trainer-API updates #1

TTS/tts/utils/speakers.py

@@ -1,7 +1,7 @@
 import json
 import os
 import random
-from typing import Union
+from typing import Union, List, Any
 
 import numpy as np
 import torch
@@ -35,9 +35,7 @@ def save_speaker_mapping(out_path, speaker_mapping):
 
 
 def get_speakers(items):
-    """Returns a sorted, unique list of speakers in a given dataset."""
-    speakers = {e[2] for e in items}
-    return sorted(speakers)
+
 
 
 def parse_speakers(c, args, meta_data_train, OUT_PATH):
@@ -121,26 +119,31 @@ class SpeakerManager:
 
     Args:
         x_vectors_file_path (str, optional): Path to the metafile including x vectors. Defaults to "".
-        speaker_id_file_path (str, optional): Path to the metafile that maps speaker names to ids used by the
-        TTS model. Defaults to "".
+        speaker_id_file_path (str, optional): Path to the metafile that maps speaker names to ids used by
+        TTS models. Defaults to "".
         encoder_model_path (str, optional): Path to the speaker encoder model file. Defaults to "".
         encoder_config_path (str, optional): Path to the spealer encoder config file. Defaults to "".
     """
 
     def __init__(
         self,
+        data_items: List[List[Any]] = None,
         x_vectors_file_path: str = "",
         speaker_id_file_path: str = "",
         encoder_model_path: str = "",
         encoder_config_path: str = "",
     ):
 
-        self.x_vectors = None
-        self.speaker_ids = None
-        self.clip_ids = None
+        self.data_items = []
+        self.x_vectors = []
+        self.speaker_ids = []
+        self.clip_ids = []
         self.speaker_encoder = None
         self.speaker_encoder_ap = None
 
+        if data_items:
+            self.speaker_ids = self.parse_speakers()
+
         if x_vectors_file_path:
             self.load_x_vectors_file(x_vectors_file_path)
 
@@ -169,10 +172,10 @@ def x_vector_dim(self):
         return len(self.x_vectors[list(self.x_vectors.keys())[0]]["embedding"])
 
     def parser_speakers_from_items(self, items: list):
-        speaker_ids = sorted({item[2] for item in items})
-        self.speaker_ids = speaker_ids
-        num_speakers = len(speaker_ids)
-        return speaker_ids, num_speakers
+        speakers = sorted({item[2] for item in items})
+        self.speaker_ids = {name: i for i, name in enumerate(speakers)}
+        num_speakers = len(self.speaker_ids)
+        return self.speaker_ids, num_speakers
 
     def save_ids_file(self, file_path: str):
         self._save_json(file_path, self.speaker_ids)

TTS/tts/utils/text/cleaners.py

@@ -1,19 +1,5 @@
-"""
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-"""
-
 import re
 
-from unidecode import unidecode
-
 from TTS.tts.utils.text.chinese_mandarin.numbers import replace_numbers_to_characters_in_text
 
 from .abbreviations import abbreviations_en, abbreviations_fr
@@ -46,8 +32,8 @@ def collapse_whitespace(text):
     return re.sub(_whitespace_re, " ", text).strip()
 
 
-def convert_to_ascii(text):
-    return unidecode(text)
+# def convert_to_ascii(text):
+#     return unidecode(text)
 
 
 def remove_aux_symbols(text):
@@ -77,7 +63,7 @@ def basic_cleaners(text):
 
 def transliteration_cleaners(text):
     """Pipeline for non-English text that transliterates to ASCII."""
-    text = convert_to_ascii(text)
+    # text = convert_to_ascii(text)
     text = lowercase(text)
     text = collapse_whitespace(text)
     return text
@@ -101,7 +87,7 @@ def basic_turkish_cleaners(text):
 
 def english_cleaners(text):
     """Pipeline for English text, including number and abbreviation expansion."""
-    text = convert_to_ascii(text)
+    # text = convert_to_ascii(text)
     text = lowercase(text)
     text = expand_time_english(text)
     text = expand_numbers(text)
@@ -141,7 +127,7 @@ def chinese_mandarin_cleaners(text: str) -> str:
 def phoneme_cleaners(text):
     """Pipeline for phonemes mode, including number and abbreviation expansion."""
     text = expand_numbers(text)
-    text = convert_to_ascii(text)
+    # text = convert_to_ascii(text)
     text = expand_abbreviations(text)
     text = replace_symbols(text)
     text = remove_aux_symbols(text)

tests/vocoder_tests/test_melgan_train.py

@@ -20,6 +20,7 @@
     eval_split_size=1,
     print_step=1,
     print_eval=True,
+    discriminator_model_params={"base_channels": 16, "max_channels": 256, "downsample_factors": [4, 4, 4]},
     data_path="tests/data/ljspeech",
     output_path=output_path,
 )