Update docs and samples

.gitignore

@@ -23,3 +23,4 @@ asr-label*
 /.cache
 /fishenv
 /.locale
+/demo-audios

docs/en/finetune.md

@@ -2,65 +2,22 @@
 
 Obviously, when you opened this page, you were not satisfied with the performance of the few-shot pre-trained model. You want to fine-tune a model to improve its performance on your dataset.
 
-`Fish Speech` consists of three modules: `VQGAN`, `LLAMA`and `VITS`.
+`Fish Speech` consists of three modules: `VQGAN`, `LLAMA`, and `VITS`.
 
 !!! info 
-    You should first conduct the following test to determine if you need to fine-tune `VQGAN`:
+    You should first conduct the following test to determine if you need to fine-tune `VITS Decoder`:
     ```bash
     python tools/vqgan/inference.py -i test.wav
+    python tools/vits_decoder/inference.py \
+        -ckpt checkpoints/vits_decoder_v1.1.ckpt \
+        -i fake.npy -r test.wav \
+        --text "The text you want to generate"
     ```
-    This test will generate a `fake.wav` file. If the timbre of this file differs from the speaker's original voice, or if the quality is not high, you need to fine-tune `VQGAN`.
+    This test will generate a `fake.wav` file. If the timbre of this file differs from the speaker's original voice, or if the quality is not high, you need to fine-tune `VITS Decoder`.
 
     Similarly, you can refer to [Inference](inference.md) to run `generate.py` and evaluate if the prosody meets your expectations. If it does not, then you need to fine-tune `LLAMA`.
 
-    It is recommended to fine-tune the LLAMA and VITS model first, then fine-tune the `VQGAN` according to your needs.
-
-## Fine-tuning VQGAN
-### 1. Prepare the Dataset
-
-```
-.
-├── SPK1
-│   ├── 21.15-26.44.mp3
-│   ├── 27.51-29.98.mp3
-│   └── 30.1-32.71.mp3
-└── SPK2
-    └── 38.79-40.85.mp3
-```
-
-You need to format your dataset as shown above and place it under `data`. Audio files can have `.mp3`, `.wav`, or `.flac` extensions.
-
-### 2. Split Training and Validation Sets
-
-```bash
-python tools/vqgan/create_train_split.py data
-```
-
-This command will create `data/vq_train_filelist.txt` and `data/vq_val_filelist.txt` in the `data/demo` directory, to be used for training and validation respectively.
-
-!!!info
-    For the VITS format, you can specify a file list using `--filelist xxx.list`.  
-    Please note that the audio files in `filelist` must also be located in the `data` folder.
-
-### 3. Start Training
-
-```bash
-python fish_speech/train.py --config-name vqgan_finetune
-```
-
-!!! note
-    You can modify training parameters by editing `fish_speech/configs/vqgan_finetune.yaml`, but in most cases, this won't be necessary.
-
-### 4. Test the Audio
-
-```bash
-python tools/vqgan/inference.py -i test.wav --checkpoint-path results/vqgan_finetune/checkpoints/step_000010000.ckpt
-```
-
-You can review `fake.wav` to assess the fine-tuning results.
-
-!!! note
-    You may also try other checkpoints. We suggest using the earliest checkpoint that meets your requirements, as they often perform better on out-of-distribution (OOD) data.
+    It is recommended to fine-tune the LLAMA first, then fine-tune the `VITS Decoder` according to your needs.
 
 ## Fine-tuning LLAMA
 ### 1. Prepare the dataset
@@ -168,8 +125,27 @@ After training is complete, you can refer to the [inference](inference.md) secti
     By default, the model will only learn the speaker's speech patterns and not the timbre. You still need to use prompts to ensure timbre stability.
     If you want to learn the timbre, you can increase the number of training steps, but this may lead to overfitting.
 
-## Fine-tuning VITS
-### 1. Prepare the dataset
+#### Fine-tuning with LoRA
+
+!!! note
+    LoRA can reduce the risk of overfitting in models, but it may also lead to underfitting on large datasets. 
+
+If you want to use LoRA, please add the following parameter: `+lora@model.lora_config=r_8_alpha_16`. 
+
+After training, you need to convert the LoRA weights to regular weights before performing inference.
+
+```bash
+python tools/llama/merge_lora.py \
+    --llama-config dual_ar_2_codebook_medium \
+    --lora-config r_8_alpha_16 \
+    --llama-weight checkpoints/text2semantic-sft-medium-v1.1-4k.pth \
+    --lora-weight results/text2semantic-finetune-medium-lora/checkpoints/step_000000200.ckpt \
+    --output checkpoints/merged.ckpt
+```
+
+
+## Fine-tuning VITS Decoder
+### 1. Prepare the Dataset
 
 ```
 .
@@ -184,32 +160,92 @@ After training is complete, you can refer to the [inference](inference.md) secti
     ├── 38.79-40.85.lab
     └── 38.79-40.85.mp3
 ```
+
 !!! note
-	The fine-tuning for VITS only support the .lab format files, please don't use .list file!
+    VITS fine-tuning currently only supports `.lab` as the label file and does not support the `filelist` format.
 
