mirror of https://github.com/coqui-ai/TTS.git
Implement `forward_tts`
- Generic API for feed-forward TTS models (FastPitch, SpeedySpeech) - Tests for `forward-tts` - Edit FastPitchConfig and SpeedySpeechConfig to use `forward_tts`
This commit is contained in:
Eren Gölge
parent
3c740d4893
commit
8b7e094bde
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@ -2,12 +2,12 @@ from dataclasses import dataclass, field
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from typing import List
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from TTS.tts.configs.shared_configs import BaseTTSConfig
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from TTS.tts.models.fast_pitch import FastPitchArgs
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from TTS.tts.models.forward_tts import ForwardTTSArgs
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@dataclass
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class FastPitchConfig(BaseTTSConfig):
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"""Defines parameters for Speedy Speech (feed-forward encoder-decoder) based models.
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"""Configure `ForwardTTS` as FastPitch model.
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Example:
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@ -36,22 +36,43 @@ class FastPitchConfig(BaseTTSConfig):
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d_vector_file (str):
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Path to the file including pre-computed speaker embeddings. Defaults to None.
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noam_schedule (bool):
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enable / disable the use of Noam LR scheduler. Defaults to False.
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d_vector_dim (int):
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Dimension of the external speaker embeddings. Defaults to 0.
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warmup_steps (int):
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Number of warm-up steps for the Noam scheduler. Defaults 4000.
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optimizer (str):
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Name of the model optimizer. Defaults to `Adam`.
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optimizer_params (dict):
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Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
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lr_scheduler (str):
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Name of the learning rate scheduler. Defaults to `Noam`.
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lr_scheduler_params (dict):
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Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
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lr (float):
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Initial learning rate. Defaults to `1e-3`.
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grad_clip (float):
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Gradient norm clipping value. Defaults to `5.0`.
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spec_loss_type (str):
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Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
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duration_loss_type (str):
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Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
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use_ssim_loss (bool):
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Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
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wd (float):
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Weight decay coefficient. Defaults to `1e-7`.
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ssim_loss_alpha (float):
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Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
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huber_loss_alpha (float):
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dur_loss_alpha (float):
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Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
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spec_loss_alpha (float):
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@ -73,9 +94,9 @@ class FastPitchConfig(BaseTTSConfig):
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Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
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"""
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model: str = "fast_pitch"
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model: str = "forward_tts"
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# model specific params
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model_args: FastPitchArgs = field(default_factory=FastPitchArgs)
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model_args: ForwardTTSArgs = field(default_factory=ForwardTTSArgs)
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# multi-speaker settings
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use_speaker_embedding: bool = False
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@ -92,11 +113,13 @@ class FastPitchConfig(BaseTTSConfig):
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grad_clip: float = 5.0
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# loss params
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spec_loss_type: str = "mse"
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duration_loss_type: str = "mse"
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use_ssim_loss: bool = True
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ssim_loss_alpha: float = 1.0
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dur_loss_alpha: float = 1.0
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spec_loss_alpha: float = 1.0
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pitch_loss_alpha: float = 1.0
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dur_loss_alpha: float = 1.0
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aligner_loss_alpha: float = 1.0
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binary_align_loss_alpha: float = 1.0
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binary_align_loss_start_step: int = 20000
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@ -2,81 +2,154 @@ from dataclasses import dataclass, field
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from typing import List
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from TTS.tts.configs.shared_configs import BaseTTSConfig
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from TTS.tts.models.speedy_speech import SpeedySpeechArgs
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from TTS.tts.models.forward_tts import ForwardTTSArgs
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@dataclass
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class SpeedySpeechConfig(BaseTTSConfig):
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"""Defines parameters for Speedy Speech (feed-forward encoder-decoder) based models.
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"""Configure `ForwardTTS` as SpeedySpeech model.
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Example:
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>>> from TTS.tts.configs import SpeedySpeechConfig
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>>> config = SpeedySpeechConfig()
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Args:
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Args:
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model (str):
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Model name used for selecting the right model at initialization. Defaults to `speedy_speech`.
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Model name used for selecting the right model at initialization. Defaults to `fast_pitch`.
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model_args (Coqpit):
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Model class arguments. Check `SpeedySpeechArgs` for more details. Defaults to `SpeedySpeechArgs()`.
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Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`.
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data_dep_init_steps (int):
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Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
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Activation Normalization that pre-computes normalization stats at the beginning and use the same values
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for the rest. Defaults to 10.
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use_speaker_embedding (bool):
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enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
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in the multi-speaker mode. Defaults to False.
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use_d_vector_file (bool):
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enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
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d_vector_file (str):
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Path to the file including pre-computed speaker embeddings. Defaults to None.
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noam_schedule (bool):
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enable / disable the use of Noam LR scheduler. Defaults to False.
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warmup_steps (int):
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Number of warm-up steps for the Noam scheduler. Defaults 4000.
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d_vector_dim (int):
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Dimension of the external speaker embeddings. Defaults to 0.
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optimizer (str):
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Name of the model optimizer. Defaults to `RAdam`.
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optimizer_params (dict):
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Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
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lr_scheduler (str):
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Name of the learning rate scheduler. Defaults to `Noam`.
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lr_scheduler_params (dict):
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Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
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lr (float):
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Initial learning rate. Defaults to `1e-3`.
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grad_clip (float):
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Gradient norm clipping value. Defaults to `5.0`.
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spec_loss_type (str):
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Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `l1`.
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duration_loss_type (str):
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Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `huber`.
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use_ssim_loss (bool):
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Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
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wd (float):
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Weight decay coefficient. Defaults to `1e-7`.
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ssim_alpha (float):
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Weight for the SSIM loss. If set <= 0, disables the SSIM loss. Defaults to 1.0.
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huber_alpha (float):
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Weight for the duration predictor's loss. Defaults to 1.0.
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l1_alpha (float):
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Weight for the L1 spectrogram loss. If set <= 0, disables the L1 loss. Defaults to 1.0.
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ssim_loss_alpha (float):
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Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
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dur_loss_alpha (float):
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Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
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spec_loss_alpha (float):
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Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
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binary_loss_alpha (float):
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Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
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binary_align_loss_start_step (int):
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Start binary alignment loss after this many steps. Defaults to 20000.
