42 lines
1.9 KiB
JSON
42 lines
1.9 KiB
JSON
{
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"model_details": {
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"manufacturer_name": "Coqui",
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"manufacturer_website": "https://github.com/coqui-ai/TTS and also updated engine https://github.com/idiap/coqui-ai-TTS/",
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"model_description": "VITS models come in single and multi-speaker variants. VITS (Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech ) is an End-to-End (encoder -> vocoder together) TTS model that takes advantage of SOTA DL techniques like GANs, VAE, Normalizing Flows. It does not require external alignment annotations and learns the text-to-audio alignment using MAS, as explained in the paper. The model architecture is a combination of GlowTTS encoder and HiFiGAN vocoder. It is a feed-forward model with x67.12 real-time factor on a GPU. \n\ntts_models--en--vctk--vits is a good example of a multi speaker VITS model."
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},
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"model_capabilties": {
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"audio_format": "wav",
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"deepspeed_capable": false,
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"generationspeed_capable": false,
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"languages_capable": false,
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"lowvram_capable": true,
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"multimodel_capable": true,
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"repetitionpenalty_capable": false,
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"streaming_capable": false,
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"temperature_capable": false,
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"multivoice_capable": true,
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"pitch_capable": false,
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"windows_capable": true,
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"linux_capable": true,
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"mac_capable": true
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},
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"settings": {
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"def_character_voice": "p225",
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"def_narrator_voice": "p226",
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"deepspeed_enabled": false,
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"engine_installed": true,
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"generationspeed_set": 1,
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"lowvram_enabled": true,
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"pitch_set": 0,
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"repetitionpenalty_set": 10,
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"temperature_set": 0.75
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},
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"openai_voices": {
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"alloy": "p225",
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"echo": "p225",
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"fable": "p225",
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"nova": "p225",
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"onyx": "p225",
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"shimmer": "p225"
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}
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} |