(synthesizing_speech)=

Synthesizing Speech

First, you need to install TTS. We recommend using PyPi. You need to call the command below:

$ pip install TTS

After the installation, 2 terminal commands are available.

TTS Command Line Interface (CLI). - tts
Local Demo Server. - tts-server
In 🐍Python. - from TTS.api import TTS

On the Commandline - `tts`

After the installation, 🐸TTS provides a CLI interface for synthesizing speech using pre-trained models. You can either use your own model or the release models under 🐸TTS.

Listing released 🐸TTS models.

tts --list_models

Run a TTS model, from the release models list, with its default vocoder. (Simply copy and paste the full model names from the list as arguments for the command below.)

tts --text "Text for TTS" \
    --model_name "<type>/<language>/<dataset>/<model_name>" \
    --out_path folder/to/save/output.wav

Run a tts and a vocoder model from the released model list. Note that not every vocoder is compatible with every TTS model.

tts --text "Text for TTS" \
    --model_name "tts_models/<language>/<dataset>/<model_name>" \
    --vocoder_name "vocoder_models/<language>/<dataset>/<model_name>" \
    --out_path folder/to/save/output.wav

Run your own TTS model (Using Griffin-Lim Vocoder)

tts --text "Text for TTS" \
    --model_path path/to/model.pth \
    --config_path path/to/config.json \
    --out_path folder/to/save/output.wav

Run your own TTS and Vocoder models

tts --text "Text for TTS" \
    --config_path path/to/config.json \
    --model_path path/to/model.pth \
    --out_path folder/to/save/output.wav \
    --vocoder_path path/to/vocoder.pth \
    --vocoder_config_path path/to/vocoder_config.json

Run a multi-speaker TTS model from the released models list.

tts --model_name "tts_models/<language>/<dataset>/<model_name>"  --list_speaker_idxs  # list the possible speaker IDs.
tts --text "Text for TTS." --out_path output/path/speech.wav --model_name "tts_models/<language>/<dataset>/<model_name>"  --speaker_idx "<speaker_id>"

Run a released voice conversion model

tts --model_name "voice_conversion/<language>/<dataset>/<model_name>"
    --source_wav "my/source/speaker/audio.wav"
    --target_wav "my/target/speaker/audio.wav"
    --out_path folder/to/save/output.wav

Note: You can use ./TTS/bin/synthesize.py if you prefer running tts from the TTS project folder.

On the Demo Server - `tts-server`

You can boot up a demo 🐸TTS server to run an inference with your models. Note that the server is not optimized for performance but gives you an easy way to interact with the models.

The demo server provides pretty much the same interface as the CLI command.

tts-server -h # see the help
tts-server --list_models  # list the available models.

Run a TTS model, from the release models list, with its default vocoder. If the model you choose is a multi-speaker TTS model, you can select different speakers on the Web interface and synthesize speech.

tts-server --model_name "<type>/<language>/<dataset>/<model_name>"

Run a TTS and a vocoder model from the released model list. Note that not every vocoder is compatible with every TTS model.

tts-server --model_name "<type>/<language>/<dataset>/<model_name>" \
           --vocoder_name "<type>/<language>/<dataset>/<model_name>"

Python 🐸TTS API

You can run a multi-speaker and multi-lingual model in Python as

import torch
from TTS.api import TTS

# Get device
device = "cuda" if torch.cuda.is_available() else "cpu"

# List available 🐸TTS models
print(TTS().list_models())

# Init TTS
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to(device)

# Run TTS
# ❗ Since this model is multi-lingual voice cloning model, we must set the target speaker_wav and language
# Text to speech list of amplitude values as output
wav = tts.tts(text="Hello world!", speaker_wav="my/cloning/audio.wav", language="en")
# Text to speech to a file
tts.tts_to_file(text="Hello world!", speaker_wav="my/cloning/audio.wav", language="en", file_path="output.wav")

Here is an example for a single speaker model.

