-c', 'import pyannote.audio']);
|
||||
proc.on('close', (code: number) => resolve(code === 0));
|
||||
proc.on('error', () => resolve(false));
|
||||
});
|
||||
};
|
||||
const p = Promise.all<any>([
|
||||
checkPython('python'),
|
||||
checkPython('python3'),
|
||||
]).then(<any>(async ([p, p3]: [boolean, boolean]) => {
|
||||
if(aborted) return;
|
||||
if(!p && !p3) throw new Error('Pyannote is not installed: pip install pyannote.audio');
|
||||
const binary = p3 ? 'python3' : 'python';
|
||||
return new Promise((resolve, reject) => {
|
||||
if(aborted) return;
|
||||
let output = '';
|
||||
const proc = spawn(binary, ['-W', 'ignore', '-c', this.pyannote, file]);
|
||||
proc.stdout.on('data', (data: Buffer) => output += data.toString());
|
||||
proc.stderr.on('data', (data: Buffer) => console.error(data.toString()));
|
||||
proc.on('close', (code: number) => {
|
||||
if(code === 0) {
|
||||
try { resolve(JSON.parse(output)); }
|
||||
catch (err) { reject(new Error('Failed to parse diarization output')); }
|
||||
} else {
|
||||
reject(new Error(`Python process exited with code ${code}`));
|
||||
}
|
||||
});
|
||||
proc.on('error', reject);
|
||||
abort = () => proc.kill('SIGTERM');
|
||||
});
|
||||
}));
|
||||
return <any>Object.assign(p, {abort});
|
||||
}
|
||||
|
||||
private async combineSpeakerTranscript(punctuatedText: string, timestampData: any, speakers: any[]): Promise<string> {
|
||||
const speakerMap = new Map();
|
||||
let speakerCount = 0;
|
||||
speakers.forEach((seg: any) => {
|
||||
if(!speakerMap.has(seg.speaker)) speakerMap.set(seg.speaker, ++speakerCount);
|
||||
});
|
||||
|
||||
if(options.speaker == 'id') {
|
||||
const sentences = punctuatedText.match(/[^.!?]+[.!?]+/g) || [punctuatedText];
|
||||
const lines: string[] = [];
|
||||
|
||||
sentences.forEach(sentence => {
|
||||
sentence = sentence.trim();
|
||||
if(!sentence) return;
|
||||
|
||||
const words = sentence.toLowerCase().replace(/[^\w\s]/g, '').split(/\s+/);
|
||||
let startTime = Infinity, endTime = 0;
|
||||
const wordTimings: {start: number, end: number}[] = [];
|
||||
|
||||
timestampData.transcription.forEach((word: any) => {
|
||||
const wordText = word.text.trim().toLowerCase();
|
||||
if(words.some(w => wordText.includes(w))) {
|
||||
const start = word.offsets.from / 1000;
|
||||
const end = word.offsets.to / 1000;
|
||||
wordTimings.push({start, end});
|
||||
if(start < startTime) startTime = start;
|
||||
if(end > endTime) endTime = end;
|
||||
}
|
||||
});
|
||||
|
||||
if(startTime === Infinity) return;
|
||||
|
||||
// Weight by word-level overlap instead of sentence span
|
||||
const speakerScores = new Map<number, number>();
|
||||
|
||||
wordTimings.forEach(wt => {
|
||||
speakers.forEach((seg: any) => {
|
||||
const overlap = Math.max(0, Math.min(wt.end, seg.end) - Math.max(wt.start, seg.start));
|
||||
const duration = wt.end - wt.start;
|
||||
if(duration > 0) {
|
||||
const score = overlap / duration; // % of word covered
|
||||
const spkNum = speakerMap.get(seg.speaker);
|
||||
speakerScores.set(spkNum, (speakerScores.get(spkNum) || 0) + score);
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
let bestSpeaker = 1;
|
||||
let maxScore = 0;
|
||||
speakerScores.forEach((score, speaker) => {
|
||||
if(score > maxScore) {
|
||||
maxScore = score;
|
||||
bestSpeaker = speaker;
|
||||
}
|
||||
});
|
||||
|
||||
lines.push(`[Speaker ${bestSpeaker}]: ${sentence}`);
|
||||
});
|
||||
|
||||
return lines.join('\n').trim();
|
||||
