performed end to end testing to the VALL-E recipe

egs/wenetspeech4tts/TTS/local/compute_neural_codec_and_prepare_text_tokens.py

@@ -516,9 +516,19 @@ def main():
             for idx, part in enumerate(cut_sets):
                 if args.audio_extractor:
                     if args.audio_extractor == "Encodec":
-                        storage_path = f"{args.output_dir}/{args.prefix}_encodec_{partition}_{idx if split > 1 else ''}"
+                        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}"
+                            )
                     else:
-                        storage_path = f"{args.output_dir}/{args.prefix}_fbank_{partition}_{idx if split > 1 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}"
+                            )
 
                     if args.prefix.lower() in [
                         "ljspeech",
@@ -587,9 +597,11 @@ def main():
                         ].normalized_text, "normalized_text is None"
 
                 # Save each part with an index if split > 1
-                cuts_filename = (
-                    f"{prefix}cuts_{partition}.{idx if split > 1 else ''}.{args.suffix}"
-                )
+                if split > 1:
+                    cuts_filename = f"{prefix}cuts_{partition}.{idx}.{args.suffix}"
+                else:
+                    cuts_filename = f"{prefix}cuts_{partition}.{args.suffix}"
+
                 part.to_file(f"{args.output_dir}/{cuts_filename}")
                 logging.info(f"Saved {cuts_filename}")
 

egs/wenetspeech4tts/TTS/valle/infer.py

@@ -86,7 +86,7 @@ def get_args():
     parser.add_argument(
         "--checkpoint",
         type=str,
-        default="exp/vallf_nano_full/checkpoint-100000.pt",
+        default="./valle/exp/checkpoint-100000.pt",
         help="Path to the saved checkpoint.",
     )
 

egs/wenetspeech4tts/TTS/valle/requirements.txt

@@ -0,0 +1,2 @@
+phonemizer==3.2.1
+git+https://github.com/facebookresearch/encodec.git

egs/wenetspeech4tts/TTS/valle/train.py

@@ -4,6 +4,7 @@
 #                                                       Mingshuang Luo)
 # Copyright    2023                           (authors: Feiteng Li)
 # Copyright    2024                           (authors: Yuekai Zhang)
+# Copyright    2024  Tsinghua University      (authors: Zengrui Jin,)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -48,10 +49,8 @@
 import argparse
 import copy
 import logging
-import os
 import random
 import warnings
-from contextlib import nullcontext
 from pathlib import Path
 from shutil import copyfile
 from typing import Any, Dict, Optional, Tuple, Union
@@ -216,7 +215,7 @@ def get_parser():
     parser.add_argument(
         "--exp-dir",
         type=str,
-        default="exp/valle_dev",
+        default="./valle/exp",
         help="""The experiment dir.
         It specifies the directory where all training related
         files, e.g., checkpoints, log, etc, are saved
@@ -686,9 +685,9 @@ def compute_validation_loss(
         output_dir = Path(f"{params.exp_dir}/eval/step-{params.batch_idx_train:06d}")
         output_dir.mkdir(parents=True, exist_ok=True)
         if isinstance(model, DDP):
-            model.module.visualize(predicts, batch, output_dir=output_dir)
+            model.module.visualize(predicts, batch, tokenizer, output_dir=output_dir)
         else:
-            model.visualize(predicts, batch, output_dir=output_dir)
+            model.visualize(predicts, batch, tokenizer, output_dir=output_dir)
 
     return tot_loss
 

egs/wenetspeech4tts/TTS/valle/valle.py

@@ -19,8 +19,11 @@
 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 tokenizer import TextTokenCollater
 from torch import Tensor
 from torch.nn import Linear, Module
 from torch.nn import functional as F
@@ -1658,6 +1661,88 @@ 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]],
+        tokenizer: TextTokenCollater,
+        output_dir: str,
+        limit: int = 4,
+    ) -> None:
+        audio_features = batch["features"].to("cpu").detach().numpy()
+        audio_features_lens = batch["features_lens"].to("cpu").detach().numpy()
+
+        tokens = batch["tokens"]
+        text_tokens, text_tokens_lens = tokenizer(tokens)
+        assert text_tokens.ndim == 2
+
+        texts = batch["text"]
+        utt_ids = [cut.id for cut in batch["cut"]]
+
+        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(