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min_seq_len (int):
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Minimum input sequence length to be used at training.
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max_seq_len (int):
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Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
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"""
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model: str = "speedy_speech"
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# model specific params
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model_args: SpeedySpeechArgs = field(default_factory=SpeedySpeechArgs)
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model: str = "forward_tts"
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# set model args as SpeedySpeech
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model_args: ForwardTTSArgs = ForwardTTSArgs(
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use_pitch=False,
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encoder_type="residual_conv_bn",
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encoder_params={
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"kernel_size": 4,
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"dilations": 4 * [1, 2, 4] + [1],
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"num_conv_blocks": 2,
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"num_res_blocks": 13,
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},
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decoder_type="residual_conv_bn",
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decoder_params={
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"kernel_size": 4,
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"dilations": 4 * [1, 2, 4, 8] + [1],
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"num_conv_blocks": 2,
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"num_res_blocks": 17,
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},
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out_channels=80,
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hidden_channels=128,
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num_speakers=0,
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positional_encoding=True,
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)
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# multi-speaker settings
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use_speaker_embedding: bool = False
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use_d_vector_file: bool = False
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d_vector_file: str = False
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d_vector_dim: int = 0
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# optimizer parameters
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optimizer: str = "RAdam"
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optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
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lr_scheduler: str = None
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lr_scheduler_params: dict = None
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lr_scheduler: str = "NoamLR"
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lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
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lr: float = 1e-4
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grad_clip: float = 5.0
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# loss params
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ssim_alpha: float = 1.0
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huber_alpha: float = 1.0
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l1_alpha: float = 1.0
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spec_loss_type: str = "l1"
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duration_loss_type: str = "huber"
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use_ssim_loss: bool = True
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ssim_loss_alpha: float = 1.0
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dur_loss_alpha: float = 1.0
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spec_loss_alpha: float = 1.0
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aligner_loss_alpha: float = 1.0
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binary_align_loss_alpha: float = 1.0
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binary_align_loss_start_step: int = 20000
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# overrides
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min_seq_len: int = 13
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max_seq_len: int = 200
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r: int = 1 # DO NOT CHANGE
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# dataset configs
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compute_f0: bool = False
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f0_cache_path: str = None
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# testing
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test_sentences: List[str] = field(
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default_factory=lambda: [
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@ -0,0 +1,695 @@
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from dataclasses import dataclass, field
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from typing import Dict, Tuple
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import torch
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from coqpit import Coqpit
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from torch import nn
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from torch.cuda.amp.autocast_mode import autocast
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from TTS.tts.layers.feed_forward.decoder import Decoder
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from TTS.tts.layers.feed_forward.encoder import Encoder
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from TTS.tts.layers.generic.aligner import AlignmentNetwork
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from TTS.tts.layers.generic.pos_encoding import PositionalEncoding
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from TTS.tts.layers.glow_tts.duration_predictor import DurationPredictor
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from TTS.tts.models.base_tts import BaseTTS
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from TTS.tts.utils.helpers import average_over_durations, generate_path, maximum_path, sequence_mask
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from TTS.tts.utils.visual import plot_alignment, plot_pitch, plot_spectrogram
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from TTS.utils.audio import AudioProcessor
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@dataclass
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class ForwardTTSArgs(Coqpit):
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"""ForwardTTS Model arguments.
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Args:
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num_chars (int):
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Number of characters in the vocabulary. Defaults to 100.
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out_channels (int):
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Number of output channels. Defaults to 80.
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hidden_channels (int):
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Number of base hidden channels of the model. Defaults to 512.
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num_speakers (int):
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Number of speakers for the speaker embedding layer. Defaults to 0.
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use_aligner (bool):
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Whether to use aligner network to learn the text to speech alignment or use pre-computed durations.
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If set False, durations should be computed by `TTS/bin/compute_attention_masks.py` and path to the
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pre-computed durations must be provided to `config.datasets[0].meta_file_attn_mask`. Defaults to True.
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use_pitch (bool):
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Use pitch predictor to learn the pitch. Defaults to True.
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duration_predictor_hidden_channels (int):
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Number of hidden channels in the duration predictor. Defaults to 256.
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duration_predictor_dropout_p (float):
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Dropout rate for the duration predictor. Defaults to 0.1.
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duration_predictor_kernel_size (int):
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Kernel size of conv layers in the duration predictor. Defaults to 3.
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pitch_predictor_hidden_channels (int):
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Number of hidden channels in the pitch predictor. Defaults to 256.
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pitch_predictor_dropout_p (float):
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Dropout rate for the pitch predictor. Defaults to 0.1.
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pitch_predictor_kernel_size (int):
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Kernel size of conv layers in the pitch predictor. Defaults to 3.
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pitch_embedding_kernel_size (int):
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Kernel size of the projection layer in the pitch predictor. Defaults to 3.
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positional_encoding (bool):
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Whether to use positional encoding. Defaults to True.
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positional_encoding_use_scale (bool):
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Whether to use a learnable scale coeff in the positional encoding. Defaults to True.
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length_scale (int):
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Length scale that multiplies the predicted durations. Larger values result slower speech. Defaults to 1.0.
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encoder_type (str):
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Type of the encoder module. One of the encoders available in :class:`TTS.tts.layers.feed_forward.encoder`.
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Defaults to `fftransformer` as in the paper.
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encoder_params (dict):
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Parameters of the encoder module. Defaults to ```{"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1}```
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decoder_type (str):
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Type of the decoder module. One of the decoders available in :class:`TTS.tts.layers.feed_forward.decoder`.
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Defaults to `fftransformer` as in the paper.
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decoder_params (str):
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Parameters of the decoder module. Defaults to ```{"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1}```
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use_d_vetor (bool):
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Whether to use precomputed d-vectors for multi-speaker training. Defaults to False.
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d_vector_dim (int):
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Number of channels of the d-vectors. Defaults to 0.
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detach_duration_predictor (bool):
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Detach the input to the duration predictor from the earlier computation graph so that the duraiton loss
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does not pass to the earlier layers. Defaults to True.
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max_duration (int):
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Maximum duration accepted by the model. Defaults to 75.
