BCI

/**
 * Batch BCI transcription from a neural-signal file.
 *
 * Reads a raw neural-signal `.bin` file, runs it through the BCI
 * (whisper.cpp) addon in one shot via `bciTranscribe`, and prints the
 * decoded transcript.
 *
 * Usage: bun run examples/bci/bci-filesystem.ts <neural-bin-file-path>
 */
import { loadModel, unloadModel, bciTranscribe, BCI_WINDOWED } from '@qvac/sdk';
const args = process.argv.slice(2);
if (!args[0]) {
    console.error('Usage: bun run examples/bci/bci-filesystem.ts <neural-bin-file-path>');
    process.exit(1);
}
const neuralFilePath = args[0];
try {
    console.log('▸ Starting BCI transcription example...');
    console.log('▸ Loading BCI model...');
    const modelId = await loadModel({
        modelSrc: BCI_WINDOWED,
        modelConfig: {
            whisperConfig: {
                language: 'en',
                n_threads: 4,
                temperature: 0.0
            },
            // Session day index selects the day-specific projection matrices.
            // Set this to match the recording session your neural file came from.
            bciConfig: {
                day_idx: 1
            }
        },
        onProgress: (p) => {
            const mb = (n) => (n / 1e6).toFixed(1);
            const line = `▸ Downloading ${p.percentage.toFixed(0)}% (${mb(p.downloaded)}/${mb(p.total)} MB)`;
            process.stderr.write(process.stderr.isTTY ? `\r${line}` : `${line}\n`);
            if (p.percentage >= 100)
                process.stderr.write('\n');
        }
    });
    console.log(`▸ BCI model loaded with ID: ${modelId}`);
    console.log('▸ Transcribing neural signal...');
    const segments = await bciTranscribe({
        modelId,
        neuralData: neuralFilePath,
        metadata: true
    });
    console.log('▸ Transcription result:');
    for (const segment of segments) {
        const start = (segment.startMs / 1000).toFixed(2);
        const end = (segment.endMs / 1000).toFixed(2);
        console.log(`  [${start}s → ${end}s] (id=${segment.id}, append=${segment.append}) ${segment.text}`);
    }
    console.log(segments
        .map((s) => s.text)
        .join('')
        .trim());
    console.log('▸ Unloading BCI model...');
    await unloadModel({ modelId });
    console.log('▸ BCI model unloaded successfully');
    process.exit(0);
}
catch (error) {
    console.error('✖', error);
    process.exit(1);
}

/**
 * Streaming BCI transcription from a neural-signal file.
 *
 * Reads a raw neural-signal `.bin` file and feeds it to the BCI
 * (whisper.cpp) addon chunk-by-chunk through a duplex `bciTranscribeStream`
 * session, printing transcript text as the sliding window decodes
 * successive windows.
 *
 * Usage: bun run examples/bci/bci-filesystem-streaming.ts <neural-bin-file-path>
 */
import { loadModel, unloadModel, bciTranscribeStream, BCI_WINDOWED } from '@qvac/sdk';
import { readFileSync } from 'fs';
const args = process.argv.slice(2);
if (!args[0]) {
    console.error('Usage: bun run examples/bci/bci-filesystem-streaming.ts <neural-bin-file-path>');
    process.exit(1);
}
const neuralFilePath = args[0];
// Feed the neural buffer in fixed-size chunks to simulate a live stream.
const CHUNK_SIZE = 64 * 1024;
try {
    console.log('▸ BCI transcribeStream file test');
    console.log(`▸ File: ${neuralFilePath}`);
    console.log(`▸ Chunk size: ${CHUNK_SIZE} bytes`);
    console.log('▸ Loading model...');
    const modelId = await loadModel({
        modelSrc: BCI_WINDOWED,
        modelConfig: {
            whisperConfig: {
                language: 'en',
                n_threads: 4,
                temperature: 0.0
            },
            // Session day index selects the day-specific projection matrices.
            // Set this to match the recording session your neural file came from.
            bciConfig: {
                day_idx: 1
            }
        }
    });
    console.log(`▸ Model loaded: ${modelId}`);
    console.log('▸ Opening live session...');
    const session = await bciTranscribeStream({ modelId, emit: 'delta' });
    console.log('▸ Session open. Streaming neural signal...');
    // Drain the session concurrently with writing so the sliding-window
    // decode can make progress as chunks arrive instead of stalling.
    const consume = (async () => {
        let transcript = '';
        for await (const text of session) {
            transcript += text;
            process.stdout.write(text);
        }
        return transcript;
    })();
    const data = readFileSync(neuralFilePath);
    let totalBytes = 0;
    for (let offset = 0; offset < data.length; offset += CHUNK_SIZE) {
        const chunk = data.subarray(offset, offset + CHUNK_SIZE);
        session.write(chunk);
        totalBytes += chunk.length;
        await new Promise((resolve) => setTimeout(resolve, 10));
    }
    console.log(`\n\n▸ Neural signal streamed: ${totalBytes} bytes`);
    console.log('▸ Waiting for transcription to finish...');
    session.end();
    const transcript = await consume;
    console.log('\n▸ Results');
    console.log(transcript.trim() || '(no text received)');
    console.log('▸ Unloading model...');
    await unloadModel({ modelId });
    console.log('▸ Done.');
    process.exit(0);
}
catch (error) {
    console.error('✖', error);
    process.exit(1);
}