PaddlePaddle/PaddleSpeech

Features

• Automatic Speech Recognition - Provides a comprehensive system for converting spoken audio into written text with streaming and punctuation support.
• Text-to-Speech Engines - Provides a complete synthesis pipeline that transforms written text into natural audio waveforms using acoustic models and neural vocoders.
• Speech-to-Text Translation - Maps source audio features directly to target language text without using an intermediate transcription step.
• Acoustic Models - Provides neural network architectures that convert linguistic representations into audio features like mel-spectrograms.
• Keyword Spotting - Detects specific predefined trigger words or phrases within continuous audio streams to initiate actions.
• Multilingual Speech Translation - Translates spoken audio from one language into text in another via an end-to-end process.
• End-to-End Speech Translation - Provides models for translating spoken audio from one language directly into another without intermediate text.
• Speaker Diarization - Partitions audio recordings into distinct segments to determine which individual is speaking at any given time.
• Speaker Embeddings - Generates fixed-dimensional numerical representations of voices to identify and verify individual speaker identities.
• Speaker Identification Frameworks - Extracts voice embeddings and partitions audio to identify and distinguish between different individual speakers.
• Speech Processing Toolkits - Offers a comprehensive toolkit of neural models for speech recognition, synthesis, and translation.
• Speech Transcription - Converts spoken audio into written text using standard and streaming automatic speech recognition methods.
• Text-to-Speech - Synthesizes natural human speech from text input with support for voice cloning and streaming output.
• Speech-to-Speech Models - Translates spoken audio directly into spoken or written target languages without intermediate transcription.
• Neural Vocoders - Transforms generated spectral data into high-fidelity time-domain audio waveforms using neural vocoders.
• Voiceprint Extraction - Creates unique digital signatures from voice samples to identify and verify specific speakers.
• Prosody Control - Modifies the timing, pitch, and energy of synthesized speech using duration predictors and conditional inputs.
• Self-Supervised Speech Representations - Learns general linguistic features from large unlabeled audio datasets via self-supervised representation learning.
• Prosodic Duration Predictors - Implements conditional duration prediction to control the timing and rhythm of synthesized speech.
• Punctuation Restoration - Automatically inserts punctuation marks into raw speech-to-text transcripts to improve readability.
• Streaming Transcription Inference - Processes audio data in small chunks for real-time speech recognition and synthesis with low latency.

Star history