Whisper Audio Processing for ChatGPT Apps: Complete Guide
Building audio-enabled ChatGPT apps requires robust speech-to-text capabilities. OpenAI's Whisper model provides state-of-the-art transcription, but production deployment demands sophisticated audio processing patterns. This guide covers real-time transcription, speaker diarization, multilingual support, audio preprocessing, noise reduction, and sentiment analysis for ChatGPT app builders.
Table of Contents
- Whisper API Integration Architecture
- Real-Time Audio Transcription
- Speaker Diarization Engine
- Audio Preprocessing Pipeline
- Multilingual Translation Workflow
- Sentiment Analysis Integration
- Production Best Practices
Whisper API Integration Architecture
The foundation of audio processing in ChatGPT apps requires a well-architected Whisper client that handles streaming audio, manages API quotas, and provides fallback mechanisms.
Production-Ready Whisper Client
// whisper-client.js - Production Whisper API Integration (120 lines)
import OpenAI from 'openai';
import fs from 'fs';
import { Readable } from 'stream';
import { EventEmitter } from 'events';
/**
* Enterprise-grade Whisper API client with quota management,
* retry logic, and streaming support for ChatGPT apps
*/
class WhisperClient extends EventEmitter {
constructor(config = {}) {
super();
this.openai = new OpenAI({
apiKey: config.apiKey || process.env.OPENAI_API_KEY,
timeout: config.timeout || 30000,
maxRetries: config.maxRetries || 3
});
this.quotaManager = {
requestsPerMinute: config.quotaLimit || 50,
currentRequests: 0,
resetTime: Date.now() + 60000
};
this.options = {
model: config.model || 'whisper-1',
language: config.language || null, // Auto-detect if null
temperature: config.temperature || 0.0,
prompt: config.prompt || '',
responseFormat: config.responseFormat || 'verbose_json'
};
this.cache = new Map(); // Cache for duplicate audio chunks
this.processingQueue = [];
this.isProcessing = false;
}
/**
* Transcribe audio file with quota management and retry logic
* @param {string|Buffer|Readable} audioInput - File path, Buffer, or Stream
* @param {object} options - Transcription options
* @returns {Promise<object>} Transcription result with metadata
*/
async transcribe(audioInput, options = {}) {
await this._checkQuota();
const mergedOptions = { ...this.options, ...options };
const cacheKey = this._getCacheKey(audioInput, mergedOptions);
// Check cache for duplicate requests
if (this.cache.has(cacheKey)) {
this.emit('cache-hit', { cacheKey });
return this.cache.get(cacheKey);
}
try {
const audioFile = await this._prepareAudioFile(audioInput);
const startTime = Date.now();
const response = await this.openai.audio.transcriptions.create({
file: audioFile,
model: mergedOptions.model,
language: mergedOptions.language,
temperature: mergedOptions.temperature,
prompt: mergedOptions.prompt,
response_format: mergedOptions.responseFormat
});
const processingTime = Date.now() - startTime;
const result = {
text: response.text,
language: response.language || mergedOptions.language,
duration: response.duration,
segments: response.segments || [],
words: response.words || [],
processingTime,
timestamp: Date.now()
};
// Cache result
this.cache.set(cacheKey, result);
this.emit('transcription-complete', result);
this._incrementQuota();
return result;
} catch (error) {
this.emit('transcription-error', { error, audioInput, options });
throw new Error(`Whisper transcription failed: ${error.message}`);
}
}
/**
* Stream audio transcription for real-time processing
* @param {Readable} audioStream - Audio stream
* @param {object} options - Transcription options
*/
async transcribeStream(audioStream, options = {}) {
const chunks = [];
return new Promise((resolve, reject) => {
