CPUでも動作する超小型 TTS「Kitten TTS」を試す

!pip install https://github.com/KittenML/KittenTTS/releases/download/0.1/kittentts-0.1.0-py3-none-any.whl

from kittentts import KittenTTS

# モデルロード
m = KittenTTS("KittenML/kitten-tts-nano-0.1")

# オーディオを生成
audio = m.generate(
    "This high quality TTS model works without a GPU.",
    # 利用可能な音声
    # - 'expr-voice-2-m'
    # - 'expr-voice-2-f'
    # - 'expr-voice-3-m'
    # - 'expr-voice-3-f'
    # - 'expr-voice-4-m'
    # - 'expr-voice-4-f'
    # - 'expr-voice-5-m'
    # - 'expr-voice-5-f'
    voice='expr-voice-2-f'
)

# オーディオを保存
import soundfile as sf
sf.write('output.wav', audio, 24000)

from IPython.display import Audio
Audio('output.wav')

import numpy as np

voices = [
    'expr-voice-2-f',
    'expr-voice-2-m',
    'expr-voice-3-f',
    'expr-voice-3-m',
    'expr-voice-4-f',
    'expr-voice-4-m',
    'expr-voice-5-f',
    'expr-voice-5-m',
]

texts = [
    "Kitten TTS is an open-source series of tiny and expressive Text-to-Speech models for on-device applications.",
    "Our smallest model is less than 25 megabytes.", 
    "The world needs frontier open source models, that are tiny enough to run on edge devices. That's what Kitten ML is here for.",
    "This project was started just two weeks ago and we're excited to launch a preview of our smallest model.",
    "This model has 15 million parameters, and we are releasing the preview just to give early users a sense of the latency and voices that will be available in next week's release.",
    "We are looking for early testers that can provide feedback to shape the future of these models.",
    "Just FYI, this model, is an early checkpoint trained on less than 10% of our total data.",
    "Our full model release will be next week, and we can't wait for you to try it out.",
]

audio_data = []
for v, t in zip(voices, texts):
    audio = m.generate(t, voice=v)
    audio_data.append(audio)
    
    print(v)
    audio_file_name = f"{v}.wav"
    sf.write(audio_file_name, audio, 24000)
    display(Audio(audio_file_name))

combined = np.concatenate(audio_data)
combined_file = 'combined.wav'
sf.write(combined_file, combined, 24000)

print("全て結合")
display(Audio(combined_file))