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Text to Speech

import gTTS import os def text_to_speech_kurdish(text, output_file="output.mp3"): # گۆڕینی نووسین بۆ دەنگ بە زمانی کوردی (هەڵبژاردنی زمانی "ku" بۆ کوردی) tts = gTTS(text=text, lang='ku', slow=False) tts.save(output_file) os.system(f"start {output_file}") # کردنەوەی فایلە دەنگییەکە (لە Windows) # نموونە: text_to_speech_kurdish("سڵاو، ئەمە دەنگی منە بە زمانی کوردی.")

Papers

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Showing 451–475 of 1419 papers

TitleStatusHype
VALL-T: Decoder-Only Generative Transducer for Robust and Decoding-Controllable Text-to-Speech—0
SpeechGPT-Gen: Scaling Chain-of-Information Speech GenerationCode5
Maximizing Data Efficiency for Cross-Lingual TTS Adaptation by Self-Supervised Representation Mixing and Embedding Initialization—0
Adversarial speech for voice privacy protection from Personalized Speech generation—0
Empowering Communication: Speech Technology for Indian and Western Accents through AI-powered Speech Synthesis—0
Benchmarking Large Multimodal Models against Common CorruptionsCode1
Data-driven grapheme-to-phoneme representations for a lexicon-free text-to-speech—0
DurFlex-EVC: Duration-Flexible Emotional Voice Conversion Leveraging Discrete Representations without Text AlignmentCode2
MCMChaos: Improvising Rap Music with MCMC Methods and Chaos Theory—0
ELLA-V: Stable Neural Codec Language Modeling with Alignment-guided Sequence Reordering—0
Multi-Task Learning for Front-End Text Processing in TTSCode1
End to end Hindi to English speech conversion using Bark, mBART and a finetuned XLSR Wav2Vec2—0
Noise-robust zero-shot text-to-speech synthesis conditioned on self-supervised speech-representation model with adapters—0
Transfer the linguistic representations from TTS to accent conversion with non-parallel data—0
Evaluating and Personalizing User-Perceived Quality of Text-to-Speech Voices for Delivering Mindfulness Meditation with Different Physical Embodiments—0
Incremental FastPitch: Chunk-based High Quality Text to Speech—0
Utilizing Neural Transducers for Two-Stage Text-to-Speech via Semantic Token Prediction—0
Boosting Large Language Model for Speech Synthesis: An Empirical Study—0
Normalization of Lithuanian Text Using Regular Expressions—0
AE-Flow: AutoEncoder Normalizing Flow—0
Creating New Voices using Normalizing Flows—0
External Knowledge Augmented Polyphone Disambiguation Using Large Language Model—0
A review-based study on different Text-to-Speech technologies—0
MM-TTS: Multi-modal Prompt based Style Transfer for Expressive Text-to-Speech Synthesis—0
Neural Text to Articulate Talk: Deep Text to Audiovisual Speech Synthesis achieving both Auditory and Photo-realismCode1
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