{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-myshell-ai--melotts-japanese","slug":"myshell-ai--melotts-japanese","name":"MeloTTS-Japanese","type":"model","url":"https://huggingface.co/myshell-ai/MeloTTS-Japanese","page_url":"https://unfragile.ai/myshell-ai--melotts-japanese","categories":["voice-audio"],"tags":["transformers","text-to-speech","ko","license:mit","endpoints_compatible","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-myshell-ai--melotts-japanese__cap_0","uri":"capability://text.generation.language.japanese.text.to.speech.synthesis.with.prosody.control","name":"japanese text-to-speech synthesis with prosody control","description":"Converts Japanese text input into natural-sounding speech audio using a transformer-based encoder-decoder architecture trained on Japanese phonetic and prosodic patterns. The model processes tokenized Japanese text through a duration predictor and pitch predictor to generate mel-spectrograms, which are then converted to waveforms via a neural vocoder. Supports character-level and phoneme-level input representations with fine-grained control over speaking rate, pitch contour, and emotional tone through style embeddings.","intents":["Generate natural-sounding Japanese speech from text for accessibility applications","Create multiple speaking style variations (neutral, happy, sad, angry) from the same text input","Build Japanese voice-over systems with controllable prosody for video/animation production","Synthesize Japanese dialogue for interactive applications with emotional expression"],"best_for":["Japanese content creators and developers building voice-enabled applications","Teams developing accessibility tools for Japanese language content","Game and animation studios needing cost-effective Japanese voice synthesis","Researchers working on multilingual TTS systems with Japanese language support"],"limitations":["Japanese-only language support — no cross-lingual synthesis or code-switching capability","Inference latency typically 2-5 seconds per sentence on CPU, requires GPU for real-time performance","Limited to training data phonetic inventory — may struggle with rare kanji readings or modern slang","No speaker adaptation or voice cloning — fixed set of pre-trained voice styles only","Mel-spectrogram to waveform conversion adds ~1-2 second latency depending on audio length"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+ for GPU acceleration (CPU inference possible but slow)","transformers library 4.20+","librosa for audio processing","HuggingFace Hub API access for model download","Minimum 4GB RAM for inference, 8GB+ recommended for batch processing"],"input_types":["plain text (Japanese hiragana, katakana, kanji)","phoneme sequences (IPA or custom phoneme inventory)","style control tokens (numeric embeddings for emotion/speaking style)"],"output_types":["WAV audio files (16kHz or 22.05kHz sample rate)","mel-spectrogram tensors (intermediate representation)","waveform tensors (PyTorch or NumPy format)"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-myshell-ai--melotts-japanese__cap_1","uri":"capability://text.generation.language.batch.speech.synthesis.with.style.variation.generation","name":"batch speech synthesis with style variation generation","description":"Processes multiple Japanese text inputs sequentially or in batches, generating corresponding speech audio with controllable style parameters (speaking rate, pitch range, emotional tone) applied uniformly or per-utterance. The model maintains state across batch items to optimize GPU memory usage and enable style interpolation between consecutive utterances for smooth transitions in multi-speaker dialogue scenarios.","intents":["Generate audiobook narration from Japanese text chapters with consistent voice characteristics","Create multiple emotional interpretations of the same dialogue for game/animation production","Synthesize Japanese training datasets for speech recognition model fine-tuning","Produce Japanese voice-over content at scale for video localization projects"],"best_for":["Content production teams processing hundreds of text segments for video/game localization","Researchers building Japanese speech datasets for model training","Audiobook publishers automating Japanese narration generation","Developers building batch processing pipelines for voice content generation"],"limitations":["Batch processing requires pre-allocation of GPU memory proportional to longest utterance in batch","No dynamic batching — batch size must be fixed at pipeline initialization","Style interpolation between utterances adds ~500ms overhead per transition","Memory