-You need to convert the dataset to the format above, and move them to the `data` , the suffix of the files can be `.mp3`, `.wav` 或 `.flac`, the label files' suffix are recommended to be  `.lab`.
+You need to format your dataset as shown above and place it under `data`. Audio files can have `.mp3`, `.wav`, or `.flac` extensions, and the annotation files should have the `.lab` extension.
 
-### 2.Start Training
+### 2. Split Training and Validation Sets
 
 ```bash
-python fish_speech/train.py --config-name vits_decoder_finetune
+python tools/vqgan/create_train_split.py data
 ```
 
+This command will create `data/vq_train_filelist.txt` and `data/vq_val_filelist.txt` in the `data/demo` directory, to be used for training and validation respectively.
 
-#### Fine-tuning with LoRA
+!!! info
+    For the VITS format, you can specify a file list using `--filelist xxx.list`.  
+    Please note that the audio files in `filelist` must also be located in the `data` folder.
+
+### 3. Start Training
+
+```bash
+python fish_speech/train.py --config-name vits_decoder_finetune
+```
 
 !!! note
-    LoRA can reduce the risk of overfitting in models, but it may also lead to underfitting on large datasets. 
+    You can modify training parameters by editing `fish_speech/configs/vits_decoder_finetune.yaml`, but in most cases, this won't be necessary.
 
-If you want to use LoRA, please add the following parameter: `+lora@model.lora_config=r_8_alpha_16`. 
+### 4. Test the Audio
+
+```bash
+python tools/vits_decoder/inference.py \
+    --checkpoint-path results/vits_decoder_finetune/checkpoints/step_000010000.ckpt \
+    -i test.npy -r test.wav \
+    --text "The text you want to generate"
+```
 
-After training, you need to convert the LoRA weights to regular weights before performing inference.
+You can review `fake.wav` to assess the fine-tuning results.
+
+
+## Fine-tuning VQGAN (Not Recommended)
+
+
+We no longer recommend using VQGAN for fine-tuning in version 1.1. Using VITS Decoder will yield better results, but if you still want to fine-tune VQGAN, you can refer to the following steps.
+
+### 1. Prepare the Dataset
+
+```
+.
+├── SPK1
+│   ├── 21.15-26.44.mp3
+│   ├── 27.51-29.98.mp3
+│   └── 30.1-32.71.mp3
+└── SPK2
+    └── 38.79-40.85.mp3
+```
+
+You need to format your dataset as shown above and place it under `data`. Audio files can have `.mp3`, `.wav`, or `.flac` extensions.
+
+### 2. Split Training and Validation Sets
 
 ```bash
-python tools/llama/merge_lora.py \
-    --llama-config dual_ar_2_codebook_medium \
-    --lora-config r_8_alpha_16 \
-    --llama-weight checkpoints/text2semantic-sft-medium-v1.1-4k.pth \
-    --lora-weight results/text2semantic-finetune-medium-lora/checkpoints/step_000000200.ckpt \
-    --output checkpoints/merged.ckpt
+python tools/vqgan/create_train_split.py data
 ```
+
+This command will create `data/vq_train_filelist.txt` and `data/vq_val_filelist.txt` in the `data/demo` directory, to be used for training and validation respectively.
+
+!!!info
+    For the VITS format, you can specify a file list using `--filelist xxx.list`.  
+    Please note that the audio files in `filelist` must also be located in the `data` folder.
+
+### 3. Start Training
+
+```bash
+python fish_speech/train.py --config-name vqgan_finetune
+```
+
+!!! note
+    You can modify training parameters by editing `fish_speech/configs/vqgan_finetune.yaml`, but in most cases, this won't be necessary.
+
+### 4. Test the Audio
+
+```bash
+python tools/vqgan/inference.py -i test.wav --checkpoint-path results/vqgan_finetune/checkpoints/step_000010000.ckpt
+```
+
+You can review `fake.wav` to assess the fine-tuning results.
+
+!!! note
+    You may also try other checkpoints. We suggest using the earliest checkpoint that meets your requirements, as they often perform better on out-of-distribution (OOD) data.

docs/en/index.md

@@ -39,13 +39,13 @@ pip3 install torch torchvision torchaudio
 # Install fish-speech
 pip3 install -e .
 