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"""
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num_chars: int = None
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out_channels: int = 80
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hidden_channels: int = 384
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num_speakers: int = 0
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use_aligner: bool = True
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use_pitch: bool = True
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pitch_predictor_hidden_channels: int = 256
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pitch_predictor_kernel_size: int = 3
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pitch_predictor_dropout_p: float = 0.1
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pitch_embedding_kernel_size: int = 3
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duration_predictor_hidden_channels: int = 256
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duration_predictor_kernel_size: int = 3
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duration_predictor_dropout_p: float = 0.1
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positional_encoding: bool = True
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poisitonal_encoding_use_scale: bool = True
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length_scale: int = 1
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encoder_type: str = "fftransformer"
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encoder_params: dict = field(
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default_factory=lambda: {"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1}
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)
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decoder_type: str = "fftransformer"
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decoder_params: dict = field(
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default_factory=lambda: {"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1}
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)
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use_d_vector: bool = False
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d_vector_dim: int = 0
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detach_duration_predictor: bool = False
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max_duration: int = 75
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class ForwardTTS(BaseTTS):
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"""General forward TTS model implementation that uses an encoder-decoder architecture with an optional alignment
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network and a pitch predictor.
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If the alignment network is used, the model learns the text-to-speech alignment
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from the data instead of using pre-computed durations.
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If the pitch predictor is used, the model trains a pitch predictor that predicts average pitch value for each
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input character as in the FastPitch model.
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`ForwardTTS` can be configured to one of these architectures,
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- FastPitch
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- SpeedySpeech
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- FastSpeech
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- TODO: FastSpeech2 (requires average speech energy predictor)
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Args:
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config (Coqpit): Model coqpit class.
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Examples:
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>>> from TTS.tts.models.fast_pitch import ForwardTTS, ForwardTTSArgs
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>>> config = ForwardTTSArgs()
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>>> model = ForwardTTS(config)
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"""
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# pylint: disable=dangerous-default-value
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def __init__(self, config: Coqpit):
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super().__init__()
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# don't use isintance not to import recursively
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if "Config" in config.__class__.__name__:
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if "characters" in config:
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# loading from FasrPitchConfig
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_, self.config, num_chars = self.get_characters(config)
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config.model_args.num_chars = num_chars
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self.args = self.config.model_args
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else:
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# loading from ForwardTTSArgs
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self.config = config
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self.args = config.model_args
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||||
elif isinstance(config, ForwardTTSArgs):
|
||||
self.args = config
|
||||
self.config = config
|
||||
else:
|
||||
raise ValueError("config must be either a *Config or ForwardTTSArgs")
|
||||
|
||||
self.max_duration = self.args.max_duration
|
||||
self.use_aligner = self.args.use_aligner
|
||||
self.use_pitch = self.args.use_pitch
|
||||
self.use_binary_alignment_loss = False
|
||||
|
||||
self.length_scale = (
|
||||
float(self.args.length_scale) if isinstance(self.args.length_scale, int) else self.args.length_scale
|
||||
)
|
||||
|
||||
self.emb = nn.Embedding(self.args.num_chars, self.args.hidden_channels)
|
||||
|
||||
self.encoder = Encoder(
|
||||
self.args.hidden_channels,
|
||||
self.args.hidden_channels,
|
||||
self.args.encoder_type,
|
||||
self.args.encoder_params,
|
||||
self.args.d_vector_dim,
|
||||
)
|
||||
|
||||
if self.args.positional_encoding:
|
||||
self.pos_encoder = PositionalEncoding(self.args.hidden_channels)
|
||||
|
||||
self.decoder = Decoder(
|
||||
self.args.out_channels,
|
||||
self.args.hidden_channels,
|
||||
self.args.decoder_type,
|
||||
self.args.decoder_params,
|
||||
)
|
||||
|
||||
self.duration_predictor = DurationPredictor(
|
||||
self.args.hidden_channels + self.args.d_vector_dim,
|
||||
self.args.duration_predictor_hidden_channels,
|
||||
self.args.duration_predictor_kernel_size,
|
||||
self.args.duration_predictor_dropout_p,
|
||||
)
|
||||
|
||||
if self.args.use_pitch:
|
||||
self.pitch_predictor = DurationPredictor(
|
||||
self.args.hidden_channels + self.args.d_vector_dim,
|
||||
self.args.pitch_predictor_hidden_channels,
|
||||
self.args.pitch_predictor_kernel_size,
|
||||
self.args.pitch_predictor_dropout_p,
|
||||
)
|
||||
self.pitch_emb = nn.Conv1d(
|
||||
1,
|
||||
self.args.hidden_channels,
|
||||
kernel_size=self.args.pitch_embedding_kernel_size,
|
||||
padding=int((self.args.pitch_embedding_kernel_size - 1) / 2),
|
||||
)
|
||||
|
||||
if self.args.num_speakers > 1 and not self.args.use_d_vector:
|
||||
# speaker embedding layer
|
||||
self.emb_g = nn.Embedding(self.args.num_speakers, self.args.d_vector_dim)
|
||||
nn.init.uniform_(self.emb_g.weight, -0.1, 0.1)
|
||||
|
||||
if self.args.d_vector_dim > 0 and self.args.d_vector_dim != self.args.hidden_channels:
|
||||
self.proj_g = nn.Conv1d(self.args.d_vector_dim, self.args.hidden_channels, 1)
|
||||
|
||||
if self.args.use_aligner:
|
||||
self.aligner = AlignmentNetwork(
|
||||
in_query_channels=self.args.out_channels, in_key_channels=self.args.hidden_channels
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def generate_attn(dr, x_mask, y_mask=None):
|
||||
"""Generate an attention mask from the durations.
