<div align="center">

## TODO (edit readme and add more info about the project; currently it is a copy of the original Matcha-TTS readme)
## WIP: Matcha-TTS-2
## 🍵 Matcha-TTS-2: A fast E2E TTS architecture with conditional flow matching (not fast enough for training yet xD) 

[x] added experimental E2E TTS support; doing some small training to verify results. Expect me to update this model completely by end of November,23.
[x] if anybody willing to help me understand cfm quicker, would be great. I have few questions. ty
[x] I am trying 2 things -> (1) cfm decoder intermideiate output gives mel, give the mel to hifigan and compare hifigan's output's mel with real mel and also use prior loss to force textenc to be near the decoder's output (Which again is the mel). (2) a more "learnable" flexible model with freedom at decoder out, no prior restrictions and just final output mel (to make hifigan robust to noise, we add small sigma to decoder output while training), ATM the code is (2) and i think it is better.
![image](https://github.com/p0p4k/Matcha-TTS-2/assets/8834712/560de995-8fbb-4155-8d1f-c8ed4200ddd6)
output from (2 ..still training) 
![image](https://github.com/p0p4k/Matcha-TTS-2/assets/8834712/1ce7bef4-96f4-4ffd-8041-a9d2cacfd85e)

### [Shivam Mehta](https://www.kth.se/profile/smehta), [Ruibo Tu](https://www.kth.se/profile/ruibo), [Jonas Beskow](https://www.kth.se/profile/beskow), [Éva Székely](https://www.kth.se/profile/szekely), and [Gustav Eje Henter](https://people.kth.se/~ghe/)

[![python](https://img.shields.io/badge/-Python_3.10-blue?logo=python&logoColor=white)](https://www.python.org/downloads/release/python-3100/)
[![pytorch](https://img.shields.io/badge/PyTorch_2.0+-ee4c2c?logo=pytorch&logoColor=white)](https://pytorch.org/get-started/locally/)
[![lightning](https://img.shields.io/badge/-Lightning_2.0+-792ee5?logo=pytorchlightning&logoColor=white)](https://pytorchlightning.ai/)
[![hydra](https://img.shields.io/badge/Config-Hydra_1.3-89b8cd)](https://hydra.cc/)
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<p style="text-align: center;">
  <img src="https://shivammehta25.github.io/Matcha-TTS/images/logo.png" height="128"/>
</p>

</div>

> This is the official code implementation of 🍵 Matcha-TTS.

We propose 🍵 Matcha-TTS, a new approach to non-autoregressive neural TTS, that uses [conditional flow matching](https://arxiv.org/abs/2210.02747) (similar to [rectified flows](https://arxiv.org/abs/2209.03003)) to speed up ODE-based speech synthesis. Our method:

- Is probabilistic
- Has compact memory footprint
- Sounds highly natural
- Is very fast to synthesise from

Check out our [demo page](https://shivammehta25.github.io/Matcha-TTS) and read [our arXiv preprint](https://arxiv.org/abs/2309.03199) for more details.

[Pre-trained models](https://drive.google.com/drive/folders/17C_gYgEHOxI5ZypcfE_k1piKCtyR0isJ?usp=sharing) will be automatically downloaded with the CLI or gradio interface.

[Try 🍵 Matcha-TTS on HuggingFace 🤗 spaces!](https://huggingface.co/spaces/shivammehta25/Matcha-TTS)

## Watch the teaser

[![Watch the video](https://img.youtube.com/vi/xmvJkz3bqw0/hqdefault.jpg)](https://youtu.be/xmvJkz3bqw0)

## Installation

1. Create an environment (suggested but optional)

```
conda create -n matcha-tts python=3.10 -y
conda activate matcha-tts
```

2. Install Matcha TTS using pip or from source

```bash
pip install matcha-tts
```

from source

```bash
pip install git+https://github.com/shivammehta25/Matcha-TTS.git
cd Matcha-TTS
pip install -e .
```

3. Run CLI / gradio app / jupyter notebook

```bash
# This will download the required models
matcha-tts --text "<INPUT TEXT>"
```

or

```bash
matcha-tts-app
```

or open `synthesis.ipynb` on jupyter notebook

### CLI Arguments

- To synthesise from given text, run:

```bash
matcha-tts --text "<INPUT TEXT>"
```

- To synthesise from a file, run:

```bash
matcha-tts --file <PATH TO FILE>
```

- To batch synthesise from a file, run:

```bash
matcha-tts --file <PATH TO FILE> --batched
```

Additional arguments

- Speaking rate

```bash
matcha-tts --text "<INPUT TEXT>" --speaking_rate 1.0
```

- Sampling temperature

```bash
matcha-tts --text "<INPUT TEXT>" --temperature 0.667
```

- Euler ODE solver steps

```bash
matcha-tts --text "<INPUT TEXT>" --steps 10
```

## Train with your own dataset

Let's assume we are training with LJ Speech

1. Download the dataset from [here](https://keithito.com/LJ-Speech-Dataset/), extract it to `data/LJSpeech-1.1`, and prepare the file lists to point to the extracted data like for [item 5 in the setup of the NVIDIA Tacotron 2 repo](https://github.com/NVIDIA/tacotron2#setup).

