diff --git a/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+40AM-debug/config.json b/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+40AM-debug/config.json new file mode 100644 index 00000000..dbea7ef2 --- /dev/null +++ b/checkpoints/libri_tts/speaker_encoder/libritts_100+360-angleproto-July-31-2020_12+40AM-debug/config.json @@ -0,0 +1,60 @@ + +"github_branch":"* dev-gst-embeddings", +{ + "run_name": "libritts_100+360-angleproto", + "run_description": "train speaker encoder for libritts 100 and 360", + "audio":{ + // Audio processing parameters + "num_mels": 80, // size of the mel spec frame. + "num_freq": 1024, // number of stft frequency levels. Size of the linear spectogram frame. + "sample_rate": 22050, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled. + "win_length": 1024, // stft window length in ms. + "hop_length": 256, // stft window hop-lengh in ms. + "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used. + "frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used. + "preemphasis": 0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis. + "min_level_db": -100, // normalization range + "ref_level_db": 20, // reference level db, theoretically 20db is the sound of air. + "power": 1.5, // value to sharpen wav signals after GL algorithm. + "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation. + // Normalization parameters + "signal_norm": true, // normalize the spec values in range [0, 1] + "symmetric_norm": true, // move normalization to range [-1, 1] + "max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm] + "clip_norm": true, // clip normalized values into the range. + "mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!! + "mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!! + "do_trim_silence": false, // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true) + "trim_db": 60 // threshold for timming silence. Set this according to your dataset. + }, + "reinit_layers": [], + "loss": "angleproto", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA) + "grad_clip": 3.0, // upper limit for gradients for clipping. + "epochs": 1000, // total number of epochs to train. + "lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate. + "lr_decay": false, // if true, Noam learning rate decaying is applied through training. + "warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr" + "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. + "steps_plot_stats": 10, // number of steps to plot embeddings. + "num_speakers_in_batch": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'. + "wd": 0.000001, // Weight decay weight. + "checkpoint": true, // If true, it saves checkpoints per "save_step" + "save_step": 1000, // Number of training steps expected to save traning stats and checkpoints. + "print_step": 1, // Number of steps to log traning on console. + "output_path": "../../checkpoints/libri_tts/speaker_encoder/", // DATASET-RELATED: output path for all training outputs. + "model": { + "input_dim": 80, // input_dim == num_mels + "proj_dim": 128, + "lstm_dim": 384, + "num_lstm_layers": 3 + }, + "datasets": + [ + { + "name": "vctk", + "path": "../../../datasets/VCTK-Corpus-removed-silence/", + "meta_file_train": null, + "meta_file_val": null + } + ] +} \ No newline at end of file diff --git a/mozilla_voice_tts/bin/train_encoder.py b/mozilla_voice_tts/bin/train_encoder.py index 1352a02e..46b7f46f 100644 --- a/mozilla_voice_tts/bin/train_encoder.py +++ b/mozilla_voice_tts/bin/train_encoder.py @@ -247,7 +247,7 @@ if __name__ == '__main__': new_fields) LOG_DIR = OUT_PATH - tb_logger = TensorboardLogger(LOG_DIR) + tb_logger = TensorboardLogger(LOG_DIR, model_name='Speaker_Encoder') try: main(args) diff --git a/mozilla_voice_tts/speaker_encoder/config.json b/mozilla_voice_tts/speaker_encoder/config.json index 281578da..37d976ce 100644 --- a/mozilla_voice_tts/speaker_encoder/config.json +++ b/mozilla_voice_tts/speaker_encoder/config.json @@ -36,6 +36,7 @@ "tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. "steps_plot_stats": 10, // number of steps to plot embeddings. "num_speakers_in_batch": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'. + "num_loader_workers": 4, // number of training data loader processes. Don't set it too big. 4-8 are good values. "wd": 0.000001, // Weight decay weight. "checkpoint": true, // If true, it saves checkpoints per "save_step" "save_step": 1000, // Number of training steps expected to save traning stats and checkpoints. diff --git a/mozilla_voice_tts/speaker_encoder/dataset.py b/mozilla_voice_tts/speaker_encoder/dataset.py index 42c75dd9..d3243c13 100644 --- a/mozilla_voice_tts/speaker_encoder/dataset.py +++ b/mozilla_voice_tts/speaker_encoder/dataset.py @@ -31,7 +31,7 @@ class MyDataset(Dataset): print(f" | > Num speakers: {len(self.speakers)}") def load_wav(self, filename): - audio = self.ap.load_wav(filename) + audio = self.ap.load_wav(filename, sr=self.ap.sample_rate) return audio def load_data(self, idx):