|
||||
|
||||
Shapes
|
||||
- dr: :math:`(B, T_{en})`
|
||||
- x_mask: :math:`(B, T_{en})`
|
||||
- y_mask: :math:`(B, T_{de})`
|
||||
"""
|
||||
# compute decode mask from the durations
|
||||
if y_mask is None:
|
||||
y_lengths = dr.sum(1).long()
|
||||
y_lengths[y_lengths < 1] = 1
|
||||
y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(dr.dtype)
|
||||
attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)
|
||||
attn = generate_path(dr, attn_mask.squeeze(1)).to(dr.dtype)
|
||||
return attn
|
||||
|
||||
def expand_encoder_outputs(self, en, dr, x_mask, y_mask):
|
||||
"""Generate attention alignment map from durations and
|
||||
expand encoder outputs
|
||||
|
||||
Shapes
|
||||
- en: :math:`(B, D_{en}, T_{en})`
|
||||
- dr: :math:`(B, T_{en})`
|
||||
- x_mask: :math:`(B, T_{en})`
|
||||
- y_mask: :math:`(B, T_{de})`
|
||||
|
||||
Examples:
|
||||
- encoder output: :math:`[a,b,c,d]`
|
||||
- durations: :math:`[1, 3, 2, 1]`
|
||||
|
||||
- expanded: :math:`[a, b, b, b, c, c, d]`
|
||||
- attention map: :math:`[[0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0]]`
|
||||
"""
|
||||
attn = self.generate_attn(dr, x_mask, y_mask)
|
||||
o_en_ex = torch.matmul(attn.squeeze(1).transpose(1, 2).to(en.dtype), en.transpose(1, 2)).transpose(1, 2)
|
||||
return o_en_ex, attn
|
||||
|
||||
def format_durations(self, o_dr_log, x_mask):
|
||||
"""Format predicted durations.
|
||||
1. Convert to linear scale from log scale
|
||||
2. Apply the length scale for speed adjustment
|
||||
3. Apply masking.
|
||||
4. Cast 0 durations to 1.
|
||||
5. Round the duration values.
|
||||
|
||||
Args:
|
||||
o_dr_log: Log scale durations.
|
||||
x_mask: Input text mask.
|
||||
|
||||
Shapes:
|
||||
- o_dr_log: :math:`(B, T_{de})`
|
||||
- x_mask: :math:`(B, T_{en})`
|
||||
"""
|
||||
o_dr = (torch.exp(o_dr_log) - 1) * x_mask * self.length_scale
|
||||
o_dr[o_dr < 1] = 1.0
|
||||
o_dr = torch.round(o_dr)
|
||||
return o_dr
|
||||
|
||||
@staticmethod
|
||||
def _concat_speaker_embedding(o_en, g):
|
||||
g_exp = g.expand(-1, -1, o_en.size(-1)) # [B, C, T_en]
|
||||
o_en = torch.cat([o_en, g_exp], 1)
|
||||
return o_en
|
||||
|
||||
def _sum_speaker_embedding(self, x, g):
|
||||
# project g to decoder dim.
|
||||
if hasattr(self, "proj_g"):
|
||||
g = self.proj_g(g)
|
||||
return x + g
|
||||
|
||||
def _forward_encoder(
|
||||
self, x: torch.LongTensor, x_mask: torch.FloatTensor, g: torch.FloatTensor = None
|
||||
) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
|
||||
"""Encoding forward pass.
|
||||
|
||||
1. Embed speaker IDs if multi-speaker mode.
|
||||
2. Embed character sequences.
|
||||
3. Run the encoder network.
|
||||
4. Concat speaker embedding to the encoder output for the duration predictor.
|
||||
|
||||
Args:
|
||||
x (torch.LongTensor): Input sequence IDs.
|
||||
x_mask (torch.FloatTensor): Input squence mask.
|
||||
g (torch.FloatTensor, optional): Conditioning vectors. In general speaker embeddings. Defaults to None.
|
||||
|
||||
Returns:
|
||||
Tuple[torch.tensor, torch.tensor, torch.tensor, torch.tensor, torch.tensor]:
|
||||
encoder output, encoder output for the duration predictor, input sequence mask, speaker embeddings,
|
||||
character embeddings
|
||||
|
||||
Shapes:
|
||||
- x: :math:`(B, T_{en})`
|
||||
- x_mask: :math:`(B, 1, T_{en})`
|
||||
- g: :math:`(B, C)`
|
||||
"""
|
||||
if hasattr(self, "emb_g"):
|
||||
g = nn.functional.normalize(self.emb_g(g)) # [B, C, 1]
|
||||
if g is not None:
|
||||
g = g.unsqueeze(-1)
|
||||
# [B, T, C]
|
||||
x_emb = self.emb(x)
|
||||
# encoder pass
|
||||
o_en = self.encoder(torch.transpose(x_emb, 1, -1), x_mask)
|
||||
# speaker conditioning for duration predictor
|
||||
if g is not None:
|
||||
o_en_dp = self._concat_speaker_embedding(o_en, g)
|
||||
else:
|
||||
o_en_dp = o_en
|
||||
return o_en, o_en_dp, x_mask, g, x_emb
|
||||
|
||||
def _forward_decoder(
|
||||
self,
|
||||
o_en: torch.FloatTensor,
|
||||
dr: torch.IntTensor,
|
||||
x_mask: torch.FloatTensor,
|
||||
y_lengths: torch.IntTensor,
|
||||
g: torch.FloatTensor,
|
||||
) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
|
||||
"""Decoding forward pass.
|
||||
|
||||
1. Compute the decoder output mask
|
||||
2. Expand encoder output with the durations.
|
||||
3. Apply position encoding.
|
||||
4. Add speaker embeddings if multi-speaker mode.
|
||||
5. Run the decoder.
|
||||
|
||||
Args:
|
||||
o_en (torch.FloatTensor): Encoder output.
|
||||
dr (torch.IntTensor): Ground truth durations or alignment network durations.
|
||||
x_mask (torch.IntTensor): Input sequence mask.
|
||||
y_lengths (torch.IntTensor): Output sequence lengths.
|
||||
g (torch.FloatTensor): Conditioning vectors. In general speaker embeddings.
|
||||
|
||||
Returns:
|
||||
Tuple[torch.FloatTensor, torch.FloatTensor]: Decoder output, attention map from durations.
