E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS
This paper introduces Embarrassingly Easy Text-to-Speech (E2 TTS), a fully
non-autoregressive zero-shot text-to-speech system that offers human-level
naturalness and state-of-the-art speaker similarity and intelligibility. In the
E2 TTS framework, the text input is converted into a character sequence with
filler tokens. The flow-matching-based mel spectrogram generator is then
trained based on the audio infilling task. Unlike many previous works, it does
not require additional components (e.g., duration model, grapheme-to-phoneme)
or complex techniques (e.g., monotonic alignment search). Despite its
simplicity, E2 TTS achieves state-of-the-art zero-shot TTS capabilities that
are comparable to or surpass previous works, including Voicebox and
NaturalSpeech 3. The simplicity of E2 TTS also allows for flexibility in the
input representation. We propose several variants of E2 TTS to improve
usability during inference. See https://aka.ms/e2tts/ for demo samples.
66835ca7e365c0f6665a5478
Sefik Emre Eskimez
claimed_verified
2024-08-15T10:12:38.419Z
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Sefik Emre Eskimez
user
emreeskimez
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Xiaofei Wang
claimed_verified
2024-07-02T07:39:05.021Z
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Xiaofei Wang
user
xiaofei-wang
66835ca7e365c0f6665a547a
Manthan Thakker
66835ca7e365c0f6665a547b
Canrun Li
66835ca7e365c0f6665a547c
Chung-Hsien Tsai
66835ca7e365c0f6665a547d
Zhen Xiao
66835ca7e365c0f6665a547e
Hemin Yang
66835ca7e365c0f6665a547f
Zirun Zhu
66835ca7e365c0f6665a5480
Min Tang
66835ca7e365c0f6665a5481
Xu Tan
claimed_verified
2024-08-28T13:47:19.699Z
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/avatars/a2f28940236ae625ed3810ad62e343ff.svg
Xu Tan
user
xutan
66835ca7e365c0f6665a5482
Yanqing Liu
66835ca7e365c0f6665a5483
Sheng Zhao
66835ca7e365c0f6665a5484
Naoyuki Kanda
claimed_verified
2024-07-02T07:39:06.943Z
64dbe5247f749b6e346663f5
/avatars/d5c7f542c1fdb31eb49478afbb3dd08d.svg
Naoyuki Kanda
user
naoyuki82
E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS
This paper introduces Embarrassingly Easy Text-to-Speech (E2 TTS), a fully
non-autoregressive zero-shot text-to-speech system that offers human-level
naturalness and state-of-the-art speaker similarity and intelligibility. In the
E2 TTS framework, the text input is converted into a character sequence with
filler tokens. The flow-matching-based mel spectrogram generator is then
trained based on the audio infilling task. Unlike many previous works, it does
not require additional components (e.g., duration model, grapheme-to-phoneme)
or complex techniques (e.g., monotonic alignment search). Despite its
simplicity, E2 TTS achieves state-of-the-art zero-shot TTS capabilities that
are comparable to or surpass previous works, including Voicebox and
NaturalSpeech 3. The simplicity of E2 TTS also allows for flexibility in the
input representation. We propose several variants of E2 TTS to improve
usability during inference. See https://aka.ms/e2tts/ for demo samples.
E2 TTS is a non-autoregressive zero-shot text-to-speech system using flow-matching-based mel spectrogram generation without additional components, achieving state-of-the-art performance.
Embarrassingly Easy Text-to-Speech E2 TTS fully non-autoregressive zero-shot text-to-speech flow-matching-based mel spectrogram audio infilling duration model grapheme-to-phoneme monotonic alignment search Voicebox NaturalSpeech 3