add tensorboard samples

.github/scripts/ljspeech/TTS/run-matcha.sh

@@ -56,7 +56,7 @@ function infer() {
 
   curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v1
 
-  ./matcha/inference.py \
+  ./matcha/infer.py \
     --epoch 1 \
     --exp-dir ./matcha/exp \
     --tokens data/tokens.txt \

egs/ljspeech/TTS/matcha/train.py

@@ -7,9 +7,10 @@ import json
 import logging
 from pathlib import Path
 from shutil import copyfile
-from typing import Any, Dict, Optional, Union
+from typing import Any, Dict, Optional, Tuple, Union
 
 import k2
+import numpy as np
 import torch
 import torch.multiprocessing as mp
 import torch.nn as nn
@@ -343,7 +344,7 @@ def compute_validation_loss(
     valid_dl: torch.utils.data.DataLoader,
     world_size: int = 1,
     rank: int = 0,
-) -> MetricsTracker:
+) -> Tuple[MetricsTracker, Tuple[np.ndarray, np.ndarray]]:
     """Run the validation process."""
     model.eval()
     device = model.device if isinstance(model, DDP) else next(model.parameters()).device
@@ -391,6 +392,23 @@ def compute_validation_loss(
             # summary stats
             tot_loss = tot_loss + loss_info
 
+            # infer for first batch:
+            if batch_idx == 0 and rank == 0:
+                inner_model = model.module if isinstance(model, DDP) else model
+                audio_pred, _, duration = inner_model.inference(
+                    text=tokens[0, : tokens_lens[0].item()]
+                )
+                audio_pred = audio_pred.data.cpu().numpy()
+                audio_len_pred = (
+                    (duration.sum(0) * params.frame_shift).to(dtype=torch.int64).item()
+                )
+                assert audio_len_pred == len(audio_pred), (
+                    audio_len_pred,
+                    len(audio_pred),
+                )
+                audio_gt = audio[0, : audio_lens[0].item()].data.cpu().numpy()
+                returned_sample = (audio_pred, audio_gt)
+
     if world_size > 1:
         tot_loss.reduce(device)
 
@@ -399,7 +417,7 @@ def compute_validation_loss(
         params.best_valid_epoch = params.cur_epoch
         params.best_valid_loss = loss_value
 
-    return tot_loss
+    return tot_loss, returned_sample
 
 
 def train_one_epoch(
@@ -559,7 +577,7 @@ def train_one_epoch(
 
         if params.batch_idx_train % params.valid_interval == 1:
             logging.info("Computing validation loss")
-            valid_info = compute_validation_loss(
+            valid_info, (speech_hat, speech) = compute_validation_loss(
                 params=params,
                 model=model,
                 tokenizer=tokenizer,
@@ -577,6 +595,18 @@ def train_one_epoch(
                 valid_info.write_summary(
                     tb_writer, "train/valid_", params.batch_idx_train
                 )
+                tb_writer.add_audio(
+                    "train/valid_speech_hat",
+                    speech_hat,
+                    params.batch_idx_train,
+                    params.sampling_rate,
+                )
+                tb_writer.add_audio(
+                    "train/valid_speech",
+                    speech,
+                    params.batch_idx_train,
+                    params.sampling_rate,
+                )
 
     loss_value = tot_loss["tot_loss"] / tot_loss["samples"]
     params.train_loss = loss_value