+
+----
+
+### 📣 Clone your voice with a single click on [🐸Coqui.ai](https://app.coqui.ai/auth/signin)
+
+----
+
+🐸TTS is a library for advanced Text-to-Speech generation. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed and quality.
+🐸TTS comes with pretrained models, tools for measuring dataset quality and already used in **20+ languages** for products and research projects.
+
+[](https://discord.gg/5eXr5seRrv)
+[
+
+## 💬 Where to ask questions
+Please use our dedicated channels for questions and discussion. Help is much more valuable if it's shared publicly so that more people can benefit from it.
+
+| Type | Platforms |
+| ------------------------------- | --------------------------------------- |
+| 🚨 **Bug Reports** | [GitHub Issue Tracker] |
+| 🎁 **Feature Requests & Ideas** | [GitHub Issue Tracker] |
+| 👩💻 **Usage Questions** | [GitHub Discussions] |
+| 🗯 **General Discussion** | [GitHub Discussions] or [Discord] |
+
+[github issue tracker]: https://github.com/coqui-ai/tts/issues
+[github discussions]: https://github.com/coqui-ai/TTS/discussions
+[discord]: https://discord.gg/5eXr5seRrv
+[Tutorials and Examples]: https://github.com/coqui-ai/TTS/wiki/TTS-Notebooks-and-Tutorials
+
+
+## 🔗 Links and Resources
+| Type | Links |
+| ------------------------------- | --------------------------------------- |
+| 💼 **Documentation** | [ReadTheDocs](https://tts.readthedocs.io/en/latest/)
+| 💾 **Installation** | [TTS/README.md](https://github.com/coqui-ai/TTS/tree/dev#install-tts)|
+| 👩💻 **Contributing** | [CONTRIBUTING.md](https://github.com/coqui-ai/TTS/blob/main/CONTRIBUTING.md)|
+| 📌 **Road Map** | [Main Development Plans](https://github.com/coqui-ai/TTS/issues/378)
+| 🚀 **Released Models** | [TTS Releases](https://github.com/coqui-ai/TTS/releases) and [Experimental Models](https://github.com/coqui-ai/TTS/wiki/Experimental-Released-Models)|
+
+## 🥇 TTS Performance
+
+
+ {% if show_details == true %}
+
+
+ Model details
+
+
+
+
+
+
+
+
+
+ CLI arguments:
+| CLI key | +Value | +
| {{ key }} | +{{ value }} | +
+
+ {% if model_config != None %}
+
+
+
+
+
+
+
+
+
+
+ {% endif %}
+
+ Model config:
+ +| Key | +Value | +
| {{ key }} | +{{ value }} | +
+ {% if vocoder_config != None %}
+
+
+ {% else %}
+
+
+
+
+ {% endif %}
+ Vocoder model config:
+ +| Key | +Value | +
| {{ key }} | +{{ value }} | +
+ Please start server with --show_details=true to see details.
+
+
+ {% endif %}
+
+
+
+
\ No newline at end of file
diff --git a/TTS/server/templates/index.html b/TTS/server/templates/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..6354d3919d9a1e9c1e22e9866c84c4eb8284bc13
--- /dev/null
+++ b/TTS/server/templates/index.html
@@ -0,0 +1,154 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/TTS/tts/__init__.py b/TTS/tts/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/TTS/tts/configs/__init__.py b/TTS/tts/configs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3146ac1c116cb807a81889b7a9ab223b9a051036
--- /dev/null
+++ b/TTS/tts/configs/__init__.py
@@ -0,0 +1,17 @@
+import importlib
+import os
+from inspect import isclass
+
+# import all files under configs/
+# configs_dir = os.path.dirname(__file__)
+# for file in os.listdir(configs_dir):
+# path = os.path.join(configs_dir, file)
+# if not file.startswith("_") and not file.startswith(".") and (file.endswith(".py") or os.path.isdir(path)):
+# config_name = file[: file.find(".py")] if file.endswith(".py") else file
+# module = importlib.import_module("TTS.tts.configs." + config_name)
+# for attribute_name in dir(module):
+# attribute = getattr(module, attribute_name)
+
+# if isclass(attribute):
+# # Add the class to this package's variables
+# globals()[attribute_name] = attribute
diff --git a/TTS/tts/configs/align_tts_config.py b/TTS/tts/configs/align_tts_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..317a01af53ce26914d83610a913eb44b5836dac2
--- /dev/null
+++ b/TTS/tts/configs/align_tts_config.py
@@ -0,0 +1,107 @@
+from dataclasses import dataclass, field
+from typing import List
+
+from TTS.tts.configs.shared_configs import BaseTTSConfig
+from TTS.tts.models.align_tts import AlignTTSArgs
+
+
+@dataclass
+class AlignTTSConfig(BaseTTSConfig):
+ """Defines parameters for AlignTTS model.
+ Example:
+
+ >>> from TTS.tts.configs.align_tts_config import AlignTTSConfig
+ >>> config = AlignTTSConfig()
+
+ Args:
+ model(str):
+ Model name used for selecting the right model at initialization. Defaults to `align_tts`.
+ positional_encoding (bool):
+ enable / disable positional encoding applied to the encoder output. Defaults to True.
+ hidden_channels (int):
+ Base number of hidden channels. Defines all the layers expect ones defined by the specific encoder or decoder
+ parameters. Defaults to 256.
+ hidden_channels_dp (int):
+ Number of hidden channels of the duration predictor's layers. Defaults to 256.
+ encoder_type (str):
+ Type of the encoder used by the model. Look at `TTS.tts.layers.feed_forward.encoder` for more details.
+ Defaults to `fftransformer`.
+ encoder_params (dict):
+ Parameters used to define the encoder network. Look at `TTS.tts.layers.feed_forward.encoder` for more details.
+ Defaults to `{"hidden_channels_ffn": 1024, "num_heads": 2, "num_layers": 6, "dropout_p": 0.1}`.
+ decoder_type (str):
+ Type of the decoder used by the model. Look at `TTS.tts.layers.feed_forward.decoder` for more details.
+ Defaults to `fftransformer`.
+ decoder_params (dict):
+ Parameters used to define the decoder network. Look at `TTS.tts.layers.feed_forward.decoder` for more details.
+ Defaults to `{"hidden_channels_ffn": 1024, "num_heads": 2, "num_layers": 6, "dropout_p": 0.1}`.
+ phase_start_steps (List[int]):
+ A list of number of steps required to start the next training phase. AlignTTS has 4 different training
+ phases. Thus you need to define 4 different values to enable phase based training. If None, it
+ trains the whole model together. Defaults to None.
+ ssim_alpha (float):
+ Weight for the SSIM loss. If set <= 0, disables the SSIM loss. Defaults to 1.0.
+ duration_loss_alpha (float):
+ Weight for the duration predictor's loss. Defaults to 1.0.
+ mdn_alpha (float):
+ Weight for the MDN loss. Defaults to 1.0.
+ spec_loss_alpha (float):
+ Weight for the MSE spectrogram loss. If set <= 0, disables the L1 loss. Defaults to 1.0.
+ use_speaker_embedding (bool):
+ enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
+ in the multi-speaker mode. Defaults to False.
+ use_d_vector_file (bool):
+ enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
+ d_vector_file (str):
+ Path to the file including pre-computed speaker embeddings. Defaults to None.
+ noam_schedule (bool):
+ enable / disable the use of Noam LR scheduler. Defaults to False.
+ warmup_steps (int):
+ Number of warm-up steps for the Noam scheduler. Defaults 4000.
+ lr (float):
+ Initial learning rate. Defaults to `1e-3`.
+ wd (float):
+ Weight decay coefficient. Defaults to `1e-7`.
+ min_seq_len (int):
+ Minimum input sequence length to be used at training.
+ max_seq_len (int):
+ Maximum input sequence length to be used at training. Larger values result in more VRAM usage."""
+
+ model: str = "align_tts"
+ # model specific params
+ model_args: AlignTTSArgs = field(default_factory=AlignTTSArgs)
+ phase_start_steps: List[int] = None
+
+ ssim_alpha: float = 1.0
+ spec_loss_alpha: float = 1.0
+ dur_loss_alpha: float = 1.0
+ mdn_alpha: float = 1.0
+
+ # multi-speaker settings
+ use_speaker_embedding: bool = False
+ use_d_vector_file: bool = False
+ d_vector_file: str = False
+
+ # optimizer parameters
+ optimizer: str = "Adam"
+ optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
+ lr_scheduler: str = None
+ lr_scheduler_params: dict = None
+ lr: float = 1e-4
+ grad_clip: float = 5.0
+
+ # overrides
+ min_seq_len: int = 13
+ max_seq_len: int = 200
+ r: int = 1
+
+ # testing
+ test_sentences: List[str] = field(
+ default_factory=lambda: [
+ "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.",
+ ]
+ )
diff --git a/TTS/tts/configs/fast_pitch_config.py b/TTS/tts/configs/fast_pitch_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..90b15021d057b4a861ca16a50f98b1e951fa661c
--- /dev/null
+++ b/TTS/tts/configs/fast_pitch_config.py
@@ -0,0 +1,183 @@
+from dataclasses import dataclass, field
+from typing import List
+
+from TTS.tts.configs.shared_configs import BaseTTSConfig
+from TTS.tts.models.forward_tts import ForwardTTSArgs
+
+
+@dataclass
+class FastPitchConfig(BaseTTSConfig):
+ """Configure `ForwardTTS` as FastPitch model.
+
+ Example:
+
+ >>> from TTS.tts.configs.fast_pitch_config import FastPitchConfig
+ >>> config = FastPitchConfig()
+
+ Args:
+ model (str):
+ Model name used for selecting the right model at initialization. Defaults to `fast_pitch`.
+
+ base_model (str):
+ Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
+ the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
+
+ model_args (Coqpit):
+ Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`.
+
+ data_dep_init_steps (int):
+ Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
+ Activation Normalization that pre-computes normalization stats at the beginning and use the same values
+ for the rest. Defaults to 10.
+
+ speakers_file (str):
+ Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
+ speaker names. Defaults to `None`.
+
+ use_speaker_embedding (bool):
+ enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
+ in the multi-speaker mode. Defaults to False.
+
+ use_d_vector_file (bool):
+ enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
+
+ d_vector_file (str):
+ Path to the file including pre-computed speaker embeddings. Defaults to None.
+
+ d_vector_dim (int):
+ Dimension of the external speaker embeddings. Defaults to 0.
+
+ optimizer (str):
+ Name of the model optimizer. Defaults to `Adam`.
+
+ optimizer_params (dict):
+ Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
+
+ lr_scheduler (str):
+ Name of the learning rate scheduler. Defaults to `Noam`.
+
+ lr_scheduler_params (dict):
+ Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
+
+ lr (float):
+ Initial learning rate. Defaults to `1e-3`.
+
+ grad_clip (float):
+ Gradient norm clipping value. Defaults to `5.0`.
+
+ spec_loss_type (str):
+ Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
+
+ duration_loss_type (str):
+ Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
+
+ use_ssim_loss (bool):
+ Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
+
+ wd (float):
+ Weight decay coefficient. Defaults to `1e-7`.
+
+ ssim_loss_alpha (float):
+ Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
+
+ dur_loss_alpha (float):
+ Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
+
+ spec_loss_alpha (float):
+ Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
+
+ pitch_loss_alpha (float):
+ Weight for the pitch predictor's loss. If set 0, disables the pitch predictor. Defaults to 1.0.
+
+ binary_align_loss_alpha (float):
+ Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
+
+ binary_loss_warmup_epochs (float):
+ Number of epochs to gradually increase the binary loss impact. Defaults to 150.
+
+ min_seq_len (int):
+ Minimum input sequence length to be used at training.
