Delete egs/tts/vits_libritts_1w

egs/tts/vits_libritts_1w/README.md

@@ -1,138 +0,0 @@
-
-# vits_libritts_1w Recipe
-
-In this recipe, we will show how to train [vits_libritts_1w](https://arxiv.org/abs/2106.06103) using Amphion's infrastructure. vits_libritts_1w is an end-to-end TTS architecture that utilizes conditional variational autoencoder with adversarial learning.
-
-There are four stages in total:
-
-1. Data preparation
-2. Features extraction
-3. Training
-4. Inference
-
-> **NOTE:** You need to run every command of this recipe in the `Amphion` root path:
-> ```bash
-> cd Amphion
-> ```
-
-## 1. Data Preparation
-
-### Dataset Download
-You can use the commonly used TTS dataset to train TTS model, e.g., LJSpeech, VCTK, LibriTTS, etc. We strongly recommend you use LJSpeech to train TTS model for the first time. How to download dataset is detailed [here](../../datasets/README.md).
-
-### Configuration
-
-After downloading the dataset, you can set the dataset paths in  `exp_config.json`. Note that you can change the `dataset` list to use your preferred datasets.
-
-```json
-    "dataset": [
-        "LJSpeech",
-    ],
-    "dataset_path": {
-        // TODO: Fill in your dataset path
-        "LJSpeech": "[LJSpeech dataset path]",
-    },
-```
-
-## 2. Features Extraction
-
-### Configuration
-
-Specify the `processed_dir` and the `log_dir` and for saving the processed data and the checkpoints in `exp_config.json`:
-
-```json
-    // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts"
-    "log_dir": "ckpts/tts",
-    "preprocess": {
-        // TODO: Fill in the output data path. The default value is "Amphion/data"
-        "processed_dir": "data",
-        ...
-    },
-```
-
-### Run
-
-Run the `run.sh` as the preproces stage (set  `--stage 1`):
-
-```bash
-sh egs/tts/vits_libritts_1w/run.sh --stage 1
-```
-
-> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "1"`.
-
-## 3. Training
-
-### Configuration
-
-We provide the default hyparameters in the `exp_config.json`. They can work on single NVIDIA-24g GPU. You can adjust them based on your GPU machines.
-
-```
-"train": {
-        "batch_size": 16,
-    }
-```
-
-### Run
-
-Run the `run.sh` as the training stage (set  `--stage 2`). Specify a experimental name to run the following command. The tensorboard logs and checkpoints will be saved in `Amphion/ckpts/tts/[YourExptName]`.
-
-```bash
-sh egs/tts/vits_libritts_1w/run.sh --stage 2 --name [YourExptName]
-```
-
-> **NOTE:** The `CUDA_VISIBLE_DEVICES` is set as `"0"` in default. You can change it when running `run.sh` by specifying such as `--gpu "0,1,2,3"`.
-
-
-## 4. Inference
-
-### Configuration
-
-For inference, you need to specify the following configurations when running `run.sh`:
-
-
-| Parameters            | Description                                                                            | Example                                                                                                                                                                         |
-| --------------------- | -------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `--infer_expt_dir`    | The experimental directory which contains `checkpoint`                                 | `Amphion/ckpts/tts/[YourExptName]`                                                                                                                                              |
-| `--infer_output_dir`  | The output directory to save inferred audios.                                          | `Amphion/ckpts/tts/[YourExptName]/result`                                                                                                                                       |
-| `--infer_mode`        | The inference mode, e.g., "`single`", "`batch`".                                       | "`single`" to generate a clip of speech, "`batch`" to generate a batch of speech at a time.                                                                                     |
-| `--infer_dataset`     | The dataset used for inference.                                                        | For LJSpeech dataset, the inference dataset would be `LJSpeech`.                                                                                                                |
-| `--infer_testing_set` | The subset of the inference dataset used for inference, e.g., train, test, golden_test | For LJSpeech dataset, the testing set would be  "`test`" split from LJSpeech at the feature extraction, or "`golden_test`" cherry-picked from test set as template testing set. |
-| `--infer_text`        | The text to be synthesized.                                                            | "`This is a clip of generated speech with the given text from a TTS model.`"                                                                                                    |
-
-### Run
-For example, if you want to generate speech of all testing set split from LJSpeech, just run:
-
-```bash
-sh egs/tts/vits_libritts_1w/run.sh --stage 3 --gpu "0" \
-	--infer_expt_dir /mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs/LibriTTS_1w \
-	--infer_output_dir /mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs/LibriTTS_1w/result \
-	--infer_mode "batch" \
-    --infer_dataset "LJSpeech" \
-    --infer_testing_set "test"
-```
-
-Or, if you want to generate a single clip of speech from a given text, just run:
-
-```bash
-sh egs/tts/vits_libritts_1w/run.sh --stage 3 --gpu "0" \
-	--infer_expt_dir /mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs/LibriTTS_1w \
-	--infer_output_dir /mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs/LibriTTS_1w/result \
-	--infer_mode "single" \
-    --infer_text "This is a clip of generated speech with the given text from a TTS model."
-
-
-sh egs/tts/vits_libritts_1w/run.sh --stage 3 --gpu "0" --infer_expt_dir /mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs/LibriTTS_1w --infer_output_dir /mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs/LibriTTS_1w/result --infer_mode "single" --infer_text "This is a clip of generated speech with the given text from a TTS model."
-```
-
-We will release a pre-trained vits_libritts_1w model trained on LJSpeech. So you can download the pre-trained model and generate speech following the above inference instruction.
-
-
-```bibtex
-@inproceedings{kim2021conditional,
-  title={Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech},
-  author={Kim, Jaehyeon and Kong, Jungil and Son, Juhee},
-  booktitle={International Conference on Machine Learning},
-  pages={5530--5540},
-  year={2021},
-}
-```

