mirror of https://github.com/coqui-ai/TTS.git
fix broken imports in speaker encoder
This commit is contained in:
Edresson
erogol
parent
ac85ccae99
commit
5e11d81e12
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@ -0,0 +1,60 @@
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"github_branch":"* dev-gst-embeddings",
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{
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"run_name": "libritts_100+360-angleproto",
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"run_description": "train speaker encoder for libritts 100 and 360",
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"audio":{
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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": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
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"sample_rate": 22050, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
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"win_length": 1024, // stft window length in ms.
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"hop_length": 256, // stft window hop-lengh in ms.
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"frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
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"frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
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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": 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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"signal_norm": true, // normalize the spec values in range [0, 1]
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"symmetric_norm": true, // move normalization to range [-1, 1]
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"max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
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"clip_norm": true, // clip normalized values into the range.
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"mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
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"mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
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"do_trim_silence": false, // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
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"trim_db": 60 // threshold for timming silence. Set this according to your dataset.
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},
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"reinit_layers": [],
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"loss": "angleproto", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
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"grad_clip": 3.0, // upper limit for gradients for clipping.
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"epochs": 1000, // total number of epochs to train.
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"lr": 0.0001, // 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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"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
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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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"steps_plot_stats": 10, // number of steps to plot embeddings.
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"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'.
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"wd": 0.000001, // Weight decay weight.
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"checkpoint": true, // If true, it saves checkpoints per "save_step"
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"save_step": 1000, // Number of training steps expected to save traning stats and checkpoints.
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"print_step": 1, // Number of steps to log traning on console.
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"output_path": "../../checkpoints/libri_tts/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
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"model": {
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"input_dim": 80, // input_dim == num_mels
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"proj_dim": 128,
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"lstm_dim": 384,
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"num_lstm_layers": 3
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},
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"datasets":
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[
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{
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"name": "vctk",
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"path": "../../../datasets/VCTK-Corpus-removed-silence/",
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"meta_file_train": null,
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"meta_file_val": null
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}
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]
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}
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@ -0,0 +1,60 @@
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"github_branch":"* dev-gst-embeddings",
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{
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"run_name": "libritts_100+360-angleproto",
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"run_description": "train speaker encoder for libritts 100 and 360",
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"audio":{
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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": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
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"sample_rate": 22050, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
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"win_length": 1024, // stft window length in ms.
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"hop_length": 256, // stft window hop-lengh in ms.
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"frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
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"frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
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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": 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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"signal_norm": true, // normalize the spec values in range [0, 1]
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"symmetric_norm": true, // move normalization to range [-1, 1]
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"max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
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"clip_norm": true, // clip normalized values into the range.
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"mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
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"mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
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"do_trim_silence": false, // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
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"trim_db": 60 // threshold for timming silence. Set this according to your dataset.
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},
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"reinit_layers": [],
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"loss": "angleproto", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
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"grad_clip": 3.0, // upper limit for gradients for clipping.
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"epochs": 1000, // total number of epochs to train.
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"lr": 0.0001, // 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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"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
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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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"steps_plot_stats": 10, // number of steps to plot embeddings.
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"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'.
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"wd": 0.000001, // Weight decay weight.
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"checkpoint": true, // If true, it saves checkpoints per "save_step"
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"save_step": 1000, // Number of training steps expected to save traning stats and checkpoints.
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"print_step": 1, // Number of steps to log traning on console.
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"output_path": "../../checkpoints/libri_tts/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
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"model": {
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"input_dim": 80, // input_dim == num_mels
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"proj_dim": 128,
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"lstm_dim": 384,
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"num_lstm_layers": 3
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},
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"datasets":
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[
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{
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"name": "vctk",
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"path": "../../../datasets/VCTK-Corpus-removed-silence/",
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"meta_file_train": null,
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"meta_file_val": null
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}
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]
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}
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@ -13,7 +13,7 @@ from torch.utils.data import DataLoader
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from mozilla_voice_tts.generic_utils import count_parameters
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from mozilla_voice_tts.speaker_encoder.dataset import MyDataset
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from mozilla_voice_tts.speaker_encoder.generic_utils import save_best_model
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from mozilla_voice_tts.speaker_encoder.loss import GE2ELoss
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from mozilla_voice_tts.speaker_encoder.losses import GE2ELoss
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from mozilla_voice_tts.speaker_encoder.model import SpeakerEncoder
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from mozilla_voice_tts.speaker_encoder.visual import plot_embeddings
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from mozilla_voice_tts.tts.datasets.preprocess import load_meta_data
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@ -50,14 +50,8 @@
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"datasets":
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[
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{
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"name": "libri_tts",
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"path": "../../datasets/LibriTTS/train-clean-360/",
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"meta_file_train": null,
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"meta_file_val": null
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},
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{
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"name": "libri_tts",
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"path": "../../datasets/LibriTTS/train-clean-100/",
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"name": "vctk",
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"path": "../../../datasets/VCTK-Corpus-removed-silence/",
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"meta_file_train": null,
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"meta_file_val": null
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}
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