+
+ max_seq_len (int):
+ Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
+
+ # dataset configs
+ compute_f0(bool):
+ Compute pitch. defaults to True
+
+ f0_cache_path(str):
+ pith cache path. defaults to None
+ """
+
+ model: str = "fast_pitch"
+ base_model: str = "forward_tts"
+
+ # model specific params
+ model_args: ForwardTTSArgs = ForwardTTSArgs()
+
+ # multi-speaker settings
+ num_speakers: int = 0
+ speakers_file: str = None
+ use_speaker_embedding: bool = False
+ use_d_vector_file: bool = False
+ d_vector_file: str = False
+ d_vector_dim: int = 0
+
+ # optimizer parameters
+ optimizer: str = "Adam"
+ optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
+ lr_scheduler: str = "NoamLR"
+ lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
+ lr: float = 1e-4
+ grad_clip: float = 5.0
+
+ # loss params
+ spec_loss_type: str = "mse"
+ duration_loss_type: str = "mse"
+ use_ssim_loss: bool = True
+ ssim_loss_alpha: float = 1.0
+ spec_loss_alpha: float = 1.0
+ aligner_loss_alpha: float = 1.0
+ pitch_loss_alpha: float = 0.1
+ dur_loss_alpha: float = 0.1
+ binary_align_loss_alpha: float = 0.1
+ binary_loss_warmup_epochs: int = 150
+
+ # overrides
+ min_seq_len: int = 13
+ max_seq_len: int = 200
+ r: int = 1 # DO NOT CHANGE
+
+ # dataset configs
+ compute_f0: bool = True
+ f0_cache_path: str = None
+
+ # testing
+ test_sentences: List[str] = field(
+ default_factory=lambda: [
+ "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.",
+ ]
+ )
+
+ def __post_init__(self):
+ # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
+ if self.num_speakers > 0:
+ self.model_args.num_speakers = self.num_speakers
+
+ # speaker embedding settings
+ if self.use_speaker_embedding:
+ self.model_args.use_speaker_embedding = True
+ if self.speakers_file:
+ self.model_args.speakers_file = self.speakers_file
+
+ # d-vector settings
+ if self.use_d_vector_file:
+ self.model_args.use_d_vector_file = True
+ if self.d_vector_dim is not None and self.d_vector_dim > 0:
+ self.model_args.d_vector_dim = self.d_vector_dim
+ if self.d_vector_file:
+ self.model_args.d_vector_file = self.d_vector_file
diff --git a/TTS/tts/configs/fast_speech_config.py b/TTS/tts/configs/fast_speech_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..16a76e215f4d47d086bea827d2b6ccc61524e5c1
--- /dev/null
+++ b/TTS/tts/configs/fast_speech_config.py
@@ -0,0 +1,177 @@
+from dataclasses import dataclass, field
+from typing import List
+
+from TTS.tts.configs.shared_configs import BaseTTSConfig
+from TTS.tts.models.forward_tts import ForwardTTSArgs
+
+
+@dataclass
+class FastSpeechConfig(BaseTTSConfig):
+ """Configure `ForwardTTS` as FastSpeech model.
+
+ Example:
+
+ >>> from TTS.tts.configs.fast_speech_config import FastSpeechConfig
+ >>> config = FastSpeechConfig()
+
+ Args:
+ model (str):
+ Model name used for selecting the right model at initialization. Defaults to `fast_pitch`.
+
+ base_model (str):
+ Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
+ the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
+
+ model_args (Coqpit):
+ Model class arguments. Check `FastSpeechArgs` for more details. Defaults to `FastSpeechArgs()`.
+
+ data_dep_init_steps (int):
+ Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
+ Activation Normalization that pre-computes normalization stats at the beginning and use the same values
+ for the rest. Defaults to 10.
+
+ speakers_file (str):
+ Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
+ speaker names. Defaults to `None`.
+
+
+ use_speaker_embedding (bool):
+ enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
+ in the multi-speaker mode. Defaults to False.
+
+ use_d_vector_file (bool):
+ enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
+
+ d_vector_file (str):
+ Path to the file including pre-computed speaker embeddings. Defaults to None.
+
+ d_vector_dim (int):
+ Dimension of the external speaker embeddings. Defaults to 0.
+
+ optimizer (str):
+ Name of the model optimizer. Defaults to `Adam`.
+
+ optimizer_params (dict):
+ Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
+
+ lr_scheduler (str):
+ Name of the learning rate scheduler. Defaults to `Noam`.
+
+ lr_scheduler_params (dict):
+ Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
+
+ lr (float):
+ Initial learning rate. Defaults to `1e-3`.
+
+ grad_clip (float):
+ Gradient norm clipping value. Defaults to `5.0`.
+
+ spec_loss_type (str):
+ Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
+
+ duration_loss_type (str):
+ Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
+
+ use_ssim_loss (bool):
+ Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
+
+ wd (float):
+ Weight decay coefficient. Defaults to `1e-7`.
+
+ ssim_loss_alpha (float):
+ Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
+
+ dur_loss_alpha (float):
+ Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
+
+ spec_loss_alpha (float):
+ Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
+
+ pitch_loss_alpha (float):
+ Weight for the pitch predictor's loss. If set 0, disables the pitch predictor. Defaults to 1.0.
+
+ binary_loss_alpha (float):
+ Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
+
+ binary_loss_warmup_epochs (float):
+ Number of epochs to gradually increase the binary loss impact. Defaults to 150.
+
+ min_seq_len (int):
+ Minimum input sequence length to be used at training.
+
+ max_seq_len (int):
+ Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
+ """
+
+ model: str = "fast_speech"
+ base_model: str = "forward_tts"
+
+ # model specific params
+ model_args: ForwardTTSArgs = ForwardTTSArgs(use_pitch=False)
+
+ # multi-speaker settings
+ num_speakers: int = 0
+ speakers_file: str = None
+ use_speaker_embedding: bool = False
+ use_d_vector_file: bool = False
+ d_vector_file: str = False
+ d_vector_dim: int = 0
+
+ # optimizer parameters
+ optimizer: str = "Adam"
+ optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
+ lr_scheduler: str = "NoamLR"
+ lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
+ lr: float = 1e-4
+ grad_clip: float = 5.0
+
+ # loss params
+ spec_loss_type: str = "mse"
+ duration_loss_type: str = "mse"
+ use_ssim_loss: bool = True
+ ssim_loss_alpha: float = 1.0
+ dur_loss_alpha: float = 1.0
+ spec_loss_alpha: float = 1.0
+ pitch_loss_alpha: float = 0.0
+ aligner_loss_alpha: float = 1.0
+ binary_align_loss_alpha: float = 1.0
+ binary_loss_warmup_epochs: int = 150
+
+ # overrides
+ min_seq_len: int = 13
+ max_seq_len: int = 200
+ r: int = 1 # DO NOT CHANGE
+
+ # dataset configs
+ compute_f0: bool = False
+ f0_cache_path: str = None
+
+ # testing
+ test_sentences: List[str] = field(
+ default_factory=lambda: [
+ "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.",
+ ]
+ )
+
+ def __post_init__(self):
+ # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
+ if self.num_speakers > 0:
+ self.model_args.num_speakers = self.num_speakers
+
+ # speaker embedding settings
+ if self.use_speaker_embedding:
+ self.model_args.use_speaker_embedding = True
+ if self.speakers_file:
+ self.model_args.speakers_file = self.speakers_file
+
+ # d-vector settings
+ if self.use_d_vector_file:
+ self.model_args.use_d_vector_file = True
+ if self.d_vector_dim is not None and self.d_vector_dim > 0:
+ self.model_args.d_vector_dim = self.d_vector_dim
+ if self.d_vector_file:
+ self.model_args.d_vector_file = self.d_vector_file
diff --git a/TTS/tts/configs/fastspeech2_config.py b/TTS/tts/configs/fastspeech2_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..68a3eec2f0424cddc2eb0a24ff5ca874ed43720d
--- /dev/null
+++ b/TTS/tts/configs/fastspeech2_config.py
@@ -0,0 +1,198 @@
+from dataclasses import dataclass, field
+from typing import List
+
+from TTS.tts.configs.shared_configs import BaseTTSConfig
+from TTS.tts.models.forward_tts import ForwardTTSArgs
+
+
+@dataclass
+class Fastspeech2Config(BaseTTSConfig):
+ """Configure `ForwardTTS` as FastPitch model.
+
+ Example:
+
+ >>> from TTS.tts.configs.fastspeech2_config import FastSpeech2Config
+ >>> config = FastSpeech2Config()
+
+ Args:
+ model (str):
+ Model name used for selecting the right model at initialization. Defaults to `fast_pitch`.
+
+ base_model (str):
+ Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
+ the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
+
+ model_args (Coqpit):
+ Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`.
+
+ data_dep_init_steps (int):
+ Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
+ Activation Normalization that pre-computes normalization stats at the beginning and use the same values
+ for the rest. Defaults to 10.
+
+ speakers_file (str):
+ Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
+ speaker names. Defaults to `None`.
+
+ use_speaker_embedding (bool):
+ enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
+ in the multi-speaker mode. Defaults to False.
+
+ use_d_vector_file (bool):
+ enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
+
+ d_vector_file (str):
+ Path to the file including pre-computed speaker embeddings. Defaults to None.
+
+ d_vector_dim (int):
+ Dimension of the external speaker embeddings. Defaults to 0.
+
+ optimizer (str):
+ Name of the model optimizer. Defaults to `Adam`.
+
+ optimizer_params (dict):
+ Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
+
+ lr_scheduler (str):
+ Name of the learning rate scheduler. Defaults to `Noam`.
+
+ lr_scheduler_params (dict):
+ Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
+
+ lr (float):
+ Initial learning rate. Defaults to `1e-3`.
+
+ grad_clip (float):
+ Gradient norm clipping value. Defaults to `5.0`.
+
+ spec_loss_type (str):
+ Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
+
+ duration_loss_type (str):
+ Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
+
+ use_ssim_loss (bool):
+ Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
+
+ wd (float):
+ Weight decay coefficient. Defaults to `1e-7`.
+
+ ssim_loss_alpha (float):
+ Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
+
+ dur_loss_alpha (float):
+ Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
+
+ spec_loss_alpha (float):
+ Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
+
+ pitch_loss_alpha (float):
+ Weight for the pitch predictor's loss. If set 0, disables the pitch predictor. Defaults to 1.0.
+
+ energy_loss_alpha (float):
+ Weight for the energy predictor's loss. If set 0, disables the energy predictor. Defaults to 1.0.
+
+ binary_align_loss_alpha (float):
+ Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
+
+ binary_loss_warmup_epochs (float):
+ Number of epochs to gradually increase the binary loss impact. Defaults to 150.
+
+ min_seq_len (int):
+ Minimum input sequence length to be used at training.
+
+ max_seq_len (int):
+ Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
+
+ # dataset configs
+ compute_f0(bool):
+ Compute pitch. defaults to True
+
+ f0_cache_path(str):
+ pith cache path. defaults to None
+
+ # dataset configs
+ compute_energy(bool):
+ Compute energy. defaults to True
+
+ energy_cache_path(str):
+ energy cache path. defaults to None
+ """
+
+ model: str = "fastspeech2"
+ base_model: str = "forward_tts"
+
+ # model specific params
+ model_args: ForwardTTSArgs = ForwardTTSArgs(use_pitch=True, use_energy=True)
+
+ # multi-speaker settings
+ num_speakers: int = 0
+ speakers_file: str = None
+ use_speaker_embedding: bool = False
+ use_d_vector_file: bool = False
+ d_vector_file: str = False
+ d_vector_dim: int = 0
+
+ # optimizer parameters
+ optimizer: str = "Adam"
+ optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
+ lr_scheduler: str = "NoamLR"
+ lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
+ lr: float = 1e-4
+ grad_clip: float = 5.0
+
+ # loss params
+ spec_loss_type: str = "mse"
+ duration_loss_type: str = "mse"
+ use_ssim_loss: bool = True
+ ssim_loss_alpha: float = 1.0
+ spec_loss_alpha: float = 1.0
+ aligner_loss_alpha: float = 1.0
+ pitch_loss_alpha: float = 0.1
+ energy_loss_alpha: float = 0.1
+ dur_loss_alpha: float = 0.1
+ binary_align_loss_alpha: float = 0.1
+ binary_loss_warmup_epochs: int = 150
+
+ # overrides
+ min_seq_len: int = 13
+ max_seq_len: int = 200
+ r: int = 1 # DO NOT CHANGE
+
+ # dataset configs
+ compute_f0: bool = True
+ f0_cache_path: str = None
+
+ # dataset configs
+ compute_energy: bool = True
+ energy_cache_path: str = None
+
+ # testing
+ test_sentences: List[str] = field(
+ default_factory=lambda: [
+ "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.",
+ ]
+ )
+
+ def __post_init__(self):
+ # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
+ if self.num_speakers > 0:
+ self.model_args.num_speakers = self.num_speakers
+
+ # speaker embedding settings
+ if self.use_speaker_embedding:
+ self.model_args.use_speaker_embedding = True
+ if self.speakers_file:
+ self.model_args.speakers_file = self.speakers_file
+
+ # d-vector settings
+ if self.use_d_vector_file:
+ self.model_args.use_d_vector_file = True
+ if self.d_vector_dim is not None and self.d_vector_dim > 0:
+ self.model_args.d_vector_dim = self.d_vector_dim
+ if self.d_vector_file:
+ self.model_args.d_vector_file = self.d_vector_file
diff --git a/TTS/tts/configs/glow_tts_config.py b/TTS/tts/configs/glow_tts_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..f42f3e5a510bacf1b2312ccea7d46201bbcb774f
--- /dev/null
+++ b/TTS/tts/configs/glow_tts_config.py
@@ -0,0 +1,182 @@
+from dataclasses import dataclass, field
+from typing import List
+
+from TTS.tts.configs.shared_configs import BaseTTSConfig
+
+
+@dataclass
+class GlowTTSConfig(BaseTTSConfig):
+ """Defines parameters for GlowTTS model.
+
+ Example:
+
+ >>> from TTS.tts.configs.glow_tts_config import GlowTTSConfig
+ >>> config = GlowTTSConfig()
+
+ Args:
+ model(str):
+ Model name used for selecting the right model at initialization. Defaults to `glow_tts`.