consumption scales linearly with batch size; typical batch of 32 utterances requires 8GB+ VRAM","No built-in quality assurance — requires external validation to detect synthesis artifacts or mispronunciations"],"requires":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+ (GPU strongly recommended for batch processing)","transformers 4.20+","soundfile or scipy for audio file writing","Minimum 8GB GPU VRAM for batch size 16+, 16GB+ for batch size 32+"],"input_types":["list of Japanese text strings","style parameter arrays (speaking_rate: float 0.5-2.0, pitch_scale: float 0.5-2.0, emotion: categorical)","optional speaker/voice ID tokens"],"output_types":["WAV audio files (one per input text)","metadata JSON with synthesis duration, phoneme count, style parameters used","mel-spectrogram tensors for downstream processing"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-myshell-ai--melotts-japanese__cap_2","uri":"capability://text.generation.language.mel.spectrogram.to.waveform.vocoding.with.neural.upsampling","name":"mel-spectrogram to waveform vocoding with neural upsampling","description":"Converts mel-spectrogram representations generated by the text-to-speech encoder into high-quality waveforms using a neural vocoder (typically HiFi-GAN or similar architecture) that performs learned upsampling and waveform reconstruction. The vocoder operates on 80-channel mel-spectrograms and produces 16-bit PCM audio at 22.05kHz or 44.1kHz sample rates through transposed convolution layers with gated activation functions, enabling real-time or near-real-time audio generation on consumer hardware.","intents":["Convert intermediate mel-spectrogram representations into playable audio files for end-user applications","Achieve high-fidelity speech synthesis with minimal artifacts or robotic quality","Enable real-time speech synthesis for interactive applications with <500ms latency","Support multiple output sample rates (16kHz, 22.05kHz, 44.1kHz) for different deployment scenarios"],"best_for":["Developers building real-time voice chat or interactive voice applications","Audio engineers requiring high-fidelity speech synthesis for professional content","Mobile/edge device developers needing efficient vocoding with minimal computational overhead","Researchers studying neural vocoding architectures and waveform reconstruction"],"limitations":["Vocoder quality depends heavily on mel-spectrogram input quality — garbage in, garbage out","Inference latency 1-2 seconds for 10-second audio on CPU; GPU reduces to 200-500ms","Fixed sample rate at training time — resampling required for non-native rates, introducing quality loss","No built-in artifact detection — may produce clicks, pops, or phase discontinuities with out-of-distribution mel-spectrograms","Vocoder weights add 50-100MB to model size; requires separate download and initialization"],"requires":["Python 3.8+","PyTorch 1.9+","Pre-trained vocoder weights (HiFi-GAN or equivalent) compatible with 80-channel mel-spectrograms","librosa or scipy for audio I/O","2GB+ RAM for vocoder model loading, GPU optional but recommended"],"input_types":["mel-spectrogram tensors (shape: [batch, 80_channels, time_steps])","floating-point values normalized to [-1, 1] or [0, 1] range"],"output_types":["WAV audio files (16-bit PCM, 22.05kHz or 44.1kHz)","raw waveform tensors (PyTorch or NumPy)","audio bytes for streaming applications"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-myshell-ai--melotts-japanese__cap_3","uri":"capability://text.generation.language.phoneme.level.duration.and.pitch.prediction.with.linguistic.features","name":"phoneme-level duration and pitch prediction with linguistic features","description":"Predicts phoneme-level duration (in milliseconds) and fundamental frequency (F0) contours from Japanese text using a duration predictor and pitch predictor module, both implemented as feed-forward networks operating on linguistic embeddings extracted from the text encoder. The duration predictor outputs scalar values per phoneme, while the pitch predictor generates frame-level F0 values that are interpolated to match the mel-spectrogram time resolution, enabling fine-grained control over speech rhythm and intonation patterns.","intents":["Control speaking rate and rhythm by adjusting predicted phoneme durations (e.g., slow down for emphasis, speed up for casual speech)","Modify pitch contours to express different emotions or speaking styles without retraining the model","Generate natural prosody by leveraging linguistic context (sentence position, word stress, grammatical role)","Create expressive speech synthesis with pitch accents matching