2. Clone and enter the Matcha-TTS repository

```bash
git clone https://github.com/shivammehta25/Matcha-TTS.git
cd Matcha-TTS
```

3. Install the package from source

```bash
pip install -e .
```

4. Go to `configs/data/ljspeech.yaml` and change

```yaml
train_filelist_path: data/filelists/ljs_audio_text_train_filelist.txt
valid_filelist_path: data/filelists/ljs_audio_text_val_filelist.txt
```

5. Generate normalisation statistics with the yaml file of dataset configuration

```bash
matcha-data-stats -i ljspeech.yaml
# Output:
#{'mel_mean': -5.53662231756592, 'mel_std': 2.1161014277038574}
```

Update these values in `configs/data/ljspeech.yaml` under `data_statistics` key.

```bash
data_statistics:  # Computed for ljspeech dataset
  mel_mean: -5.536622
  mel_std: 2.116101
```

to the paths of your train and validation filelists.

6. Run the training script

```bash
make train-ljspeech
```

or

```bash
python matcha/train.py experiment=ljspeech
```

- for a minimum memory run

```bash
python matcha/train.py experiment=ljspeech_min_memory
```

- for multi-gpu training, run

```bash
python matcha/train.py experiment=ljspeech trainer.devices=[0,1]
```

7. Synthesise from the custom trained model

```bash
matcha-tts --text "<INPUT TEXT>" --checkpoint_path <PATH TO CHECKPOINT>
```

## ONNX support

> Special thanks to [@mush42](https://github.com/mush42) for implementing ONNX export and inference support.

It is possible to export Matcha checkpoints to [ONNX](https://onnx.ai/), and run inference on the exported ONNX graph.

### ONNX export

To export a checkpoint to ONNX, first install ONNX with

```bash
pip install onnx
```

then run the following:

```bash
python3 -m matcha.onnx.export matcha.ckpt model.onnx --n-timesteps 5
```

Optionally, the ONNX exporter accepts **vocoder-name** and **vocoder-checkpoint** arguments. This enables you to embed the vocoder in the exported graph and generate waveforms in a single run (similar to end-to-end TTS systems).

**Note** that `n_timesteps` is treated as a hyper-parameter rather than a model input. This means you should specify it during export (not during inference). If not specified, `n_timesteps` is set to **5**.

**Important**: for now, torch>=2.1.0 is needed for export since the `scaled_product_attention` operator is not exportable in older versions. Until the final version is released, those who want to export their models must install torch>=2.1.0 manually as a pre-release.

### ONNX Inference

To run inference on the exported model, first install `onnxruntime` using

```bash
pip install onnxruntime
pip install onnxruntime-gpu  # for GPU inference
```

then use the following:

```bash
python3 -m matcha.onnx.infer model.onnx --text "hey" --output-dir ./outputs
```

You can also control synthesis parameters:

```bash
python3 -m matcha.onnx.infer model.onnx --text "hey" --output-dir ./outputs --temperature 0.4 --speaking_rate 0.9 --spk 0
```

To run inference on **GPU**, make sure to install **onnxruntime-gpu** package, and then pass `--gpu` to the inference command:

```bash
python3 -m matcha.onnx.infer model.onnx --text "hey" --output-dir ./outputs --gpu
```

If you exported only Matcha to ONNX, this will write mel-spectrogram as graphs and `numpy` arrays to the output directory.
If you embedded the vocoder in the exported graph, this will write `.wav` audio files to the output directory.

If you exported only Matcha to ONNX, and you want to run a full TTS pipeline, you can pass a path to a vocoder model in `ONNX` format:

```bash
python3 -m matcha.onnx.infer model.onnx --text "hey" --output-dir ./outputs --vocoder hifigan.small.onnx
```

This will write `.wav` audio files to the output directory.

## Citation information

If you use our code or otherwise find this work useful, please cite our paper:

```text
@article{mehta2023matcha,
  title={Matcha-TTS: A fast TTS architecture with conditional flow matching},
  author={Mehta, Shivam and Tu, Ruibo and Beskow, Jonas and Sz{\'e}kely, {\'E}va and Henter, Gustav Eje},
  journal={arXiv preprint arXiv:2309.03199},
  year={2023}
}
```

## Acknowledgements

Since this code uses [Lightning-Hydra-Template](https://github.com/ashleve/lightning-hydra-template), you have all the powers that come with it.

Other source code I would like to acknowledge:

- [Coqui-TTS](https://github.com/coqui-ai/TTS/tree/dev): For helping me figure out how to make cython binaries pip installable and encouragement
- [Hugging Face Diffusers](https://huggingface.co/): For their awesome diffusers library and its components
- [Grad-TTS](https://github.com/huawei-noah/Speech-Backbones/tree/main/Grad-TTS): For the monotonic alignment search source code
- [torchdyn](https://github.com/DiffEqML/torchdyn): Useful for trying other ODE solvers during research and development
- [labml.ai](https://nn.labml.ai/transformers/rope/index.html): For the RoPE implementation