+ encoder_type (str):
+ Type of the encoder used by the model. Look at `TTS.tts.layers.glow_tts.encoder` for more details.
+ Defaults to `rel_pos_transformers`.
+ encoder_params (dict):
+ Parameters used to define the encoder network. Look at `TTS.tts.layers.glow_tts.encoder` for more details.
+ Defaults to `{"kernel_size": 3, "dropout_p": 0.1, "num_layers": 6, "num_heads": 2, "hidden_channels_ffn": 768}`
+ use_encoder_prenet (bool):
+ enable / disable the use of a prenet for the encoder. Defaults to True.
+ hidden_channels_enc (int):
+ Number of base hidden channels used by the encoder network. It defines the input and the output channel sizes,
+ and for some encoder types internal hidden channels sizes too. Defaults to 192.
+ hidden_channels_dec (int):
+ Number of base hidden channels used by the decoder WaveNet network. Defaults to 192 as in the original work.
+ hidden_channels_dp (int):
+ Number of layer channels of the duration predictor network. Defaults to 256 as in the original work.
+ mean_only (bool):
+ If true predict only the mean values by the decoder flow. Defaults to True.
+ out_channels (int):
+ Number of channels of the model output tensor. Defaults to 80.
+ num_flow_blocks_dec (int):
+ Number of decoder blocks. Defaults to 12.
+ inference_noise_scale (float):
+ Noise scale used at inference. Defaults to 0.33.
+ kernel_size_dec (int):
+ Decoder kernel size. Defaults to 5
+ dilation_rate (int):
+ Rate to increase dilation by each layer in a decoder block. Defaults to 1.
+ num_block_layers (int):
+ Number of decoder layers in each decoder block. Defaults to 4.
+ dropout_p_dec (float):
+ Dropout rate for decoder. Defaults to 0.1.
+ num_speaker (int):
+ Number of speaker to define the size of speaker embedding layer. Defaults to 0.
+ c_in_channels (int):
+ Number of speaker embedding channels. It is set to 512 if embeddings are learned. Defaults to 0.
+ num_splits (int):
+ Number of split levels in inversible conv1x1 operation. Defaults to 4.
+ num_squeeze (int):
+ Number of squeeze levels. When squeezing channels increases and time steps reduces by the factor
+ 'num_squeeze'. Defaults to 2.
+ sigmoid_scale (bool):
+ enable/disable sigmoid scaling in decoder. Defaults to False.
+ mean_only (bool):
+ If True, encoder only computes mean value and uses constant variance for each time step. Defaults to true.
+ encoder_type (str):
+ Encoder module type. Possible values are`["rel_pos_transformer", "gated_conv", "residual_conv_bn", "time_depth_separable"]`
+ Check `TTS.tts.layers.glow_tts.encoder` for more details. Defaults to `rel_pos_transformers` as in the original paper.
+ encoder_params (dict):
+ Encoder module parameters. Defaults to None.
+ d_vector_dim (int):
+ Channels of external speaker embedding vectors. Defaults to 0.
+ data_dep_init_steps (int):
+ Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
+ Activation Normalization that pre-computes normalization stats at the beginning and use the same values
+ for the rest. Defaults to 10.
+ style_wav_for_test (str):
+ Path to the wav file used for changing the style of the speech. Defaults to None.
+ inference_noise_scale (float):
+ Variance used for sampling the random noise added to the decoder's input at inference. Defaults to 0.0.
+ length_scale (float):
+ Multiply the predicted durations with this value to change the speech speed. Defaults to 1.
+ use_speaker_embedding (bool):
+ enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
+ in the multi-speaker mode. Defaults to False.
+ use_d_vector_file (bool):
+ enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
+ d_vector_file (str):
+ Path to the file including pre-computed speaker embeddings. Defaults to None.
+ noam_schedule (bool):
+ enable / disable the use of Noam LR scheduler. Defaults to False.
+ warmup_steps (int):
+ Number of warm-up steps for the Noam scheduler. Defaults 4000.
+ lr (float):
+ Initial learning rate. Defaults to `1e-3`.
+ wd (float):
+ Weight decay coefficient. Defaults to `1e-7`.
+ min_seq_len (int):
+ Minimum input sequence length to be used at training.
+ max_seq_len (int):
+ Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
+ """
+
+ model: str = "glow_tts"
+
+ # model params
+ num_chars: int = None
+ encoder_type: str = "rel_pos_transformer"
+ encoder_params: dict = field(
+ default_factory=lambda: {
+ "kernel_size": 3,
+ "dropout_p": 0.1,
+ "num_layers": 6,
+ "num_heads": 2,
+ "hidden_channels_ffn": 768,
+ }
+ )
+ use_encoder_prenet: bool = True
+ hidden_channels_enc: int = 192
+ hidden_channels_dec: int = 192
+ hidden_channels_dp: int = 256
+ dropout_p_dp: float = 0.1
+ dropout_p_dec: float = 0.05
+ mean_only: bool = True
+ out_channels: int = 80
+ num_flow_blocks_dec: int = 12
+ inference_noise_scale: float = 0.33
+ kernel_size_dec: int = 5
+ dilation_rate: int = 1
+ num_block_layers: int = 4
+ num_speakers: int = 0
+ c_in_channels: int = 0
+ num_splits: int = 4
+ num_squeeze: int = 2
+ sigmoid_scale: bool = False
+ encoder_type: str = "rel_pos_transformer"
+ encoder_params: dict = field(
+ default_factory=lambda: {
+ "kernel_size": 3,
+ "dropout_p": 0.1,
+ "num_layers": 6,
+ "num_heads": 2,
+ "hidden_channels_ffn": 768,
+ "input_length": None,
+ }
+ )
+ d_vector_dim: int = 0
+
+ # training params
+ data_dep_init_steps: int = 10
+
+ # inference params
+ style_wav_for_test: str = None
+ inference_noise_scale: float = 0.0
+ length_scale: float = 1.0
+
+ # multi-speaker settings
+ use_speaker_embedding: bool = False
+ speakers_file: str = None
+ use_d_vector_file: bool = False
+ d_vector_file: str = False
+
+ # optimizer parameters
+ optimizer: str = "RAdam"
+ optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
+ lr_scheduler: str = "NoamLR"
+ lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
+ grad_clip: float = 5.0
+ lr: float = 1e-3
+
+ # overrides
+ min_seq_len: int = 3
+ max_seq_len: int = 500
+ r: int = 1 # DO NOT CHANGE - TODO: make this immutable once coqpit implements it.
+
+ # testing
+ test_sentences: List[str] = field(
+ default_factory=lambda: [
+ "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.",
+ ]
+ )
diff --git a/TTS/tts/configs/neuralhmm_tts_config.py b/TTS/tts/configs/neuralhmm_tts_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..50f72847ed3e1c7089915ef8fd77ae5775c5b260
--- /dev/null
+++ b/TTS/tts/configs/neuralhmm_tts_config.py
@@ -0,0 +1,170 @@
+from dataclasses import dataclass, field
+from typing import List
+
+from TTS.tts.configs.shared_configs import BaseTTSConfig
+
+
+@dataclass
+class NeuralhmmTTSConfig(BaseTTSConfig):
+ """
+ Define parameters for Neural HMM TTS model.
+
+ Example:
+
+ >>> from TTS.tts.configs.overflow_config import OverflowConfig
+ >>> config = OverflowConfig()
+
+ Args:
+ model (str):
+ Model name used to select the right model class to initilize. Defaults to `Overflow`.
+ run_eval_steps (int):
+ Run evalulation epoch after N steps. If None, waits until training epoch is completed. Defaults to None.
+ save_step (int):
+ Save local checkpoint every save_step steps. Defaults to 500.
+ plot_step (int):
+ Plot training stats on the logger every plot_step steps. Defaults to 1.
+ model_param_stats (bool):
+ Log model parameters stats on the logger dashboard. Defaults to False.
+ force_generate_statistics (bool):
+ Force generate mel normalization statistics. Defaults to False.
+ mel_statistics_parameter_path (str):
+ Path to the mel normalization statistics.If the model doesn't finds a file there it will generate statistics.
+ Defaults to None.
+ num_chars (int):
+ Number of characters used by the model. It must be defined before initializing the model. Defaults to None.
+ state_per_phone (int):
+ Generates N states per phone. Similar, to `add_blank` parameter in GlowTTS but in Overflow it is upsampled by model's encoder. Defaults to 2.
+ encoder_in_out_features (int):
+ Channels of encoder input and character embedding tensors. Defaults to 512.
+ encoder_n_convolutions (int):
+ Number of convolution layers in the encoder. Defaults to 3.
+ out_channels (int):
+ Channels of the final model output. It must match the spectragram size. Defaults to 80.
+ ar_order (int):
+ Autoregressive order of the model. Defaults to 1. In ablations of Neural HMM it was found that more autoregression while giving more variation hurts naturalness of the synthesised audio.
+ sampling_temp (float):
+ Variation added to the sample from the latent space of neural HMM. Defaults to 0.334.
+ deterministic_transition (bool):
+ deterministic duration generation based on duration quantiles as defiend in "S. Ronanki, O. Watts, S. King, and G. E. Henter, “Medianbased generation of synthetic speech durations using a nonparametric approach,” in Proc. SLT, 2016.". Defaults to True.
+ duration_threshold (float):
+ Threshold for duration quantiles. Defaults to 0.55. Tune this to change the speaking rate of the synthesis, where lower values defines a slower speaking rate and higher values defines a faster speaking rate.
+ use_grad_checkpointing (bool):
+ Use gradient checkpointing to save memory. In a multi-GPU setting currently pytorch does not supports gradient checkpoint inside a loop so we will have to turn it off then.Adjust depending on whatever get more batch size either by using a single GPU or multi-GPU. Defaults to True.
+ max_sampling_time (int):
+ Maximum sampling time while synthesising latents from neural HMM. Defaults to 1000.
+ prenet_type (str):
+ `original` or `bn`. `original` sets the default Prenet and `bn` uses Batch Normalization version of the
+ Prenet. Defaults to `original`.
+ prenet_dim (int):
+ Dimension of the Prenet. Defaults to 256.
+ prenet_n_layers (int):
+ Number of layers in the Prenet. Defaults to 2.
+ prenet_dropout (float):
+ Dropout rate of the Prenet. Defaults to 0.5.
+ prenet_dropout_at_inference (bool):
+ Use dropout at inference time. Defaults to False.
+ memory_rnn_dim (int):
+ Dimension of the memory LSTM to process the prenet output. Defaults to 1024.
+ outputnet_size (list[int]):
+ Size of the output network inside the neural HMM. Defaults to [1024].
+ flat_start_params (dict):
+ Parameters for the flat start initialization of the neural HMM. Defaults to `{"mean": 0.0, "std": 1.0, "transition_p": 0.14}`.
+ It will be recomputed when you pass the dataset.
+ std_floor (float):
+ Floor value for the standard deviation of the neural HMM. Prevents model cheating by putting point mass and getting infinite likelihood at any datapoint. Defaults to 0.01.
+ It is called `variance flooring` in standard HMM literature.
+ optimizer (str):
+ Optimizer to use for training. Defaults to `adam`.
+ optimizer_params (dict):
+ Parameters for the optimizer. Defaults to `{"weight_decay": 1e-6}`.
+ grad_clip (float):
+ Gradient clipping threshold. Defaults to 40_000.
+ lr (float):
+ Learning rate. Defaults to 1e-3.
+ lr_scheduler (str):
+ Learning rate scheduler for the training. Use one from `torch.optim.Scheduler` schedulers or
+ `TTS.utils.training`. Defaults to `None`.
+ min_seq_len (int):
+ Minimum input sequence length to be used at training.
+ max_seq_len (int):
+ Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
+ """
+
+ model: str = "NeuralHMM_TTS"
+
+ # Training and Checkpoint configs
+ run_eval_steps: int = 100
+ save_step: int = 500
+ plot_step: int = 1
+ model_param_stats: bool = False
+
+ # data parameters
+ force_generate_statistics: bool = False
+ mel_statistics_parameter_path: str = None
+
+ # Encoder parameters
+ num_chars: int = None
+ state_per_phone: int = 2
+ encoder_in_out_features: int = 512
+ encoder_n_convolutions: int = 3
+
+ # HMM parameters
+ out_channels: int = 80
+ ar_order: int = 1
+ sampling_temp: float = 0
+ deterministic_transition: bool = True
+ duration_threshold: float = 0.43
+ use_grad_checkpointing: bool = True
+ max_sampling_time: int = 1000
+
+ ## Prenet parameters
+ prenet_type: str = "original"
+ prenet_dim: int = 256
+ prenet_n_layers: int = 2
+ prenet_dropout: float = 0.5
+ prenet_dropout_at_inference: bool = True
+ memory_rnn_dim: int = 1024
+
+ ## Outputnet parameters
+ outputnet_size: List[int] = field(default_factory=lambda: [1024])
+ flat_start_params: dict = field(default_factory=lambda: {"mean": 0.0, "std": 1.0, "transition_p": 0.14})
+ std_floor: float = 0.001
+
+ # optimizer parameters
+ optimizer: str = "Adam"
+ optimizer_params: dict = field(default_factory=lambda: {"weight_decay": 1e-6})
+ grad_clip: float = 40000.0
+ lr: float = 1e-3
+ lr_scheduler: str = None
+
+ # overrides
+ min_text_len: int = 10
+ max_text_len: int = 500
+ min_audio_len: int = 512
+
+ # testing
+ test_sentences: List[str] = field(
+ default_factory=lambda: [
+ "Be a voice, not an echo.",
+ ]
+ )
+
+ # Extra needed config
+ r: int = 1
+ use_d_vector_file: bool = False
+ use_speaker_embedding: bool = False
+
+ def check_values(self):
+ """Validate the hyperparameters.
