Use absolute paths of the attention masks

TTS/bin/compute_attention_masks.py

@@ -148,10 +148,12 @@ Example run:
                 alignment = alignment[: mel_lengths[idx], : text_lengths[idx]].cpu().numpy()
                 # set file paths
                 wav_file_name = os.path.basename(item_idx)
-                align_file_name = os.path.splitext(wav_file_name)[0] + ".npy"
+                align_file_name = os.path.splitext(wav_file_name)[0] + "_attn.npy"
                 file_path = item_idx.replace(wav_file_name, align_file_name)
                 # save output
-                file_paths.append([item_idx, file_path])
+                wav_file_abs_path = os.path.abspath(item_idx)
+                file_abs_path = os.path.abspath(file_path)
+                file_paths.append([wav_file_abs_path, file_abs_path])
                 np.save(file_path, alignment)
 
         # ourput metafile

TTS/tts/datasets/__init__.py

@@ -1,6 +1,7 @@
 import sys
 from collections import Counter
 from pathlib import Path
+from typing import Dict, List, Tuple
 
 import numpy as np
 
@@ -30,7 +31,17 @@ def split_dataset(items):
     return items[:eval_split_size], items[eval_split_size:]
 
 
-def load_meta_data(datasets, eval_split=True):
+def load_meta_data(datasets: List[Dict], eval_split=True) -> Tuple[List[List], List[List]]:
+    """Parse the dataset, load the samples as a list and load the attention alignments if provided.
+
+    Args:
+        datasets (List[Dict]): A list of dataset dictionaries or dataset configs.
+        eval_split (bool, optional): If true, create a evaluation split. If an eval split provided explicitly, generate
+            an eval split automatically. Defaults to True.
+
+    Returns:
+        Tuple[List[List], List[List]: training and evaluation splits of the dataset.
+    """
     meta_data_train_all = []
     meta_data_eval_all = [] if eval_split else None
     for dataset in datasets:
@@ -51,15 +62,15 @@ def load_meta_data(datasets, eval_split=True):
                 meta_data_eval, meta_data_train = split_dataset(meta_data_train)
             meta_data_eval_all += meta_data_eval
         meta_data_train_all += meta_data_train
-        # load attention masks for duration predictor training
+        # load attention masks for the duration predictor training
         if dataset.meta_file_attn_mask:
             meta_data = dict(load_attention_mask_meta_data(dataset["meta_file_attn_mask"]))
             for idx, ins in enumerate(meta_data_train_all):
-                attn_file = meta_data[ins[1]].strip()
+                attn_file = meta_data[os.path.abspath(ins[1])].strip()
                 meta_data_train_all[idx].append(attn_file)
             if meta_data_eval_all:
                 for idx, ins in enumerate(meta_data_eval_all):
-                    attn_file = meta_data[ins[1]].strip()
+                    attn_file = meta_data[os.path.abspath(ins[1])].strip()
                     meta_data_eval_all[idx].append(attn_file)
     return meta_data_train_all, meta_data_eval_all
 

recipes/ljspeech/fast_pitch/train_fast_pitch.py

@@ -3,8 +3,11 @@ import os
 from TTS.config import BaseAudioConfig, BaseDatasetConfig
 from TTS.trainer import Trainer, TrainingArgs, init_training
 from TTS.tts.configs import FastPitchConfig
+from TTS.utils.manage import ModelManager
 
 output_path = os.path.dirname(os.path.abspath(__file__))
+
+# init configs
 dataset_config = BaseDatasetConfig(name="ljspeech", meta_file_train="metadata.csv",  meta_file_attn_mask=os.path.join(output_path, "../LJSpeech-1.1/metadata_attn_mask.txt"), path=os.path.join(output_path, "../LJSpeech-1.1/"))
 audio_config = BaseAudioConfig(
     sample_rate=22050,
@@ -40,6 +43,14 @@ config = FastPitchConfig(
     output_path=output_path,
     datasets=[dataset_config]
 )
+
+# compute alignments
+manager = ModelManager()
+model_path, config_path, _ = manager.download_model("tts_models/en/ljspeech/tacotron2-DCA")
+# TODO: make compute_attention python callable
+os.system(f"python TTS/bin/compute_attention_masks.py --model_path {model_path} --config_path {config_path} --dataset ljspeech --dataset_metafile metadata.csv --data_path ./recipes/ljspeech/LJSpeech-1.1/  --use_cuda true")
+
+# train the model
 args, config, output_path, _, c_logger, tb_logger = init_training(TrainingArgs(), config)
 trainer = Trainer(args, config, output_path, c_logger, tb_logger)
 trainer.fit()