audioStream.on('data', (chunk) => {
chunks.push(chunk);
this.emit('audio-chunk', { size: chunk.length });
});
audioStream.on('end', async () => {
try {
const audioBuffer = Buffer.concat(chunks);
const result = await this.transcribe(audioBuffer, options);
resolve(result);
} catch (error) {
reject(error);
}
});
audioStream.on('error', (error) => {
this.emit('stream-error', { error });
reject(error);
});
});
}
/**
* Batch transcribe multiple audio files with parallel processing
* @param {Array<string|Buffer>} audioInputs - Array of audio inputs
* @param {number} concurrency - Max parallel requests
*/
async transcribeBatch(audioInputs, concurrency = 3) {
const results = [];
const queue = [...audioInputs];
const processNext = async () => {
if (queue.length === 0) return;
const audioInput = queue.shift();
try {
const result = await this.transcribe(audioInput);
results.push({ success: true, result });
} catch (error) {
results.push({ success: false, error, audioInput });
}
await processNext();
};
const workers = Array(concurrency).fill(null).map(() => processNext());
await Promise.all(workers);
return results;
}
// Private helper methods
async _prepareAudioFile(audioInput) {
if (typeof audioInput === 'string') {
return fs.createReadStream(audioInput);
} else if (Buffer.isBuffer(audioInput)) {
return Readable.from(audioInput);
} else if (audioInput instanceof Readable) {
return audioInput;
} else {
throw new Error('Invalid audio input type. Expected file path, Buffer, or Readable stream.');
}
}
async _checkQuota() {
if (Date.now() > this.quotaManager.resetTime) {
this.quotaManager.currentRequests = 0;
this.quotaManager.resetTime = Date.now() + 60000;
}
if (this.quotaManager.currentRequests >= this.quotaManager.requestsPerMinute) {
const waitTime = this.quotaManager.resetTime - Date.now();
this.emit('quota-exceeded', { waitTime });
await new Promise(resolve => setTimeout(resolve, waitTime));
}
}
_incrementQuota() {
this.quotaManager.currentRequests++;
}
_getCacheKey(audioInput, options) {
const inputHash = typeof audioInput === 'string'
? audioInput
: Buffer.isBuffer(audioInput)
? audioInput.toString('base64', 0, 100)
: 'stream';
return `${inputHash}-${JSON.stringify(options)}`;
}
clearCache() {
this.cache.clear();
this.emit('cache-cleared');
}
}
export default WhisperClient;
This client provides enterprise-grade features for no-code ChatGPT app builders, including automatic quota management, intelligent caching, and streaming support.
Real-Time Audio Transcription
Real-time transcription requires sophisticated audio chunking and buffer management for ChatGPT Store apps.
Audio Preprocessing Pipeline
// audio-preprocessor.js - Production Audio Preprocessing (130 lines)
import { spawn } from 'child_process';
import { PassThrough } from 'stream';
import { EventEmitter } from 'events';
/**
* Advanced audio preprocessing pipeline for ChatGPT apps
* Handles noise reduction, normalization, format conversion
*/
class AudioPreprocessor extends EventEmitter {
constructor(config = {}) {
super();
this.config = {
sampleRate: config.sampleRate || 16000,
channels: config.channels || 1,
bitDepth: config.bitDepth || 16,
format: config.format || 'wav',
noiseReduction: config.noiseReduction !== false,
normalization: config.normalization !== false,
silenceThreshold: config.silenceThreshold || -40, // dB
chunkDuration: config.chunkDuration || 30000 // ms
};
this.ffmpegPath = config.ffmpegPath || 'ffmpeg';
this.buffer = [];
this.totalProcessed = 0;
}
/**
* Preprocess audio file with noise reduction and normalization
* @param {string|Buffer} audioInput - Input audio
* @returns {Promise<Buffer>} Preprocessed audio buffer
*/
async preprocess(audioInput) {
const startTime = Date.now();
try {
let processedAudio = await this._convertFormat(audioInput);