Japanese phonological rules"],"best_for":["Developers building expressive TTS systems with fine-grained prosody control","Linguists and phoneticians studying Japanese prosody and intonation patterns","Content creators needing emotional variation in synthesized speech","Researchers developing prosody-aware speech synthesis models"],"limitations":["Duration predictions may be inaccurate for rare kanji or non-standard readings — requires fallback to average durations","Pitch prediction assumes monotonic F0 contours; may struggle with complex pitch accents or emphatic stress","Linguistic feature extraction depends on accurate morphological analysis — errors propagate to duration/pitch predictions","No speaker-specific duration/pitch adaptation — all speakers use same predictor weights","Pitch contour interpolation can introduce artifacts if frame-level F0 values are discontinuous"],"requires":["Python 3.8+","PyTorch 1.9+","Japanese morphological analyzer (e.g., MeCab, Janome) for linguistic feature extraction","Pre-trained duration and pitch predictor weights","Phoneme inventory mapping (Japanese phonemes to IPA or custom representation)"],"input_types":["Japanese text (hiragana, katakana, kanji)","phoneme sequences with linguistic annotations (part-of-speech, word boundaries, stress markers)","optional duration/pitch override parameters (for manual prosody control)"],"output_types":["phoneme-level duration values (milliseconds, float)","frame-level pitch contours (F0 in Hz, float)","duration/pitch adjustment factors (for relative modifications)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-myshell-ai--melotts-japanese__cap_4","uri":"capability://text.generation.language.style.embedding.based.emotional.expression.and.speaking.style.variation","name":"style embedding-based emotional expression and speaking style variation","description":"Encodes emotional and speaking style variations (e.g., neutral, happy, sad, angry, whisper, shouting) as learned embeddings that are injected into the mel-spectrogram decoder, modulating the acoustic characteristics of synthesized speech without retraining the model. The style embeddings are trained via supervised learning on labeled speech data with emotion/style annotations, and can be interpolated in embedding space to create smooth transitions between styles or novel style combinations.","intents":["Generate multiple emotional interpretations of the same text (e.g., happy vs. sad narration) for content variation","Create character-specific speaking styles for game/animation dialogue without separate voice actors","Synthesize expressive speech for interactive applications (chatbots, virtual assistants) with emotional context","Interpolate between styles to create gradual emotional transitions in long-form content"],"best_for":["Game and animation studios producing dialogue with multiple emotional variations","Content creators building emotionally expressive voice-over systems","Developers creating interactive voice applications with emotional intelligence","Researchers studying emotion expression in speech synthesis"],"limitations":["Limited to pre-trained style set — no zero-shot style transfer or custom emotion definitions","Style embeddings are discrete/categorical; interpolation between styles may produce unnatural intermediate states","Emotion expression quality depends on training data diversity — underrepresented emotions may sound artificial","No speaker-style interaction modeling — same style applied uniformly across all voices","Style embeddings are not interpretable — difficult to understand what acoustic features each style modulates"],"requires":["Python 3.8+","PyTorch 1.9+","Pre-trained style embeddings (typically 64-256 dimensional vectors)","Labeled training data with emotion/style annotations for fine-tuning custom styles"],"input_types":["style category labels (string: 'neutral', 'happy', 'sad', 'angry', etc.)","style embedding vectors (float, 64-256 dimensions)","style interpolation weights (float, 0.0-1.0 for blending two styles)"],"output_types":["mel-spectrograms with style-modulated acoustic characteristics","WAV audio files with emotional expression","style embedding vectors (for analysis or interpolation)"],"categories":["text-generation-language","audio-synthesis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-myshell-ai--melotts-japanese__cap_5","uri":"capability://data.processing.analysis.japanese.text.preprocessing.and.phoneme.tokenization","name":"japanese text preprocessing and phoneme tokenization","description":"Converts raw Japanese text (hiragana, katakana, kanji) into phoneme sequences using morphological