Big Speak

Converts written text into natural-sounding speech audio across multiple languages by applying neural vocoder architecture with language-specific prosody models. The system processes input text through linguistic feature extraction, phoneme conversion, and mel-spectrogram generation, then synthesizes waveforms using deep learning models trained on native speaker datasets. Supports SSML markup for fine-grained control over speech rate, pitch, emphasis, and pause timing at the phoneme level.

Extracts speaker-specific acoustic characteristics from short audio recordings (typically 30 seconds to 2 minutes) and applies them to synthesize new speech in the target speaker's voice. Uses speaker embedding extraction via deep neural networks to capture voice timbre, pitch baseline, and speaking style, then conditions the TTS vocoder on these embeddings during synthesis. The cloned voice can generate speech in multiple languages while preserving the original speaker's acoustic identity.

Parses SSML (Speech Synthesis Markup Language) tags embedded in input text to apply granular control over speech parameters including pitch, rate, volume, emphasis, pauses, and phonetic pronunciation. The system tokenizes SSML-annotated text, extracts control directives from tags, and applies them as conditioning signals to the neural vocoder during synthesis, enabling frame-level manipulation of acoustic output. Supports standard SSML tags (prosody, break, emphasis, phoneme) plus potential custom extensions for voice-specific parameters.

Converts audio input (speech recordings) into written text using automatic speech recognition (ASR) models with automatic language detection. The system processes audio through acoustic feature extraction (mel-spectrograms or similar), runs inference on multilingual ASR models to identify language and generate transcriptions, and optionally applies post-processing for punctuation and capitalization. Supports batch transcription of multiple audio files and streaming transcription for real-time use cases.

Processes multiple audio files or text-to-speech requests in parallel using a job queue and asynchronous execution model. Users submit batch requests with multiple items, receive a job ID, and poll or webhook-subscribe for completion status. The system distributes jobs across worker nodes, manages resource allocation, and stores results in a retrievable format. Supports both TTS batch generation (multiple texts to audio) and transcription batch processing (multiple audio files to text).

Maintains separate voice libraries for 50+ languages and language variants, with each voice trained on native speaker data to capture language-specific phonetics and prosody. The system selects appropriate voice models based on target language, applies language-specific phoneme conversion, and synthesizes audio with native-like intonation. Supports both language-generic voices (can speak multiple languages) and language-specific voices (optimized for single language) with explicit language parameter in API requests.

Generates speech audio in real-time by streaming synthesized audio chunks to the client as they are produced, rather than waiting for full synthesis completion. The system processes input text incrementally, generates mel-spectrograms in chunks, synthesizes audio frames through the vocoder, and streams raw audio bytes or encoded chunks (MP3, Opus) to the client with minimal buffering. Enables interactive voice applications with perceived latency under 500ms from text input to audio playback.

Provides metrics and reporting on synthesized audio quality including MOS (Mean Opinion Score) estimates, prosody consistency scores, and speaker identity preservation metrics. The system evaluates each synthesis output against quality benchmarks, compares cloned voices against original samples for identity preservation, and generates quality reports. Supports A/B comparison of different voice settings or models to help users optimize synthesis parameters.

Provides REST API endpoints for managing custom voices, organizing voices into collections or projects, and retrieving voice metadata and capabilities. Users can create voice profiles, upload voice samples for cloning, list available voices with filtering by language/gender/characteristics, and manage voice permissions and sharing. The system maintains voice metadata (language support, characteristics, quality metrics) and enables programmatic voice discovery and selection.