Parler TTS

Parler TTS Ikiganiro

Describe the voice you want in plain English and Parler generates speech matching that description.

Kwiyandikisha kugirango Inyuguti

Umwandiko in Itagi: ya: Igenzura:

<speak><prosody rate="slow">Slow speech</prosody></speak>

i Byahiswemo Urugero - Kanda Kuri Gukuraho Rimwe Umwandiko:

Urugero: Bisanzwe Umwandiko, Umurongo: Itagi:. Itagi: -, Hindura Kuri Urugero: Nka Cyangwa.

Kugena (Ijambo =):

-12 +12
0.5x 2.0x
Na:,,
Audio Kugaragara. A Urugero:, Injiza Umwandiko, na Kanda.
Byaremwe
0:00
Iyimura Iyimura Ihuza in
Bwite Koresha Kuva: 5 /
TTS.ai? Abayobozi!

Ikiganiro Parler TTS

Parler TTS, developed by Hugging Face, replaces voice presets with natural-language control: instead of picking from a fixed list, you write a description such as "a warm female voice with a slight British accent, speaking slowly and clearly," and the model synthesizes speech to match. This makes it unusually flexible for creative work where you need a specific, custom voice character without recording or cloning anyone. It is an 880M-parameter transformer encoder-decoder trained on roughly 45,000 hours of speech, and it is released under the permissive Apache 2.0 license. Parler is English-focused and best suited to applications that benefit from on-demand, describable voice characteristics.

kugirango: Creative applications where you need custom voice characteristics

Gushakisha byose Parler TTS Amashusho

A

Mukoraporogaramu
Hugging Face
Inyandiko y'Iyemererakoresha
Apache 2.0
Itariki
standard
Umuvuduko
medium
Guhindura izina
Oya
Ururimi:
English
Inyuguti
500

Parler TTS Amashusho

Default

English
Bisanzwe Neutral

Parler TTS -

You describe it in natural language — gender, accent, pace, tone, and recording quality — and Parler generates speech matching the description. There are no preset voices to choose from.

Hugging Face. It is an 880M-parameter transformer encoder-decoder trained on around 45,000 hours of speech and released under Apache 2.0.

No. Parler generates a voice from a text description rather than from a reference recording. For cloning a specific voice, use a model like Chatterbox or GPT-SoVITS.
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