}
|
||||
|
||||
asr(file: string, options: { model?: string; diarization?: boolean | 'id' } = {}): AbortablePromise<string | null> {
|
||||
if(!this.ai.options.whisper) throw new Error('Whisper not configured');
|
||||
|
||||
const tmp = join(mkdtempSync(join(tmpdir(), 'audio-')), 'converted.wav');
|
||||
execSync(`ffmpeg -i "${file}" -ar 16000 -ac 1 -f wav "${tmp}"`, { stdio: 'ignore' });
|
||||
const clean = () => fs.rm(Path.dirname(tmp), {recursive: true, force: true}).catch(() => {});
|
||||
const transcript = this.runAsr(tmp, {model: options.model, diarization: false});
|
||||
const timestamps: any = !options.diarization ? Promise.resolve(null) : this.runAsr(tmp, {model: options.model, diarization: true});
|
||||
const diarization: any = !options.diarization ? Promise.resolve(null) : this.runDiarization(tmp);
|
||||
let aborted = false, abort = () => {
|
||||
aborted = true;
|
||||
transcript.abort();
|
||||
timestamps?.abort?.();
|
||||
diarization?.abort?.();
|
||||
clean();
|
||||
};
|
||||
|
||||
const response = Promise.allSettled([transcript, timestamps, diarization]).then(async ([t, ts, d]) => {
|
||||
if(t.status == 'rejected') throw new Error('Whisper.cpp punctuated:\n' + t.reason);
|
||||
if(ts.status == 'rejected') throw new Error('Whisper.cpp timestamps:\n' + ts.reason);
|
||||
if(d.status == 'rejected') throw new Error('Pyannote:\n' + d.reason);
|
||||
if(aborted || !options.diarization) return t.value;
|
||||
|
||||
let transcript = await this.combineSpeakerTranscript(t.value, ts.value, d.value);
|
||||
if(!aborted && options.diarization === 'id') {
|
||||
if(!this.ai.language.defaultModel) throw new Error('Configure an LLM for advanced ASR speaker detection');
|
||||
p = p.then(async transcript => {
|
||||
if(!transcript) return transcript;
|
||||
let chunks = this.ai.language.chunk(transcript, 500, 0);
|
||||
if(chunks.length > 4) chunks = [...chunks.slice(0, 3), <string>chunks.at(-1)];
|
||||
const names = await this.ai.language.json(chunks.join('\n'), '{1: "Detected Name", 2: "Second Name"}', {
|
||||
system: 'Use the following transcript to identify speakers. Only identify speakers you are positive about, dont mention speakers you are unsure about in your response',
|
||||
temperature: 0.1,
|
||||
});
|
||||
Object.entries(names).forEach(([speaker, name]) => {
|
||||
transcript = (<string>transcript).replaceAll(`[Speaker ${speaker}]`, `[${name}]`);
|
||||
});
|
||||
Object.entries(names).forEach(([speaker, name]) => transcript = transcript.replaceAll(`[Speaker ${speaker}]`, `[${name}]`));
|
||||
}
|
||||
return transcript;
|
||||
})
|
||||
}).finally(() => clean());
|
||||
return <any>Object.assign(response, {abort});
|
||||
}
|
||||
|
||||
return Object.assign(p, { abort });
|
||||
async downloadAsrModel(model: string = this.whisperModel): Promise<string> {
|
||||
if(!this.ai.options.whisper) throw new Error('Whisper not configured');
|
||||
if(!model.endsWith('.bin')) model += '.bin';
|
||||
const p = Path.join(<string>this.ai.options.path, model);
|
||||
if(await fs.stat(p).then(() => true).catch(() => false)) return p;
|
||||
if(!!this.downloads[model]) return this.downloads[model];
|
||||
this.downloads[model] = fetch(`https://huggingface.co/ggerganov/whisper.cpp/resolve/main/${model}`)
|
||||
.then(resp => resp.arrayBuffer())
|
||||
.then(arr => Buffer.from(arr)).then(async buffer => {
|
||||
await fs.writeFile(p, buffer);
|
||||
delete this.downloads[model];
|
||||
return p;
|
||||
});
|
||||