|
||||
"""
|
||||
y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(o_en.dtype)
|
||||
# expand o_en with durations
|
||||
o_en_ex, attn = self.expand_encoder_outputs(o_en, dr, x_mask, y_mask)
|
||||
# positional encoding
|
||||
if hasattr(self, "pos_encoder"):
|
||||
o_en_ex = self.pos_encoder(o_en_ex, y_mask)
|
||||
# speaker embedding
|
||||
if g is not None:
|
||||
o_en_ex = self._sum_speaker_embedding(o_en_ex, g)
|
||||
# decoder pass
|
||||
o_de = self.decoder(o_en_ex, y_mask, g=g)
|
||||
return o_de.transpose(1, 2), attn.transpose(1, 2)
|
||||
|
||||
def _forward_pitch_predictor(
|
||||
self,
|
||||
o_en: torch.FloatTensor,
|
||||
x_mask: torch.IntTensor,
|
||||
pitch: torch.FloatTensor = None,
|
||||
dr: torch.IntTensor = None,
|
||||
) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
|
||||
"""Pitch predictor forward pass.
|
||||
|
||||
1. Predict pitch from encoder outputs.
|
||||
2. In training - Compute average pitch values for each input character from the ground truth pitch values.
|
||||
3. Embed average pitch values.
|
||||
|
||||
Args:
|
||||
o_en (torch.FloatTensor): Encoder output.
|
||||
x_mask (torch.IntTensor): Input sequence mask.
|
||||
pitch (torch.FloatTensor, optional): Ground truth pitch values. Defaults to None.
|
||||
dr (torch.IntTensor, optional): Ground truth durations. Defaults to None.
|
||||
|
||||
Returns:
|
||||
Tuple[torch.FloatTensor, torch.FloatTensor]: Pitch embedding, pitch prediction.
|
||||
|
||||
Shapes:
|
||||
- o_en: :math:`(B, C, T_{en})`
|
||||
- x_mask: :math:`(B, 1, T_{en})`
|
||||
- pitch: :math:`(B, 1, T_{de})`
|
||||
- dr: :math:`(B, T_{en})`
|
||||
"""
|
||||
o_pitch = self.pitch_predictor(o_en, x_mask)
|
||||
if pitch is not None:
|
||||
avg_pitch = average_over_durations(pitch, dr)
|
||||
o_pitch_emb = self.pitch_emb(avg_pitch)
|
||||
return o_pitch_emb, o_pitch, avg_pitch
|
||||
o_pitch_emb = self.pitch_emb(o_pitch)
|
||||
return o_pitch_emb, o_pitch
|
||||
|
||||
def _forward_aligner(
|
||||
self, x: torch.FloatTensor, y: torch.FloatTensor, x_mask: torch.IntTensor, y_mask: torch.IntTensor
|
||||
) -> Tuple[torch.IntTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
|
||||
"""Aligner forward pass.
|
||||
|
||||
1. Compute a mask to apply to the attention map.
|
||||
2. Run the alignment network.
|
||||
3. Apply MAS to compute the hard alignment map.
|
||||
4. Compute the durations from the hard alignment map.
|
||||
|
||||
Args:
|
||||
x (torch.FloatTensor): Input sequence.
|
||||
y (torch.FloatTensor): Output sequence.
|
||||
x_mask (torch.IntTensor): Input sequence mask.
|
||||
y_mask (torch.IntTensor): Output sequence mask.
|
||||
|
||||
Returns:
|
||||
Tuple[torch.IntTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
|
||||
Durations from the hard alignment map, soft alignment potentials, log scale alignment potentials,
|
||||
hard alignment map.
|
||||
|
||||
Shapes:
|
||||
- x: :math:`[B, T_en, C_en]`
|
||||
- y: :math:`[B, T_de, C_de]`
|
||||
- x_mask: :math:`[B, 1, T_en]`
|
||||
- y_mask: :math:`[B, 1, T_de]`
|
||||
|
||||
- o_alignment_dur: :math:`[B, T_en]`
|
||||
- alignment_soft: :math:`[B, T_en, T_de]`
|
||||
- alignment_logprob: :math:`[B, 1, T_de, T_en]`
|
||||
- alignment_mas: :math:`[B, T_en, T_de]`
|
||||
"""
|
||||
attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)
|
||||
alignment_soft, alignment_logprob = self.aligner(y.transpose(1, 2), x.transpose(1, 2), x_mask, None)
|
||||
alignment_mas = maximum_path(
|
||||
alignment_soft.squeeze(1).transpose(1, 2).contiguous(), attn_mask.squeeze(1).contiguous()
|
||||
)
|
||||
o_alignment_dur = torch.sum(alignment_mas, -1).int()
|
||||
alignment_soft = alignment_soft.squeeze(1).transpose(1, 2)
|
||||
return o_alignment_dur, alignment_soft, alignment_logprob, alignment_mas
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: torch.LongTensor,
|
||||
x_lengths: torch.LongTensor,
|
||||
y_lengths: torch.LongTensor,
|
||||
y: torch.FloatTensor = None,
|
||||
dr: torch.IntTensor = None,
|
||||
pitch: torch.FloatTensor = None,
|
||||
aux_input: Dict = {"d_vectors": None, "speaker_ids": None}, # pylint: disable=unused-argument
|
||||
) -> Dict:
|
||||
"""Model's forward pass.
|
||||
|
||||
Args:
|
||||
x (torch.LongTensor): Input character sequences.
|
||||
x_lengths (torch.LongTensor): Input sequence lengths.
|
||||
y_lengths (torch.LongTensor): Output sequnce lengths. Defaults to None.
|
||||
y (torch.FloatTensor): Spectrogram frames. Only used when the alignment network is on. Defaults to None.
|
||||
dr (torch.IntTensor): Character durations over the spectrogram frames. Only used when the alignment network is off. Defaults to None.
|
||||
pitch (torch.FloatTensor): Pitch values for each spectrogram frame. Only used when the pitch predictor is on. Defaults to None.
|
||||
aux_input (Dict): Auxiliary model inputs for multi-speaker training. Defaults to `{"d_vectors": 0, "speaker_ids": None}`.
