PeerTube/packages/transcription-devtools/src/benchmark.ts

143 lines
4.6 KiB
TypeScript

import { millisecondsToTime } from '@peertube/peertube-core-utils'
import { SUUID, buildAbsoluteFixturePath, buildSUUID } from '@peertube/peertube-node-utils'
import {
TranscriptFile,
TranscriptionEngine,
TranscriptionEngineName,
TranscriptionModel,
transcriberFactory
} from '@peertube/peertube-transcription'
import { ensureDir, remove } from 'fs-extra/esm'
import { tmpdir } from 'node:os'
import { join } from 'node:path'
import { PerformanceObserver, performance } from 'node:perf_hooks'
import { createLogger, format, transports } from 'winston'
import { TranscriptFileEvaluator } from './transcript-file-evaluator.js'
interface BenchmarkResult {
uuid: SUUID
WER?: number
CER?: number
duration?: number
engine?: TranscriptionEngine
model?: string
}
type Benchmark = Record<SUUID, BenchmarkResult>
const benchmarkReducer = (benchmark: Benchmark = {}, benchmarkResult: BenchmarkResult) => ({
...benchmark,
[benchmarkResult.uuid]: {
...benchmark[benchmarkResult.uuid],
...benchmarkResult
}
})
const groupBenchmarkResultsByModel = (benchmarkResults: Record<string, BenchmarkResult>) => (benchmarksGroupedByModel, uuid) => ({
...benchmarksGroupedByModel,
[benchmarkResults[uuid].model]: {
...benchmarksGroupedByModel[benchmarkResults[uuid].model],
[uuid]: formatBenchmarkResult(benchmarkResults[uuid])
}
})
interface FormattedBenchmarkResult {
WER?: string
CER?: string
duration?: string
model?: string
engine?: string
}
const formatBenchmarkResult = ({ WER, CER, duration, engine, model }: Partial<BenchmarkResult>): FormattedBenchmarkResult => ({
WER: WER ? `${WER * 100}%` : undefined,
CER: CER ? `${CER * 100}%` : undefined,
duration: duration ? millisecondsToTime(duration) : undefined,
model,
engine: engine.name
})
void (async () => {
const logger = createLogger()
logger.add(new transports.Console({ format: format.printf(log => log.message) }))
const transcribers: TranscriptionEngineName[] = [ 'openai-whisper', 'whisper-ctranslate2' ]
const models = process.env.MODELS
? process.env.MODELS.trim().split(',').map(modelName => modelName.trim()).filter(modelName => modelName)
: [ 'tiny' ]
const transcriptDirectory = join(tmpdir(), 'peertube-transcription', 'benchmark')
const pipDirectory = join(tmpdir(), 'peertube-transcription', 'pip')
const mediaFilePath = buildAbsoluteFixturePath('transcription/videos/derive_sectaire.mp4')
const referenceTranscriptFile = new TranscriptFile({
path: buildAbsoluteFixturePath('transcription/videos/derive_sectaire.txt'),
language: 'fr',
format: 'txt'
})
let benchmarkResults: Record<string, BenchmarkResult> = {}
// before
await ensureDir(transcriptDirectory)
const performanceObserver = new PerformanceObserver((items) => {
items
.getEntries()
.forEach((entry) => {
benchmarkResults = benchmarkReducer(benchmarkResults, {
uuid: entry.name as SUUID,
duration: entry.duration
})
})
})
performanceObserver.observe({ type: 'measure' })
// benchmark
logger.info(`Running transcribers benchmark with the following models: ${models.join(', ')}`)
for (const transcriberName of transcribers) {
logger.info(`Create "${transcriberName}" transcriber for the benchmark...`)
const transcriber = transcriberFactory.createFromEngineName({
engineName: transcriberName,
logger: createLogger({ transports: [ new transports.Console() ] }),
binDirectory: join(pipDirectory, 'bin')
})
await transcriber.install(pipDirectory)
for (const modelName of models) {
logger.info(`Run benchmark with "${modelName}" model:`)
const model = new TranscriptionModel(modelName)
const uuid = buildSUUID()
const transcriptFile = await transcriber.transcribe({
mediaFilePath,
model,
transcriptDirectory,
language: 'fr',
format: 'txt',
runId: uuid
})
const evaluator = new TranscriptFileEvaluator(referenceTranscriptFile, transcriptFile)
await new Promise(resolve => setTimeout(resolve, 1))
benchmarkResults = benchmarkReducer(benchmarkResults, {
uuid,
engine: transcriber.engine,
WER: await evaluator.wer(),
CER: await evaluator.cer(),
model: model.name
})
}
}
// display
const benchmarkResultsGroupedByModel = Object
.keys(benchmarkResults)
.reduce(groupBenchmarkResultsByModel(benchmarkResults), {})
Object.values(benchmarkResultsGroupedByModel).forEach(benchmark => console.table(benchmark))
// after
await remove(transcriptDirectory)
performance.clearMarks()
})()