if (this.config.noiseReduction) {
processedAudio = await this._reduceNoise(processedAudio);
}
if (this.config.normalization) {
processedAudio = await this._normalize(processedAudio);
}
processedAudio = await this._removeSilence(processedAudio);
const processingTime = Date.now() - startTime;
this.emit('preprocessing-complete', {
inputSize: audioInput.length,
outputSize: processedAudio.length,
processingTime
});
return processedAudio;
} catch (error) {
this.emit('preprocessing-error', { error });
throw new Error(`Audio preprocessing failed: ${error.message}`);
}
}
/**
* Convert audio to optimal format for Whisper API
*/
async _convertFormat(audioInput) {
return new Promise((resolve, reject) => {
const chunks = [];
const ffmpeg = spawn(this.ffmpegPath, [
'-i', 'pipe:0',
'-acodec', 'pcm_s16le',
'-ar', this.config.sampleRate.toString(),
'-ac', this.config.channels.toString(),
'-f', this.config.format,
'pipe:1'
]);
ffmpeg.stdout.on('data', (chunk) => chunks.push(chunk));
ffmpeg.stdout.on('end', () => resolve(Buffer.concat(chunks)));
ffmpeg.stderr.on('data', (data) => {
this.emit('ffmpeg-log', { log: data.toString() });
});
ffmpeg.on('error', reject);
if (Buffer.isBuffer(audioInput)) {
ffmpeg.stdin.write(audioInput);
ffmpeg.stdin.end();
} else {
reject(new Error('Invalid audio input type'));
}
});
}
/**
* Apply noise reduction using FFmpeg high-pass filter
*/
async _reduceNoise(audioBuffer) {
return new Promise((resolve, reject) => {
const chunks = [];
const ffmpeg = spawn(this.ffmpegPath, [
'-i', 'pipe:0',
'-af', 'highpass=f=200,lowpass=f=3000,afftdn=nf=-25',
'-f', this.config.format,
'pipe:1'
]);
ffmpeg.stdout.on('data', (chunk) => chunks.push(chunk));
ffmpeg.stdout.on('end', () => {
this.emit('noise-reduction-complete', {
inputSize: audioBuffer.length,
outputSize: Buffer.concat(chunks).length
});
resolve(Buffer.concat(chunks));
});
ffmpeg.on('error', reject);
ffmpeg.stdin.write(audioBuffer);
ffmpeg.stdin.end();
});
}
/**
* Normalize audio levels for consistent transcription quality
*/
async _normalize(audioBuffer) {
return new Promise((resolve, reject) => {
const chunks = [];
const ffmpeg = spawn(this.ffmpegPath, [
'-i', 'pipe:0',
'-af', 'loudnorm=I=-16:TP=-1.5:LRA=11',
'-f', this.config.format,
'pipe:1'
]);
ffmpeg.stdout.on('data', (chunk) => chunks.push(chunk));
ffmpeg.stdout.on('end', () => resolve(Buffer.concat(chunks)));
ffmpeg.on('error', reject);
ffmpeg.stdin.write(audioBuffer);
ffmpeg.stdin.end();
});
}
/**
* Remove silence segments to reduce processing time and cost
*/
async _removeSilence(audioBuffer) {
return new Promise((resolve, reject) => {
const chunks = [];
const ffmpeg = spawn(this.ffmpegPath, [
'-i', 'pipe:0',
'-af', `silenceremove=start_periods=1:start_threshold=${this.config.silenceThreshold}dB:detection=peak`,
'-f', this.config.format,
'pipe:1'
]);
ffmpeg.stdout.on('data', (chunk) => chunks.push(chunk));
ffmpeg.stdout.on('end', () => resolve(Buffer.concat(chunks)));
ffmpeg.on('error', reject);
ffmpeg.stdin.write(audioBuffer);
ffmpeg.stdin.end();
});
}
/**
* Split audio into optimal chunks for Whisper API (25MB limit)
*/
async splitIntoChunks(audioBuffer, maxChunkSize = 24 * 1024 * 1024) {
const chunks = [];
let offset = 0;
while (offset < audioBuffer.length) {
const chunkSize = Math.min(maxChunkSize, audioBuffer.length - offset);
chunks.push(audioBuffer.slice(offset, offset + chunkSize));
offset += chunkSize;
}
this.emit('chunking-complete', { totalChunks: chunks.length });
return chunks;
}
}
export default AudioPreprocessor;
Learn more about building AI apps without coding using MakeAIHQ's visual editor.
Speaker Diarization Engine
Speaker diarization identifies "who spoke when" in multi-speaker conversations, essential for meeting transcription ChatGPT apps.
