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--- |
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license: mit |
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tags: |
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- vocoder |
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- audio |
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- speech |
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- tts |
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--- |
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# Model Card for Model ID |
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[HuggingFace 🤗 - Repository](https://huggingface.co/Respair/RiFornet_Vocoder) |
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**DDP is very un-stable, please use the single-gpu training script** - if you still want to do it, I suggest uncommenting the grad clipping lines; that should help a lot. |
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This Vocoder, is a combination of [HiFTnet](https://github.com/yl4579/HiFTNet) and [Ringformer](https://github.com/seongho608/RingFormer). it supports Ring Attention, Conformer and Neural Source Filtering etc. |
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This repository is experimental, expect some bugs and some hardcoded params. |
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The default setting is 44.1khz - 128 Mel bins. but I have provided the necessary script for the 24khz version in the LibriTTS checkpoint's folder. |
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Huge Thanks to [Johnathan Duering](https://github.com/duerig) for his help. I mostly implemented this based on his [STTS2 Fork](https://github.com/duerig/StyleTTS2/tree/main). |
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**NOTE**: |
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There are Three checkpoints so far in this repository: |
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- RiFornet 24khz (trained for roughly 117K~ steps on LibriTTS (360 + 100) and 40 hours of other English datasets.) |
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- RiFornet 44.1khz (trained for roughly 280K~ steps on a Large (more than 1100 hours) private Multilingual dataset, covering Arabic, Persian, Japanese, English, Russian and also Singing voice in Chinese and Japanese with Quranic recitations in Arabic. |
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- HiFTNet 44.1khz (trained for ~100K steps, on a similar dataset to RiFornet 44.1khz, but slightly smaller and no singing voice). |
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1. Python >= 3.10 |
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2. Clone this repository: |
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```bash |
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git clone https://github.com/Respaired/RiFornet_Vocoder |
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cd RiFornet_Vocoder/Ringformer |
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``` |
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3. Install python requirements: |
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```bash |
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pip install -r requirements.txt |
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``` |
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## Training |
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```bash |
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CUDA_VISIBLE_DEVICES=0 python train_single_gpu.py --config config_v1.json --[args] |
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``` |
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For the F0 model training, please refer to [yl4579/PitchExtractor](https://github.com/yl4579/PitchExtractor). This repo includes a pre-trained F0 model on a Mixture of Multilingual data for the previously mentioned configuration. I'm going to quote the HiFTnet's Author: "Still, you may want to train your own F0 model for the best performance, particularly for noisy or non-speech data, as we found that F0 estimation accuracy is essential for the vocoder performance." |
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## Inference |
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Please refer to the notebook [inference.ipynb](https://github.com/Respaired/RiFornet_Vocoder/blob/main/RingFormer/inference.ipynb) for details. |