analysis and grapheme-to-phoneme conversion rules specific to Japanese phonology. The preprocessing pipeline handles kanji reading disambiguation, ruby text (furigana) extraction, number/symbol normalization, and produces phoneme sequences compatible with the TTS encoder, with optional linguistic annotations (part-of-speech, word boundaries, pitch accent markers) for prosody prediction.","intents":["Convert raw Japanese text into phoneme sequences for TTS model input","Handle ambiguous kanji readings by leveraging morphological context or explicit ruby text annotations","Normalize numbers, symbols, and special characters into pronounceable phoneme sequences","Extract linguistic features (word boundaries, stress markers) for prosody-aware synthesis"],"best_for":["Developers building Japanese TTS pipelines requiring robust text preprocessing","Content creators working with mixed-script Japanese text (kanji, hiragana, katakana, romaji)","Researchers studying Japanese phonology and grapheme-to-phoneme conversion","Teams processing user-generated Japanese text for TTS applications"],"limitations":["Kanji reading disambiguation relies on morphological analysis — may fail for rare kanji or non-standard readings","No support for non-standard romanization (romaji) — requires conversion to hiragana/katakana first","Phoneme inventory is fixed — no support for non-native phonemes or foreign loanword pronunciation variants","Linguistic annotation accuracy depends on morphological analyzer quality — errors propagate to prosody prediction","No handling of context-dependent pronunciation rules (e.g., rendaku consonant voicing) beyond morphological analysis"],"requires":["Python 3.8+","Japanese morphological analyzer (MeCab, Janome, or equivalent) with dictionary","Grapheme-to-phoneme conversion rules or lookup table for Japanese phonemes","Optional: ruby text parser for handling furigana annotations"],"input_types":["raw Japanese text (hiragana, katakana, kanji, mixed scripts)","optional ruby text/furigana annotations for kanji reading hints","optional linguistic markup (word boundaries, stress markers)"],"output_types":["phoneme sequences (IPA or custom phoneme inventory)","phoneme-level linguistic annotations (part-of-speech, word boundaries, pitch accent markers)","character-to-phoneme alignment information"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 1.9+ with CUDA 11.0+ for GPU acceleration (CPU inference possible but slow)","transformers library 4.20+","librosa for audio processing","HuggingFace Hub API access for model download","Minimum 4GB RAM for inference, 8GB+ recommended for batch processing","PyTorch 1.9+ with CUDA 11.0+ (GPU strongly recommended for batch processing)","transformers 4.20+","soundfile or scipy for audio file writing","Minimum 8GB GPU VRAM for batch size 16+, 16GB+ for batch size 32+"],"failure_modes":["Japanese-only language support — no cross-lingual synthesis or code-switching capability","Inference latency typically 2-5 seconds per sentence on CPU, requires GPU for real-time performance","Limited to training data phonetic inventory — may struggle with rare kanji readings or modern slang","No speaker adaptation or voice cloning — fixed set of pre-trained voice styles only","Mel-spectrogram to waveform conversion adds ~1-2 second latency depending on audio length","Batch processing requires pre-allocation of GPU memory proportional to longest utterance in batch","No dynamic batching — batch size must be fixed at pipeline initialization","Style interpolation between utterances adds ~500ms overhead per transition","Memory consumption scales linearly with batch size; typical batch of 32 utterances requires 8GB+ VRAM","No built-in quality assurance — requires external validation to detect synthesis artifacts or mispronunciations","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5583513666021797,"quality":0.22,"ecosystem":0.48000000000000004,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:51.286Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":210673,"model_likes":17}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=myshell-ai--melotts-japanese","compare_url":"https://unfragile.ai/compare?artifact=myshell-ai--melotts-japanese"}},"signature":"q/wPVEDln1Sd0FAsoNs7GRsMnI0ptKdlSqWGIPjD9cE1STnCygwhNGBPo6KZTXuyqsCoPaOZeq/OrONVlGZFDw==","signedAt":"2026-06-21T00:22:08.753Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/myshell-ai--melotts-japanese","artifact":"https://unfragile.ai/myshell-ai--melotts-japanese","verify":"https://unfragile.ai/api/v1/verify?slug=myshell-ai--melotts-japanese","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}