return this.downloads[model];
|
||||
}
|
||||
|
||||
canDiarization = () => canDiarization().then(resp => !!resp);
|
||||
}
|
||||
|
||||
@@ -1,11 +1,13 @@
|
||||
import { pipeline } from '@xenova/transformers';
|
||||
import { parentPort } from 'worker_threads';
|
||||
|
||||
let embedder: any;
|
||||
const [modelDir, model] = process.argv.slice(2);
|
||||
|
||||
parentPort?.on('message', async ({text, model, modelDir }) => {
|
||||
if(!embedder) embedder = await pipeline('feature-extraction', 'Xenova/' + model, {quantized: true, cache_dir: modelDir});
|
||||
let text = '';
|
||||
process.stdin.on('data', chunk => text += chunk);
|
||||
process.stdin.on('end', async () => {
|
||||
const embedder = await pipeline('feature-extraction', 'Xenova/' + model, {quantized: true, cache_dir: modelDir});
|
||||
const output = await embedder(text, { pooling: 'mean', normalize: true });
|
||||
const embedding = Array.from(output.data);
|
||||
parentPort?.postMessage({embedding});
|
||||
console.log(JSON.stringify({embedding}));
|
||||
process.exit();
|
||||
});
|
||||
|
||||
@@ -1,8 +1,6 @@
|
||||
export * from './ai';
|
||||
export * from './antrhopic';
|
||||
export * from './asr';
|
||||
export * from './audio';
|
||||
export * from './embedder'
|
||||
export * from './llm';
|
||||
export * from './open-ai';
|
||||
export * from './provider';
|
||||
|
||||
52
src/llm.ts
52
src/llm.ts
@@ -4,9 +4,9 @@ import {Anthropic} from './antrhopic.ts';
|
||||
import {OpenAi} from './open-ai.ts';
|
||||
import {LLMProvider} from './provider.ts';
|
||||
import {AiTool} from './tools.ts';
|
||||
import {Worker} from 'worker_threads';
|
||||
import {fileURLToPath} from 'url';
|
||||
import {dirname, join} from 'path';
|
||||
import { spawn } from 'node:child_process';
|
||||
|
||||
export type AnthropicConfig = {proto: 'anthropic', token: string};
|
||||
export type OllamaConfig = {proto: 'ollama', host: string};
|
||||
@@ -258,34 +258,54 @@ class LLM {
|
||||
* @param {maxTokens?: number, overlapTokens?: number} opts Options for embedding such as chunk sizes
|
||||
* @returns {Promise<Awaited<{index: number, embedding: number[], text: string, tokens: number}>[]>} Chunked embeddings
|
||||
*/
|
||||
async embedding(target: object | string, opts: {maxTokens?: number, overlapTokens?: number} = {}) {
|
||||
embedding(target: object | string, opts: {maxTokens?: number, overlapTokens?: number} = {}): AbortablePromise<any[]> {
|
||||
let {maxTokens = 500, overlapTokens = 50} = opts;
|
||||
let aborted = false;
|
||||
const abort = () => { aborted = true; };
|
||||
|
||||
const embed = (text: string): Promise<number[]> => {
|
||||
return new Promise((resolve, reject) => {
|
||||
const worker = new Worker(join(dirname(fileURLToPath(import.meta.url)), 'embedder.js'));
|
||||
const handleMessage = ({ embedding }: any) => {
|
||||
worker.terminate();
|
||||
resolve(embedding);
|
||||
};
|
||||
const handleError = (err: Error) => {
|
||||
worker.terminate();
|
||||
reject(err);
|
||||
};
|
||||
worker.on('message', handleMessage);
|
||||
worker.on('error', handleError);
|
||||
worker.on('exit', (code) => {
|
||||
if(code !== 0) reject(new Error(`Worker exited with code ${code}`));
|
||||
if(aborted) return reject(new Error('Aborted'));
|
||||
|
||||
const args: string[] = [
|
||||
join(dirname(fileURLToPath(import.meta.url)), 'embedder.js'),
|
||||
<string>this.ai.options.path,
|
||||
this.ai.options?.embedder || 'bge-small-en-v1.5'
|
||||
];
|
||||