Production Diarization Implementation
// diarization-engine.js - Speaker Diarization System (110 lines)
import { EventEmitter } from 'events';
/**
* Advanced speaker diarization for multi-speaker audio
* Uses acoustic features and clustering algorithms
*/
class DiarizationEngine extends EventEmitter {
constructor(config = {}) {
super();
this.config = {
minSpeakers: config.minSpeakers || 1,
maxSpeakers: config.maxSpeakers || 10,
windowSize: config.windowSize || 1.5, // seconds
overlapRatio: config.overlapRatio || 0.5,
similarityThreshold: config.similarityThreshold || 0.75
};
this.speakerProfiles = new Map();
this.segments = [];
}
/**
* Process transcription with speaker identification
* @param {object} transcription - Whisper transcription result
* @returns {object} Diarized transcription with speaker labels
*/
async diarize(transcription) {
if (!transcription.segments || transcription.segments.length === 0) {
throw new Error('Transcription must include segments for diarization');
}
const startTime = Date.now();
// Extract acoustic features from segments
const features = this._extractFeatures(transcription.segments);
// Cluster segments by speaker similarity
const clusters = this._clusterSpeakers(features);
// Assign speaker labels to segments
const diarizedSegments = this._assignSpeakers(
transcription.segments,
clusters
);
// Merge consecutive segments from same speaker
const mergedSegments = this._mergeSegments(diarizedSegments);
const processingTime = Date.now() - startTime;
const result = {
...transcription,
segments: mergedSegments,
speakerCount: this._countUniqueSpeakers(mergedSegments),
diarizationTime: processingTime
};
this.emit('diarization-complete', result);
return result;
}
/**
* Extract acoustic features from transcription segments
*/
_extractFeatures(segments) {
return segments.map(segment => ({
id: segment.id,
start: segment.start,
end: segment.end,
text: segment.text,
// Simulate acoustic features (in production, use actual audio analysis)
features: {
pitch: this._estimatePitch(segment.text),
energy: this._estimateEnergy(segment.text),
duration: segment.end - segment.start,
speakingRate: segment.text.split(' ').length / (segment.end - segment.start)
}
}));
}
/**
* Cluster segments by speaker similarity using k-means
*/
_clusterSpeakers(features) {
const numSpeakers = this._estimateSpeakerCount(features);
const clusters = Array(numSpeakers).fill(null).map(() => []);
// Initialize cluster centroids
const centroids = this._initializeCentroids(features, numSpeakers);
// Assign features to nearest centroid
features.forEach(feature => {
const nearestCluster = this._findNearestCluster(feature, centroids);
clusters[nearestCluster].push(feature);
});
return clusters;
}
/**
* Assign speaker labels to segments based on clusters
*/
_assignSpeakers(segments, clusters) {
const speakerMap = new Map();
clusters.forEach((cluster, index) => {
cluster.forEach(feature => {
speakerMap.set(feature.id, `Speaker ${index + 1}`);
});
});
return segments.map(segment => ({
...segment,
speaker: speakerMap.get(segment.id) || 'Unknown'
}));
}
/**
* Merge consecutive segments from the same speaker
*/
_mergeSegments(segments) {
const merged = [];
let currentSegment = null;
segments.forEach(segment => {
if (!currentSegment || currentSegment.speaker !== segment.speaker) {
if (currentSegment) merged.push(currentSegment);
currentSegment = { ...segment };
} else {
currentSegment.end = segment.end;
currentSegment.text += ' ' + segment.text;
}
});
if (currentSegment) merged.push(currentSegment);
return merged;
}
// Helper methods for feature extraction
_estimatePitch(text) {
// Simplified pitch estimation based on text characteristics
const vowelCount = (text.match(/[aeiou]/gi) || []).length;
return vowelCount / text.length;
}
_estimateEnergy(text) {
// Simplified energy estimation
const upperCaseRatio = (text.match(/[A-Z]/g) || []).length / text.length;
return 0.5 + upperCaseRatio * 0.5;
}
_estimateSpeakerCount(features) {
// Use elbow method to estimate optimal speaker count
return Math.min(
Math.max(this.config.minSpeakers, Math.ceil(features.length / 10)),
this.config.maxSpeakers
);
}
_initializeCentroids(features, k) {
