How to Train Your Own Voice Model with Tortoise-TTS

a very expressive TTS system with impressive voice cloning capabilities

How to Train Your Own Voice Model with Tortoise-TTS

1. Background

Tortoise is a text-to-speech program built with the following priorities:

  • Strong multi-voice capabilities.
  • Highly realistic prosody and intonation.

2. Pre-requisites

To train your own voice model using Tortoise-TTS, make sure you have:

  • GPU runtime
  • Basic knowledge of Python
  • Enough high quality voice samples
RAM and VRAM usage

3. (Optional) Use your own voice as voice model

If you’d like to use your own voice as voice model, personally I recommend you to record them based on Harvard Sentences

The Harvard sentences, sometimes called Harvard lines, is a collection of 720 sample phrases, divided into lists of 10, used for standardized testing of Voice over IP, cellular, and other telephone systems. They are phonetically balanced sentences that use specific phonemes at the same frequency they appear in English. (example below)

List 1
1 The birch canoe slid on the smooth planks.
2 Glue the sheet to the dark blue background.
3 It's easy to tell the depth of a well.
4 These days a chicken leg is a rare dish.
5 Rice is often served in round bowls.
6 The juice of lemons makes fine punch.
7 The box was thrown beside the parked truck.
8 The hogs were fed chopped corn and garbage.
9 Four hours of steady work faced us.
10 A large size in stockings is hard to sell.

You can download Audacity
Record at least 3 voice samples (preferably over 10) in .wav format. The default sample rate may be 44100 Hz. You better re-sample it to 22050 Hz.

Resample to 22050 Hz

4. Main python

I’m using Google Colab for demo purposes.

  • Pre-steps
# the scipy version packaged with colab is not tolerant of misformated WAV files.
# install the latest version.
pip3 install -U scipy
git clone
cd tortoise-tts
pip3 install -r requirements.txt
pip3 install transformers==4.19.0 einops==0.5.0 rotary_embedding_torch==0.1.5 unidecode==1.3.5
python3 install
  • Import libraries
# Imports used through the rest of the notebook.
import torch
import torchaudio
import torch.nn as nn
import torch.nn.functional as F

import IPython

from tortoise.api import TextToSpeech
from import load_audio, load_voice, load_voices

# This will download all the models used by Tortoise from the HuggingFace hub.
tts = TextToSpeech()

The output can be:

Downloading: 100%
2.06k/2.06k [00:00<00:00, 143kB/s]
Downloading: 100%
1.18G/1.18G [00:19<00:00, 57.7MB/s]
Downloading: 100%
159/159 [00:00<00:00, 12.0kB/s]
Downloading: 100%
1.57k/1.57k [00:00<00:00, 23.5kB/s]
Downloading: 100%
181/181 [00:00<00:00, 12.2kB/s]
Downloading: 100%
85.0/85.0 [00:00<00:00, 6.30kB/s]
Downloading autoregressive.pth from

Downloading classifier.pth from

Downloading clvp2.pth from

Downloading cvvp.pth from

Downloading diffusion_decoder.pth from

Downloading vocoder.pth from

Downloading rlg_auto.pth from

Downloading rlg_diffuser.pth from

  • Input the message you want to be converted to voice, also determine the quality (the higher the slower)
# This is the text that will be spoken.
text = "Poggers, bro! The weather is good today. What is your plan for the weekend? Shall we go to see a movie?"

# Pick a "preset mode" to determine quality. Options: {"ultra_fast", "fast" (default), "standard", "high_quality"}. See docs in
preset = "fast"
  • Upload your voice samples (change your name). You can multi-select your wav files.
# Optionally, upload use your own voice by running the next two cells. I recommend
# you upload at least 2 audio clips. They must be a WAV file, 6-10 seconds long.

import os
from google.colab import files

custom_voice_folder = f"tortoise/voices/{CUSTOM_VOICE_NAME}"
for i, file_data in enumerate(files.upload().values()):
  with open(os.path.join(custom_voice_folder, f'{i}.wav'), 'wb') as f:

The output can be:

10 files
1.wav(audio/wav) - 1943360 bytes, last modified: 10/6/2023 - 100% done
2.wav(audio/wav) - 1948336 bytes, last modified: 10/6/2023 - 100% done
3.wav(audio/wav) - 1838908 bytes, last modified: 10/6/2023 - 100% done
4.wav(audio/wav) - 1938388 bytes, last modified: 10/6/2023 - 100% done
5.wav(audio/wav) - 1953308 bytes, last modified: 10/6/2023 - 100% done
6.wav(audio/wav) - 1943360 bytes, last modified: 10/6/2023 - 100% done
7.wav(audio/wav) - 1823984 bytes, last modified: 10/6/2023 - 100% done
8.wav(audio/wav) - 1928440 bytes, last modified: 10/6/2023 - 100% done
9.wav(audio/wav) - 1843880 bytes, last modified: 10/6/2023 - 100% done
10.wav(audio/wav) - 1828960 bytes, last modified: 10/6/2023 - 100% done
Saving 1.wav to 1.wav
Saving 2.wav to 2.wav
Saving 3.wav to 3.wav
Saving 4.wav to 4.wav
Saving 5.wav to 5.wav
Saving 6.wav to 6.wav
Saving 7.wav to 7.wav
Saving 8.wav to 8.wav
Saving 9.wav to 9.wav
Saving 10.wav to 10.wav
  • Generate speech. The duration of this step varies, highly depending on your quality, number of samples, length of your message, etc. Make sure you have enough RAM & VRAM.
# Generate speech with the custotm voice.
voice_samples, conditioning_latents = load_voice(CUSTOM_VOICE_NAME)
gen = tts.tts_with_preset(text, voice_samples=voice_samples, conditioning_latents=conditioning_latents,
                          preset=preset)'generated-{CUSTOM_VOICE_NAME}.wav', gen.squeeze(0).cpu(), 24000)

The output can be:

Generating autoregressive samples..
100%|██████████| 6/6 [00:45<00:00,  7.52s/it]
Computing best candidates using CLVP and CVVP
  0%|          | 0/6 [00:00<?, ?it/s]/usr/local/lib/python3.10/dist-packages/torch/utils/ UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")
100%|██████████| 6/6 [00:18<00:00,  3.14s/it]
Transforming autoregressive outputs into audio..
100%|██████████| 80/80 [00:34<00:00,  2.35it/s]

5. Demo result

waveform of generated audio


Poggers, bro! The weather is good today. What is your plan for the weekend? Shall we go to see a movie?


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