added the missing visualize function

egs/wenetspeech4tts/TTS/local/compute_neural_codec_and_prepare_text_tokens.py

@@ -519,12 +519,16 @@ def main():
                         if split > 1:
                             storage_path = f"{args.output_dir}/{args.prefix}_encodec_{partition}_{idx}"
                         else:
-                            storage_path = f"{args.output_dir}/{args.prefix}_encodec_{partition}"
+                            storage_path = (
+                                f"{args.output_dir}/{args.prefix}_encodec_{partition}"
+                            )
                     else:
                         if split > 1:
                             storage_path = f"{args.output_dir}/{args.prefix}_fbank_{partition}_{idx}"
                         else:
-                            storage_path = f"{args.output_dir}/{args.prefix}_fbank_{partition}"
+                            storage_path = (
+                                f"{args.output_dir}/{args.prefix}_fbank_{partition}"
+                            )
 
                     if args.prefix.lower() in [
                         "ljspeech",

egs/wenetspeech4tts/TTS/valle/valle.py

@@ -19,6 +19,8 @@
 from functools import partial
 from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union
 
+import matplotlib.pyplot as plt
+import numpy as np
 import torch
 import torch.nn as nn
 from torch import Tensor
@@ -1658,6 +1660,89 @@ def continual(
         assert len(codes) == 8
         return torch.stack(codes, dim=-1)
 
+    def visualize(
+        self,
+        predicts: Tuple[torch.Tensor],
+        batch: Dict[str, Union[List, torch.Tensor]],
+        output_dir: str,
+        limit: int = 4,
+    ) -> None:
+        text_tokens = batch["text_tokens"].to("cpu").detach().numpy()
+        text_tokens_lens = batch["text_tokens_lens"].to("cpu").detach().numpy()
+        audio_features = batch["audio_features"].to("cpu").detach().numpy()
+        audio_features_lens = (
+            batch["audio_features_lens"].to("cpu").detach().numpy()
+        )
+        assert text_tokens.ndim == 2
+
+        utt_ids, texts = batch["utt_id"], batch["text"]
+
+        encoder_outputs = predicts[0].to("cpu").type(torch.float32).detach().numpy()
+        decoder_outputs = predicts[1]
+        if isinstance(decoder_outputs, list):
+            decoder_outputs = decoder_outputs[-1]
+        decoder_outputs = (
+            decoder_outputs.to("cpu").type(torch.float32).detach().numpy()
+        )
+
+        vmin, vmax = 0, 1024  # Encodec
+        if decoder_outputs.dtype == np.float32:
+            vmin, vmax = -6, 0  # Fbank
+
+        num_figures = 3
+        for b, (utt_id, text) in enumerate(zip(utt_ids[:limit], texts[:limit])):
+            _ = plt.figure(figsize=(14, 8 * num_figures))
+
+            S = text_tokens_lens[b]
+            T = audio_features_lens[b]
+
+            # encoder
+            plt.subplot(num_figures, 1, 1)
+            plt.title(f"Text: {text}")
+            plt.imshow(
+                X=np.transpose(encoder_outputs[b]),
+                cmap=plt.get_cmap("jet"),
+                aspect="auto",
+                interpolation="nearest",
+            )
+            plt.gca().invert_yaxis()
+            plt.axvline(x=S - 0.4, linewidth=2, color="r")
+            plt.xlabel("Encoder Output")
+            plt.colorbar()
+
+            # decoder
+            plt.subplot(num_figures, 1, 2)
+            plt.imshow(
+                X=np.transpose(decoder_outputs[b]),
+                cmap=plt.get_cmap("jet"),
+                aspect="auto",
+                interpolation="nearest",
+                vmin=vmin,
+                vmax=vmax,
+            )
+            plt.gca().invert_yaxis()
+            plt.axvline(x=T - 0.4, linewidth=2, color="r")
+            plt.xlabel("Decoder Output")
+            plt.colorbar()
+
+            # target
+            plt.subplot(num_figures, 1, 3)
+            plt.imshow(
+                X=np.transpose(audio_features[b]),
+                cmap=plt.get_cmap("jet"),
+                aspect="auto",
+                interpolation="nearest",
+                vmin=vmin,
+                vmax=vmax,
+            )
+            plt.gca().invert_yaxis()
+            plt.axvline(x=T - 0.4, linewidth=2, color="r")
+            plt.xlabel("Decoder Target")
+            plt.colorbar()
+
+            plt.savefig(f"{output_dir}/{utt_id}.png")
+            plt.close()
+
 
 # https://github.com/microsoft/unilm/blob/master/xtune/src/transformers/modeling_utils.py
 def top_k_top_p_filtering(