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| Pricing | Free | Free |
| Capabilities | 9 decomposed | 10 decomposed |
| Times Matched | 0 | 0 |
Automatically isolates individual audio stems (vocals, drums, bass, instruments) from a mixed audio track using AI-powered source separation. Produces clean, artifact-minimal separated tracks suitable for remixing and production work.
Allows users to shift the pitch of separated audio stems up or down in real-time without affecting playback speed. Enables key transposition and harmonic experimentation on individual stems.
Enables users to change the playback speed and tempo of separated audio stems without affecting pitch. Allows time-stretching for synchronizing stems to different BPMs.
Provides an intuitive interface for importing audio files into the application without requiring technical knowledge. Users can simply drag files into the interface to begin processing.
Manages user export limits on the free tier, allowing a generous monthly quota of processed audio exports while watermarking free-tier outputs. Tracks and enforces usage limits to encourage premium upgrades.
Allows users to choose between different processing quality levels, with free tier limited to lower quality and paid tiers offering higher fidelity stem separation. Quality selection affects processing time and output clarity.
Supports import and export of common audio formats, converting between formats as needed for compatibility with user workflows. Limited to common formats; does not support professional lossless codecs.
Enables processing of multiple audio tracks sequentially or in queue, though processing times remain slow (5-15 minutes per track). Useful for processing multiple songs but requires patience for large batches.
+1 more capabilities
Generates natural-sounding speech from text using a lightweight 82-million parameter transformer-based neural model (KModel class) that operates on phoneme sequences rather than raw text, with parallel Python and JavaScript implementations enabling deployment from CLI to web browsers. The KPipeline orchestrates text processing through language-specific G2P conversion (misaki or espeak-ng backends) followed by neural synthesis and ONNX-based audio waveform generation via istftnet modules.
Unique: Combines 82M parameter efficiency (vs 1B+ parameter competitors) with dual Python/JavaScript architecture enabling both server and browser deployment; uses misaki + espeak-ng hybrid G2P pipeline for language-agnostic phoneme conversion rather than language-specific models
vs alternatives: Smaller model size and Apache 2.0 licensing enable unrestricted commercial deployment where cloud-dependent TTS (Google Cloud, Azure) or GPL-licensed alternatives (Coqui) are impractical; JavaScript support gives browser-native synthesis unavailable in most open-source TTS
Converts text characters to phoneme sequences using a dual-backend architecture: misaki library as primary G2P engine for most languages, with espeak-ng fallback for Hindi and other languages requiring rule-based phonetic conversion. The text processing pipeline (in kokoro/pipeline.py) selects the appropriate G2P backend based on language code, handles text chunking for long inputs, and produces phoneme sequences that feed into neural synthesis.
Unique: Hybrid G2P architecture using misaki as primary engine with espeak-ng fallback provides better phonetic accuracy than single-backend approaches; language-specific backend selection (misaki for most, espeak-ng for Hindi) optimizes for each language's phonetic complexity rather than one-size-fits-all approach
vs alternatives: More flexible than single-backend G2P (e.g., pure espeak-ng) by combining neural-trained misaki with rule-based espeak-ng; avoids dependency on large language models for phoneme conversion, reducing latency vs LLM-based G2P approaches
Kokoro TTS scores higher at 59/100 vs RipX at 46/100.
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Generates raw audio waveforms from phoneme token sequences using ONNX-optimized istftnet modules that perform inverse short-time Fourier transform (ISTFT) synthesis. The KModel class produces mel-spectrogram embeddings from phoneme tokens, which are then converted to linear spectrograms and finally to waveforms via the ONNX-compiled istftnet vocoder, enabling efficient CPU/GPU inference without PyTorch overhead.
