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()