shigabeev commented 2 years ago

Describe the bug

Hey! I'm trying to run models with speaker consistency loss and the inference doesn't run:

import torch
from TTS.tts.utils.synthesis import synthesis

model_root = path/to/model
C = load_config(model_root + '/config.json')
state_dict = torch.load(model_root + '/best_model.pth')
model = setup_model(config=C)
model.load_state_dict(state_dict['model'])

res = synthesis(model, text="This is an example of the voice produced by multi speaker model.", 
          CONFIG=C, 
          use_cuda=False,
          d_vector=torch.randn(512),
          )

It returns the same error as in #1457

AttributeError: 'StochasticDurationPredictor' object has no attribute 'cond'

Fun fact: defining d_vector_dim doesn't help anymore.

When I change config to:

C.model_args['use_d_vector_file'] = True
C.model_args['d_vector_file'] = TTS_SPEAKERS

it allows to partially load weights of model with strict=false: model.load_state_dict(model_weights, strict=False) But the inference is of understandably terrible quality since some layers didn't load and contain pure noise.

Another option is to change config with:

C.model_args['d_vector_file'] = None
C.model_args['speaker_encoder_model_path'] = None
C.model_args['speaker_encoder_config_path'] = None
C.model_args['use_speaker_encoder_as_loss'] = False
C.model_args['use_d_vector_file'] = False
C.use_speaker_embedding = True

This way, weights will load without raising error, but since there's no trace of speaker encoder left in model, it would raise the same error as in #1457

AttributeError: 'StochasticDurationPredictor' object has no attribute 'cond'

Related Issues:

2059 - if use_speaker_encoder_as_loss set to True, model is either able to train itself, either to run inference. But I never seen a config that allows for both. Even an original YourTTS one.

To Reproduce

Steps to reproduce:

launch the config for a few steps, i.e. 2, it would generate weights for the model.
Run inference through CLI/Python.

Expected behavior

Inference runs without any modifications of the config

Logs

No response

Environment

Coqui-TTS version: 0.8.0
OS: Ubuntu
CUDA: 11.5
Python: 3.8.13
Pytorch: 1.12.1

Additional context

My model config:

