TTS

Introduction

This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.

The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain.

The above information is from the LJSpeech website.

VITS

This recipe provides a VITS model trained on the LJSpeech dataset.

Pretrained model can be found here.

For tutorial and more details, please refer to the VITS documentation.

The training command is given below:

export CUDA_VISIBLE_DEVICES=0,1,2,3
./vits/train.py \
  --world-size 4 \
  --num-epochs 1000 \
  --start-epoch 1 \
  --use-fp16 1 \
  --exp-dir vits/exp \
  --max-duration 500

To inference, use:

./vits/infer.py \
  --exp-dir vits/exp \
  --epoch 1000 \
  --tokens data/tokens.txt

Quality vs speed

If you feel that the trained model is slow at runtime, you can specify the argument --model-type during training. Possible values are:

• low, means low quality. The resulting model is very small in file size and runs very fast. The following is a wave file generatd by a low quality model

  low.mp4

  The text is Ask not what your country can do for you; ask what you can do for your country.

  The exported onnx model has a file size of 26.8 MB (float32).

• medium, means medium quality. The following is a wave file generatd by a medium quality model

  medium.mp4

  The text is Ask not what your country can do for you; ask what you can do for your country.

  The exported onnx model has a file size of 70.9 MB (float32).

• high, means high quality. This is the default value.

  The following is a wave file generatd by a high quality model

  high.mp4

  The text is Ask not what your country can do for you; ask what you can do for your country.

  The exported onnx model has a file size of 113 MB (float32).

A pre-trained low model trained using 4xV100 32GB GPU with the following command can be found at https://huggingface.co/csukuangfj/icefall-tts-ljspeech-vits-low-2024-03-12

export CUDA_VISIBLE_DEVICES=0,1,2,3
./vits/train.py \
  --world-size 4 \
  --num-epochs 1601 \
  --start-epoch 1 \
  --use-fp16 1 \
  --exp-dir vits/exp \
  --model-type low \
  --max-duration 800

A pre-trained medium model trained using 4xV100 32GB GPU with the following command can be found at https://huggingface.co/csukuangfj/icefall-tts-ljspeech-vits-medium-2024-03-12

export CUDA_VISIBLE_DEVICES=4,5,6,7
./vits/train.py \
  --world-size 4 \
  --num-epochs 1000 \
  --start-epoch 1 \
  --use-fp16 1 \
  --exp-dir vits/exp-medium \
  --model-type medium \
  --max-duration 500

# (Note it is killed after `epoch-820.pt`)

matcha

./matcha contains the code for training Matcha-TTS

This recipe provides a Matcha-TTS model trained on the LJSpeech dataset.

Checkpoints and training logs can be found here. The pull-request for this recipe can be found at #1773

The training command is given below:

export CUDA_VISIBLE_DEVICES=0,1,2,3

python3 ./matcha/train.py \
  --exp-dir ./matcha/exp-new-3/ \
  --num-workers 4 \
  --world-size 4 \
  --num-epochs 4000 \
  --max-duration 1000 \
  --bucketing-sampler 1 \
  --start-epoch 1

To inference, use:

# Download Hifigan vocoder. We use Hifigan v1 below. You can select from v1, v2, or v3

wget https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v1

./matcha/infer.py \
  --exp-dir ./matcha/exp-new-3 \
  --epoch 4000 \
  --tokens ./data/tokens.txt \
  --vocoder ./generator_v1 \
  --input-text "how are you doing?" \
  --output-wav ./generated.wav

soxi ./generated.wav

prints:

Input File     : './generated.wav'
Channels       : 1
Sample Rate    : 22050
Precision      : 16-bit
Duration       : 00:00:01.29 = 28416 samples ~ 96.6531 CDDA sectors
File Size      : 56.9k
Bit Rate       : 353k
Sample Encoding: 16-bit Signed Integer PCM

To export the checkpoint to onnx:

# export the acoustic model to onnx

./matcha/export_onnx.py \
  --exp-dir ./matcha/exp-new-3 \
  --epoch 4000 \
  --tokens ./data/tokens.txt

The above command generate the following files:

• model-steps-2.onnx
• model-steps-3.onnx
• model-steps-4.onnx
• model-steps-5.onnx
• model-steps-6.onnx

where the 2 in model-steps-2.onnx means it uses 2 steps for the ODE solver.

To export the Hifigan vocoder to onnx, please use:

wget https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v1
wget https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v2
wget https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v3

python3 ./matcha/export_onnx_hifigan.py

The above command generates 3 files:

• hifigan_v1.onnx
• hifigan_v2.onnx
• hifigan_v3.onnx

To use the generated onnx files to generate speech from text, please run:

python3 ./matcha/onnx_pretrained.py \
 --acoustic-model ./model-steps-6.onnx \
 --vocoder ./hifigan_v1.onnx \
 --tokens ./data/tokens.txt \
 --input-text "Ask not what your country can do for you; ask what you can do for your country." \
 --output-wav ./matcha-epoch-4000-step6-hfigian-v1.wav

soxi ./matcha-epoch-4000-step6-hfigian-v1.wav

Input File     : './matcha-epoch-4000-step6-hfigian-v1.wav'
Channels       : 1
Sample Rate    : 22050
Precision      : 16-bit
Duration       : 00:00:05.46 = 120320 samples ~ 409.252 CDDA sectors
File Size      : 241k
Bit Rate       : 353k
Sample Encoding: 16-bit Signed Integer PCM

matcha-epoch-4000-step6-hfigian-v1.mov