diff --git a/recipes/ljspeech/align_tts/train_aligntts.py b/recipes/ljspeech/align_tts/train_aligntts.py index f1b29025..d27d0fa1 100644 --- a/recipes/ljspeech/align_tts/train_aligntts.py +++ b/recipes/ljspeech/align_tts/train_aligntts.py @@ -49,7 +49,7 @@ tokenizer, config = TTSTokenizer.init_from_config(config) # You can define your custom sample loader returning the list of samples. # Or define your custom formatter and pass it to the `load_tts_samples`. # Check `TTS.tts.datasets.load_tts_samples` for more details. -train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size) # init model model = AlignTTS(config, ap, tokenizer) diff --git a/recipes/ljspeech/fast_pitch/train_fast_pitch.py b/recipes/ljspeech/fast_pitch/train_fast_pitch.py index a3fc35c9..1f10ef07 100644 --- a/recipes/ljspeech/fast_pitch/train_fast_pitch.py +++ b/recipes/ljspeech/fast_pitch/train_fast_pitch.py @@ -84,7 +84,7 @@ tokenizer, config = TTSTokenizer.init_from_config(config) # You can define your custom sample loader returning the list of samples. # Or define your custom formatter and pass it to the `load_tts_samples`. # Check `TTS.tts.datasets.load_tts_samples` for more details. -train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size) # init the model model = ForwardTTS(config, ap, tokenizer, speaker_manager=None) diff --git a/recipes/ljspeech/fast_speech/train_fast_speech.py b/recipes/ljspeech/fast_speech/train_fast_speech.py index 560d3de2..e5a601a7 100644 --- a/recipes/ljspeech/fast_speech/train_fast_speech.py +++ b/recipes/ljspeech/fast_speech/train_fast_speech.py @@ -83,7 +83,7 @@ tokenizer, config = TTSTokenizer.init_from_config(config) # You can define your custom sample loader returning the list of samples. # Or define your custom formatter and pass it to the `load_tts_samples`. # Check `TTS.tts.datasets.load_tts_samples` for more details. -train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size) # init the model model = ForwardTTS(config, ap, tokenizer) diff --git a/recipes/ljspeech/glow_tts/train_glowtts.py b/recipes/ljspeech/glow_tts/train_glowtts.py index c47cd00a..47d03fe3 100644 --- a/recipes/ljspeech/glow_tts/train_glowtts.py +++ b/recipes/ljspeech/glow_tts/train_glowtts.py @@ -60,7 +60,7 @@ tokenizer, config = TTSTokenizer.init_from_config(config) # You can define your custom sample loader returning the list of samples. # Or define your custom formatter and pass it to the `load_tts_samples`. # Check `TTS.tts.datasets.load_tts_samples` for more details. -train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size) # INITIALIZE THE MODEL # Models take a config object and a speaker manager as input diff --git a/recipes/ljspeech/speedy_speech/train_speedy_speech.py b/recipes/ljspeech/speedy_speech/train_speedy_speech.py index 7ad132b2..a19e9053 100644 --- a/recipes/ljspeech/speedy_speech/train_speedy_speech.py +++ b/recipes/ljspeech/speedy_speech/train_speedy_speech.py @@ -67,7 +67,7 @@ tokenizer, config = TTSTokenizer.init_from_config(config) # You can define your custom sample loader returning the list of samples. # Or define your custom formatter and pass it to the `load_tts_samples`. # Check `TTS.tts.datasets.load_tts_samples` for more details. -train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size) # init model model = ForwardTTS(config, ap, tokenizer) diff --git a/recipes/ljspeech/tacotron2-DCA/train_tacotron_dca.py b/recipes/ljspeech/tacotron2-DCA/train_tacotron_dca.py index ea1b0874..19a9f315 100644 --- a/recipes/ljspeech/tacotron2-DCA/train_tacotron_dca.py +++ b/recipes/ljspeech/tacotron2-DCA/train_tacotron_dca.py @@ -77,7 +77,7 @@ tokenizer, config = TTSTokenizer.init_from_config(config) # You can define your custom sample loader returning the list of samples. # Or define your custom formatter and pass it to the `load_tts_samples`. # Check `TTS.tts.datasets.load_tts_samples` for more