{
    "output_path": "/home/frappuccino/recipes/out/",
    "logger_uri": null,
    "run_name": "vits_spk_consist",
    "project_name": null,
    "run_description": "\ud83d\udc38Coqui trainer run.",
    "print_step": 25,
    "plot_step": 100,
    "model_param_stats": false,
    "wandb_entity": null,
    "dashboard_logger": "tensorboard",
    "log_model_step": 10000,
    "save_step": 10000,
    "save_n_checkpoints": 5,
    "save_checkpoints": true,
    "save_all_best": false,
    "save_best_after": 10000,
    "target_loss": null,
    "print_eval": false,
    "test_delay_epochs": -1,
    "run_eval": true,
    "run_eval_steps": null,
    "distributed_backend": "nccl",
    "distributed_url": "tcp://localhost:54321",
    "mixed_precision": false,
    "epochs": 1000,
    "batch_size": 32,
    "eval_batch_size": 32,
    "grad_clip": [
        5.0,
        5.0
    ],
    "scheduler_after_epoch": true,
    "lr": 0.001,
    "optimizer": "AdamW",
    "optimizer_params": {
        "betas": [
            0.8,
            0.99
        ],
        "eps": 1e-09,
        "weight_decay": 0.01
    },
    "lr_scheduler": "",
    "lr_scheduler_params": {},
    "use_grad_scaler": false,
    "cudnn_enable": true,
    "cudnn_deterministic": false,
    "cudnn_benchmark": false,
    "training_seed": 54321,
    "model": "vits",
    "num_loader_workers": 12,
    "num_eval_loader_workers": 1,
    "use_noise_augment": false,
    "audio": {
        "fft_size": 1024,
        "sample_rate": 16000,
        "win_length": 1024,
        "hop_length": 256,
        "num_mels": 80,
        "mel_fmin": 0,
        "mel_fmax": null
    },
    "use_phonemes": true,
    "phonemizer": "espeak",
    "phoneme_language": "en",
    "compute_input_seq_cache": true,
    "text_cleaner": "english_cleaners",
    "enable_eos_bos_chars": false,
    "test_sentences_file": "",
    "phoneme_cache_path": "/home/frappuccino/recipes/out/phoneme_cache",
    "characters": {
        "characters_class": "TTS.tts.utils.text.characters.IPAPhonemes",
        "vocab_dict": null,
        "pad": "#",
        "eos": "$",
        "bos": "^",
        "blank": "_",
        "characters": "iy\u0268\u0289\u026fu\u026a\u028f\u028ae\u00f8\u0258\u0259\u0275\u0264o\u025b\u0153\u025c\u025e\u028c\u0254\u00e6\u0250a\u0276\u0251\u0252\u1d7b\u0298\u0253\u01c0\u0257\u01c3\u0284\u01c2\u0260\u01c1\u029bpbtd\u0288\u0256c\u025fk\u0261q\u0262\u0294\u0274\u014b\u0272\u0273n\u0271m\u0299r\u0280\u2c71\u027e\u027d\u0278\u03b2fv\u03b8\u00f0sz\u0283\u0292\u0282\u0290\u00e7\u029dx\u0263\u03c7\u0281\u0127\u0295h\u0266\u026c\u026e\u028b\u0279\u027bj\u0270l\u026d\u028e\u029f\u02c8\u02cc\u02d0\u02d1\u028dw\u0265\u029c\u02a2\u02a1\u0255\u0291\u027a\u0267\u02b2\u025a\u02de\u026b",
        "punctuations": "!'(),-.:;? ",
        "phonemes": null,
        "is_unique": true,
        "is_sorted": true
    },
    "add_blank": true,
    "batch_group_size": 0,
    "loss_masking": null,
    "min_audio_len": 1,
    "max_audio_len": Infinity,
    "min_text_len": 1,
    "max_text_len": 500,
    "compute_f0": false,
    "compute_linear_spec": true,
    "precompute_num_workers": 0,
    "start_by_longest": false,
    "datasets": [
        {
            "name": "libri_tts",
            "path": "/home/frappuccino/data/librispeech16k/train-clean-360-16k/",
            "meta_file_train": "",
            "ignored_speakers": null,
            "language": "en",
            "meta_file_val": "",
            "meta_file_attn_mask": ""
        }
    ],
    "test_sentences": [
        [
            "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."
        ],
        [
            "Be a voice, not an echo."
        ],
        [
            "I'm sorry Dave. I'm afraid I can't do that."
        ],
        [
            "This cake is great. It's so delicious and moist."
        ],
        [
            "Prior to November 22, 1963."
        ]
    ],
    "eval_split_max_size": null,
    "eval_split_size": 0.01,
    "use_speaker_weighted_sampler": false,
    "speaker_weighted_sampler_alpha": 1.0,
    "use_language_weighted_sampler": false,
    "language_weighted_sampler_alpha": 1.0,
    "use_length_weighted_sampler": false,
    "length_weighted_sampler_alpha": 1.0,
    "model_args": {