|
||||
|
||||
Shapes:
|
||||
- x: :math:`[B, T_max]`
|
||||
- x_lengths: :math:`[B]`
|
||||
- y_lengths: :math:`[B]`
|
||||
- y: :math:`[B, T_max2]`
|
||||
- dr: :math:`[B, T_max]`
|
||||
- g: :math:`[B, C]`
|
||||
- pitch: :math:`[B, 1, T]`
|
||||
"""
|
||||
g = aux_input["d_vectors"] if "d_vectors" in aux_input else None
|
||||
# compute sequence masks
|
||||
y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).float()
|
||||
x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.shape[1]), 1).float()
|
||||
# encoder pass
|
||||
o_en, o_en_dp, x_mask, g, x_emb = self._forward_encoder(x, x_mask, g)
|
||||
# duration predictor pass
|
||||
if self.args.detach_duration_predictor:
|
||||
o_dr_log = self.duration_predictor(o_en_dp.detach(), x_mask)
|
||||
else:
|
||||
o_dr_log = self.duration_predictor(o_en_dp, x_mask)
|
||||
o_dr = torch.clamp(torch.exp(o_dr_log) - 1, 0, self.max_duration)
|
||||
# generate attn mask from predicted durations
|
||||
o_attn = self.generate_attn(o_dr.squeeze(1), x_mask)
|
||||
# aligner
|
||||
o_alignment_dur = None
|
||||
alignment_soft = None
|
||||
alignment_logprob = None
|
||||
alignment_mas = None
|
||||
if self.use_aligner:
|
||||
o_alignment_dur, alignment_soft, alignment_logprob, alignment_mas = self._forward_aligner(
|
||||
x_emb, y, x_mask, y_mask
|
||||
)
|
||||
alignment_soft = alignment_soft.transpose(1, 2)
|
||||
alignment_mas = alignment_mas.transpose(1, 2)
|
||||
dr = o_alignment_dur
|
||||
# pitch predictor pass
|
||||
o_pitch = None
|
||||
avg_pitch = None
|
||||
if self.args.use_pitch:
|
||||
o_pitch_emb, o_pitch, avg_pitch = self._forward_pitch_predictor(o_en_dp, x_mask, pitch, dr)
|
||||
o_en = o_en + o_pitch_emb
|
||||
# decoder pass
|
||||
o_de, attn = self._forward_decoder(o_en, dr, x_mask, y_lengths, g=g)
|
||||
outputs = {
|
||||
"model_outputs": o_de, # [B, T, C]
|
||||
"durations_log": o_dr_log.squeeze(1), # [B, T]
|
||||
"durations": o_dr.squeeze(1), # [B, T]
|
||||
"attn_durations": o_attn, # for visualization [B, T_en, T_de']
|
||||
"pitch_avg": o_pitch,
|
||||
"pitch_avg_gt": avg_pitch,
|
||||
"alignments": attn, # [B, T_de, T_en]
|
||||
"alignment_soft": alignment_soft,
|
||||
"alignment_mas": alignment_mas,
|
||||
"o_alignment_dur": o_alignment_dur,
|
||||
"alignment_logprob": alignment_logprob,
|
||||
"x_mask": x_mask,
|
||||
"y_mask": y_mask,
|
||||
}
|
||||
return outputs
|
||||
|
||||
@torch.no_grad()
|
||||
def inference(self, x, aux_input={"d_vectors": None, "speaker_ids": None}): # pylint: disable=unused-argument
|
||||
"""Model's inference pass.
|
||||
|
||||
Args:
|
||||
x (torch.LongTensor): Input character sequence.
|
||||
aux_input (Dict): Auxiliary model inputs. Defaults to `{"d_vectors": None, "speaker_ids": None}`.
|
||||
|
||||
Shapes:
|
||||
- x: [B, T_max]
|
||||
- x_lengths: [B]
|
||||
- g: [B, C]
|
||||
"""
|
||||
g = aux_input["d_vectors"] if "d_vectors" in aux_input else None
|
||||
x_lengths = torch.tensor(x.shape[1:2]).to(x.device)
|
||||
x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.shape[1]), 1).to(x.dtype).float()
|
||||
# encoder pass
|
||||
o_en, o_en_dp, x_mask, g, _ = self._forward_encoder(x, x_mask, g)
|
||||
# duration predictor pass
|
||||
o_dr_log = self.duration_predictor(o_en_dp, x_mask)
|
||||
o_dr = self.format_durations(o_dr_log, x_mask).squeeze(1)
|
||||
y_lengths = o_dr.sum(1)
|
||||
# pitch predictor pass
|
||||
o_pitch = None
|
||||
if self.args.use_pitch:
|
||||
o_pitch_emb, o_pitch = self._forward_pitch_predictor(o_en_dp, x_mask)
|
||||
o_en = o_en + o_pitch_emb
|
||||
# decoder pass
|
||||
o_de, attn = self._forward_decoder(o_en, o_dr, x_mask, y_lengths, g=g)
|
||||
outputs = {
|
||||
"model_outputs": o_de,
|
||||
"alignments": attn,
|
||||
"pitch": o_pitch,
|
||||
"durations_log": o_dr_log,
|
||||
}
|
||||
return outputs
|
||||
|
||||
def train_step(self, batch: dict, criterion: nn.Module):
|
||||
text_input = batch["text_input"]
|
||||
text_lengths = batch["text_lengths"]
|
||||
mel_input = batch["mel_input"]
|
||||
mel_lengths = batch["mel_lengths"]
|
||||
pitch = batch["pitch"] if self.args.use_pitch else None
|
||||
d_vectors = batch["d_vectors"]
|
||||
speaker_ids = batch["speaker_ids"]
|
||||
durations = batch["durations"]
|
||||
aux_input = {"d_vectors": d_vectors, "speaker_ids": speaker_ids}
|
||||
|
||||
# forward pass
|
||||
outputs = self.forward(
|
||||
text_input, text_lengths, mel_lengths, y=mel_input, dr=durations, pitch=pitch, aux_input=aux_input
|
||||
)
|
||||
# use aligner's output as the duration target
|
||||
if self.use_aligner:
|
||||
durations = outputs["o_alignment_dur"]
|
||||
# use float32 in AMP
|
||||
with autocast(enabled=False):
|
||||
# compute loss
|
||||
loss_dict = criterion(
|
||||
decoder_output=outputs["model_outputs"],
|
||||
decoder_target=mel_input,
|
||||
decoder_output_lens=mel_lengths,
|
||||
dur_output=outputs["durations_log"],
|
||||
dur_target=durations,
|
||||
pitch_output=outputs["pitch_avg"] if self.use_pitch else None,