# Init TTS with the target model name
tts = TTS(model_name="tts_models/de/thorsten/tacotron2-DDC", progress_bar=False)
# Run TTS
tts.tts_to_file(text="Ich bin eine Testnachricht.", file_path=OUTPUT_PATH)

Example voice cloning with YourTTS in English, French and Portuguese:

tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False).to("cuda")
tts.tts_to_file("This is voice cloning.", speaker_wav="my/cloning/audio.wav", language="en", file_path="output.wav")
tts.tts_to_file("C'est le clonage de la voix.", speaker_wav="my/cloning/audio.wav", language="fr", file_path="output.wav")
tts.tts_to_file("Isso é clonagem de voz.", speaker_wav="my/cloning/audio.wav", language="pt", file_path="output.wav")

Example voice conversion converting speaker of the `source_wav` to the speaker of the `target_wav`

tts = TTS(model_name="voice_conversion_models/multilingual/vctk/freevc24", progress_bar=False).to("cuda")
tts.voice_conversion_to_file(source_wav="my/source.wav", target_wav="my/target.wav", file_path="output.wav")

Example voice cloning by a single speaker TTS model combining with the voice conversion model.

This way, you can clone voices by using any model in 🐸TTS.

tts = TTS("tts_models/de/thorsten/tacotron2-DDC")
tts.tts_with_vc_to_file(
    "Wie sage ich auf Italienisch, dass ich dich liebe?",
    speaker_wav="target/speaker.wav",
    file_path="ouptut.wav"
)

Example text to speech using 🐸Coqui Studio models.

You can use all of your available speakers in the studio. 🐸Coqui Studio API token is required. You can get it from the account page. You should set the COQUI_STUDIO_TOKEN environment variable to use the API token.

# If you have a valid API token set you will see the studio speakers as separate models in the list.
# The name format is coqui_studio/en/<studio_speaker_name>/coqui_studio
models = TTS().list_models()
# Init TTS with the target studio speaker
tts = TTS(model_name="coqui_studio/en/Torcull Diarmuid/coqui_studio", progress_bar=False)
# Run TTS
tts.tts_to_file(text="This is a test.", file_path=OUTPUT_PATH)
# Run TTS with emotion and speed control
tts.tts_to_file(text="This is a test.", file_path=OUTPUT_PATH, emotion="Happy", speed=1.5)

If you just need 🐸 Coqui Studio speakers, you can use CS_API. It is a wrapper around the 🐸 Coqui Studio API.

from TTS.api import CS_API

# Init 🐸 Coqui Studio API
# you can either set the API token as an environment variable `COQUI_STUDIO_TOKEN` or pass it as an argument.

# XTTS - Best quality and life-like speech in multiple languages. See https://docs.coqui.ai/reference/samples_xtts_create for supported languages.
api = CS_API(api_token=<token>, model="XTTS")
api.speakers  # all the speakers are available with all the models.
api.list_speakers()
api.list_voices()
wav, sample_rate = api.tts(text="This is a test.", speaker=api.speakers[0].name, emotion="Happy", language="en", speed=1.5)

# V1 - Fast and lightweight TTS in EN with emotion control.
api = CS_API(api_token=<token>, model="V1")
api.speakers
api.emotions  # emotions are only for the V1 model.
api.list_speakers()
api.list_voices()
wav, sample_rate = api.tts(text="This is a test.", speaker=api.speakers[0].name, emotion="Happy", speed=1.5)

Example text to speech using Fairseq models in ~1100 languages 🤯.

For these models use the following name format: tts_models/<lang-iso_code>/fairseq/vits.

You can find the list of language ISO codes here and learn about the Fairseq models here.

from TTS.api import TTS
api = TTS(model_name="tts_models/eng/fairseq/vits").to("cuda")
api.tts_to_file("This is a test.", file_path="output.wav")

# TTS with on the fly voice conversion
api = TTS("tts_models/deu/fairseq/vits")
api.tts_with_vc_to_file(
    "Wie sage ich auf Italienisch, dass ich dich liebe?",
    speaker_wav="target/speaker.wav",
    file_path="ouptut.wav"
)