-#install sox
+# (Ubuntu / Debian User) Install sox
 apt install libsox-dev
 ```
 
 ## Changelog
 
-- 2024/05/10: Updated Fish-Speech to 1.1 version, importing VITS as the Decoder part.
+- 2024/05/10: Updated Fish-Speech to 1.1 version, implement VITS decoder to reduce WER and improve timbre similarity.
 - 2024/04/22: Finished Fish-Speech 1.0 version, significantly modified VQGAN and LLAMA models.
 - 2023/12/28: Added `lora` fine-tuning support.
 - 2023/12/27: Add `gradient checkpointing`, `causual sampling`, and `flash-attn` support.

docs/en/inference.md

@@ -5,10 +5,12 @@ Inference support command line, HTTP API and web UI.
 !!! note
     Overall, reasoning consists of several parts:
 
-    1. Encode a given 5-10 seconds of voice using VQGAN.
+    1. Encode a given ~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.
+    4. Input the generated semantic tokens into VITS / VQGAN to decode and generate the corresponding voice.
+
+In version 1.1, we recommend using VITS for decoding, as it performs better than VQGAN in both timbre and pronunciation.
 
 ## Command Line Inference
 
@@ -17,6 +19,7 @@ Download the required `vqgan` and `text2semantic` models from our Hugging Face r
 ```bash
 huggingface-cli download fishaudio/fish-speech-1 vq-gan-group-fsq-2x1024.pth --local-dir checkpoints
 huggingface-cli download fishaudio/fish-speech-1 text2semantic-sft-medium-v1.1-4k.pth --local-dir checkpoints
+huggingface-cli download fishaudio/fish-speech-1 vits_decoder_v1.1.ckpt --local-dir checkpoints
 ```
 
 ### 1. Generate prompt from voice:
@@ -56,6 +59,16 @@ This command will create a `codes_N` file in the working directory, where N is a
     If you are using your own fine-tuned model, please be sure to carry the `--speaker` parameter to ensure the stability of pronunciation.
 
 ### 3. Generate vocals from semantic tokens:
+
+#### VITS Decoder
+```bash
+python tools/vits_decoder/inference.py \
+    --checkpoint-path checkpoints/vits_decoder_v1.1.ckpt \
+    -i codes_0.npy -r ref.wav \
+    --text "The text you want to generate"
+```
+
+#### VQGAN Decoder (not recommended)
 ```bash
 python tools/vqgan/inference.py \
     -i "codes_0.npy" \
@@ -71,11 +84,20 @@ python -m tools.api \
     --listen 0.0.0.0:8000 \
     --llama-checkpoint-path "checkpoints/text2semantic-sft-medium-v1.1-4k.pth" \
     --llama-config-name dual_ar_2_codebook_medium \
-    --vqgan-checkpoint-path "checkpoints/vq-gan-group-fsq-2x1024.pth"
+    --decoder-checkpoint-path "checkpoints/vq-gan-group-fsq-2x1024.pth" \
+    --decoder-config-name vqgan_pretrain
 ```
 
 After that, you can view and test the API at http://127.0.0.1:8000/.  
 
+!!! info
+    You should use following parameters to start VITS decoder:
+
+    ```bash
+    --decoder-config-name vits_decoder_finetune \
+    --decoder-checkpoint-path "checkpoints/vits_decoder_v1.1.ckpt" # or your own model
+    ```
+
 ## WebUI Inference
 
 You can start the WebUI using the following command:
@@ -84,7 +106,19 @@ You can start the WebUI using the following command:
 python -m tools.webui \
     --llama-checkpoint-path "checkpoints/text2semantic-sft-medium-v1.1-4k.pth" \
     --llama-config-name dual_ar_2_codebook_medium \
-    --vqgan-checkpoint-path "checkpoints/vq-gan-group-fsq-2x1024.pth"
+    --vqgan-checkpoint-path "checkpoints/vq-gan-group-fsq-2x1024.pth" \
+    --vits-checkpoint-path "checkpoints/vits_decoder_v1.1.ckpt"
 ```
 
+!!! info
+    You should use following parameters to start VITS decoder:
+
+    ```bash
+    --decoder-config-name vits_decoder_finetune \
+    --decoder-checkpoint-path "checkpoints/vits_decoder_v1.1.ckpt" # or your own model
+    ```
+
+!!! note
+    You can use Gradio environment variables, such as `GRADIO_SHARE`, `GRADIO_SERVER_PORT`, `GRADIO_SERVER_NAME` to configure WebUI.
+
 Enjoy!