Does TranscriptG store my uploaded audio files or transcripts?

No. TranscriptG operates on an immutable zero-data-retention architecture. Media files reside strictly in volatile RAM and are deallocated immediately upon request completion.

What audio and video file formats can I transcribe?

TranscriptG supports MP3, WAV, FLAC, AAC, M4A, OGG, WebM, and MP4 files up to 25MB directly in the browser.

How accurate is TranscriptG compared to human transcribers?

Under standard recording conditions (clear microphone, minimal background noise), TranscriptG achieves a 98% to 99.5% Word Error Rate accuracy across 90+ spoken languages.

Can I export millisecond-accurate subtitle files?

Yes. TranscriptG exports SubRip (.SRT), WebVTT (.VTT), structured JSON with millisecond timecodes, Markdown (.MD), and Microsoft Word (.DOCX) formats.

How TranscriptG Works: Inside Our Acoustic Processing Architecture

1. The Dilemma of Modern Cloud Transcription

For decades, converting spoken acoustic vibrations into written text required massive compromise. Early automatic speech recognition (ASR) relied on Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) that broke down under noisy conditions, accented phonetics, or multi-speaker conversations (as explored in our historical retrospective on ASR evolution).

When the deep learning revolution brought neural end-to-end models into production, cloud providers solved the accuracy problem by building massive, opaque data pipelines. However, this introduced a severe privacy dilemma for enterprise and consumer users alike:

Persistent Media Storing: Uploaded audio files were saved to persistent cloud object storage buckets for caching and asynchronous queue processing.
Model Training Exploitation: Confidential customer calls, legal depositions, and proprietary strategy meetings were routinely ingested into training sets for future model generations without explicit consent.
Excessive Latency: Traditional transcription APIs required multi-stage asynchronous webhooks, resulting in minutes of latency for standard media files.

TranscriptG was engineered from the ground up to eliminate this compromise. By coupling high-throughput neural acoustic decoders with an immutable, ephemeral in-memory processing architecture (detailed in our Zero Data Retention security whitepaper), TranscriptG achieves sub-second inference, 99%+ linguistic accuracy, and verifiable zero data retention.

2. Acoustic Ingestion & Codec Demuxing

The transcription lifecycle begins the moment an audio or video payload is supplied to TranscriptG. The browser initiates an instantaneous client-side inspection to validate container headers without uploading uncompressed bloat. For an in-depth breakdown of audio containers and compression algorithms, see our Audio Formats & Codecs Guide.

Codec Validation & Demuxing Matrix

TranscriptG handles a wide spectrum of media containers, demuxing raw audio tracks across diverse containers:

Container Format	Typical Codec	Compression Profile	Header Parsing Standard
.WAV	Linear PCM	Uncompressed Lossless	RIFF header validation (44-byte chunk)
.FLAC	FLAC	Lossless Entropy (Rice)	`fLaC` stream marker & metadata block
.MP3	MPEG-1 Audio Layer III	Lossy MDCT Filterbank	ID3v2 tag parsing & sync frame search
.M4A / .AAC	Advanced Audio Coding	Lossy MDCT / Temporal	ISO/IEC 14496-12 MP4 atoms (`moov`/`mdat`)
.OGG / .OPUS	Opus Speech Codec	Lossy Hybrid Silk/CELT	OggS encapsulation page packets
.MP4 / .WEBM	AAC / Opus (Video Track)	Demuxed Audio Extraction	Video demuxing via WebAssembly FFmpeg

Client-Side Rate Normalization

Before the audio stream is passed to the neural processing pipeline, it is resampled to a standardized 16,000 Hz single-channel (mono) 16-bit linear PCM stream. This ensures optimal spectral density while reducing unnecessary payload transmission bandwidth by over 75%:

Raw Audio Payload (MP3/WAV/MP4) 
   │
   ▼
[ Web Audio Demuxer & Mono Downmixer ]
   │
   ▼
[ Resampling Engine (16,000 Hz Linear PCM) ]
   │
   ▼
[ Ephemeral Linear Memory Buffer (RAM Only) ]

3. Mel-Spectrogram Extraction & Normalization

Human speech frequency ranges between 85 Hz (fundamental frequency for deep male voices) and 8,000 Hz (fricatives and sibilants like 's' and 'th'). The human ear does not perceive pitch linearly; instead, our cochlea perceives pitch logarithmically. Preparing clean audio before recording also makes a dramatic difference, as covered in our 10 Acoustic Calibration Tips.

