Add LJSpeech SpeedySpeech recipe

recipes/ljspeech/speedy_speech/train_speedy_speech.py

@@ -0,0 +1,68 @@
+import os
+
+from TTS.config import BaseAudioConfig, BaseDatasetConfig
+from TTS.trainer import Trainer, TrainingArgs, init_training
+from TTS.tts.configs import SpeedySpeechConfig
+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,
+    do_trim_silence=True,
+    trim_db=60.0,
+    signal_norm=False,
+    mel_fmin=0.0,
+    mel_fmax=8000,
+    spec_gain=1.0,
+    log_func="np.log",
+    ref_level_db=20,
+    preemphasis=0.0,
+)
+
+config = SpeedySpeechConfig(
+    run_name="speedy_speech_ljspeech",
+    audio=audio_config,
+    batch_size=32,
+    eval_batch_size=16,
+    num_loader_workers=4,
+    num_eval_loader_workers=4,
+    compute_input_seq_cache=True,
+    run_eval=True,
+    test_delay_epochs=-1,
+    epochs=1000,
+    text_cleaner="english_cleaners",
+    use_phonemes=True,
+    use_espeak_phonemes=False,
+    phoneme_language="en-us",
+    phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
+    print_step=50,
+    print_eval=False,
+    mixed_precision=False,
+    sort_by_audio_len=True,
+    max_seq_len=500000,
+    output_path=output_path,
+    datasets=[dataset_config],
+)
+
+# compute alignments
+if not config.model_args.use_aligner:
+    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()