+
+ Raises:
+ AssertionError: when the parameters network is not defined
+ AssertionError: transition probability is not between 0 and 1
+ """
+ assert self.ar_order > 0, "AR order must be greater than 0 it is an autoregressive model."
+ assert (
+ len(self.outputnet_size) >= 1
+ ), f"Parameter Network must have atleast one layer check the config file for parameter network. Provided: {self.parameternetwork}"
+ assert (
+ 0 < self.flat_start_params["transition_p"] < 1
+ ), f"Transition probability must be between 0 and 1. Provided: {self.flat_start_params['transition_p']}"
diff --git a/TTS/tts/configs/overflow_config.py b/TTS/tts/configs/overflow_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc3e5548b8f62f76c88acca85d19e2cee8687ebd
--- /dev/null
+++ b/TTS/tts/configs/overflow_config.py
@@ -0,0 +1,201 @@
+from dataclasses import dataclass, field
+from typing import List
+
+from TTS.tts.configs.shared_configs import BaseTTSConfig
+
+
+@dataclass
+class OverflowConfig(BaseTTSConfig): # The classname has to be camel case
+ """
+ Define parameters for OverFlow model.
+
+ Example:
+
+ >>> from TTS.tts.configs.overflow_config import OverflowConfig
+ >>> config = OverflowConfig()
+
+ Args:
+ model (str):
+ Model name used to select the right model class to initilize. Defaults to `Overflow`.
+ run_eval_steps (int):
+ Run evalulation epoch after N steps. If None, waits until training epoch is completed. Defaults to None.
+ save_step (int):
+ Save local checkpoint every save_step steps. Defaults to 500.
+ plot_step (int):
+ Plot training stats on the logger every plot_step steps. Defaults to 1.
+ model_param_stats (bool):
+ Log model parameters stats on the logger dashboard. Defaults to False.
+ force_generate_statistics (bool):
+ Force generate mel normalization statistics. Defaults to False.
+ mel_statistics_parameter_path (str):
+ Path to the mel normalization statistics.If the model doesn't finds a file there it will generate statistics.
+ Defaults to None.
+ num_chars (int):
+ Number of characters used by the model. It must be defined before initializing the model. Defaults to None.
+ state_per_phone (int):
+ Generates N states per phone. Similar, to `add_blank` parameter in GlowTTS but in Overflow it is upsampled by model's encoder. Defaults to 2.
+ encoder_in_out_features (int):
+ Channels of encoder input and character embedding tensors. Defaults to 512.
+ encoder_n_convolutions (int):
+ Number of convolution layers in the encoder. Defaults to 3.
+ out_channels (int):
+ Channels of the final model output. It must match the spectragram size. Defaults to 80.
+ ar_order (int):
+ Autoregressive order of the model. Defaults to 1. In ablations of Neural HMM it was found that more autoregression while giving more variation hurts naturalness of the synthesised audio.
+ sampling_temp (float):
+ Variation added to the sample from the latent space of neural HMM. Defaults to 0.334.
+ deterministic_transition (bool):
+ deterministic duration generation based on duration quantiles as defiend in "S. Ronanki, O. Watts, S. King, and G. E. Henter, “Medianbased generation of synthetic speech durations using a nonparametric approach,” in Proc. SLT, 2016.". Defaults to True.
+ duration_threshold (float):
+ Threshold for duration quantiles. Defaults to 0.55. Tune this to change the speaking rate of the synthesis, where lower values defines a slower speaking rate and higher values defines a faster speaking rate.
+ use_grad_checkpointing (bool):
+ Use gradient checkpointing to save memory. In a multi-GPU setting currently pytorch does not supports gradient checkpoint inside a loop so we will have to turn it off then.Adjust depending on whatever get more batch size either by using a single GPU or multi-GPU. Defaults to True.
+ max_sampling_time (int):
+ Maximum sampling time while synthesising latents from neural HMM. Defaults to 1000.
+ prenet_type (str):
+ `original` or `bn`. `original` sets the default Prenet and `bn` uses Batch Normalization version of the
+ Prenet. Defaults to `original`.
+ prenet_dim (int):
+ Dimension of the Prenet. Defaults to 256.
+ prenet_n_layers (int):
+ Number of layers in the Prenet. Defaults to 2.
+ prenet_dropout (float):
+ Dropout rate of the Prenet. Defaults to 0.5.
+ prenet_dropout_at_inference (bool):
+ Use dropout at inference time. Defaults to False.
+ memory_rnn_dim (int):
+ Dimension of the memory LSTM to process the prenet output. Defaults to 1024.
+ outputnet_size (list[int]):
+ Size of the output network inside the neural HMM. Defaults to [1024].
+ flat_start_params (dict):
+ Parameters for the flat start initialization of the neural HMM. Defaults to `{"mean": 0.0, "std": 1.0, "transition_p": 0.14}`.
+ It will be recomputed when you pass the dataset.
+ std_floor (float):
+ Floor value for the standard deviation of the neural HMM. Prevents model cheating by putting point mass and getting infinite likelihood at any datapoint. Defaults to 0.01.
+ It is called `variance flooring` in standard HMM literature.
+ hidden_channels_dec (int):
+ Number of base hidden channels used by the decoder WaveNet network. Defaults to 150.
+ kernel_size_dec (int):
+ Decoder kernel size. Defaults to 5
+ dilation_rate (int):
+ Rate to increase dilation by each layer in a decoder block. Defaults to 1.
+ num_flow_blocks_dec (int):
+ Number of decoder layers in each decoder block. Defaults to 4.
+ dropout_p_dec (float):
+ Dropout rate of the decoder. Defaults to 0.05.
+ num_splits (int):
+ Number of split levels in inversible conv1x1 operation. Defaults to 4.
+ num_squeeze (int):
+ Number of squeeze levels. When squeezing channels increases and time steps reduces by the factor
+ 'num_squeeze'. Defaults to 2.
+ sigmoid_scale (bool):
+ enable/disable sigmoid scaling in decoder. Defaults to False.
+ c_in_channels (int):
+ Unused parameter from GlowTTS's decoder. Defaults to 0.
+ optimizer (str):
+ Optimizer to use for training. Defaults to `adam`.
+ optimizer_params (dict):
+ Parameters for the optimizer. Defaults to `{"weight_decay": 1e-6}`.
+ grad_clip (float):
+ Gradient clipping threshold. Defaults to 40_000.
+ lr (float):
+ Learning rate. Defaults to 1e-3.
+ lr_scheduler (str):
+ Learning rate scheduler for the training. Use one from `torch.optim.Scheduler` schedulers or
+ `TTS.utils.training`. Defaults to `None`.
+ min_seq_len (int):
+ Minimum input sequence length to be used at training.
+ max_seq_len (int):
+ Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
+ """
+
+ model: str = "Overflow"
+
+ # Training and Checkpoint configs
+ run_eval_steps: int = 100
+ save_step: int = 500
+ plot_step: int = 1
+ model_param_stats: bool = False
+
+ # data parameters
+ force_generate_statistics: bool = False
+ mel_statistics_parameter_path: str = None
+
+ # Encoder parameters
+ num_chars: int = None
+ state_per_phone: int = 2
+ encoder_in_out_features: int = 512
+ encoder_n_convolutions: int = 3
+
+ # HMM parameters
+ out_channels: int = 80
+ ar_order: int = 1
+ sampling_temp: float = 0.334
+ deterministic_transition: bool = True
+ duration_threshold: float = 0.55
+ use_grad_checkpointing: bool = True
+ max_sampling_time: int = 1000
+
+ ## Prenet parameters
+ prenet_type: str = "original"
+ prenet_dim: int = 256
+ prenet_n_layers: int = 2
+ prenet_dropout: float = 0.5
+ prenet_dropout_at_inference: bool = False
+ memory_rnn_dim: int = 1024
+
+ ## Outputnet parameters
+ outputnet_size: List[int] = field(default_factory=lambda: [1024])
+ flat_start_params: dict = field(default_factory=lambda: {"mean": 0.0, "std": 1.0, "transition_p": 0.14})
+ std_floor: float = 0.01
+
+ # Decoder parameters
+ hidden_channels_dec: int = 150
+ kernel_size_dec: int = 5
+ dilation_rate: int = 1
+ num_flow_blocks_dec: int = 12
+ num_block_layers: int = 4
+ dropout_p_dec: float = 0.05
+ num_splits: int = 4
+ num_squeeze: int = 2
+ sigmoid_scale: bool = False
+ c_in_channels: int = 0
+
+ # optimizer parameters
+ optimizer: str = "Adam"
+ optimizer_params: dict = field(default_factory=lambda: {"weight_decay": 1e-6})
+ grad_clip: float = 40000.0
+ lr: float = 1e-3
+ lr_scheduler: str = None
+
+ # overrides
+ min_text_len: int = 10
+ max_text_len: int = 500
+ min_audio_len: int = 512
+
+ # testing
+ test_sentences: List[str] = field(
+ default_factory=lambda: [
+ "Be a voice, not an echo.",
+ ]
+ )
+
+ # Extra needed config
+ r: int = 1
+ use_d_vector_file: bool = False
+ use_speaker_embedding: bool = False
+
+ def check_values(self):
+ """Validate the hyperparameters.
+
+ Raises:
+ AssertionError: when the parameters network is not defined
+ AssertionError: transition probability is not between 0 and 1
+ """
+ assert self.ar_order > 0, "AR order must be greater than 0 it is an autoregressive model."
+ assert (
+ len(self.outputnet_size) >= 1
+ ), f"Parameter Network must have atleast one layer check the config file for parameter network. Provided: {self.parameternetwork}"
+ assert (
+ 0 < self.flat_start_params["transition_p"] < 1
+ ), f"Transition probability must be between 0 and 1. Provided: {self.flat_start_params['transition_p']}"
diff --git a/TTS/tts/configs/shared_configs.py b/TTS/tts/configs/shared_configs.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf17322c190bb234d4e27c6196e53b276fb5f09d
--- /dev/null
+++ b/TTS/tts/configs/shared_configs.py
@@ -0,0 +1,344 @@
+from dataclasses import asdict, dataclass, field
+from typing import Dict, List
+
+from coqpit import Coqpit, check_argument
+
+from TTS.config import BaseAudioConfig, BaseDatasetConfig, BaseTrainingConfig
+
+
+@dataclass
+class GSTConfig(Coqpit):
+ """Defines the Global Style Token Module
+
+ Args:
+ gst_style_input_wav (str):
+ Path to the wav file used to define the style of the output speech at inference. Defaults to None.
+
+ gst_style_input_weights (dict):
+ Defines the weights for each style token used at inference. Defaults to None.
+
+ gst_embedding_dim (int):
+ Defines the size of the GST embedding vector dimensions. Defaults to 256.
+
+ gst_num_heads (int):
+ Number of attention heads used by the multi-head attention. Defaults to 4.
+
+ gst_num_style_tokens (int):
+ Number of style token vectors. Defaults to 10.
+ """
+
+ gst_style_input_wav: str = None
+ gst_style_input_weights: dict = None
+ gst_embedding_dim: int = 256
+ gst_use_speaker_embedding: bool = False
+ gst_num_heads: int = 4
+ gst_num_style_tokens: int = 10
+
+ def check_values(
+ self,
+ ):
+ """Check config fields"""
+ c = asdict(self)
+ super().check_values()
+ check_argument("gst_style_input_weights", c, restricted=False)
+ check_argument("gst_style_input_wav", c, restricted=False)
+ check_argument("gst_embedding_dim", c, restricted=True, min_val=0, max_val=1000)
+ check_argument("gst_use_speaker_embedding", c, restricted=False)
+ check_argument("gst_num_heads", c, restricted=True, min_val=2, max_val=10)
+ check_argument("gst_num_style_tokens", c, restricted=True, min_val=1, max_val=1000)
+
+
+@dataclass
+class CapacitronVAEConfig(Coqpit):
+ """Defines the capacitron VAE Module
+ Args:
+ capacitron_capacity (int):
+ Defines the variational capacity limit of the prosody embeddings. Defaults to 150.