Mathematical Transformation Pipeline

Short-Time Fourier Transform (STFT): The continuous audio signal is segmented into overlapping windows using a Hann window function. We employ a window size of 25 milliseconds (400 samples at 16 kHz) with a frame stride of 10 milliseconds (160 samples), guaranteeing continuous temporal resolution.
Mel-Scale Triangular Filter Bank: The linear frequency spectrum is mapped to the perceptual Mel scale using an 80-channel triangular filter bank that integrates energy across adjacent bins, compressing irrelevant acoustic noise while accentuating vocal formant transitions.
Logarithmic Dynamic Compression: Natural human speech exhibits vast dynamic ranges. Applying natural log compression mimics the ear's logarithmic loudness response, ensuring quiet consonants receive proportional attention alongside loud vowels.

4. Neural Transformer Speech Recognition

Once the log-Mel spectrogram is computed, it is fed into our deep neural Transformer encoder-decoder architecture:

  [ 80-Channel Log-Mel Spectrogram ]
                │
                ▼
  [ 2D Convolutional Subsampling (4x Reduction) ]
                │
                ▼
  [ Conformer / Transformer Encoder (32 Layers) ]
    ├─ Multi-Head Self-Attention (Global Context)
    ├─ Depthwise Convolution (Local Phonetics)
    └─ Feed-Forward SwiGLU Gating
                │
                ▼
  [ Cross-Attention Autoregressive Decoder ]
                │
                ▼
  [ Byte-Pair Encoded (BPE) Linguistic Tokens ]

Deep Encoder Mechanics

Convolutional Downsampling: Two 2D-convolution layers reduce the time dimension by a factor of 4, compressing 10ms frame strides into 40ms tokens to accelerate matrix multiplication.
32-Layer Multi-Head Self-Attention: Each attention head computes dependencies across the entire utterance, resolving ambiguous phonemes based on trailing syntactic cues.
Convolutional Local Context: Depthwise separable convolutions capture fine-grained phonetic transitions, vocal vibrato, and transient plosives.

Autoregressive Cross-Attention Decoder

The decoder emits text tokens from a 32,000-vocabulary Byte-Pair Encoding (BPE) dictionary. During token emission, the model simultaneously resolves:

Homophone Disambiguation: Differentiating their, there, and they're by modeling syntactic grammar trees.
Punctuation Restoration: Emitting period, comma, question mark, and quotation tokens directly within the neural beam search.
Truecasing & Capitalization: Automatically capitalizing proper nouns, acronyms, and sentence boundaries without secondary rule engines.

5. Dynamic Time Warping & Millisecond Alignment

Subtitles (SRT vs. WebVTT) and interactive audio players require exact millisecond timestamps. TranscriptG utilizes Dynamic Time Warping (DTW) on the cross-attention matrix generated between the encoder's acoustic frames and the decoder's text tokens. By tracing the optimal cost path through the attention weight matrix, the system aligns every word boundary to within ±15 milliseconds of audio ground truth (developers can learn how to parse these in our Subtitle Parsing Guide):

Segment ID	Timecode Start	Timecode End	Word Count	Confidence Score
#001	`00:00:00.120`	`00:00:03.480`	7 words	99.8%
#002	`00:00:03.950`	`00:00:08.120`	11 words	99.4%
#003	`00:00:08.500`	`00:00:12.840`	9 words	99.7%

This sub-frame precision ensures subtitles never drift out of sync during long-form videos or high-speed podcast dialogues.

