mirror of https://github.com/coqui-ai/TTS.git
Merge branch 'dev-embedding-init' into dev
Eren Golge
commit
ebc166b54f
31
config.json
31
config.json
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@ -1,18 +1,18 @@
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{
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"model_name": "TTS-phoneme",
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"model_description": "Training with phonemes created by phonemizer.",
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"model_name": "embed_init_loss_weight",
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"model_description": "Lower loss freq is weight 0.0, higher weight decay, ref_level_db: 40->20, sample_rate:16000 -> 22050",
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"audio":{
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"audio_processor": "audio", // to use dictate different audio processors, if available.
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// Audio processing parameters
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"num_mels": 80, // size of the mel spec frame.
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"num_freq": 1025, // number of stft frequency levels. Size of the linear spectogram frame.
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"sample_rate": 16000, // wav sample-rate. If different than the original data, it is resampled.
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"sample_rate": 22050, // wav sample-rate. If different than the original data, it is resampled.
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"frame_length_ms": 50, // stft window length in ms.
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"frame_shift_ms": 12.5, // stft window hop-lengh in ms.
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"preemphasis": 0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
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"min_level_db": -100, // normalization range
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"ref_level_db": 40, // reference level db, theoretically 20db is the sound of air.
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"ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
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"power": 1.5, // value to sharpen wav signals after GL algorithm.
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"griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
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// Normalization parameters
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@ -30,29 +30,30 @@
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"epochs": 1000, // total number of epochs to train.
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"lr": 0.001, // Initial learning rate. If Noam decay is active, maximum learning rate.
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"lr_decay": false, // if true, Noam learning rate decaying is applied through training.
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"loss_weight": 0.0, // loss weight to emphasize lower frequencies. Lower frequencies are in general more important for speech signals.
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"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
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"windowing": true, // Enables attention windowing. Used only in eval mode.
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"windowing": false, // Enables attention windowing. Used only in eval mode.
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"batch_size": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention.
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"eval_batch_size":32,
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"r": 5, // Number of frames to predict for step.
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"wd": 0.000001, // Weight decay weight.
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"wd": 0.00001, // Weight decay weight.
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"checkpoint": true, // If true, it saves checkpoints per "save_step"
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"save_step": 5000, // Number of training steps expected to save traning stats and checkpoints.
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"print_step": 10, // Number of steps to log traning on console.
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"tb_model_param_stats": true, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
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"print_step": 50, // Number of steps to log traning on console.
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"tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
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"run_eval": true,
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"data_path": "/home/erogol/Data/en_UK/by_book/female/elizabeth_klett/", // DATASET-RELATED: can overwritten from command argument
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"meta_file_train": "jane_eyre/metadata_train.csv, wives_and_daughters/metadata_train.csv", // DATASET-RELATED: metafile for training dataloader.
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"meta_file_val": "jane_eyre/metadata_val.csv, wives_and_daughters/metadata_val.csv", // DATASET-RELATED: metafile for evaluation dataloader.
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"dataset": "mailabs", // DATASET-RELATED: one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
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"data_path": "/media/erogol/data_ssd/Data/LJSpeech-1.1", // DATASET-RELATED: can overwritten from command argument
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"meta_file_train": "metadata_train.csv", // DATASET-RELATED: metafile for training dataloader.
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"meta_file_val": "metadata_val.csv", // DATASET-RELATED: metafile for evaluation dataloader.
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"dataset": "ljspeech", // DATASET-RELATED: one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
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"min_seq_len": 0, // DATASET-RELATED: minimum text length to use in training
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"max_seq_len": 300, // DATASET-RELATED: maximum text length
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"output_path": "/media/erogol/data_ssd/Data/models/en_UK/", // DATASET-RELATED: output path for all training outputs.
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"output_path": "/media/erogol/data_ssd/Data/models/ljspeech_models/", // DATASET-RELATED: output path for all training outputs.
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"num_loader_workers": 8, // number of training data loader processes. Don't set it too big. 4-8 are good values.
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"num_val_loader_workers": 4, // number of evaluation data loader processes.
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"phoneme_cache_path": "tmp_phonemes_gb", // phoneme computation is slow, therefore, it caches results in the given folder.
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"phoneme_cache_path": "ljspeech_us_phonemes", // phoneme computation is slow, therefore, it caches results in the given folder.
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"use_phonemes": true, // use phonemes instead of raw characters. It is suggested for better pronounciation.
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"phoneme_language": "en-gb" // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
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"phoneme_language": "en-us" // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
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}
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@ -323,6 +323,10 @@ class Decoder(nn.Module):
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[nn.GRUCell(256, 256) for _ in range(2)])
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# RNN_state -> |Linear| -> mel_spec
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self.proj_to_mel = nn.Linear(256, memory_dim * r)
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# learn init values instead of zero init.
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self.attention_rnn_init = nn.Embedding(1, 256)
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self.memory_init = nn.Embedding(1, r * memory_dim)
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self.decoder_rnn_inits = nn.Embedding(2, 256)
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self.stopnet = StopNet(256 + memory_dim * r)
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# self.init_layers()
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@ -354,12 +358,12 @@ class Decoder(nn.Module):
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B = inputs.size(0)
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T = inputs.size(1)
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# go frame as zeros matrix
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initial_memory = inputs.data.new(B, self.memory_dim * self.r).zero_()
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initial_memory = self.memory_init(inputs.data.new_zeros(B).long())
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# decoder states
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attention_rnn_hidden = inputs.data.new(B, 256).zero_()
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attention_rnn_hidden = self.attention_rnn_init(inputs.data.new_zeros(B).long())
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decoder_rnn_hiddens = [
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inputs.data.new(B, 256).zero_()
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for _ in range(len(self.decoder_rnns))
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self.decoder_rnn_inits(inputs.data.new_tensor([idx]*B).long())
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for idx in range(len(self.decoder_rnns))
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]
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current_context_vec = inputs.data.new(B, self.in_features).zero_()
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# attention states
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4
train.py
4
train.py
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@ -126,8 +126,8 @@ def train(model, criterion, criterion_st, optimizer, optimizer_st,
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# loss computation
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stop_loss = criterion_st(stop_tokens, stop_targets)
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mel_loss = criterion(mel_output, mel_input, mel_lengths)
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linear_loss = 0.5 * criterion(linear_output, linear_input, mel_lengths)\
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+ 0.5 * criterion(linear_output[:, :, :n_priority_freq],
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linear_loss = (1 - c.loss_weight) * criterion(linear_output, linear_input, mel_lengths)\
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+ c.loss_weight * criterion(linear_output[:, :, :n_priority_freq],
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linear_input[:, :, :n_priority_freq],
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mel_lengths)
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loss = mel_loss + linear_loss
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