+ capacitron_VAE_embedding_dim (int):
+ Defines the size of the Capacitron embedding vector dimension. Defaults to 128.
+ capacitron_use_text_summary_embeddings (bool):
+ If True, use a text summary embedding in Capacitron. Defaults to True.
+ capacitron_text_summary_embedding_dim (int):
+ Defines the size of the capacitron text embedding vector dimension. Defaults to 128.
+ capacitron_use_speaker_embedding (bool):
+ if True use speaker embeddings in Capacitron. Defaults to False.
+ capacitron_VAE_loss_alpha (float):
+ Weight for the VAE loss of the Tacotron model. If set less than or equal to zero, it disables the
+ corresponding loss function. Defaults to 0.25
+ capacitron_grad_clip (float):
+ Gradient clipping value for all gradients except beta. Defaults to 5.0
+ """
+
+ capacitron_loss_alpha: int = 1
+ capacitron_capacity: int = 150
+ capacitron_VAE_embedding_dim: int = 128
+ capacitron_use_text_summary_embeddings: bool = True
+ capacitron_text_summary_embedding_dim: int = 128
+ capacitron_use_speaker_embedding: bool = False
+ capacitron_VAE_loss_alpha: float = 0.25
+ capacitron_grad_clip: float = 5.0
+
+ def check_values(
+ self,
+ ):
+ """Check config fields"""
+ c = asdict(self)
+ super().check_values()
+ check_argument("capacitron_capacity", c, restricted=True, min_val=10, max_val=500)
+ check_argument("capacitron_VAE_embedding_dim", c, restricted=True, min_val=16, max_val=1024)
+ check_argument("capacitron_use_speaker_embedding", c, restricted=False)
+ check_argument("capacitron_text_summary_embedding_dim", c, restricted=False, min_val=16, max_val=512)
+ check_argument("capacitron_VAE_loss_alpha", c, restricted=False)
+ check_argument("capacitron_grad_clip", c, restricted=False)
+
+
+@dataclass
+class CharactersConfig(Coqpit):
+ """Defines arguments for the `BaseCharacters` or `BaseVocabulary` and their subclasses.
+
+ Args:
+ characters_class (str):
+ Defines the class of the characters used. If None, we pick ```Phonemes``` or ```Graphemes``` based on
+ the configuration. Defaults to None.
+
+ vocab_dict (dict):
+ Defines the vocabulary dictionary used to encode the characters. Defaults to None.
+
+ pad (str):
+ characters in place of empty padding. Defaults to None.
+
+ eos (str):
+ characters showing the end of a sentence. Defaults to None.
+
+ bos (str):
+ characters showing the beginning of a sentence. Defaults to None.
+
+ blank (str):
+ Optional character used between characters by some models for better prosody. Defaults to `_blank`.
+
+ characters (str):
+ character set used by the model. Characters not in this list are ignored when converting input text to
+ a list of sequence IDs. Defaults to None.
+
+ punctuations (str):
+ characters considered as punctuation as parsing the input sentence. Defaults to None.
+
+ phonemes (str):
+ characters considered as parsing phonemes. This is only for backwards compat. Use `characters` for new
+ models. Defaults to None.
+
+ is_unique (bool):
+ remove any duplicate characters in the character lists. It is a bandaid for compatibility with the old
+ models trained with character lists with duplicates. Defaults to True.
+
+ is_sorted (bool):
+ Sort the characters in alphabetical order. Defaults to True.
+ """
+
+ characters_class: str = None
+
+ # using BaseVocabulary
+ vocab_dict: Dict = None
+
+ # using on BaseCharacters
+ pad: str = None
+ eos: str = None
+ bos: str = None
+ blank: str = None
+ characters: str = None
+ punctuations: str = None
+ phonemes: str = None
+ is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates
+ is_sorted: bool = True
+
+
+@dataclass
+class BaseTTSConfig(BaseTrainingConfig):
+ """Shared parameters among all the tts models.
+
+ Args:
+
+ audio (BaseAudioConfig):
+ Audio processor config object instance.
+
+ use_phonemes (bool):
+ enable / disable phoneme use.
+
+ phonemizer (str):
+ Name of the phonemizer to use. If set None, the phonemizer will be selected by `phoneme_language`.
+ Defaults to None.
+
+ phoneme_language (str):
+ Language code for the phonemizer. You can check the list of supported languages by running
+ `python TTS/tts/utils/text/phonemizers/__init__.py`. Defaults to None.
+
+ compute_input_seq_cache (bool):
+ enable / disable precomputation of the phoneme sequences. At the expense of some delay at the beginning of
+ the training, It allows faster data loader time and precise limitation with `max_seq_len` and
+ `min_seq_len`.
+
+ text_cleaner (str):
+ Name of the text cleaner used for cleaning and formatting transcripts.
+
+ enable_eos_bos_chars (bool):
+ enable / disable the use of eos and bos characters.
+
+ test_senteces_file (str):
+ Path to a txt file that has sentences used at test time. The file must have a sentence per line.
+
+ phoneme_cache_path (str):
+ Path to the output folder caching the computed phonemes for each sample.
+
+ characters (CharactersConfig):
+ Instance of a CharactersConfig class.
+
+ batch_group_size (int):
+ Size of the batch groups used for bucketing. By default, the dataloader orders samples by the sequence
+ length for a more efficient and stable training. If `batch_group_size > 1` then it performs bucketing to
+ prevent using the same batches for each epoch.
+
+ loss_masking (bool):
+ enable / disable masking loss values against padded segments of samples in a batch.
+
+ min_text_len (int):
+ Minimum length of input text to be used. All shorter samples will be ignored. Defaults to 0.
+
+ max_text_len (int):
+ Maximum length of input text to be used. All longer samples will be ignored. Defaults to float("inf").
+
+ min_audio_len (int):
+ Minimum length of input audio to be used. All shorter samples will be ignored. Defaults to 0.
+
+ max_audio_len (int):
+ Maximum length of input audio to be used. All longer samples will be ignored. The maximum length in the
+ dataset defines the VRAM used in the training. Hence, pay attention to this value if you encounter an
+ OOM error in training. Defaults to float("inf").
+
+ compute_f0 (int):
+ (Not in use yet).
+
+ compute_energy (int):
+ (Not in use yet).
+
+ compute_linear_spec (bool):
+ If True data loader computes and returns linear spectrograms alongside the other data.
+
+ precompute_num_workers (int):
+ Number of workers to precompute features. Defaults to 0.
+
+ use_noise_augment (bool):
+ Augment the input audio with random noise.
+
+ start_by_longest (bool):
+ If True, the data loader will start loading the longest batch first. It is useful for checking OOM issues.
+ Defaults to False.
+
+ shuffle (bool):
+ If True, the data loader will shuffle the dataset when there is not sampler defined. Defaults to True.
+
+ drop_last (bool):
+ If True, the data loader will drop the last batch if it is not complete. It helps to prevent
+ issues that emerge from the partial batch statistics. Defaults to True.
+
+ add_blank (bool):
+ Add blank characters between each other two characters. It improves performance for some models at expense
+ of slower run-time due to the longer input sequence.
+
+ datasets (List[BaseDatasetConfig]):
+ List of datasets used for training. If multiple datasets are provided, they are merged and used together
+ for training.
+
+ optimizer (str):
+ Optimizer used for the training. Set one from `torch.optim.Optimizer` or `TTS.utils.training`.
+ Defaults to ``.
+
+ optimizer_params (dict):
+ Optimizer kwargs. Defaults to `{"betas": [0.8, 0.99], "weight_decay": 0.0}`
+
+ lr_scheduler (str):
+ Learning rate scheduler for the training. Use one from `torch.optim.Scheduler` schedulers or
+ `TTS.utils.training`. Defaults to ``.
+
+ lr_scheduler_params (dict):
+ Parameters for the generator learning rate scheduler. Defaults to `{"warmup": 4000}`.
+
+ test_sentences (List[str]):
+ List of sentences to be used at testing. Defaults to '[]'
+
+ eval_split_max_size (int):
+ Number maximum of samples to be used for evaluation in proportion split. Defaults to None (Disabled).
+
+ eval_split_size (float):
+ If between 0.0 and 1.0 represents the proportion of the dataset to include in the evaluation set.
+ If > 1, represents the absolute number of evaluation samples. Defaults to 0.01 (1%).
+
+ use_speaker_weighted_sampler (bool):
+ Enable / Disable the batch balancer by speaker. Defaults to ```False```.
+
+ speaker_weighted_sampler_alpha (float):
+ Number that control the influence of the speaker sampler weights. Defaults to ```1.0```.
+
+ use_language_weighted_sampler (bool):
+ Enable / Disable the batch balancer by language. Defaults to ```False```.
+
+ language_weighted_sampler_alpha (float):
+ Number that control the influence of the language sampler weights. Defaults to ```1.0```.
+
+ use_length_weighted_sampler (bool):
+ Enable / Disable the batch balancer by audio length. If enabled the dataset will be divided
+ into 10 buckets considering the min and max audio of the dataset. The sampler weights will be
+ computed forcing to have the same quantity of data for each bucket in each training batch. Defaults to ```False```.
+
+ length_weighted_sampler_alpha (float):
+ Number that control the influence of the length sampler weights. Defaults to ```1.0```.
+ """
+
+ audio: BaseAudioConfig = field(default_factory=BaseAudioConfig)
+ # phoneme settings
+ use_phonemes: bool = False
+ phonemizer: str = None
+ phoneme_language: str = None
+ compute_input_seq_cache: bool = False
+ text_cleaner: str = None
+ enable_eos_bos_chars: bool = False
+ test_sentences_file: str = ""
+ phoneme_cache_path: str = None
+ # vocabulary parameters
+ characters: CharactersConfig = None
+ add_blank: bool = False
+ # training params
+ batch_group_size: int = 0
+ loss_masking: bool = None
+ # dataloading
+ min_audio_len: int = 1
+ max_audio_len: int = float("inf")
+ min_text_len: int = 1
+ max_text_len: int = float("inf")
+ compute_f0: bool = False
+ compute_energy: bool = False
+ compute_linear_spec: bool = False
+ precompute_num_workers: int = 0
+ use_noise_augment: bool = False
+ start_by_longest: bool = False
+ shuffle: bool = False
+ drop_last: bool = False
+ # dataset
+ datasets: List[BaseDatasetConfig] = field(default_factory=lambda: [BaseDatasetConfig()])
+ # optimizer
+ optimizer: str = "radam"
+ optimizer_params: dict = None
+ # scheduler
+ lr_scheduler: str = None
+ lr_scheduler_params: dict = field(default_factory=lambda: {})
+ # testing
+ test_sentences: List[str] = field(default_factory=lambda: [])
+ # evaluation
+ eval_split_max_size: int = None
+ eval_split_size: float = 0.01
+ # weighted samplers
+ use_speaker_weighted_sampler: bool = False
+ speaker_weighted_sampler_alpha: float = 1.0
+ use_language_weighted_sampler: bool = False
+ language_weighted_sampler_alpha: float = 1.0
+ use_length_weighted_sampler: bool = False
+ length_weighted_sampler_alpha: float = 1.0
diff --git a/TTS/tts/configs/speedy_speech_config.py b/TTS/tts/configs/speedy_speech_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bf5101fcad2479e87836c827658c88addfd7cc6
--- /dev/null
+++ b/TTS/tts/configs/speedy_speech_config.py
@@ -0,0 +1,192 @@
+from dataclasses import dataclass, field
+from typing import List
+
+from TTS.tts.configs.shared_configs import BaseTTSConfig
+from TTS.tts.models.forward_tts import ForwardTTSArgs
+
+
+@dataclass
+class SpeedySpeechConfig(BaseTTSConfig):
+ """Configure `ForwardTTS` as SpeedySpeech model.
+
+ Example:
+
+ >>> from TTS.tts.configs.speedy_speech_config import SpeedySpeechConfig
+ >>> config = SpeedySpeechConfig()
+
+ Args:
+ model (str):
+ Model name used for selecting the right model at initialization. Defaults to `speedy_speech`.
+
+ base_model (str):
+ Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
+ the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
+
+ model_args (Coqpit):
+ Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`.
+
+ data_dep_init_steps (int):
+ Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
+ Activation Normalization that pre-computes normalization stats at the beginning and use the same values
+ for the rest. Defaults to 10.
+
+ speakers_file (str):
+ Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
+ speaker names. Defaults to `None`.
+
+ use_speaker_embedding (bool):
+ enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
+ in the multi-speaker mode. Defaults to False.
+
+ use_d_vector_file (bool):
+ enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
+
+ d_vector_file (str):
+ Path to the file including pre-computed speaker embeddings. Defaults to None.