|
||||
pitch_target=outputs["pitch_avg_gt"] if self.use_pitch else None,
|
||||
input_lens=text_lengths,
|
||||
alignment_logprob=outputs["alignment_logprob"] if self.use_aligner else None,
|
||||
alignment_soft=outputs["alignment_soft"] if self.use_binary_alignment_loss else None,
|
||||
alignment_hard=outputs["alignment_mas"] if self.use_binary_alignment_loss else None,
|
||||
)
|
||||
# compute duration error
|
||||
durations_pred = outputs["durations"]
|
||||
duration_error = torch.abs(durations - durations_pred).sum() / text_lengths.sum()
|
||||
loss_dict["duration_error"] = duration_error
|
||||
|
||||
return outputs, loss_dict
|
||||
|
||||
def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict): # pylint: disable=no-self-use
|
||||
model_outputs = outputs["model_outputs"]
|
||||
alignments = outputs["alignments"]
|
||||
mel_input = batch["mel_input"]
|
||||
|
||||
pred_spec = model_outputs[0].data.cpu().numpy()
|
||||
gt_spec = mel_input[0].data.cpu().numpy()
|
||||
align_img = alignments[0].data.cpu().numpy()
|
||||
|
||||
figures = {
|
||||
"prediction": plot_spectrogram(pred_spec, ap, output_fig=False),
|
||||
"ground_truth": plot_spectrogram(gt_spec, ap, output_fig=False),
|
||||
"alignment": plot_alignment(align_img, output_fig=False),
|
||||
}
|
||||
|
||||
# plot pitch figures
|
||||
if self.args.use_pitch:
|
||||
pitch = batch["pitch"]
|
||||
pitch_avg_expanded, _ = self.expand_encoder_outputs(
|
||||
outputs["pitch_avg"], outputs["durations"], outputs["x_mask"], outputs["y_mask"]
|
||||
)
|
||||
pitch = pitch[0, 0].data.cpu().numpy()
|
||||
# TODO: denormalize before plotting
|
||||
pitch = abs(pitch)
|
||||
pitch_avg_expanded = abs(pitch_avg_expanded[0, 0]).data.cpu().numpy()
|
||||
pitch_figures = {
|
||||
"pitch_ground_truth": plot_pitch(pitch, gt_spec, ap, output_fig=False),
|
||||
"pitch_avg_predicted": plot_pitch(pitch_avg_expanded, pred_spec, ap, output_fig=False),
|
||||
}
|
||||
figures.update(pitch_figures)
|
||||
|
||||
# plot the attention mask computed from the predicted durations
|
||||
if "attn_durations" in outputs:
|
||||
alignments_hat = outputs["attn_durations"][0].data.cpu().numpy()
|
||||
figures["alignment_hat"] = plot_alignment(alignments_hat.T, output_fig=False)
|
||||
|
||||
# Sample audio
|
||||
train_audio = ap.inv_melspectrogram(pred_spec.T)
|
||||
return figures, {"audio": train_audio}
|
||||
|
||||
def eval_step(self, batch: dict, criterion: nn.Module):
|
||||
return self.train_step(batch, criterion)
|
||||
|
||||
def eval_log(self, ap: AudioProcessor, batch: dict, outputs: dict):
|
||||
return self.train_log(ap, batch, outputs)
|
||||
|
||||
def load_checkpoint(
|
||||
self, config, checkpoint_path, eval=False
|
||||
): # pylint: disable=unused-argument, redefined-builtin
|
||||
state = torch.load(checkpoint_path, map_location=torch.device("cpu"))
|
||||
self.load_state_dict(state["model"])
|
||||
if eval:
|
||||
self.eval()
|
||||
assert not self.training
|
||||
|
||||
def get_criterion(self):
|
||||
from TTS.tts.layers.losses import ForwardTTSLoss # pylint: disable=import-outside-toplevel
|
||||
|
||||
return ForwardTTSLoss(self.config)
|
||||
|
||||
def on_train_step_start(self, trainer):
|
||||
"""Enable binary alignment loss when needed"""
|
||||
if trainer.total_steps_done > self.config.binary_align_loss_start_step:
|
||||
self.use_binary_alignment_loss = True
|
||||
|
|
@ -0,0 +1,149 @@
|
|||
import unittest
|
||||
|
||||
import torch as T
|
||||
|
||||
from TTS.tts.models.forward_tts import ForwardTTS, ForwardTTSArgs
|
||||
from TTS.tts.utils.helpers import sequence_mask
|
||||
|
||||
# pylint: disable=unused-variable
|
||||
|
||||
|
||||
def expand_encoder_outputs_test():
|
||||
model = ForwardTTS(ForwardTTSArgs(num_chars=10))
|
||||
|
||||
inputs = T.rand(2, 5, 57)
|
||||
durations = T.randint(1, 4, (2, 57))
|
||||
|
||||
x_mask = T.ones(2, 1, 57)
|
||||
y_mask = T.ones(2, 1, durations.sum(1).max())
|
||||
|
||||
expanded, _ = model.expand_encoder_outputs(inputs, durations, x_mask, y_mask)
|
||||
|
||||
for b in range(durations.shape[0]):
|
||||
index = 0
|
||||
for idx, dur in enumerate(durations[b]):
|
||||
diff = (
|
||||
expanded[b, :, index : index + dur.item()]
|
||||
- inputs[b, :, idx].repeat(dur.item()).view(expanded[b, :, index : index + dur.item()].shape)
|
||||
).sum()
|
||||
assert abs(diff) < 1e-6, diff
|
||||
index += dur
|
||||
|
||||
|
||||
def model_input_output_test():
|
||||
"""Assert the output shapes of the model in different modes"""
|
||||
|
||||
# VANILLA MODEL
|
||||
model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=False, use_aligner=False))
|
||||
|
||||
x = T.randint(0, 10, (2, 21))
|
||||
x_lengths = T.randint(10, 22, (2,))
|
||||
x_lengths[-1] = 21
|
||||
x_mask = sequence_mask(x_lengths).unsqueeze(1).long()
|
||||
durations = T.randint(1, 4, (2, 21))
|
||||
durations = durations * x_mask.squeeze(1)
|
||||
y_lengths = durations.sum(1)
|
||||
