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Fala PB

Retomando experimentos com Síntese de Fala

O foco nesta seção é retomar experimentos iniciais de síntese de fala utilizando o docker.

Coqui AI

Nesta seção retomamos a parte do coqui AI, já há um experimento com exemplos do coqui ai.

Português Brasileiro

tts_models/multilingual/multi-dataset/xtts_v2
Suporta 16 idiomas, incluindo o português brasileiro (“pt”), usando embeddings de idioma e de falante para geração zero-shot com alta fidelidade na prosódia e entonação.

tts_models/multilingual/multi-dataset/xtts_v1.1
Cobre 14 idiomas, entre eles “pt”, com uma versão otimizada do Transformer-TTS que oferece síntese expressiva em pt-BR, ideal para protótipos e testes rápidos.

tts_models/multilingual/multi-dataset/your_tts
Treinado com o TTS-Portuguese Corpus (TPC) para português brasileiro, baseado em VITS com adversarial learning, garantindo naturalidade, clareza e variedade de entonações típicas do pt-BR.

Português de Portugal

tts_models/pt/cv/vits
Modelo VITS exclusivo para português europeu (pt-PT), treinado no Common Voice, que entrega voz articulada e ritmo consistente, perfeito para público de Portugal.

Lista de modelos disponíveis pelo coqui ai

Name format: type/language/dataset/model 1: tts_models/multilingual/multi-dataset/xtts_v2 2: tts_models/multilingual/multi-dataset/xtts_v1.1 3: tts_models/multilingual/multi-dataset/your_tts 4: tts_models/multilingual/multi-dataset/bark 5: tts_models/bg/cv/vits 6: tts_models/cs/cv/vits 7: tts_models/da/cv/vits 8: tts_models/et/cv/vits 9: tts_models/ga/cv/vits 10: tts_models/en/ek1/tacotron2 11: tts_models/en/ljspeech/tacotron2-DDC 12: tts_models/en/ljspeech/tacotron2-DDC_ph 13: tts_models/en/ljspeech/glow-tts 14: tts_models/en/ljspeech/speedy-speech 15: tts_models/en/ljspeech/tacotron2-DCA 16: tts_models/en/ljspeech/vits 17: tts_models/en/ljspeech/vits--neon 18: tts_models/en/ljspeech/fast_pitch 19: tts_models/en/ljspeech/overflow 20: tts_models/en/ljspeech/neural_hmm 21: tts_models/en/vctk/vits 22: tts_models/en/vctk/fast_pitch 23: tts_models/en/sam/tacotron-DDC 24: tts_models/en/blizzard2013/capacitron-t2-c50 25: tts_models/en/blizzard2013/capacitron-t2-c150_v2 26: tts_models/en/multi-dataset/tortoise-v2 27: tts_models/en/jenny/jenny 28: tts_models/es/mai/tacotron2-DDC 29: tts_models/es/css10/vits 30: tts_models/fr/mai/tacotron2-DDC 31: tts_models/fr/css10/vits 32: tts_models/uk/mai/glow-tts 33: tts_models/uk/mai/vits 34: tts_models/zh-CN/baker/tacotron2-DDC-GST 35: tts_models/nl/mai/tacotron2-DDC 36: tts_models/nl/css10/vits 37: tts_models/de/thorsten/tacotron2-DCA 38: tts_models/de/thorsten/vits 39: tts_models/de/thorsten/tacotron2-DDC 40: tts_models/de/css10/vits-neon 41: tts_models/ja/kokoro/tacotron2-DDC 42: tts_models/tr/common-voice/glow-tts 43: tts_models/it/mai_female/glow-tts 44: tts_models/it/mai_female/vits 45: tts_models/it/mai_male/glow-tts 46: tts_models/it/mai_male/vits 47: tts_models/ewe/openbible/vits 48: tts_models/hau/openbible/vits 49: tts_models/lin/openbible/vits 50: tts_models/tw_akuapem/openbible/vits 51: tts_models/tw_asante/openbible/vits 52: tts_models/yor/openbible/vits 53: tts_models/hu/css10/vits 54: tts_models/el/cv/vits 55: tts_models/fi/css10/vits 56: tts_models/hr/cv/vits 57: tts_models/lt/cv/vits 58: tts_models/lv/cv/vits 59: tts_models/mt/cv/vits 60: tts_models/pl/mai_female/vits 61: tts_models/pt/cv/vits 62: tts_models/ro/cv/vits 63: tts_models/sk/cv/vits 64: tts_models/sl/cv/vits 65: tts_models/sv/cv/vits 66: tts_models/ca/custom/vits 67: tts_models/fa/custom/glow-tts 68: tts_models/bn/custom/vits-male 69: tts_models/bn/custom/vits-female 70: tts_models/be/common-voice/glow-tts Name format: type/language/dataset/model 1: vocoder_models/universal/libri-tts/wavegrad 2: vocoder_models/universal/libri-tts/fullband-melgan 3: vocoder_models/en/ek1/wavegrad 4: vocoder_models/en/ljspeech/multiband-melgan 5: vocoder_models/en/ljspeech/hifigan_v2 6: vocoder_models/en/ljspeech/univnet 7: vocoder_models/en/blizzard2013/hifigan_v2 8: vocoder_models/en/vctk/hifigan_v2 9: vocoder_models/en/sam/hifigan_v2 10: vocoder_models/nl/mai/parallel-wavegan 11: vocoder_models/de/thorsten/wavegrad 12: vocoder_models/de/thorsten/fullband-melgan 13: vocoder_models/de/thorsten/hifigan_v1 14: vocoder_models/ja/kokoro/hifigan_v1 15: vocoder_models/uk/mai/multiband-melgan 16: vocoder_models/tr/common-voice/hifigan 17: vocoder_models/be/common-voice/hifigan

Tabela de Áudios XTTS

Nesta tabela temos todos os speakers possiveis falando a frase “Eu te amo”. Também é possível fazer few shot que fiz com 5 audios usando minha própria voz:

Speaker	Exemplo
Abrahan Mack
Aaron Dreschner
Adde Michal
Alison Dietlinde
Alexandra Hisakawa
Alma María
Andrew Chipper
Ana Florence
Annmarie Nele
Asya Anara
Badr Odhiambo
Barbora MacLean
Baldur Sanjin
Brenda Stern
Camilla Holmström
Chandra MacFarland
Claribel Dervla
Craig Gutsy
Damien Black
Damjan Chapman
Daisy Studious
Dionisio Schuyler
Eugenio Mataracı
Ferran Simen
Filip Traverse
Gilberto Mathias
Gitta Nikolina
Henriette Usha
Ilkin Urbano
Ige Behringer
Kumar Dahl
Kazuhiko Atallah
Lidiya Szekeres
Lilya Stainthorpe
Ludvig Milivoj
Maja Ruoho
Marcos Rudaski
Nova Hogarth
Narelle Moon
Rosemary Okafor
Royston Min
Sofia Hellen
Suad Qasim
Szofi Granger
Tammie Ema
Tammy Grit
Tanja Adelina
Torcull Diarmuid
Uta Obando
Viktor Eka
Viktor Menelaos
Vjollca Johnnie
Wulf Carlevaro
Xavier Hayasaka
Zacharie Aimilios
Zofija Kendrick