+
+ d_vector_dim (int):
+ Dimension of the external speaker embeddings. Defaults to 0.
+
+ optimizer (str):
+ Name of the model optimizer. Defaults to `RAdam`.
+
+ optimizer_params (dict):
+ Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
+
+ lr_scheduler (str):
+ Name of the learning rate scheduler. Defaults to `Noam`.
+
+ lr_scheduler_params (dict):
+ Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
+
+ lr (float):
+ Initial learning rate. Defaults to `1e-3`.
+
+ grad_clip (float):
+ Gradient norm clipping value. Defaults to `5.0`.
+
+ spec_loss_type (str):
+ Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `l1`.
+
+ duration_loss_type (str):
+ Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `huber`.
+
+ use_ssim_loss (bool):
+ Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
+
+ wd (float):
+ Weight decay coefficient. Defaults to `1e-7`.
+
+ ssim_loss_alpha (float):
+ Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
+
+ dur_loss_alpha (float):
+ Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
+
+ spec_loss_alpha (float):
+ Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
+
+ binary_loss_alpha (float):
+ Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
+
+ binary_loss_warmup_epochs (float):
+ Number of epochs to gradually increase the binary loss impact. Defaults to 150.
+
+ min_seq_len (int):
+ Minimum input sequence length to be used at training.
+
+ max_seq_len (int):
+ Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
+ """
+
+ model: str = "speedy_speech"
+ base_model: str = "forward_tts"
+
+ # set model args as SpeedySpeech
+ model_args: ForwardTTSArgs = ForwardTTSArgs(
+ use_pitch=False,
+ encoder_type="residual_conv_bn",
+ encoder_params={
+ "kernel_size": 4,
+ "dilations": 4 * [1, 2, 4] + [1],
+ "num_conv_blocks": 2,
+ "num_res_blocks": 13,
+ },
+ decoder_type="residual_conv_bn",
+ decoder_params={
+ "kernel_size": 4,
+ "dilations": 4 * [1, 2, 4, 8] + [1],
+ "num_conv_blocks": 2,
+ "num_res_blocks": 17,
+ },
+ out_channels=80,
+ hidden_channels=128,
+ positional_encoding=True,
+ detach_duration_predictor=True,
+ )
+
+ # multi-speaker settings
+ num_speakers: int = 0
+ speakers_file: str = None
+ use_speaker_embedding: bool = False
+ use_d_vector_file: bool = False
+ d_vector_file: str = False
+ d_vector_dim: int = 0
+
+ # optimizer parameters
+ optimizer: str = "Adam"
+ optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
+ lr_scheduler: str = "NoamLR"
+ lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
+ lr: float = 1e-4
+ grad_clip: float = 5.0
+
+ # loss params
+ spec_loss_type: str = "l1"
+ duration_loss_type: str = "huber"
+ use_ssim_loss: bool = False
+ ssim_loss_alpha: float = 1.0
+ dur_loss_alpha: float = 1.0
+ spec_loss_alpha: float = 1.0
+ aligner_loss_alpha: float = 1.0
+ binary_align_loss_alpha: float = 0.3
+ binary_loss_warmup_epochs: int = 150
+
+ # overrides
+ min_seq_len: int = 13
+ max_seq_len: int = 200
+ r: int = 1 # DO NOT CHANGE
+
+ # dataset configs
+ compute_f0: bool = False
+ f0_cache_path: str = None
+
+ # testing
+ test_sentences: List[str] = field(
+ default_factory=lambda: [
+ "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.",
+ ]
+ )
+
+ def __post_init__(self):
+ # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
+ if self.num_speakers > 0:
+ self.model_args.num_speakers = self.num_speakers
+
+ # speaker embedding settings
+ if self.use_speaker_embedding:
+ self.model_args.use_speaker_embedding = True
+ if self.speakers_file:
+ self.model_args.speakers_file = self.speakers_file
+
+ # d-vector settings
+ if self.use_d_vector_file:
+ self.model_args.use_d_vector_file = True
+ if self.d_vector_dim is not None and self.d_vector_dim > 0:
+ self.model_args.d_vector_dim = self.d_vector_dim
+ if self.d_vector_file:
+ self.model_args.d_vector_file = self.d_vector_file
diff --git a/TTS/tts/configs/tacotron2_config.py b/TTS/tts/configs/tacotron2_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..95b65202218cf3aa0dd70c8d8cd55a3f913ed308
--- /dev/null
+++ b/TTS/tts/configs/tacotron2_config.py
@@ -0,0 +1,21 @@
+from dataclasses import dataclass
+
+from TTS.tts.configs.tacotron_config import TacotronConfig
+
+
+@dataclass
+class Tacotron2Config(TacotronConfig):
+ """Defines parameters for Tacotron2 based models.
+
+ Example:
+
+ >>> from TTS.tts.configs.tacotron2_config import Tacotron2Config
+ >>> config = Tacotron2Config()
+
+ Check `TacotronConfig` for argument descriptions.
+ """
+
+ model: str = "tacotron2"
+ out_channels: int = 80
+ encoder_in_features: int = 512
+ decoder_in_features: int = 512
diff --git a/TTS/tts/configs/tacotron_config.py b/TTS/tts/configs/tacotron_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..350b5ea99633569d6977851875d5d8d83175ac36
--- /dev/null
+++ b/TTS/tts/configs/tacotron_config.py
@@ -0,0 +1,235 @@
+from dataclasses import dataclass, field
+from typing import List
+
+from TTS.tts.configs.shared_configs import BaseTTSConfig, CapacitronVAEConfig, GSTConfig
+
+
+@dataclass
+class TacotronConfig(BaseTTSConfig):
+ """Defines parameters for Tacotron based models.
+
+ Example:
+
+ >>> from TTS.tts.configs.tacotron_config import TacotronConfig
+ >>> config = TacotronConfig()
+
+ Args:
+ model (str):
+ Model name used to select the right model class to initilize. Defaults to `Tacotron`.
+ use_gst (bool):
+ enable / disable the use of Global Style Token modules. Defaults to False.
+ gst (GSTConfig):
+ Instance of `GSTConfig` class.
+ gst_style_input (str):
+ Path to the wav file used at inference to set the speech style through GST. If `GST` is enabled and
+ this is not defined, the model uses a zero vector as an input. Defaults to None.
+ use_capacitron_vae (bool):
+ enable / disable the use of Capacitron modules. Defaults to False.
+ capacitron_vae (CapacitronConfig):
+ Instance of `CapacitronConfig` class.
+ num_chars (int):
+ Number of characters used by the model. It must be defined before initializing the model. Defaults to None.
+ num_speakers (int):
+ Number of speakers for multi-speaker models. Defaults to 1.
+ r (int):
+ Initial number of output frames that the decoder computed per iteration. Larger values makes training and inference
+ faster but reduces the quality of the output frames. This must be equal to the largest `r` value used in
+ `gradual_training` schedule. Defaults to 1.
+ gradual_training (List[List]):
+ Parameters for the gradual training schedule. It is in the form `[[a, b, c], [d ,e ,f] ..]` where `a` is
+ the step number to start using the rest of the values, `b` is the `r` value and `c` is the batch size.
+ If sets None, no gradual training is used. Defaults to None.
+ memory_size (int):
+ Defines the number of previous frames used by the Prenet. If set to < 0, then it uses only the last frame.
+ Defaults to -1.
+ prenet_type (str):
+ `original` or `bn`. `original` sets the default Prenet and `bn` uses Batch Normalization version of the
+ Prenet. Defaults to `original`.
+ prenet_dropout (bool):
+ enables / disables the use of dropout in the Prenet. Defaults to True.
+ prenet_dropout_at_inference (bool):
+ enable / disable the use of dropout in the Prenet at the inference time. Defaults to False.
+ stopnet (bool):
+ enable /disable the Stopnet that predicts the end of the decoder sequence. Defaults to True.
+ stopnet_pos_weight (float):
+ Weight that is applied to over-weight positive instances in the Stopnet loss. Use larger values with
+ datasets with longer sentences. Defaults to 0.2.
+ max_decoder_steps (int):
+ Max number of steps allowed for the decoder. Defaults to 50.
+ encoder_in_features (int):
+ Channels of encoder input and character embedding tensors. Defaults to 256.
+ decoder_in_features (int):
+ Channels of decoder input and encoder output tensors. Defaults to 256.
+ out_channels (int):
+ Channels of the final model output. It must match the spectragram size. Defaults to 80.
+ separate_stopnet (bool):
+ Use a distinct Stopnet which is trained separately from the rest of the model. Defaults to True.
+ attention_type (str):
+ attention type. Check ```TTS.tts.layers.attentions.init_attn```. Defaults to 'original'.
+ attention_heads (int):
+ Number of attention heads for GMM attention. Defaults to 5.
+ windowing (bool):
+ It especially useful at inference to keep attention alignment diagonal. Defaults to False.
+ use_forward_attn (bool):
+ It is only valid if ```attn_type``` is ```original```. Defaults to False.
+ forward_attn_mask (bool):
+ enable/disable extra masking over forward attention. It is useful at inference to prevent
+ possible attention failures. Defaults to False.
+ transition_agent (bool):
+ enable/disable transition agent in forward attention. Defaults to False.
+ location_attn (bool):
+ enable/disable location sensitive attention as in the original Tacotron2 paper.
+ It is only valid if ```attn_type``` is ```original```. Defaults to True.
+ bidirectional_decoder (bool):
+ enable/disable bidirectional decoding. Defaults to False.
+ double_decoder_consistency (bool):
+ enable/disable double decoder consistency. Defaults to False.
+ ddc_r (int):
+ reduction rate used by the coarse decoder when `double_decoder_consistency` is in use. Set this
+ as a multiple of the `r` value. Defaults to 6.
+ speakers_file (str):
+ Path to the speaker mapping file for the Speaker Manager. Defaults to None.
+ use_speaker_embedding (bool):
+ enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
+ in the multi-speaker mode. Defaults to False.
+ use_d_vector_file (bool):
+ enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
+ d_vector_file (str):
+ Path to the file including pre-computed speaker embeddings. Defaults to None.
+ optimizer (str):
+ Optimizer used for the training. Set one from `torch.optim.Optimizer` or `TTS.utils.training`.
+ Defaults to `RAdam`.
+ optimizer_params (dict):
+ Optimizer kwargs. Defaults to `{"betas": [0.8, 0.99], "weight_decay": 0.0}`
+ lr_scheduler (str):
+ Learning rate scheduler for the training. Use one from `torch.optim.Scheduler` schedulers or
+ `TTS.utils.training`. Defaults to `NoamLR`.
+ lr_scheduler_params (dict):
+ Parameters for the generator learning rate scheduler. Defaults to `{"warmup": 4000}`.
+ lr (float):
+ Initial learning rate. Defaults to `1e-4`.
+ wd (float):
+ Weight decay coefficient. Defaults to `1e-6`.
+ grad_clip (float):
+ Gradient clipping threshold. Defaults to `5`.
+ seq_len_norm (bool):
+ enable / disable the sequnce length normalization in the loss functions. If set True, loss of a sample
+ is divided by the sequence length. Defaults to False.
+ loss_masking (bool):
+ enable / disable masking the paddings of the samples in loss computation. Defaults to True.
+ decoder_loss_alpha (float):
+ Weight for the decoder loss of the Tacotron model. If set less than or equal to zero, it disables the
+ corresponding loss function. Defaults to 0.25
+ postnet_loss_alpha (float):
+ Weight for the postnet loss of the Tacotron model. If set less than or equal to zero, it disables the
+ corresponding loss function. Defaults to 0.25
+ postnet_diff_spec_alpha (float):
+ Weight for the postnet differential loss of the Tacotron model. If set less than or equal to zero, it disables the
+ corresponding loss function. Defaults to 0.25
+ decoder_diff_spec_alpha (float):
+
+ Weight for the decoder differential loss of the Tacotron model. If set less than or equal to zero, it disables the
+ corresponding loss function. Defaults to 0.25
+ decoder_ssim_alpha (float):
+ Weight for the decoder SSIM loss of the Tacotron model. If set less than or equal to zero, it disables the
+ corresponding loss function. Defaults to 0.25
+ postnet_ssim_alpha (float):
+ Weight for the postnet SSIM loss of the Tacotron model. If set less than or equal to zero, it disables the
+ corresponding loss function. Defaults to 0.25
+ ga_alpha (float):
+ Weight for the guided attention loss. If set less than or equal to zero, it disables the corresponding loss
+ function. Defaults to 5.