y_mask = sequence_mask(y_lengths).unsqueeze(1).long()
|
||||
|
||||
outputs = model.forward(x, x_lengths, y_lengths, dr=durations)
|
||||
|
||||
assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80)
|
||||
assert outputs["durations_log"].shape == (2, 21)
|
||||
assert outputs["durations"].shape == (2, 21)
|
||||
assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21)
|
||||
assert (outputs["x_mask"] - x_mask).sum() == 0.0
|
||||
assert (outputs["y_mask"] - y_mask).sum() == 0.0
|
||||
|
||||
assert outputs["alignment_soft"] == None
|
||||
assert outputs["alignment_mas"] == None
|
||||
assert outputs["alignment_logprob"] == None
|
||||
assert outputs["o_alignment_dur"] == None
|
||||
assert outputs["pitch_avg"] == None
|
||||
assert outputs["pitch_avg_gt"] == None
|
||||
|
||||
# USE PITCH
|
||||
model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=True, use_aligner=False))
|
||||
|
||||
x = T.randint(0, 10, (2, 21))
|
||||
x_lengths = T.randint(10, 22, (2,))
|
||||
x_lengths[-1] = 21
|
||||
x_mask = sequence_mask(x_lengths).unsqueeze(1).long()
|
||||
durations = T.randint(1, 4, (2, 21))
|
||||
durations = durations * x_mask.squeeze(1)
|
||||
y_lengths = durations.sum(1)
|
||||
y_mask = sequence_mask(y_lengths).unsqueeze(1).long()
|
||||
pitch = T.rand(2, 1, y_lengths.max())
|
||||
|
||||
outputs = model.forward(x, x_lengths, y_lengths, dr=durations, pitch=pitch)
|
||||
|
||||
assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80)
|
||||
assert outputs["durations_log"].shape == (2, 21)
|
||||
assert outputs["durations"].shape == (2, 21)
|
||||
assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21)
|
||||
assert (outputs["x_mask"] - x_mask).sum() == 0.0
|
||||
assert (outputs["y_mask"] - y_mask).sum() == 0.0
|
||||
assert outputs["pitch_avg"].shape == (2, 1, 21)
|
||||
assert outputs["pitch_avg_gt"].shape == (2, 1, 21)
|
||||
|
||||
assert outputs["alignment_soft"] == None
|
||||
assert outputs["alignment_mas"] == None
|
||||
assert outputs["alignment_logprob"] == None
|
||||
assert outputs["o_alignment_dur"] == None
|
||||
|
||||
# USE ALIGNER NETWORK
|
||||
model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=False, use_aligner=True))
|
||||
|
||||
x = T.randint(0, 10, (2, 21))
|
||||
x_lengths = T.randint(10, 22, (2,))
|
||||
x_lengths[-1] = 21
|
||||
x_mask = sequence_mask(x_lengths).unsqueeze(1).long()
|
||||
durations = T.randint(1, 4, (2, 21))
|
||||
durations = durations * x_mask.squeeze(1)
|
||||
y_lengths = durations.sum(1)
|
||||
y_mask = sequence_mask(y_lengths).unsqueeze(1).long()
|
||||
y = T.rand(2, y_lengths.max(), 80)
|
||||
|
||||
outputs = model.forward(x, x_lengths, y_lengths, dr=durations, y=y)
|
||||
|
||||
assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80)
|
||||
assert outputs["durations_log"].shape == (2, 21)
|
||||
assert outputs["durations"].shape == (2, 21)
|
||||
assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21)
|
||||
assert (outputs["x_mask"] - x_mask).sum() == 0.0
|
||||
assert (outputs["y_mask"] - y_mask).sum() == 0.0
|
||||
assert outputs["alignment_soft"].shape == (2, durations.sum(1).max(), 21)
|
||||
assert outputs["alignment_mas"].shape == (2, durations.sum(1).max(), 21)
|
||||
assert outputs["alignment_logprob"].shape == (2, 1, durations.sum(1).max(), 21)
|
||||
assert outputs["o_alignment_dur"].shape == (2, 21)
|
||||
|
||||
assert outputs["pitch_avg"] == None
|
||||
assert outputs["pitch_avg_gt"] == None
|
||||
|
||||
# USE ALIGNER NETWORK AND PITCH
|
||||
model = ForwardTTS(ForwardTTSArgs(num_chars=10, use_pitch=True, use_aligner=True))
|
||||
|
||||
x = T.randint(0, 10, (2, 21))
|
||||
x_lengths = T.randint(10, 22, (2,))
|
||||
x_lengths[-1] = 21
|
||||
x_mask = sequence_mask(x_lengths).unsqueeze(1).long()
|
||||
durations = T.randint(1, 4, (2, 21))
|
||||
durations = durations * x_mask.squeeze(1)
|
||||
y_lengths = durations.sum(1)
|
||||
y_mask = sequence_mask(y_lengths).unsqueeze(1).long()
|
||||
y = T.rand(2, y_lengths.max(), 80)
|
||||
pitch = T.rand(2, 1, y_lengths.max())
|
||||
|
||||
outputs = model.forward(x, x_lengths, y_lengths, dr=durations, pitch=pitch, y=y)
|
||||
|
||||
assert outputs["model_outputs"].shape == (2, durations.sum(1).max(), 80)
|
||||
assert outputs["durations_log"].shape == (2, 21)
|
||||
assert outputs["durations"].shape == (2, 21)
|
||||
assert outputs["alignments"].shape == (2, durations.sum(1).max(), 21)
|
||||
assert (outputs["x_mask"] - x_mask).sum() == 0.0
|
||||
assert (outputs["y_mask"] - y_mask).sum() == 0.0
|
||||
assert outputs["alignment_soft"].shape == (2, durations.sum(1).max(), 21)
|
||||
assert outputs["alignment_mas"].shape == (2, durations.sum(1).max(), 21)
|
||||
assert outputs["alignment_logprob"].shape == (2, 1, durations.sum(1).max(), 21)
|
||||
assert outputs["o_alignment_dur"].shape == (2, 21)
|
||||
assert outputs["pitch_avg"].shape == (2, 1, 21)
|
||||
assert outputs["pitch_avg_gt"].shape == (2, 1, 21)
|
||||
Loading…
Reference in New Issue