# Inference In the plan, inference is expected to support both command line and webui methods, but currently, only the command-line reasoning function has been completed. !!! note Overall, reasoning consists of several parts: 1. Encode a given 5-10 seconds of voice using VQGAN. 2. Input the encoded semantic tokens and the corresponding text into the language model as an example. 3. Given a new piece of text, let the model generate the corresponding semantic tokens. 4. Input the generated semantic tokens into VQGAN to decode and generate the corresponding voice. ## Command Line Inference Download the required `vqgan` and `text2semantic` models from our Hugging Face repository. ```bash huggingface-cli download fishaudio/speech-lm-v1 vqgan-v1.pth --local-dir checkpoints huggingface-cli download fishaudio/speech-lm-v1 text2semantic-400m-v0.2-4k.pth --local-dir checkpoints ``` ### 1. Generate prompt from voice: !!! note If you plan to let the model randomly choose a voice timbre, you can skip this step. ```bash python tools/vqgan/inference.py \ -i "paimon.wav" \ --checkpoint-path "checkpoints/vqgan-v1.pth" ``` You should get a `fake.npy` file. ### 2. Generate semantic tokens from text: ```bash python tools/llama/generate.py \ --text "The text you want to convert" \ --prompt-text "Your reference text" \ --prompt-tokens "fake.npy" \ --checkpoint-path "checkpoints/text2semantic-400m-v0.2-4k.pth" \ --num-samples 2 \ --compile ``` This command will create a `codes_N` file in the working directory, where N is an integer starting from 0. !!! note You may want to use `--compile` to fuse CUDA kernels for faster inference (~30 tokens/second -> ~500 tokens/second). Correspondingly, if you do not plan to use acceleration, you can comment out the `--compile` parameter. !!! info For GPUs that do not support bf16, you may need to use the `--half` parameter. ### 3. Generate vocals from semantic tokens: ```bash python tools/vqgan/inference.py \ -i "codes_0.npy" \ --checkpoint-path "checkpoints/vqgan-v1.pth" ```