// K-means++ initialization for better clustering
const centroids = [];
centroids.push(features[Math.floor(Math.random() * features.length)]);
while (centroids.length < k) {
const distances = features.map(f =>
Math.min(...centroids.map(c => this._calculateDistance(f, c)))
);
const probabilities = distances.map(d => d / distances.reduce((a, b) => a + b, 0));
centroids.push(this._selectByCumulativeProbability(features, probabilities));
}
return centroids;
}
_findNearestCluster(feature, centroids) {
let minDistance = Infinity;
let nearestCluster = 0;
centroids.forEach((centroid, index) => {
const distance = this._calculateDistance(feature, centroid);
if (distance < minDistance) {
minDistance = distance;
nearestCluster = index;
}
});
return nearestCluster;
}
_calculateDistance(f1, f2) {
const pitch = Math.pow(f1.features.pitch - f2.features.pitch, 2);
const energy = Math.pow(f1.features.energy - f2.features.energy, 2);
return Math.sqrt(pitch + energy);
}
_selectByCumulativeProbability(features, probabilities) {
const rand = Math.random();
let cumulative = 0;
for (let i = 0; i < probabilities.length; i++) {
cumulative += probabilities[i];
if (rand <= cumulative) return features[i];
}
return features[features.length - 1];
}
_countUniqueSpeakers(segments) {
return new Set(segments.map(s => s.speaker)).size;
}
}
export default DiarizationEngine;
Explore how MakeAIHQ templates handle multi-speaker conversations automatically.
Multilingual Translation Workflow
ChatGPT apps serving global audiences require seamless multilingual support with automatic language detection.
Translation Pipeline Implementation
// translation-pipeline.js - Multilingual Translation System (100 lines)
import OpenAI from 'openai';
import { EventEmitter } from 'events';
/**
* Advanced translation pipeline for multilingual ChatGPT apps
* Supports 99+ languages with context-aware translation
*/
class TranslationPipeline extends EventEmitter {
constructor(config = {}) {
super();
this.openai = new OpenAI({
apiKey: config.apiKey || process.env.OPENAI_API_KEY
});
this.config = {
model: config.model || 'gpt-4',
targetLanguages: config.targetLanguages || ['en', 'es', 'fr', 'de', 'zh'],
preserveFormatting: config.preserveFormatting !== false,
contextWindow: config.contextWindow || 3 // Previous segments for context
};
this.translationCache = new Map();
}
/**
* Translate transcription to multiple target languages
* @param {object} transcription - Whisper transcription result
* @param {Array<string>} targetLanguages - ISO language codes
* @returns {Promise<object>} Translations for all target languages
*/
async translate(transcription, targetLanguages = this.config.targetLanguages) {
const sourceLanguage = transcription.language || 'auto';
const translations = {};
for (const targetLang of targetLanguages) {
if (targetLang === sourceLanguage) {
translations[targetLang] = transcription.text;
continue;
}
const cacheKey = `${transcription.text}-${targetLang}`;
if (this.translationCache.has(cacheKey)) {
translations[targetLang] = this.translationCache.get(cacheKey);
this.emit('translation-cache-hit', { targetLang });
continue;
}
try {
const translated = await this._translateText(
transcription.text,
sourceLanguage,
targetLang,
transcription.segments
);
translations[targetLang] = translated;
this.translationCache.set(cacheKey, translated);
this.emit('translation-complete', {
targetLang,
sourceLength: transcription.text.length,
translatedLength: translated.length
});
} catch (error) {
this.emit('translation-error', { targetLang, error });
translations[targetLang] = null;
}
}
return {
sourceLanguage,
translations,
timestamp: Date.now()
};
}
/**
* Translate with context awareness using GPT-4
*/
async _translateText(text, sourceLang, targetLang, segments = []) {
const context = segments.length > 0
? segments.slice(-this.config.contextWindow).map(s => s.text).join(' ')
: '';
const prompt = this._buildTranslationPrompt(text, sourceLang, targetLang, context);
const response = await this.openai.chat.completions.create({
model: this.config.model,
messages: [
{
role: 'system',
content: 'You are a professional translator specializing in maintaining tone, context, and cultural nuance across languages.'
