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Merge branch 'ONNX_BRANCH' into dev

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Shivam Mehta
2023-09-29 14:43:52 +00:00
parent 51ea36d271 269609003b
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@@ -36,7 +36,6 @@ Check out our [demo page](https://shivammehta25.github.io/Matcha-TTS) and read [
[![Watch the video](https://img.youtube.com/vi/xmvJkz3bqw0/hqdefault.jpg)](https://youtu.be/xmvJkz3bqw0)
## Installation
1. Create an environment (suggested but optional)
@@ -46,7 +45,7 @@ conda create -n matcha-tts python=3.10 -y
conda activate matcha-tts
```
2. Install Matcha TTS using pip or from source
2. Install Matcha TTS using pip or from source
```bash
pip install matcha-tts
@@ -191,11 +190,19 @@ matcha-tts --text "<INPUT TEXT>" --checkpoint_path <PATH TO CHECKPOINT>
## ONNX support
> Special thanks to @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, run the following:
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
@@ -205,11 +212,18 @@ Optionally, the ONNX exporter accepts **vocoder-name** and **vocoder-checkpoint*
**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.
**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, use the following:
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