diff --git a/TTS/bin/compute_vits_alignments.py b/TTS/bin/compute_vits_alignments.py
new file mode 100644
index 00000000..d908b341
--- /dev/null
+++ b/TTS/bin/compute_vits_alignments.py
@@ -0,0 +1,133 @@
+#!/usr/bin/env python3
+"""Extract Mel spectrograms with teacher forcing."""
+
+import argparse
+import os
+
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from TTS.config import load_config
+from TTS.tts.datasets import TTSDataset, load_tts_samples
+from TTS.tts.models import setup_model
+from TTS.tts.utils.speakers import SpeakerManager
+from TTS.tts.utils.text.tokenizer import TTSTokenizer
+from TTS.utils.audio import AudioProcessor
+from TTS.utils.generic_utils import count_parameters
+from trainer.generic_utils import to_cuda
+
+use_cuda = torch.cuda.is_available()
+
+def extract_aligments(
+    data_loader, model, output_path, use_cuda=True
+):
+    model.eval()
+    export_metadata = []
+    for _, batch in tqdm(enumerate(data_loader), total=len(data_loader)):
+
+        batch = model.format_batch(batch)        
+        if use_cuda:
+            for k, v in batch.items():
+                    batch[k] = to_cuda(v)
+
+        batch = model.format_batch_on_device(batch)
+
+        spec_lens = batch["spec_lens"]
+        tokens = batch["tokens"]
+        token_lenghts = batch["token_lens"]
+        spec = batch["spec"]
+
+        d_vectors = batch["d_vectors"]
+        speaker_ids = batch["speaker_ids"]
+        language_ids = batch["language_ids"]
+        emotion_embeddings = batch["emotion_embeddings"]
+        emotion_ids = batch["emotion_ids"]
+        waveform = batch["waveform"]
+        item_idx = batch["audio_files"]
+        # generator pass
+        outputs = model.forward(
+            tokens,
+            token_lenghts,
+            spec,
+            spec_lens,
+            waveform,
+            aux_input={
+                "d_vectors": d_vectors,
+                "speaker_ids": speaker_ids,
+                "language_ids": language_ids,
+                "emotion_embeddings": emotion_embeddings,
+                "emotion_ids": emotion_ids,
+            },
+        )
+
+        alignments = outputs["alignments"].detach().cpu().numpy()
+
+        for idx in range(tokens.shape[0]):
+            wav_file_path = item_idx[idx]
+            alignment = alignments[idx]
+            # set paths
+            align_file_name = os.path.splitext(os.path.basename(wav_file_path))[0] + ".npy"
+            os.makedirs(os.path.join(output_path, "alignments"), exist_ok=True)
+            align_file_path = os.path.join(output_path, "alignments", align_file_name)
+            np.save(align_file_path, alignment)
+
+
+def main(args):  # pylint: disable=redefined-outer-name
+    # pylint: disable=global-variable-undefined
+    global meta_data, speaker_manager
+
+    # load data instances
+    meta_data_train, meta_data_eval = load_tts_samples(
+        c.datasets, eval_split=args.eval, eval_split_max_size=c.eval_split_max_size, eval_split_size=c.eval_split_size
+    )
+
+    # use eval and training partitions
+    meta_data = meta_data_train + meta_data_eval
+
+    # setup model
+    model = setup_model(c, meta_data)
+
+    # restore model
+    model.load_checkpoint(c, args.checkpoint_path, eval=False)
+    model = model.eval()
+
+    if use_cuda:
+        model.cuda()
+
+    num_params = count_parameters(model)
+    print("\n > Model has {} parameters".format(num_params), flush=True)
+
+    own_loader = model.get_data_loader(config=model.config,
+        assets={},
+        is_eval=False,
+        samples=meta_data,
+        verbose=True,
+        num_gpus=1,
+    )
+
+    extract_aligments(
+        own_loader,
+        model,
+        args.output_path,
+        use_cuda=use_cuda,
+    )
+
+
+if __name__ == "__main__":
+    # python3 TTS/bin/extract_tts_audio.py --config_path /raid/edresson/dev/Checkpoints/YourTTS/new_vctk_trimmed_silence/upsampling/YourTTS_22khz--\>44khz_vocoder_approach_frozen/YourTTS_22khz--\>44khz_vocoder_approach_frozen-April-02-2022_08+23PM-a5f5ebae/config.json --checkpoint_path /raid/edresson/dev/Checkpoints/YourTTS/new_vctk_trimmed_silence/upsampling/YourTTS_22khz--\>44khz_vocoder_approach_frozen/YourTTS_22khz--\>44khz_vocoder_approach_frozen-April-02-2022_08+23PM-a5f5ebae/checkpoint_1600000.pth --output_path ../Test_extract_audio_script/
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config_path", type=str, help="Path to config file for training.", required=True)
+    parser.add_argument("--checkpoint_path", type=str, help="Model file to be restored.", required=True)
+    parser.add_argument("--output_path", type=str, help="Path to save mel specs", required=True)
+    parser.add_argument("--eval", type=bool, help="compute eval.", default=True)
+    args = parser.parse_args()
+
+    c = load_config(args.config_path)
+    # disable samplers
+    c.use_speaker_weighted_sampler = False
+    c.use_language_weighted_sampler = False
+    c.use_length_weighted_sampler = False
+    c.use_style_weighted_sampler = False
+    main(args)
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