egs/tts/vits_libritts_1w/exp_config.json

@@ -1,31 +0,0 @@
-{
-  "base_config": "config/vits.json",
-  "model_type": "VITS",
-  "dataset": [
-    "libritts"
-  ],
-  "dataset_path": {
-    // TODO: Fill in your dataset path
-    "libritts": "/mnt/workspace/xueliumeng/data/libritts/raw/LibriTTS"
-  },
-  // TODO: Fill in the output log path. The default value is "Amphion/ckpts/tts"
-  "log_dir": "/mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/logs",
-  "preprocess": {
-    "extract_audio": true,
-    "use_phone": true,
-    // linguistic features
-    "extract_phone": true,
-    "phone_extractor": "espeak", // "espeak, pypinyin, pypinyin_initials_finals, lexicon (only for language=en-us right now)"
-    // TODO: Fill in the output data path. The default value is "Amphion/data"
-    "processed_dir": "/mnt/workspace/wangmingxuan/vits_on_libritts_hifitts/processed_data",
-    "sample_rate": 24000,
-    "train_file": "train_seen_spk_5k_cleaned.json",
-    "valid_file": "valid_100.json", // validattion set
-    "use_spkid": true, // True: use speaker id for multi-speaker dataset
-  },
-  "train": {
-    "batch_size": 16,
-    "multi_speaker_training": true, // True: train multi-speaker model; False: training single-speaker model;
-    // "n_speakers": 2500, // number of speakers, while be automatically set if n_speakers is 0 and multi_speaker_training is true
-  }
-}

