diff --git a/TTS/tts/configs/tacotron_config.py b/TTS/tts/configs/tacotron_config.py
index 53f5739e10..9121301249 100644
--- a/TTS/tts/configs/tacotron_config.py
+++ b/TTS/tts/configs/tacotron_config.py
@@ -24,10 +24,10 @@ class TacotronConfig(BaseTTSConfig):
             Path to the wav file used at inference to set the speech style through GST. If `GST` is enabled and
             this is not defined, the model uses a zero vector as an input. Defaults to None.
         r (int):
-            Number of output frames that the decoder computed per iteration. Larger values makes training and inference
-            faster but reduces the quality of the output frames. This needs to be tuned considering your own needs.
-            Defaults to 1.
-        gradual_trainin (List[List]):
+            Initial number of output frames that the decoder computed per iteration. Larger values makes training and inference
+            faster but reduces the quality of the output frames. This must be equal to the largest `r` value used in
+            `gradual_training` schedule. Defaults to 1.
+        gradual_training (List[List]):
             Parameters for the gradual training schedule. It is in the form `[[a, b, c], [d ,e ,f] ..]` where `a` is
             the step number to start using the rest of the values, `b` is the `r` value and `c` is the batch size.
             If sets None, no gradual training is used. Defaults to None.
@@ -168,3 +168,8 @@ class TacotronConfig(BaseTTSConfig):
     decoder_ssim_alpha: float = 0.25
     postnet_ssim_alpha: float = 0.25
     ga_alpha: float = 5.0
+
+
+    def check_values(self):
+        if self.gradual_training:
+            assert self.gradual_training[0][1] == self.r, f"[!] the first scheduled gradual training `r` must be equal to the model's `r` value. {self.gradual_training[0][1]} vs {self.r}"
\ No newline at end of file
diff --git a/recipes/ljspeech/tacotron/run.sh b/recipes/ljspeech/tacotron/run.sh
new file mode 100644
index 0000000000..9f5435db3d
--- /dev/null
+++ b/recipes/ljspeech/tacotron/run.sh
@@ -0,0 +1,21 @@
+#!/bin/bash
+RUN_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
+echo $RUN_DIR
+# download LJSpeech dataset
+wget http://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
+# extract
+tar -xjf LJSpeech-1.1.tar.bz2
+# create train-val splits
+shuf LJSpeech-1.1/metadata.csv > LJSpeech-1.1/metadata_shuf.csv
+head -n 12000 LJSpeech-1.1/metadata_shuf.csv > LJSpeech-1.1/metadata_train.csv
+tail -n 1100 LJSpeech-1.1/metadata_shuf.csv > LJSpeech-1.1/metadata_val.csv
+mv LJSpeech-1.1 $RUN_DIR/
+rm LJSpeech-1.1.tar.bz2
+# compute dataset mean and variance for normalization
+python TTS/bin/compute_statistics.py $RUN_DIR/tacotron2-DCA.json $RUN_DIR/scale_stats.npy --data_path $RUN_DIR/LJSpeech-1.1/wavs/
+# training ....
+# change the GPU id if needed
+CUDA_VISIBLE_DEVICES="0" python TTS/bin/train_tacotron.py --config_path $RUN_DIR/tacotron2-DDC.json \
+                                                          --output_path $RUN_DIR  \
+                                                          --coqpit.datasets.0.path $RUN_DIR/LJSpeech-1.1/    \
+                                                          --coqpit.audio.stats_path $RUN_DIR/scale_stats.npy \
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diff --git a/recipes/ljspeech/tacotron/tacotron2-DDC.json b/recipes/ljspeech/tacotron/tacotron2-DDC.json
new file mode 100644
index 0000000000..0e29040507
--- /dev/null
+++ b/recipes/ljspeech/tacotron/tacotron2-DDC.json
@@ -0,0 +1,91 @@
+{
+    "datasets": [
+        {
+            "name": "ljspeech",
+            "path": "DEFINE THIS",
+            "meta_file_train": "metadata.csv",
+            "meta_file_val": null
+        }
+    ],
+    "audio": {
+        "fft_size": 1024,
+        "win_length": 1024,
+        "hop_length": 256,
+        "frame_length_ms": null,
+        "frame_shift_ms": null,
+        "sample_rate": 22050,
+        "preemphasis": 0.0,
+        "ref_level_db": 20,
+        "do_trim_silence": true,
+        "trim_db": 60,
+        "power": 1.5,
+        "griffin_lim_iters": 60,
+        "num_mels": 80,
+        "mel_fmin": 50.0,
+        "mel_fmax": 7600.0,
+        "spec_gain": 1,
+        "signal_norm": true,
+        "min_level_db": -100,
+        "symmetric_norm": true,
+        "max_norm": 4.0,
+        "clip_norm": true,
+        "stats_path": "scale_stats.npy"
+    },
+    "gst":{
+        "gst_embedding_dim": 256,
+        "gst_num_heads": 4,
+        "gst_num_style_tokens": 10
+    },
+    "model": "Tacotron2",
+    "run_name": "ljspeech-dcattn",
+    "run_description": "tacotron2 with dynamic convolution attention.",
+    "batch_size": 64,
+    "eval_batch_size": 16,
+    "r": 2,
+    "mixed_precision": true,
+    "loss_masking": true,
+    "decoder_loss_alpha": 0.25,
+    "postnet_loss_alpha": 0.25,
+    "postnet_diff_spec_alpha": 0.25,
+    "decoder_diff_spec_alpha": 0.25,
+    "decoder_ssim_alpha": 0.25,
+    "postnet_ssim_alpha": 0.25,
+    "ga_alpha": 5.0,
+    "stopnet_pos_weight": 15.0,
+    "run_eval": true,
+    "test_delay_epochs": 10,
+    "test_sentences_file": null,
+    "noam_schedule": true,
+    "grad_clip": 0.05,
+    "epochs": 1000,
+    "lr": 0.001,
+    "wd": 1e-06,
+    "warmup_steps": 4000,
+    "memory_size": -1,
+    "prenet_type": "original",
+    "prenet_dropout": true,
+    "attention_type": "original",
+    "location_attn": true,
+    "double_decoder_consistency": true,
+    "ddc_r": 6,
+    "attention_norm": "sigmoid",
+    "gradual_training": [[0, 6, 64], [10000, 4, 32], [50000, 3, 32], [100000, 2, 32]],
+    "stopnet": true,
+    "separate_stopnet": true,
+    "print_step": 25,
+    "tb_plot_step": 100,
+    "print_eval": false,
+    "save_step": 10000,
+    "checkpoint": true,
+    "text_cleaner": "phoneme_cleaners",
+    "num_loader_workers": 4,
+    "num_val_loader_workers": 4,
+    "batch_group_size": 4,
+    "min_seq_len": 6,
+    "max_seq_len": 180,
+    "compute_input_seq_cache": true,
+    "output_path": "DEFINE THIS",
+    "phoneme_cache_path": "DEFINE THIS",
+    "use_phonemes": false,
+    "phoneme_language": "en-us"
+}
\ No newline at end of file