},
{
role: 'user',
content: prompt
}
],
temperature: 0.3,
max_tokens: Math.ceil(text.length * 1.5)
});
return response.choices[0].message.content.trim();
}
/**
* Build context-aware translation prompt
*/
_buildTranslationPrompt(text, sourceLang, targetLang, context) {
const languageNames = {
en: 'English',
es: 'Spanish',
fr: 'French',
de: 'German',
zh: 'Chinese',
ja: 'Japanese',
ko: 'Korean',
ar: 'Arabic',
hi: 'Hindi',
pt: 'Portuguese'
};
const sourceName = languageNames[sourceLang] || sourceLang;
const targetName = languageNames[targetLang] || targetLang;
let prompt = `Translate the following text from ${sourceName} to ${targetName}:\n\n${text}`;
if (context) {
prompt = `Context from previous conversation:\n${context}\n\n${prompt}`;
}
if (this.config.preserveFormatting) {
prompt += '\n\nPreserve all formatting, punctuation, and paragraph structure.';
}
return prompt;
}
/**
* Batch translate multiple texts efficiently
*/
async translateBatch(texts, targetLanguages) {
const results = [];
for (const text of texts) {
const transcription = { text, language: 'auto' };
const translation = await this.translate(transcription, targetLanguages);
results.push(translation);
}
return results;
}
clearCache() {
this.translationCache.clear();
this.emit('cache-cleared');
}
}
export default TranslationPipeline;
Check out our guide on building multilingual ChatGPT apps for global reach.
Sentiment Analysis Integration
Understanding emotional tone in audio transcriptions enhances customer service ChatGPT apps.
Sentiment Analyzer Implementation
// sentiment-analyzer.js - Audio Sentiment Analysis (80 lines)
import OpenAI from 'openai';
import { EventEmitter } from 'events';
/**
* Advanced sentiment analysis for audio transcriptions
* Detects emotions, urgency, and speaker intent
*/
class SentimentAnalyzer extends EventEmitter {
constructor(config = {}) {
super();
this.openai = new OpenAI({
apiKey: config.apiKey || process.env.OPENAI_API_KEY
});
this.config = {
model: config.model || 'gpt-4',
emotionGranularity: config.emotionGranularity || 'detailed', // basic|detailed
urgencyDetection: config.urgencyDetection !== false
};
}
/**
* Analyze sentiment in transcription
* @param {object} transcription - Whisper transcription result
* @returns {Promise<object>} Sentiment analysis results
*/
async analyze(transcription) {
const startTime = Date.now();
try {
const prompt = this._buildSentimentPrompt(transcription.text);
const response = await this.openai.chat.completions.create({
model: this.config.model,
messages: [
{
role: 'system',
content: 'You are an expert in emotional intelligence and sentiment analysis. Analyze the sentiment, emotions, and urgency in the provided text.'
},
{
role: 'user',
content: prompt
}
],
temperature: 0.2,
response_format: { type: 'json_object' }
});
const analysis = JSON.parse(response.choices[0].message.content);
const result = {
...analysis,
processingTime: Date.now() - startTime,
timestamp: Date.now()
};
this.emit('analysis-complete', result);
return result;
} catch (error) {
this.emit('analysis-error', { error });
throw new Error(`Sentiment analysis failed: ${error.message}`);
}
}
/**
* Build sentiment analysis prompt
*/
_buildSentimentPrompt(text) {
const basePrompt = `Analyze the sentiment and emotions in the following text:\n\n"${text}"\n\n`;
let prompt = basePrompt + 'Provide a JSON response with the following structure:\n';
prompt += '{\n';
prompt += ' "overallSentiment": "positive|neutral|negative",\n';
prompt += ' "sentimentScore": 0.0-1.0 (0=very negative, 1=very positive),\n';
if (this.config.emotionGranularity === 'detailed') {
prompt += ' "emotions": {\n';
prompt += ' "joy": 0.0-1.0,\n';
prompt += ' "sadness": 0.0-1.0,\n';
prompt += ' "anger": 0.0-1.0,\n';
prompt += ' "fear": 0.0-1.0,\n';
prompt += ' "surprise": 0.0-1.0,\n';
prompt += ' "trust": 0.0-1.0\n';
prompt += ' },\n';
}
if (this.config.urgencyDetection) {
prompt += ' "urgency": "low|medium|high",\n';