details. -train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size) # INITIALIZE THE MODEL # Models take a config object and a speaker manager as input diff --git a/recipes/ljspeech/tacotron2-DDC/train_tacotron_ddc.py b/recipes/ljspeech/tacotron2-DDC/train_tacotron_ddc.py index 04e6150e..029698d8 100644 --- a/recipes/ljspeech/tacotron2-DDC/train_tacotron_ddc.py +++ b/recipes/ljspeech/tacotron2-DDC/train_tacotron_ddc.py @@ -74,7 +74,7 @@ tokenizer, config = TTSTokenizer.init_from_config(config) # You can define your custom sample loader returning the list of samples. # Or define your custom formatter and pass it to the `load_tts_samples`. # Check `TTS.tts.datasets.load_tts_samples` for more details. -train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size) # INITIALIZE THE MODEL # Models take a config object and a speaker manager as input diff --git a/recipes/ljspeech/vits_tts/train_vits.py b/recipes/ljspeech/vits_tts/train_vits.py index cfb3351d..e38dc200 100644 --- a/recipes/ljspeech/vits_tts/train_vits.py +++ b/recipes/ljspeech/vits_tts/train_vits.py @@ -69,7 +69,7 @@ tokenizer, config = TTSTokenizer.init_from_config(config) # You can define your custom sample loader returning the list of samples. # Or define your custom formatter and pass it to the `load_tts_samples`. # Check `TTS.tts.datasets.load_tts_samples` for more details. -train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size) # init model model = Vits(config, ap, tokenizer, speaker_manager=None) diff --git a/recipes/multilingual/vits_tts/train_vits_tts.py b/recipes/multilingual/vits_tts/train_vits_tts.py index 26eb46be..9e0cb4c8 100644 --- a/recipes/multilingual/vits_tts/train_vits_tts.py +++ b/recipes/multilingual/vits_tts/train_vits_tts.py @@ -109,7 +109,7 @@ config.from_dict(config.to_dict()) ap = AudioProcessor(**config.audio.to_dict()) # load training samples -train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size) # init speaker manager for multi-speaker training # it maps speaker-id to speaker-name in the model and data-loader diff --git a/recipes/vctk/fast_pitch/train_fast_pitch.py b/recipes/vctk/fast_pitch/train_fast_pitch.py index 986202c5..d066a539 100644 --- a/recipes/vctk/fast_pitch/train_fast_pitch.py +++ b/recipes/vctk/fast_pitch/train_fast_pitch.py @@ -71,7 +71,7 @@ tokenizer, config = TTSTokenizer.init_from_config(config) # You can define your custom sample loader returning the list of samples. # Or define your custom formatter and pass it to the `load_tts_samples`. # Check `TTS.tts.datasets.load_tts_samples` for more details. -train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size) # init speaker manager for multi-speaker training # it maps speaker-id to speaker-name in the model and data-loader diff --git a/recipes/vctk/fast_speech/train_fast_speech.py b/recipes/vctk/fast_speech/train_fast_speech.py index fe785a41..dbe23351 100644 --- a/recipes/vctk/fast_speech/train_fast_speech.py +++ b/recipes/vctk/fast_speech/train_fast_speech.py @@ -69,7 +69,7 @@ tokenizer, config = TTSTokenizer.init_from_config(config) # You can define your custom sample loader returning the list of samples. # Or define your custom formatter and pass it to the `load_tts_samples`. # Check `TTS.tts.datasets.load_tts_samples` for more details. -train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size) # init speaker manager for multi-speaker training # it maps speaker-id to speaker-name in the model and data-loader diff --git a/recipes/vctk/glow_tts/train_glow_tts.py b/recipes/vctk/glow_tts/train_glow_tts.py index ebdbfb37..8a891e5d 100644 --- a/recipes/vctk/glow_tts/train_glow_tts.py +++ b/recipes/vctk/glow_tts/train_glow_tts.py @@ -69,7 +69,7 @@ tokenizer, config = TTSTokenizer.init_from_config(config) # You can define your custom sample loader returning the list of samples. # Or define your custom formatter and pass it to