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An implementation of Microsoft's "FastSpeech 2: Fast and High-Quality End-to-End Text to Speech"
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| 2026-07-31 | 2185 |
| 2026-08-06 | 2186 |
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# FastSpeech 2 - PyTorch Implementation This is a PyTorch implementation of Microsoft's text-to-speech system [**FastSpeech 2: Fast and High-Quality End-to-End Text to Speech**](https://arxiv.org/abs/2006.04558v1). This project is based on [xcmyz's implementation](https://github.com/xcmyz/FastSpeech) of FastSpeech. Feel free to use/modify the code. There are several versions of FastSpeech 2. This implementation is more similar to [version 1](https://arxiv.org/abs/2006.04558v1), which uses F0 values as the pitch features. On the other hand, pitch spectrograms extracted by continuous wavelet transform are used as the pitch features in the [later versions](https://arxiv.org/abs/2006.04558).  # Updates - 2021/7/8: Release the checkpoint and audio samples of a multi-speaker English TTS model trained on LibriTTS - 2021/2/26: Support English and Mandarin TTS - 2021/2/26: Support multi-speaker TTS (AISHELL-3 and LibriTTS) - 2021/2/26: Support MelGAN and HiFi-GAN vocoder # Audio Samples Audio samples generated by this implementation can be found [here](https://ming024.github.io/FastSpeech2/). # Quickstart ## Dependencies You can install the Python dependencies with ``` pip3 install -r requirements.txt ``` ## Inference You have to download the [pretrained models](https://drive.google.com/drive/folders/1DOhZGlTLMbbAAFZmZGDdc77kz1PloS7F?usp=sharing) and put them in ``output/ckpt/LJSpeech/``, ``output/ckpt/AISHELL3``, or ``output/ckpt/LibriTTS/``. For English single-speaker TTS, run ``` python3 synthesize.py --text "YOUR_DESIRED_TEXT" --restore_step 900000 --mode single -p config/LJSpeech/preprocess.yaml -m config/LJSpeech/model.yaml -t config/LJSpeech/train.yaml ``` For Mandarin multi-speaker TTS, try ``` python3 synthesize.py --text "大家好" --speaker_id SPEAKER_ID --restore_step 600000 --mode single -p config/AISHELL3/preprocess.yaml -m config/AISHELL3/model.yaml -t config/AISHELL3/train.yaml ``` For English multi-speaker TTS, run ``` python3 synthesize.py --text "YOUR_DESIRED_TEXT" --speaker_id SPEAKER_ID --restore_step 800000 --mode single -p config/LibriTTS/preprocess.yaml -m config/LibriTTS/model.yaml -t config/LibriTTS/train.yaml ``` The generated utterances will be put in ``output/result/``. Here is an example of synthesized mel-spectrogram of the sentence "Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition", with the English single-speaker TTS model.  ## Batch Inference Batch inference is also supported, try ``` python3 synthesize.py --source preprocessed_data/LJSpeech/val.txt --restore_step 900000 --mode batch -p config/LJSpeech/preprocess.yaml -m config/LJSpeech/model.yaml -t config/LJSpeech/train.yaml ``` to synthesize all utterances in ``preprocessed_data/LJSpeech/val.txt`` ## Controllability The pitch/volume/speaking rate of the synthesized utterances can be controlled by specifying the desired pitch/energy/duration ratios. For example, one can increase the speaking rate by 20 % and decrease the volume by 20 % by ``` python3 synthesize.py --text "YOUR_DESIRED_TEXT" --restore_step 900000 --mode single -p config/LJSpeech/preprocess.yaml -m config/LJSpeech/model.yaml -t config/LJSpeech/train.yaml --duration_control 0.8 --energy_control 0.8 ``` # Training ## Datasets The supported datasets are - [LJSpeech](https://keithito.com/LJ-Speech-Dataset/): a single-speaker English dataset consists of 13100 short audio clips of a female speaker reading passages from 7 non-fiction books, approximately 24 hours in total. - [AISHELL-3](http://www.aishelltech.com/aishell_3): a Mandarin TTS dataset with 218 male and female speakers, roughly 85 hours in total. - [LibriTTS](https://research.google/tools/datasets/libri-tts/): a multi-speaker English dataset containing 585 hours of speech by 2456 speakers. We take LJSpeech as an example hereafter. ## Preprocessi
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