Unique: Uses ONNX-compiled istftnet vocoder for inference optimization rather than PyTorch-based vocoding, reducing memory footprint and enabling deployment on ONNX Runtime across heterogeneous hardware (CPU, GPU, mobile); istftnet provides direct spectrogram-to-waveform synthesis without intermediate neural vocoder layers
vs alternatives: ONNX vocoding is faster than PyTorch-based vocoders (HiFi-GAN, Glow-TTS) on CPU inference; smaller model size than end-to-end neural vocoders enables edge deployment where alternatives require significant computational overhead
Enables selection from multiple pre-trained voice styles (e.g., 'af_heart' for American female, various British voices) by conditioning the neural model with voice-specific embeddings. The KModel class accepts a voice identifier parameter that retrieves corresponding embeddings from HuggingFace Hub, which are concatenated with phoneme embeddings during synthesis to produce voice-specific speech characteristics without retraining the base model.
Unique: Implements speaker conditioning via pre-trained voice embeddings rather than speaker ID tokens or speaker-specific model variants, enabling voice selection without model duplication; embeddings are downloaded on-demand from HuggingFace Hub rather than bundled, reducing package size
vs alternatives: More efficient than maintaining separate model checkpoints per voice (as some TTS systems do); embedding-based conditioning is lighter-weight than speaker encoder networks used in some alternatives, reducing inference latency
Provides parallel Python (KPipeline, KModel classes) and JavaScript (KokoroTTS class) implementations with identical functional semantics, enabling code portability and consistent behavior across environments. Both implementations share the same text processing pipeline, model inference logic, and audio synthesis approach, with language-specific optimizations (PyTorch for Python, ONNX.js for JavaScript) while maintaining API compatibility.
Unique: Maintains semantic equivalence between Python and JavaScript implementations through shared pipeline design (KPipeline abstraction) rather than transpilation or wrapper layers; both implementations use identical text processing and model inference logic with language-specific runtime optimization
vs alternatives: More maintainable than separate Python/JavaScript implementations because core logic is unified; avoids transpilation overhead and complexity of maintaining two codebases with different semantics, unlike some TTS projects with separate Python and JS versions
Provides CLI tools for text-to-speech synthesis without programmatic API usage, supporting both interactive input and batch file processing. The CLI wraps the KPipeline class, accepting text input via stdin or file arguments, language/voice parameters, and output file specifications, enabling integration into shell scripts and data processing pipelines.
Unique: CLI implementation wraps KPipeline class directly without separate CLI-specific code, maintaining consistency with programmatic API; supports both interactive and batch modes through unified interface
vs alternatives: Simpler than cloud-based TTS CLIs (Google Cloud, Azure) because no authentication or API key management required; more accessible than programmatic APIs for non-developers and shell script integration
Provides utilities (examples/export.py) to export the KModel neural network and istftnet vocoder to ONNX format for optimized inference across different hardware and runtime environments. The export process converts PyTorch models to ONNX intermediate representation, enabling deployment on ONNX Runtime (CPU, GPU, mobile) without PyTorch dependency, reducing model size and inference latency.
Unique: Provides explicit export utilities rather than automatic ONNX export, giving developers control over export parameters and optimization settings; separates export from inference, enabling offline optimization workflows
vs alternatives: More flexible than automatic export because developers can customize export parameters; avoids runtime overhead of on-demand export compared to systems that export during first inference
Implements generator-based processing pipeline that yields audio segments incrementally as they are synthesized, rather than buffering entire output. The KPipeline class returns Python generators that yield tuples of (graphemes, phonemes, audio_segment) for each text chunk, enabling memory-efficient processing of long texts and streaming output to audio devices or files.
Unique: Uses Python generators to yield audio segments incrementally rather than buffering entire output, enabling memory-efficient processing of arbitrarily long texts; generator pattern provides both phoneme and audio output for each segment, enabling downstream analysis or processing
vs alternatives: More memory-efficient than batch processing entire texts; enables real-time streaming output unavailable in systems that require complete synthesis before output; generator pattern is more Pythonic than callback-based streaming
+2 more capabilities