the `load_tts_samples`. # Check `TTS.tts.datasets.load_tts_samples` for more details. -train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size) # init speaker manager for multi-speaker training # it maps speaker-id to speaker-name in the model and data-loader diff --git a/recipes/vctk/speedy_speech/train_speedy_speech.py b/recipes/vctk/speedy_speech/train_speedy_speech.py index 80d21ca2..d9353af2 100644 --- a/recipes/vctk/speedy_speech/train_speedy_speech.py +++ b/recipes/vctk/speedy_speech/train_speedy_speech.py @@ -69,7 +69,7 @@ tokenizer, config = TTSTokenizer.init_from_config(config) # You can define your custom sample loader returning the list of samples. # Or define your custom formatter and pass it to the `load_tts_samples`. # Check `TTS.tts.datasets.load_tts_samples` for more details. -train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size) # init speaker manager for multi-speaker training # it maps speaker-id to speaker-name in the model and data-loader diff --git a/recipes/vctk/tacotron-DDC/train_tacotron-DDC.py b/recipes/vctk/tacotron-DDC/train_tacotron-DDC.py index bed21ad9..14007239 100644 --- a/recipes/vctk/tacotron-DDC/train_tacotron-DDC.py +++ b/recipes/vctk/tacotron-DDC/train_tacotron-DDC.py @@ -72,7 +72,7 @@ tokenizer, config = TTSTokenizer.init_from_config(config) # You can define your custom sample loader returning the list of samples. # Or define your custom formatter and pass it to the `load_tts_samples`. # Check `TTS.tts.datasets.load_tts_samples` for more details. -train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size) # init speaker manager for multi-speaker training # it mainly handles speaker-id to speaker-name for the model and the data-loader diff --git a/recipes/vctk/tacotron2-DDC/train_tacotron2-ddc.py b/recipes/vctk/tacotron2-DDC/train_tacotron2-ddc.py index caa745b3..ab2e1bc9 100644 --- a/recipes/vctk/tacotron2-DDC/train_tacotron2-ddc.py +++ b/recipes/vctk/tacotron2-DDC/train_tacotron2-ddc.py @@ -78,7 +78,7 @@ tokenizer, config = TTSTokenizer.init_from_config(config) # You can define your custom sample loader returning the list of samples. # Or define your custom formatter and pass it to the `load_tts_samples`. # Check `TTS.tts.datasets.load_tts_samples` for more details. -train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size) # init speaker manager for multi-speaker training # it mainly handles speaker-id to speaker-name for the model and the data-loader diff --git a/recipes/vctk/tacotron2/train_tacotron2.py b/recipes/vctk/tacotron2/train_tacotron2.py index 43f5d4e6..48934e2a 100644 --- a/recipes/vctk/tacotron2/train_tacotron2.py +++ b/recipes/vctk/tacotron2/train_tacotron2.py @@ -78,7 +78,7 @@ tokenizer, config = TTSTokenizer.init_from_config(config) # You can define your custom sample loader returning the list of samples. # Or define your custom formatter and pass it to the `load_tts_samples`. # Check `TTS.tts.datasets.load_tts_samples` for more details. -train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size) # init speaker manager for multi-speaker training # it mainly handles speaker-id to speaker-name for the model and the data-loader diff --git a/recipes/vctk/vits/train_vits.py b/recipes/vctk/vits/train_vits.py index 84e8a058..443dbbd1 100644 --- a/recipes/vctk/vits/train_vits.py +++ b/recipes/vctk/vits/train_vits.py @@ -79,7 +79,7 @@ tokenizer, config = TTSTokenizer.init_from_config(config) # You can define your custom sample loader returning the list of samples. # Or define your custom formatter and pass it to the `load_tts_samples`. # Check `TTS.tts.datasets.load_tts_samples` for more details. -train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) +train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size) # init speaker manager for multi-speaker training # it maps speaker-id to speaker-name in the model and data-loader