Coqui TTS — Deep Learning Text-to-Speech Engine

What it is

Coqui TTS is an open-source deep learning text-to-speech engine that supports over 1100 languages. Its XTTS v2 model enables voice cloning from short audio samples with streaming output under 200ms latency. You can generate speech from text, clone voices, and fine-tune models on custom datasets.

Coqui TTS targets developers building voice interfaces, accessibility tools, content creation pipelines, and any application that needs high-quality synthesized speech without proprietary API costs.

How it saves time or tokens

Coqui TTS runs locally, eliminating per-request API costs from cloud TTS services. The pre-trained models cover most languages out of the box. Voice cloning requires only a few seconds of reference audio, avoiding expensive studio recording sessions. The streaming API enables real-time voice output for interactive applications.

How to use

1. Install Coqui TTS:

pip install TTS

1. Generate speech from the command line:

tts --text 'Hello, this is a test.' \
    --model_name tts_models/en/ljspeech/tacotron2-DDC \
    --out_path output.wav

1. Clone a voice with XTTS:

from TTS.api import TTS

tts = TTS('tts_models/multilingual/multi-dataset/xtts_v2')
tts.tts_to_file(
    text='Hello, this is my cloned voice.',
    speaker_wav='reference_audio.wav',
    language='en',
    file_path='cloned_output.wav'
)

Example

Streaming TTS for real-time applications:

from TTS.api import TTS
import sounddevice as sd
import numpy as np

tts = TTS('tts_models/multilingual/multi-dataset/xtts_v2')

wav = tts.tts(
    text='Streaming text to speech in real time.',
    speaker_wav='reference.wav',
    language='en'
)

sd.play(np.array(wav), samplerate=24000)
sd.wait()

Related on TokRepo

• Voice tools — text-to-speech and voice AI resources
• AI coding tools — developer tools and libraries

Common pitfalls

• XTTS v2 requires a GPU for reasonable inference speed. CPU inference works but is too slow for real-time applications.
• Voice cloning quality depends on reference audio quality. Use clean, noise-free recordings of at least 6 seconds for best results.
• Model downloads are large (several GB). Plan for storage and bandwidth when deploying to new environments.