6. Acoustic Clustering & Speaker Diarization

In multi-person recordings (interviews, board meetings, depositions), knowing who spoke what is critical. TranscriptG employs an advanced diarization pipeline:

Voice Activity Detection (VAD): Neural energy gates isolate active speech intervals, filtering out dead silence, laughter, and ambient room noise.
d-Vector Speaker Embeddings: A lightweight convolutional network extracts 256-dimensional speaker embeddings for every 1.5-second speech window.
Spectral Clustering & Cosine Similarity: Embeddings are projected into a latent vector space. Unsupervised spectral clustering groups vectors into discrete speaker IDs (Speaker 1, Speaker 2), even when voices share similar pitch. For legal depositions, see our Legal Deposition Standards Guide.

Once the raw transcript is generated, TranscriptG unlocks high-leverage linguistic processing:

Executive Summaries: Synthesizing hour-long discussions into 3-paragraph executive briefs (see our AI Meeting Summarizer Guide).
Action Item Matrices: Extracting deliverables, assigned owners, and target deadlines into structured tables.
Grammar Polish & Filler Removal: Stripping vocal disfluencies (um, uh, like, you know) while preserving original speaker intent.
Multilingual Translation: Translating timecoded transcripts into 90+ languages while preserving exact timecode markers (see our Multilingual AI Guide).

8. Ephemeral Memory Security & Zero Storage

Data privacy is the core pillar of TranscriptG's architecture. Unlike traditional platforms that store files in persistent cloud buckets, TranscriptG implements a Zero-Data-Retention (ZDR) guarantee:

Strict Ephemeral Execution Guarantees

No Disk I/O: Audio bytes reside exclusively in volatile server RAM for the duration of the HTTP connection.
No Relational Databases: Transcripts, user metadata, and uploaded media are never stored in databases, logs, or cache layers.
Immediate Garbage Collection: As soon as the client receives the completed transcript or subtitle file, memory buffers are overwritten and deallocated.
Zero Training Policy: User audio and transcripts are never utilized to train or fine-tune public foundation models. Read the full ZDR Architectural Whitepaper for threat models and compliance verification.

Client Request ──► [ Ephemeral RAM ] ──► [ Neural Inference ]
                          │                       │
                          ▼                       ▼
                   [ HTTP Response ] ──► [ Explicit Buffer Overwrite & Purge ]

9. Latency & Word Error Rate Benchmarks

Our production acoustic engine is benchmarked continuously against standard linguistic datasets (LibriSpeech, Common Voice, TED-LIUM 3):

Benchmark Dataset	Acoustic Environment	TranscriptG WER	Industry Average WER	Real-Time Factor (RTF)
LibriSpeech Clean	Studio Audiobook	1.8%	3.2%	0.08x (1h audio in 4.8 min)
LibriSpeech Other	Accented / Noisy	3.9%	6.5%	0.09x
Common Voice 15	Global Multi-Accent	4.2%	7.8%	0.10x
TED-LIUM 3	Natural Stage Speech	2.6%	4.9%	0.08x

Real-Time Factor (RTF) of 0.08 means 60 minutes of audio is completely processed, timecoded, and transcribed in under 5 minutes. Test it yourself on our Free Speech-to-Text Transcriber.

10. Summary & Production Implementation

TranscriptG combines digital signal processing, Mel-spectrogram extraction, Conformer self-attention networks, and zero-retention memory management to deliver an enterprise-grade transcription platform accessible directly in your web browser.

Whether you need frame-accurate SRT subtitles, formatted JSON arrays for web audio players, or structured executive meeting summaries, TranscriptG executes the entire pipeline with unmatched precision and absolute privacy. Convert existing subtitle files instantly with our Subtitle Converter Tool.

How TranscriptG Works: Inside Our Privacy-First Neural Acoustic Processing Pipeline