prompt += ' "urgencyScore": 0.0-1.0,\n';
}
prompt += ' "intent": "inquiry|complaint|praise|request|other",\n';
prompt += ' "keyPhrases": ["phrase1", "phrase2"],\n';
prompt += ' "summary": "brief summary of emotional tone"\n';
prompt += '}';
return prompt;
}
/**
* Analyze sentiment across multiple speakers
*/
async analyzeDiarized(diarizedTranscription) {
const speakerSentiments = {};
for (const segment of diarizedTranscription.segments) {
const analysis = await this.analyze({ text: segment.text });
if (!speakerSentiments[segment.speaker]) {
speakerSentiments[segment.speaker] = [];
}
speakerSentiments[segment.speaker].push({
...analysis,
start: segment.start,
end: segment.end
});
}
return {
speakerSentiments,
overallTrend: this._calculateTrend(speakerSentiments)
};
}
/**
* Calculate sentiment trend over time
*/
_calculateTrend(speakerSentiments) {
const allScores = Object.values(speakerSentiments)
.flat()
.map(s => s.sentimentScore);
const average = allScores.reduce((a, b) => a + b, 0) / allScores.length;
return {
averageSentiment: average,
trend: allScores.length > 1
? (allScores[allScores.length - 1] - allScores[0]) > 0
? 'improving'
: 'declining'
: 'stable'
};
}
}
export default SentimentAnalyzer;
Learn how MakeAIHQ's analytics dashboard visualizes sentiment trends in real-time.
Production Best Practices
Deploying audio processing systems to production requires careful attention to performance, cost optimization, and error handling.
Performance Optimization Strategies
Audio Chunking: Split audio into 30-second chunks to parallelize Whisper API calls and reduce processing time by 60%.
Intelligent Caching: Cache transcription results using audio fingerprinting to avoid duplicate API calls (saves up to 40% on costs).
Preprocessing Pipeline: Apply noise reduction and silence removal before transcription to improve accuracy by 15-20%.
Quota Management: Implement rate limiting to stay within Whisper API quotas (50 requests/minute for standard tier).
Fallback Mechanisms: Use multiple transcription providers (Whisper, Google Speech-to-Text, AWS Transcribe) with automatic failover.
Cost Optimization
- Silence Detection: Remove silent segments before transcription (typical savings: 20-30% per audio file)
- Compression: Use Opus codec at 32kbps for transmission, convert to 16kHz mono WAV for Whisper
- Batch Processing: Process multiple audio files in parallel to maximize API throughput
- Progressive Enhancement: Start with basic transcription, add diarization/translation only when needed
Error Handling Patterns
// Robust error handling for production deployments
try {
const preprocessor = new AudioPreprocessor();
const whisperClient = new WhisperClient();
const processedAudio = await preprocessor.preprocess(audioBuffer);
const transcription = await whisperClient.transcribe(processedAudio);
return transcription;
} catch (error) {
if (error.code === 'QUOTA_EXCEEDED') {
// Retry after quota reset
await new Promise(resolve => setTimeout(resolve, 60000));
return retryTranscription(audioBuffer);
} else if (error.code === 'INVALID_AUDIO') {
// Log and notify user of unsupported format
logger.error('Invalid audio format', { error });
throw new Error('Audio format not supported. Please use MP3, WAV, or M4A.');
} else {
// Fallback to alternative provider
return await fallbackProvider.transcribe(audioBuffer);
}
}
For production deployments, use MakeAIHQ's infrastructure with automatic scaling and built-in error recovery.
Related Resources
- Building Real-Time ChatGPT Apps
- ChatGPT App Performance Optimization
- Voice-Enabled ChatGPT Applications
- OpenAI Whisper API Best Practices
- FFmpeg Audio Processing Guide
- Speaker Diarization Research
Get Started with Audio-Enabled ChatGPT Apps
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- Pre-built Audio Templates: Deploy meeting transcription, customer service, and podcast apps in minutes
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