egs/tts/vits_libritts_1w/run.sh

@@ -1,146 +0,0 @@
-# Copyright (c) 2023 Amphion.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-######## Build Experiment Environment ###########
-exp_dir=$(cd `dirname $0`; pwd)
-work_dir=$(dirname $(dirname $(dirname $exp_dir)))
-
-export WORK_DIR=$work_dir
-export PYTHONPATH=$work_dir
-export PYTHONIOENCODING=UTF-8
-
-cd $work_dir/modules/monotonic_align
-mkdir -p monotonic_align
-python setup.py build_ext --inplace
-cd $work_dir
-
-######## Parse the Given Parameters from the Commond ###########
-# options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_source_file:,infer_source_audio_dir:,infer_target_speaker:,infer_key_shift:,infer_vocoder_dir:,name:,stage: -- "$@")
-options=$(getopt -o c:n:s --long gpu:,config:,infer_expt_dir:,infer_output_dir:,infer_mode:,infer_dataset:,infer_testing_set:,infer_text:,infer_speaker_name:,name:,stage: -- "$@")
-eval set -- "$options"
-
-while true; do
-  case $1 in
-    # Experimental Configuration File
-    -c | --config) shift; exp_config=$1 ; shift ;;
-    # Experimental Name
-    -n | --name) shift; exp_name=$1 ; shift ;;
-    # Running Stage
-    -s | --stage) shift; running_stage=$1 ; shift ;;
-    # Visible GPU machines. The default value is "0".
-    --gpu) shift; gpu=$1 ; shift ;;
-
-    # [Only for Inference] The experiment dir. The value is like "[Your path to save logs and checkpoints]/[YourExptName]"
-    --infer_expt_dir) shift; infer_expt_dir=$1 ; shift ;;
-    # [Only for Inference] The output dir to save inferred audios. Its default value is "$expt_dir/result"
-    --infer_output_dir) shift; infer_output_dir=$1 ; shift ;;
-    # [Only for Inference] The inference mode. It can be "batch" to generate speech by batch, or "single" to generage a single clip of speech.
-    --infer_mode) shift; infer_mode=$1 ; shift ;;
-    # [Only for Inference] The inference dataset. It is only used when the inference model is "batch".
-    --infer_dataset) shift; infer_dataset=$1 ; shift ;;
-    # [Only for Inference] The inference testing set. It is only used when the inference model is "batch". It can be "test" set split from the dataset, or "golden_test" carefully selected from the testing set.
-    --infer_testing_set) shift; infer_testing_set=$1 ; shift ;;
-    # [Only for Inference] The text to be synthesized from. It is only used when the inference model is "single". 
-    --infer_text) shift; infer_text=$1 ; shift ;;
-    # [Only for Inference] The speaker voice to be delivered in the synthesized speech. It is only used when the inference model is "single". 
-    --infer_speaker_name) shift; infer_speaker_name=$1 ; shift ;;
-
-    --) shift ; break ;;
-    *) echo "Invalid option: $1" exit 1 ;;
-  esac
-done
-
-
-### Value check ###
-if [ -z "$running_stage" ]; then
-    echo "[Error] Please specify the running stage"
-    exit 1
-fi
-
-if [ -z "$exp_config" ]; then
-    exp_config="${exp_dir}"/exp_config.json
-fi
-echo "Exprimental Configuration File: $exp_config"
-
-if [ -z "$gpu" ]; then
-    gpu="0"
-fi
-
-######## Features Extraction ###########
-if [ $running_stage -eq 1 ]; then
-    CUDA_VISIBLE_DEVICES=$gpu /home/pai/envs/amphion/bin/python "${work_dir}"/bins/tts/preprocess.py \
-        --config=$exp_config \
-        --num_workers=4
-fi
-
-######## Training ###########
-if [ $running_stage -eq 2 ]; then
-    if [ -z "$exp_name" ]; then
-        echo "[Error] Please specify the experiments name"
-        exit 1
-    fi
-    echo "Exprimental Name: $exp_name"
-
-    CUDA_VISIBLE_DEVICES=$gpu accelerate launch "${work_dir}"/bins/tts/train.py \
-        --config $exp_config \
-        --exp_name $exp_name \
-        --log_level debug
-fi
-
-######## Inference ###########
-if [ $running_stage -eq 3 ]; then
-    if [ -z "$infer_expt_dir" ]; then
-        echo "[Error] Please specify the experimental directionary. The value is like [Your path to save logs and checkpoints]/[YourExptName]"
-        exit 1
-    fi
-
-    if [ -z "$infer_output_dir" ]; then
-        infer_output_dir="$expt_dir/result"
-    fi
-
-    if [ -z "$infer_mode" ]; then
-        echo "[Error] Please specify the inference mode, e.g., "batch", "single""
-        exit 1
-    fi
-
-    if [ "$infer_mode" = "batch" ] && [ -z "$infer_dataset" ]; then
-        echo "[Error] Please specify the dataset used in inference when the inference mode is batch"
-        exit 1
-    fi
-
-    if [ "$infer_mode" = "batch" ] && [ -z "$infer_testing_set" ]; then
-        echo "[Error] Please specify the testing set used in inference when the inference mode is batch"
-        exit 1
-    fi
- 
-    if [ "$infer_mode" = "single" ] && [ -z "$infer_text" ]; then
-        echo "[Error] Please specify the text to be synthesized when the inference mode is single"
-        exit 1
-    fi
-
-    if [ "$infer_mode" = "single" ]; then
-        echo 'Text: ' ${infer_text}
-        infer_dataset=None
-        infer_testing_set=None
-    elif [ "$infer_mode" = "batch" ]; then
-        infer_text=''
-        infer_speaker_name=None
-    fi
-
-
-    CUDA_VISIBLE_DEVICES=$gpu accelerate launch "$work_dir"/bins/tts/inference.py \
-        --config $exp_config \
-        --acoustics_dir $infer_expt_dir \
-        --output_dir $infer_output_dir  \
-        --mode $infer_mode \
-        --dataset $infer_dataset \
-        --testing_set $infer_testing_set \
-        --text "$infer_text" \
-        --speaker_name $infer_speaker_name \
-        --log_level debug
-
-
-
-fi