+ """
+
+ model: str = "tacotron"
+ # model_params: TacotronArgs = field(default_factory=lambda: TacotronArgs())
+ use_gst: bool = False
+ gst: GSTConfig = None
+ gst_style_input: str = None
+
+ use_capacitron_vae: bool = False
+ capacitron_vae: CapacitronVAEConfig = None
+
+ # model specific params
+ num_speakers: int = 1
+ num_chars: int = 0
+ r: int = 2
+ gradual_training: List[List[int]] = None
+ memory_size: int = -1
+ prenet_type: str = "original"
+ prenet_dropout: bool = True
+ prenet_dropout_at_inference: bool = False
+ stopnet: bool = True
+ separate_stopnet: bool = True
+ stopnet_pos_weight: float = 0.2
+ max_decoder_steps: int = 10000
+ encoder_in_features: int = 256
+ decoder_in_features: int = 256
+ decoder_output_dim: int = 80
+ out_channels: int = 513
+
+ # attention layers
+ attention_type: str = "original"
+ attention_heads: int = None
+ attention_norm: str = "sigmoid"
+ attention_win: bool = False
+ windowing: bool = False
+ use_forward_attn: bool = False
+ forward_attn_mask: bool = False
+ transition_agent: bool = False
+ location_attn: bool = True
+
+ # advance methods
+ bidirectional_decoder: bool = False
+ double_decoder_consistency: bool = False
+ ddc_r: int = 6
+
+ # multi-speaker settings
+ speakers_file: str = None
+ use_speaker_embedding: bool = False
+ speaker_embedding_dim: int = 512
+ use_d_vector_file: bool = False
+ d_vector_file: str = False
+ d_vector_dim: int = None
+
+ # optimizer parameters
+ optimizer: str = "RAdam"
+ optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
+ lr_scheduler: str = "NoamLR"
+ lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
+ lr: float = 1e-4
+ grad_clip: float = 5.0
+ seq_len_norm: bool = False
+ loss_masking: bool = True
+
+ # loss params
+ decoder_loss_alpha: float = 0.25
+ postnet_loss_alpha: float = 0.25
+ postnet_diff_spec_alpha: float = 0.25
+ decoder_diff_spec_alpha: float = 0.25
+ decoder_ssim_alpha: float = 0.25
+ postnet_ssim_alpha: float = 0.25
+ ga_alpha: float = 5.0
+
+ # testing
+ test_sentences: List[str] = field(
+ default_factory=lambda: [
+ "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.",
+ ]
+ )
+
+ def check_values(self):
+ if self.gradual_training:
+ assert (
+ self.gradual_training[0][1] == self.r
+ ), f"[!] the first scheduled gradual training `r` must be equal to the model's `r` value. {self.gradual_training[0][1]} vs {self.r}"
+ if self.model == "tacotron" and self.audio is not None:
+ assert self.out_channels == (
+ self.audio.fft_size // 2 + 1
+ ), f"{self.out_channels} vs {self.audio.fft_size // 2 + 1}"
+ if self.model == "tacotron2" and self.audio is not None:
+ assert self.out_channels == self.audio.num_mels
diff --git a/TTS/tts/configs/vits_config.py b/TTS/tts/configs/vits_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e574c5aa081e7bc1c0c7e1e647d0e0d5e2efa82
--- /dev/null
+++ b/TTS/tts/configs/vits_config.py
@@ -0,0 +1,176 @@
+from dataclasses import dataclass, field
+from typing import List
+
+from TTS.tts.configs.shared_configs import BaseTTSConfig
+from TTS.tts.models.vits import VitsArgs, VitsAudioConfig
+
+
+@dataclass
+class VitsConfig(BaseTTSConfig):
+ """Defines parameters for VITS End2End TTS model.
+
+ Args:
+ model (str):
+ Model name. Do not change unless you know what you are doing.
+
+ model_args (VitsArgs):
+ Model architecture arguments. Defaults to `VitsArgs()`.
+
+ audio (VitsAudioConfig):
+ Audio processing configuration. Defaults to `VitsAudioConfig()`.
+
+ grad_clip (List):
+ Gradient clipping thresholds for each optimizer. Defaults to `[1000.0, 1000.0]`.
+
+ lr_gen (float):
+ Initial learning rate for the generator. Defaults to 0.0002.
+
+ lr_disc (float):
+ Initial learning rate for the discriminator. Defaults to 0.0002.
+
+ lr_scheduler_gen (str):
+ Name of the learning rate scheduler for the generator. One of the `torch.optim.lr_scheduler.*`. Defaults to
+ `ExponentialLR`.
+
+ lr_scheduler_gen_params (dict):
+ Parameters for the learning rate scheduler of the generator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`.
+
+ lr_scheduler_disc (str):
+ Name of the learning rate scheduler for the discriminator. One of the `torch.optim.lr_scheduler.*`. Defaults to
+ `ExponentialLR`.
+
+ lr_scheduler_disc_params (dict):
+ Parameters for the learning rate scheduler of the discriminator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`.
+
+ scheduler_after_epoch (bool):
+ If true, step the schedulers after each epoch else after each step. Defaults to `False`.
+
+ optimizer (str):
+ Name of the optimizer to use with both the generator and the discriminator networks. One of the
+ `torch.optim.*`. Defaults to `AdamW`.
+
+ kl_loss_alpha (float):
+ Loss weight for KL loss. Defaults to 1.0.
+
+ disc_loss_alpha (float):
+ Loss weight for the discriminator loss. Defaults to 1.0.
+
+ gen_loss_alpha (float):
+ Loss weight for the generator loss. Defaults to 1.0.
+
+ feat_loss_alpha (float):
+ Loss weight for the feature matching loss. Defaults to 1.0.
+
+ mel_loss_alpha (float):
+ Loss weight for the mel loss. Defaults to 45.0.
+
+ return_wav (bool):
+ If true, data loader returns the waveform as well as the other outputs. Do not change. Defaults to `True`.
+
+ compute_linear_spec (bool):
+ If true, the linear spectrogram is computed and returned alongside the mel output. Do not change. Defaults to `True`.
+
+ use_weighted_sampler (bool):
+ If true, use weighted sampler with bucketing for balancing samples between datasets used in training. Defaults to `False`.
+
+ weighted_sampler_attrs (dict):
+ Key retuned by the formatter to be used for weighted sampler. For example `{"root_path": 2.0, "speaker_name": 1.0}` sets sample probabilities
+ by overweighting `root_path` by 2.0. Defaults to `{}`.
+
+ weighted_sampler_multipliers (dict):
+ Weight each unique value of a key returned by the formatter for weighted sampling.
+ For example `{"root_path":{"/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-100/":1.0, "/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-360/": 0.5}`.
+ It will sample instances from `train-clean-100` 2 times more than `train-clean-360`. Defaults to `{}`.
+
+ r (int):
+ Number of spectrogram frames to be generated at a time. Do not change. Defaults to `1`.
+
+ add_blank (bool):
+ If true, a blank token is added in between every character. Defaults to `True`.
+
+ test_sentences (List[List]):
+ List of sentences with speaker and language information to be used for testing.
+
+ language_ids_file (str):
+ Path to the language ids file.
+
+ use_language_embedding (bool):
+ If true, language embedding is used. Defaults to `False`.
+
+ Note:
+ Check :class:`TTS.tts.configs.shared_configs.BaseTTSConfig` for the inherited parameters.
+
+ Example:
+
+ >>> from TTS.tts.configs.vits_config import VitsConfig
+ >>> config = VitsConfig()
+ """
+
+ model: str = "vits"
+ # model specific params
+ model_args: VitsArgs = field(default_factory=VitsArgs)
+ audio: VitsAudioConfig = VitsAudioConfig()
+
+ # optimizer
+ grad_clip: List[float] = field(default_factory=lambda: [1000, 1000])
+ lr_gen: float = 0.0002
+ lr_disc: float = 0.0002
+ lr_scheduler_gen: str = "ExponentialLR"
+ lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1})
+ lr_scheduler_disc: str = "ExponentialLR"
+ lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1})
+ scheduler_after_epoch: bool = True
+ optimizer: str = "AdamW"
+ optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01})
+
+ # loss params
+ kl_loss_alpha: float = 1.0
+ disc_loss_alpha: float = 1.0
+ gen_loss_alpha: float = 1.0
+ feat_loss_alpha: float = 1.0
+ mel_loss_alpha: float = 45.0
+ dur_loss_alpha: float = 1.0
+ speaker_encoder_loss_alpha: float = 1.0
+
+ # data loader params
+ return_wav: bool = True
+ compute_linear_spec: bool = True
+
+ # sampler params
+ use_weighted_sampler: bool = False # TODO: move it to the base config
+ weighted_sampler_attrs: dict = field(default_factory=lambda: {})
+ weighted_sampler_multipliers: dict = field(default_factory=lambda: {})
+
+ # overrides
+ r: int = 1 # DO NOT CHANGE
+ add_blank: bool = True
+
+ # testing
+ test_sentences: List[List] = field(
+ default_factory=lambda: [
+ ["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."],
+ ]
+ )
+
+ # multi-speaker settings
+ # use speaker embedding layer
+ num_speakers: int = 0
+ use_speaker_embedding: bool = False
+ speakers_file: str = None
+ speaker_embedding_channels: int = 256
+ language_ids_file: str = None
+ use_language_embedding: bool = False
+
+ # use d-vectors
+ use_d_vector_file: bool = False
+ d_vector_file: List[str] = None
+ d_vector_dim: int = None
+
+ def __post_init__(self):
+ for key, val in self.model_args.items():
+ if hasattr(self, key):
+ self[key] = val
diff --git a/TTS/tts/datasets/__init__.py b/TTS/tts/datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..192138561fdb4e85978fe8beb52eae2edf73888e
--- /dev/null
+++ b/TTS/tts/datasets/__init__.py
@@ -0,0 +1,181 @@
+import os
+import sys
+from collections import Counter
+from pathlib import Path
+from typing import Callable, Dict, List, Tuple, Union
+
+import numpy as np
+
+from TTS.tts.datasets.dataset import *
+from TTS.tts.datasets.formatters import *
+
+
+def split_dataset(items, eval_split_max_size=None, eval_split_size=0.01):
+ """Split a dataset into train and eval. Consider speaker distribution in multi-speaker training.
+
+ Args:
+ items (List[List]):
+ A list of samples. Each sample is a list of `[audio_path, text, speaker_id]`.
+
+ eval_split_max_size (int):
+ Number maximum of samples to be used for evaluation in proportion split. Defaults to None (Disabled).
+
+ eval_split_size (float):
+ If between 0.0 and 1.0 represents the proportion of the dataset to include in the evaluation set.
+ If > 1, represents the absolute number of evaluation samples. Defaults to 0.01 (1%).
+ """
+ speakers = [item["speaker_name"] for item in items]
+ is_multi_speaker = len(set(speakers)) > 1
+ if eval_split_size > 1:
+ eval_split_size = int(eval_split_size)
+ else:
+ if eval_split_max_size:
+ eval_split_size = min(eval_split_max_size, int(len(items) * eval_split_size))
+ else:
+ eval_split_size = int(len(items) * eval_split_size)
+
+ assert (
+ eval_split_size > 0
+ ), " [!] You do not have enough samples for the evaluation set. You can work around this setting the 'eval_split_size' parameter to a minimum of {}".format(
+ 1 / len(items)
+ )
+ np.random.seed(0)
+ np.random.shuffle(items)
+ if is_multi_speaker:
+ items_eval = []
+ speakers = [item["speaker_name"] for item in items]
+ speaker_counter = Counter(speakers)
+ while len(items_eval) < eval_split_size:
+ item_idx = np.random.randint(0, len(items))
+ speaker_to_be_removed = items[item_idx]["speaker_name"]
+ if speaker_counter[speaker_to_be_removed] > 1:
+ items_eval.append(items[item_idx])
+ speaker_counter[speaker_to_be_removed] -= 1
+ del items[item_idx]
+ return items_eval, items
+ return items[:eval_split_size], items[eval_split_size:]
+
+
+def add_extra_keys(metadata, language, dataset_name):
+ for item in metadata:
+ # add language name
+ item["language"] = language
+ # add unique audio name
+ relfilepath = os.path.splitext(os.path.relpath(item["audio_file"], item["root_path"]))[0]
+ audio_unique_name = f"{dataset_name}#{relfilepath}"
+ item["audio_unique_name"] = audio_unique_name
+ return metadata
+
+
+def load_tts_samples(
+ datasets: Union[List[Dict], Dict],
+ eval_split=True,
+ formatter: Callable = None,
+ eval_split_max_size=None,
+ eval_split_size=0.01,
+) -> Tuple[List[List], List[List]]:
+ """Parse the dataset from the datasets config, load the samples as a List and load the attention alignments if provided.
+ If `formatter` is not None, apply the formatter to the samples else pick the formatter from the available ones based
+ on the dataset name.
+
+ Args:
+ datasets (List[Dict], Dict): A list of datasets or a single dataset dictionary. If multiple datasets are
+ in the list, they are all merged.
+
+ eval_split (bool, optional): If true, create a evaluation split. If an eval split provided explicitly, generate
+ an eval split automatically. Defaults to True.
