diff --git a/recipes/vctk/yourtts/train_yourtts.py b/recipes/vctk/yourtts/train_yourtts.py
new file mode 100644
index 00000000..0d3ec12c
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+++ b/recipes/vctk/yourtts/train_yourtts.py
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+import torch
+import os
+from trainer import Trainer, TrainerArgs
+
+from TTS.config.shared_configs import BaseDatasetConfig
+from TTS.tts.datasets import load_tts_samples
+from TTS.tts.models.vits import VitsArgs, VitsAudioConfig, Vits
+from TTS.tts.configs.vits_config import VitsConfig
+
+torch.set_num_threads(24)
+
+# Name of the run for the Trainer
+RUN_NAME = "YourTTS-EN-VCTK"
+
+# Path where you want to save the models outputs (configs, checkpoints and tensorboard logs)
+OUT_PATH = os.path.dirname(os.path.abspath(__file__))  # "/raid/coqui/Checkpoints/original-YourTTS/"
+
+# If you want to do transfer learning and speedup your training you can set here the path to the original YourTTS model
+RESTORE_PATH = None  # "/raid/coqui/Checkpoints/YourTTS/checkpoint.pth"
+
+# This paramter is usefull to debug, it skips the training epochs and just do the evaluation  and produce the test sentences
+SKIP_TRAIN_EPOCH = False
+
+# Set here the batch size to be used in training and evaluation
+BATCH_SIZE = 32
+
+# To get the speakers.json or speakers.pth you need to follow the steps described at: https://github.com/Edresson/YourTTS#reproducibility
+# or you can check the extract embedding script guidelines here: https://github.com/coqui-ai/TTS/blob/dev/TTS/bin/compute_embeddings.py#L20
+D_VECTOR_FILES = [
+    "/raid/datasets/VCTK/speakers.json",
+]
+
+# Change our dataset paths to the VCTK dataset or replace it for others
+# init configs
+vctk_config = BaseDatasetConfig(
+    formatter="vctk", dataset_name="vctk", meta_file_train="metadata.csv", path="/raid/datasets/VCTK/", language="en"
+)
+
+# add here all datasets configs, in our case we just want to train with the VCTK dataset then we need to add just VCTK
+datasets_list = [vctk_config]
+
+# Audio config used in training. Please: Check if your dataset sampling rate and the parameter sample_rate here are matching, otherwise resample your audios
+audio_config = VitsAudioConfig(
+    sample_rate=22050,
+    hop_length=256,
+    win_length=1024,
+    fft_size=1024,
+    mel_fmin=0.0,
+    mel_fmax=None,
+    num_mels=80,
+)
+
+# Init VITSArgs setting the arguments that is needed for the YourTTS model
+model_args = VitsArgs(
+    d_vector_file=D_VECTOR_FILES,
+    use_d_vector_file=True,
+    d_vector_dim=512,
+    num_layers_text_encoder=10,
+    # usefull parameters to the enable multilingual training
+    # use_language_embedding=True,
+    # embedded_language_dim=4,
+)
+
+# General training config, here you can change the batch size and others usefull parameters
+config = VitsConfig(
+    output_path=OUT_PATH,
+    model_args=model_args,
+    run_name=RUN_NAME,
+    project_name="YourTTS",
+    run_description="""
+            - Original YourTTS trained using VCTK dataset
+        """,
+    dashboard_logger="tensorboard",
+    logger_uri=None,
+    audio=audio_config,
+    batch_size=BATCH_SIZE,
+    batch_group_size=48,
+    eval_batch_size=BATCH_SIZE,
+    num_loader_workers=8,
+    eval_split_max_size=256,
+    print_step=50,
+    plot_step=100,
+    log_model_step=1000,
+    save_step=5000,
+    save_n_checkpoints=2,
+    save_checkpoints=True,
+    target_loss="loss_1",
+    print_eval=False,
+    use_phonemes=False,
+    phonemizer="espeak",
+    phoneme_language="en",
+    compute_input_seq_cache=True,
+    add_blank=True,
+    text_cleaner="english_cleaners",
+    phoneme_cache_path=None,
+    precompute_num_workers=12,
+    start_by_longest=True,
+    datasets=datasets_list,
+    cudnn_benchmark=False,
+    max_audio_len=220500,  # it should be: sampling rate * max audio in sec. So it is 22050 * 10 = 220500
+    mixed_precision=False,
+    test_sentences=[
+        [
+            "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
+            "VCTK_p277",
+            None,
+            "en",
+        ],
+        [
+            "Be a voice, not an echo.",
+            "VCTK_p239",
+            None,
+            "en",
+        ],
+        [
+            "I'm sorry Dave. I'm afraid I can't do that.",
+            "VCTK_p258",
+            None,
+            "en",
+        ],
+        [
+            "This cake is great. It's so delicious and moist.",
+            "VCTK_p244",
+            None,
+            "en",
+        ],
+        [
+            "Prior to November 22, 1963.",
+            "VCTK_p305",
+            None,
+            "en",
+        ],
+    ],
+    # Enable the weighted sampler
+    use_weighted_sampler=True,
+    # Ensures that all speakers are seen in the training batch equally no matter how many samples each speaker has
+    weighted_sampler_attrs={"speaker_name": 1.0},
+)
+
+# Load all the datasets samples and split traning and evaluation sets
+train_samples, eval_samples = load_tts_samples(
+    config.datasets,
+    eval_split=True,
+    eval_split_max_size=config.eval_split_max_size,
+    eval_split_size=config.eval_split_size,
+)
+
+# Init the model
+model = Vits.init_from_config(config)
+
+# Init the trainer and 🚀
+trainer = Trainer(
+    TrainerArgs(restore_path=RESTORE_PATH, skip_train_epoch=SKIP_TRAIN_EPOCH),
+    config,
+    output_path=OUT_PATH,
+    model=model,
+    train_samples=train_samples,
+    eval_samples=eval_samples,
+)
+trainer.fit()