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metadata 
license: agpl-3.0
base_model:
  - H5N1AIDS/F5-TTS-ONNX
tags:
  - rknn

F5-TTS-RKNN2

(English README see below)

在RK3588上运行超高质量的F5-TTS文字转语音/零样本音色克隆模型!

  • 推理速度(RK3588, 生成9秒音频): 每次迭代用时11s, 迭代32步, 总用时352s
  • 内存占用(RK3588): 2.2GB

使用方法

  1. 克隆或者下载此仓库到本地. 模型较大, 请确保有足够的磁盘空间.

  2. 安装依赖

pip install "numpy<2" rknn-toolkit-lite2 jieba torch onnxruntime soundfile pydub pypinyin tqdm
  1. 运行
python F5-TTS-ONNX-Inference-rknn2.py

你可以修改F5-TTS-ONNX-Inference-rknn2.py中的文本等参数来生成不同的音频。

模型转换

  1. 下载ONNX模型文件

  2. 安装依赖

pip install "numpy<2" rknn-toolkit2==2.3.0 onnx onnxruntime
  1. 转换模型
python convert_opset.py
python convert_F5_Transformer_opset19.py

已知问题

  • 由于RKNN2不支持动态输入，这里把序列长度固定为了1536，并通过缩放音频速度来补偿。在差距不大的情况下效果可以接受。
  • 模型中DiT中的RoPE位置编码部分有一个Transpose操作无法在NPU上运行，造成推理速度下降~15%。这个问题应该可以通过修改原模型来解决，但我懒得改了，因为改完之后推理还是会非常慢，因为序列长度实在太长了。
  • 只有DiT部分使用了NPU，其他部分都是CPU推理，但其他部分运行速度快，总体上不会对推理速度有太大影响。

参考

English README

Run the ultra-high-quality F5-TTS text-to-speech / zero-shot voice cloning model on RK3588!

  • Inference Speed (on RK3588, generating 9 seconds of audio): 11s per iteration, 32 iterations, total time ~352s
  • Memory Usage (on RK3588): 2.2GB

Usage

  1. Clone or download this repository locally. The models are large, ensure you have sufficient disk space.

  2. Install dependencies:

    pip install "numpy<2" rknn-toolkit-lite2 jieba torch onnxruntime soundfile pydub pypinyin tqdm

  3. Run:

    python F5-TTS-ONNX-Inference-rknn2.py

    You can modify parameters such as the text within F5-TTS-ONNX-Inference-rknn2.py to generate different audio.

Model Conversion

  1. Download the ONNX model files.

  2. Install dependencies:

    pip install "numpy<2" rknn-toolkit2==2.3.0 onnx onnxruntime

  3. Convert the models:

    python convert_opset.py
python convert_F5_Transformer_opset19.py

Known Issues

  • Due to RKNN2 limitations with dynamic inputs, the sequence length is fixed at 1536. Audio speed scaling is used to compensate. The effect is acceptable when the difference isn't significant.
  • A Transpose operation within the RoPE (Rotary Positional Embedding) part of the DiT (Diffusion Transformer) model cannot run on the NPU, causing an approximate 15% decrease in inference speed. This could potentially be resolved by modifying the original model, but I chose not to, as inference would still be very slow due to the extremely long sequence length.
  • Only the DiT part utilizes the NPU; other parts run on the CPU. However, these CPU parts are fast and do not significantly impact the overall inference speed.

References