const proc = spawn('node', args, {stdio: ['pipe', 'pipe', 'ignore']});
|
||||
proc.stdin.write(text);
|
||||
proc.stdin.end();
|
||||
|
||||
let output = '';
|
||||
proc.stdout.on('data', (data: Buffer) => output += data.toString());
|
||||
proc.on('close', (code: number) => {
|
||||
if(aborted) return reject(new Error('Aborted'));
|
||||
if(code === 0) {
|
||||
try {
|
||||
const result = JSON.parse(output);
|
||||
resolve(result.embedding);
|
||||
} catch(err) {
|
||||
reject(new Error('Failed to parse embedding output'));
|
||||
}
|
||||
} else {
|
||||
reject(new Error(`Embedder process exited with code ${code}`));
|
||||
}
|
||||
});
|
||||
worker.postMessage({text, model: this.ai.options?.embedder || 'bge-small-en-v1.5', modelDir: this.ai.options.path});
|
||||
proc.on('error', reject);
|
||||
});
|
||||
};
|
||||
|
||||
const p = (async () => {
|
||||
const chunks = this.chunk(target, maxTokens, overlapTokens), results: any[] = [];
|
||||
for(let i = 0; i < chunks.length; i++) {
|
||||
if(aborted) break;
|
||||
const text = chunks[i];
|
||||
const embedding = await embed(text);
|
||||
results.push({index: i, embedding, text, tokens: this.estimateTokens(text)});
|
||||
}
|
||||
return results;
|
||||
})();
|
||||
return Object.assign(p, { abort });
|
||||
}
|
||||
|
||||
/**
|
||||
|
||||
@@ -2,43 +2,22 @@ import {createWorker} from 'tesseract.js';
|
||||
import {AbortablePromise, Ai} from './ai.ts';
|
||||
|
||||
export class Vision {
|
||||
private worker: any = null;
|
||||
private queue: Array<{ path: string, resolve: any, reject: any }> = [];
|
||||
private busy = false;
|
||||
|
||||
constructor(private ai: Ai) {}
|
||||
|
||||
private async processQueue() {
|
||||
if(this.busy || !this.queue.length) return;
|
||||
this.busy = true;
|
||||
const job = this.queue.shift()!;
|
||||
if(!this.worker) this.worker = await createWorker(this.ai.options.ocr || 'eng', 2, {cachePath: this.ai.options.path});
|
||||
try {
|
||||
const {data} = await this.worker.recognize(job.path);
|
||||
job.resolve(data.text.trim() || null);
|
||||
} catch(err) {
|
||||
job.reject(err);
|
||||
}
|
||||
this.busy = false;
|
||||
this.processQueue();
|
||||
}
|
||||
|
||||
/**
|
||||
* Convert image to text using Optical Character Recognition
|
||||
* @param {string} path Path to image
|
||||
* @returns {AbortablePromise<string | null>} Promise of extracted text with abort method
|
||||
*/
|
||||
ocr(path: string): AbortablePromise<string | null> {
|
||||
let aborted = false;
|
||||
const abort = () => { aborted = true; };
|
||||
const p = new Promise<string | null>((resolve, reject) => {
|
||||
this.queue.push({
|
||||
path,
|
||||
resolve: (text: string | null) => !aborted && resolve(text),
|
||||
reject: (err: Error) => !aborted && reject(err)
|
||||
let worker: any;
|
||||
const p = new Promise<string | null>(async res => {
|
||||
worker = await createWorker(this.ai.options.ocr || 'eng', 2, {cachePath: this.ai.options.path});
|
||||
const {data} = await worker.recognize(path);
|
||||
await worker.terminate();
|
||||
res(data.text.trim() || null);
|
||||
});
|
||||
this.processQueue();
|
||||
});
|
||||
return Object.assign(p, {abort});
|
||||
return Object.assign(p, {abort: () => worker?.terminate()});
|
||||
}
|
||||
}
|
||||
|
||||
@@ -5,7 +5,6 @@ export default defineConfig({
|
||||
build: {
|
||||
lib: {
|
||||
entry: {
|
||||
asr: './src/asr.ts',
|
||||
index: './src/index.ts',
|
||||
embedder: './src/embedder.ts',
|
||||
},
|
||||
|
||||
Reference in New Issue
Block a user