        "num_chars": 131,
        "out_channels": 513,
        "spec_segment_size": 62,
        "hidden_channels": 192,
        "hidden_channels_ffn_text_encoder": 768,
        "num_heads_text_encoder": 2,
        "num_layers_text_encoder": 10,
        "kernel_size_text_encoder": 3,
        "dropout_p_text_encoder": 0.1,
        "dropout_p_duration_predictor": 0.5,
        "kernel_size_posterior_encoder": 5,
        "dilation_rate_posterior_encoder": 1,
        "num_layers_posterior_encoder": 16,
        "kernel_size_flow": 5,
        "dilation_rate_flow": 1,
        "num_layers_flow": 4,
        "resblock_type_decoder": "2",
        "resblock_kernel_sizes_decoder": [
            3,
            7,
            11
        ],
        "resblock_dilation_sizes_decoder": [
            [
                1,
                3,
                5
            ],
            [
                1,
                3,
                5
            ],
            [
                1,
                3,
                5
            ]
        ],
        "upsample_rates_decoder": [
            8,
            8,
            2,
            2
        ],
        "upsample_initial_channel_decoder": 512,
        "upsample_kernel_sizes_decoder": [
            16,
            16,
            4,
            4
        ],
        "periods_multi_period_discriminator": [
            2,
            3,
            5,
            7,
            11
        ],
        "use_sdp": true,
        "noise_scale": 1.0,
        "inference_noise_scale": 0.3,
        "length_scale": 1.5,
        "noise_scale_dp": 0.6,
        "inference_noise_scale_dp": 0.3,
        "max_inference_len": null,
        "init_discriminator": true,
        "use_spectral_norm_disriminator": false,
        "use_speaker_embedding": false,
        "num_speakers": 902,
        "speakers_file": "out/vits_spk_consist-October-20-2022_07+43AM-9017f31/speakers.pth",
        "d_vector_file": null,
        "speaker_embedding_channels": 512,
        "use_d_vector_file": false,
        "d_vector_dim": 512,
        "detach_dp_input": true,
        "use_language_embedding": false,
        "embedded_language_dim": 4,
        "num_languages": 0,
        "language_ids_file": null,
        "use_speaker_encoder_as_loss": true,
        "speaker_encoder_config_path": "config_se.json",
        "speaker_encoder_model_path": "SE_checkpoint.pth.tar",
        "condition_dp_on_speaker": true,
        "freeze_encoder": false,
        "freeze_DP": false,
        "freeze_PE": false,
        "freeze_flow_decoder": false,
        "freeze_waveform_decoder": false,
        "encoder_sample_rate": null,
        "interpolate_z": true,
        "reinit_DP": false,
        "reinit_text_encoder": false
    },
    "lr_gen": 0.0002,
    "lr_disc": 0.0002,
    "lr_scheduler_gen": "ExponentialLR",
    "lr_scheduler_gen_params": {
        "gamma": 0.999875,
        "last_epoch": -1
    },
    "lr_scheduler_disc": "ExponentialLR",
    "lr_scheduler_disc_params": {
        "gamma": 0.999875,
        "last_epoch": -1
    },
    "kl_loss_alpha": 1.0,
    "disc_loss_alpha": 1.0,
    "gen_loss_alpha": 1.0,
    "feat_loss_alpha": 1.0,
    "mel_loss_alpha": 45.0,
    "dur_loss_alpha": 1.0,
    "speaker_encoder_loss_alpha": 9.0,
    "return_wav": true,
    "use_weighted_sampler": false,
    "weighted_sampler_attrs": {},
    "weighted_sampler_multipliers": {},
    "r": 1,
    "num_speakers": 902,
    "use_speaker_embedding": false,
    "speakers_file": "out/vits_spk_consist-October-20-2022_07+43AM-9017f31/speakers.pth",
    "speaker_embedding_channels": 512,
    "language_ids_file": null,
    "use_language_embedding": false,
    "use_d_vector_file": false,
    "d_vector_file": null,
    "d_vector_dim": 512
}

erogol commented 2 years ago

Can you check this @Edresson ?

Edresson commented 2 years ago

Hi @shigabeev,

Looks like you are trying to do inference providing d-vectors (speaker embeddings) on a model that wasn't trained with external d-vectors.

Your training config need to have use_d_vector_file = True and you should provide the "d_vector_file" as well.

Here a short tutorial on how to generate the "d_vector_file": https://github.com/Edresson/YourTTS#reproducibility