+
+ formatter (Callable, optional): The preprocessing function to be applied to create the list of samples. It
+ must take the root_path and the meta_file name and return a list of samples in the format of
+ `[[text, audio_path, speaker_id], ...]]`. See the available formatters in `TTS.tts.dataset.formatter` as
+ example. Defaults to None.
+
+ eval_split_max_size (int):
+ Number maximum of samples to be used for evaluation in proportion split. Defaults to None (Disabled).
+
+ eval_split_size (float):
+ If between 0.0 and 1.0 represents the proportion of the dataset to include in the evaluation set.
+ If > 1, represents the absolute number of evaluation samples. Defaults to 0.01 (1%).
+
+ Returns:
+ Tuple[List[List], List[List]: training and evaluation splits of the dataset.
+ """
+ meta_data_train_all = []
+ meta_data_eval_all = [] if eval_split else None
+ if not isinstance(datasets, list):
+ datasets = [datasets]
+ for dataset in datasets:
+ formatter_name = dataset["formatter"]
+ dataset_name = dataset["dataset_name"]
+ root_path = dataset["path"]
+ meta_file_train = dataset["meta_file_train"]
+ meta_file_val = dataset["meta_file_val"]
+ ignored_speakers = dataset["ignored_speakers"]
+ language = dataset["language"]
+
+ # setup the right data processor
+ if formatter is None:
+ formatter = _get_formatter_by_name(formatter_name)
+ # load train set
+ meta_data_train = formatter(root_path, meta_file_train, ignored_speakers=ignored_speakers)
+ assert len(meta_data_train) > 0, f" [!] No training samples found in {root_path}/{meta_file_train}"
+
+ meta_data_train = add_extra_keys(meta_data_train, language, dataset_name)
+
+ print(f" | > Found {len(meta_data_train)} files in {Path(root_path).resolve()}")
+ # load evaluation split if set
+ if eval_split:
+ if meta_file_val:
+ meta_data_eval = formatter(root_path, meta_file_val, ignored_speakers=ignored_speakers)
+ meta_data_eval = add_extra_keys(meta_data_eval, language, dataset_name)
+ else:
+ eval_size_per_dataset = eval_split_max_size // len(datasets) if eval_split_max_size else None
+ meta_data_eval, meta_data_train = split_dataset(meta_data_train, eval_size_per_dataset, eval_split_size)
+ meta_data_eval_all += meta_data_eval
+ meta_data_train_all += meta_data_train
+ # load attention masks for the duration predictor training
+ if dataset.meta_file_attn_mask:
+ meta_data = dict(load_attention_mask_meta_data(dataset["meta_file_attn_mask"]))
+ for idx, ins in enumerate(meta_data_train_all):
+ attn_file = meta_data[ins["audio_file"]].strip()
+ meta_data_train_all[idx].update({"alignment_file": attn_file})
+ if meta_data_eval_all:
+ for idx, ins in enumerate(meta_data_eval_all):
+ attn_file = meta_data[ins["audio_file"]].strip()
+ meta_data_eval_all[idx].update({"alignment_file": attn_file})
+ # set none for the next iter
+ formatter = None
+ return meta_data_train_all, meta_data_eval_all
+
+
+def load_attention_mask_meta_data(metafile_path):
+ """Load meta data file created by compute_attention_masks.py"""
+ with open(metafile_path, "r", encoding="utf-8") as f:
+ lines = f.readlines()
+
+ meta_data = []
+ for line in lines:
+ wav_file, attn_file = line.split("|")
+ meta_data.append([wav_file, attn_file])
+ return meta_data
+
+
+def _get_formatter_by_name(name):
+ """Returns the respective preprocessing function."""
+ thismodule = sys.modules[__name__]
+ return getattr(thismodule, name.lower())
+
+
+def find_unique_chars(data_samples, verbose=True):
+ texts = "".join(item[0] for item in data_samples)
+ chars = set(texts)
+ lower_chars = filter(lambda c: c.islower(), chars)
+ chars_force_lower = [c.lower() for c in chars]
+ chars_force_lower = set(chars_force_lower)
+
+ if verbose:
+ print(f" > Number of unique characters: {len(chars)}")
+ print(f" > Unique characters: {''.join(sorted(chars))}")
+ print(f" > Unique lower characters: {''.join(sorted(lower_chars))}")
+ print(f" > Unique all forced to lower characters: {''.join(sorted(chars_force_lower))}")
+ return chars_force_lower
diff --git a/TTS/tts/datasets/dataset.py b/TTS/tts/datasets/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..df01d66323e51d5e76d5f7adb335a722edc2d635
--- /dev/null
+++ b/TTS/tts/datasets/dataset.py
@@ -0,0 +1,961 @@
+import base64
+import collections
+import os
+import random
+from typing import Dict, List, Union
+
+import numpy as np
+import torch
+import tqdm
+from torch.utils.data import Dataset
+
+from TTS.tts.utils.data import prepare_data, prepare_stop_target, prepare_tensor
+from TTS.utils.audio import AudioProcessor
+from TTS.utils.audio.numpy_transforms import compute_energy as calculate_energy
+
+# to prevent too many open files error as suggested here
+# https://github.com/pytorch/pytorch/issues/11201#issuecomment-421146936
+torch.multiprocessing.set_sharing_strategy("file_system")
+
+
+def _parse_sample(item):
+ language_name = None
+ attn_file = None
+ if len(item) == 5:
+ text, wav_file, speaker_name, language_name, attn_file = item
+ elif len(item) == 4:
+ text, wav_file, speaker_name, language_name = item
+ elif len(item) == 3:
+ text, wav_file, speaker_name = item
+ else:
+ raise ValueError(" [!] Dataset cannot parse the sample.")
+ return text, wav_file, speaker_name, language_name, attn_file
+
+
+def noise_augment_audio(wav):
+ return wav + (1.0 / 32768.0) * np.random.rand(*wav.shape)
+
+
+def string2filename(string):
+ # generate a safe and reversible filename based on a string
+ filename = base64.urlsafe_b64encode(string.encode("utf-8")).decode("utf-8", "ignore")
+ return filename
+
+
+class TTSDataset(Dataset):
+ def __init__(
+ self,
+ outputs_per_step: int = 1,
+ compute_linear_spec: bool = False,
+ ap: AudioProcessor = None,
+ samples: List[Dict] = None,
+ tokenizer: "TTSTokenizer" = None,
+ compute_f0: bool = False,
+ compute_energy: bool = False,
+ f0_cache_path: str = None,
+ energy_cache_path: str = None,
+ return_wav: bool = False,
+ batch_group_size: int = 0,
+ min_text_len: int = 0,
+ max_text_len: int = float("inf"),
+ min_audio_len: int = 0,
+ max_audio_len: int = float("inf"),
+ phoneme_cache_path: str = None,
+ precompute_num_workers: int = 0,
+ speaker_id_mapping: Dict = None,
+ d_vector_mapping: Dict = None,
+ language_id_mapping: Dict = None,
+ use_noise_augment: bool = False,
+ start_by_longest: bool = False,
+ verbose: bool = False,
+ ):
+ """Generic 📂 data loader for `tts` models. It is configurable for different outputs and needs.
+
+ If you need something different, you can subclass and override.
+
+ Args:
+ outputs_per_step (int): Number of time frames predicted per step.
+
+ compute_linear_spec (bool): compute linear spectrogram if True.
+
+ ap (TTS.tts.utils.AudioProcessor): Audio processor object.
+
+ samples (list): List of dataset samples.
+
+ tokenizer (TTSTokenizer): tokenizer to convert text to sequence IDs. If None init internally else
+ use the given. Defaults to None.
+
+ compute_f0 (bool): compute f0 if True. Defaults to False.
+
+ compute_energy (bool): compute energy if True. Defaults to False.
+
+ f0_cache_path (str): Path to store f0 cache. Defaults to None.
+
+ energy_cache_path (str): Path to store energy cache. Defaults to None.
+
+ return_wav (bool): Return the waveform of the sample. Defaults to False.
+
+ batch_group_size (int): Range of batch randomization after sorting
+ sequences by length. It shuffles each batch with bucketing to gather similar lenght sequences in a
+ batch. Set 0 to disable. Defaults to 0.
+
+ min_text_len (int): Minimum length of input text to be used. All shorter samples will be ignored.
+ Defaults to 0.
+
+ max_text_len (int): Maximum length of input text to be used. All longer samples will be ignored.
+ Defaults to float("inf").
+
+ min_audio_len (int): Minimum length of input audio to be used. All shorter samples will be ignored.
+ Defaults to 0.
+
+ max_audio_len (int): Maximum length of input audio to be used. All longer samples will be ignored.
+ The maximum length in the dataset defines the VRAM used in the training. Hence, pay attention to
+ this value if you encounter an OOM error in training. Defaults to float("inf").
+
+ phoneme_cache_path (str): Path to cache computed phonemes. It writes phonemes of each sample to a
+ separate file. Defaults to None.
+
+ precompute_num_workers (int): Number of workers to precompute features. Defaults to 0.
+
+ speaker_id_mapping (dict): Mapping of speaker names to IDs used to compute embedding vectors by the
+ embedding layer. Defaults to None.
+
+ d_vector_mapping (dict): Mapping of wav files to computed d-vectors. Defaults to None.
+
+ use_noise_augment (bool): Enable adding random noise to wav for augmentation. Defaults to False.
+
+ start_by_longest (bool): Start by longest sequence. It is especially useful to check OOM. Defaults to False.
+
+ verbose (bool): Print diagnostic information. Defaults to false.
+ """
+ super().__init__()
+ self.batch_group_size = batch_group_size
+ self._samples = samples
+ self.outputs_per_step = outputs_per_step
+ self.compute_linear_spec = compute_linear_spec
+ self.return_wav = return_wav
+ self.compute_f0 = compute_f0
+ self.compute_energy = compute_energy
+ self.f0_cache_path = f0_cache_path
+ self.energy_cache_path = energy_cache_path
+ self.min_audio_len = min_audio_len
+ self.max_audio_len = max_audio_len
+ self.min_text_len = min_text_len
+ self.max_text_len = max_text_len
+ self.ap = ap
+ self.phoneme_cache_path = phoneme_cache_path
+ self.speaker_id_mapping = speaker_id_mapping
+ self.d_vector_mapping = d_vector_mapping
+ self.language_id_mapping = language_id_mapping
+ self.use_noise_augment = use_noise_augment
+ self.start_by_longest = start_by_longest
+
+ self.verbose = verbose
+ self.rescue_item_idx = 1
+ self.pitch_computed = False
+ self.tokenizer = tokenizer
+
+ if self.tokenizer.use_phonemes:
+ self.phoneme_dataset = PhonemeDataset(
+ self.samples, self.tokenizer, phoneme_cache_path, precompute_num_workers=precompute_num_workers
+ )
+
+ if compute_f0:
+ self.f0_dataset = F0Dataset(
+ self.samples, self.ap, cache_path=f0_cache_path, precompute_num_workers=precompute_num_workers
+ )
+ if compute_energy:
+ self.energy_dataset = EnergyDataset(
+ self.samples, self.ap, cache_path=energy_cache_path, precompute_num_workers=precompute_num_workers
+ )
+ if self.verbose:
+ self.print_logs()
+
+ @property
+ def lengths(self):
+ lens = []
+ for item in self.samples:
+ _, wav_file, *_ = _parse_sample(item)
+ audio_len = os.path.getsize(wav_file) / 16 * 8 # assuming 16bit audio
+ lens.append(audio_len)
+ return lens
+
+ @property
+ def samples(self):
+ return self._samples
+
+ @samples.setter
+ def samples(self, new_samples):
+ self._samples = new_samples
+ if hasattr(self, "f0_dataset"):
+ self.f0_dataset.samples = new_samples
+ if hasattr(self, "energy_dataset"):
+ self.energy_dataset.samples = new_samples
+ if hasattr(self, "phoneme_dataset"):
+ self.phoneme_dataset.samples = new_samples
+
+ def __len__(self):
+ return len(self.samples)
+
+ def __getitem__(self, idx):
+ return self.load_data(idx)
+
+ def print_logs(self, level: int = 0) -> None:
+ indent = "\t" * level
+ print("\n")
+ print(f"{indent}> DataLoader initialization")
+ print(f"{indent}| > Tokenizer:")
+ self.tokenizer.print_logs(level + 1)
+ print(f"{indent}| > Number of instances : {len(self.samples)}")
+
+ def load_wav(self, filename):
+ waveform = self.ap.load_wav(filename)
+ assert waveform.size > 0
+ return waveform
+
+ def get_phonemes(self, idx, text):
+ out_dict = self.phoneme_dataset[idx]
+ assert text == out_dict["text"], f"{text} != {out_dict['text']}"
+ assert len(out_dict["token_ids"]) > 0
+ return out_dict
+
+ def get_f0(self, idx):
+ out_dict = self.f0_dataset[idx]
+ item = self.samples[idx]
+ assert item["audio_unique_name"] == out_dict["audio_unique_name"]
+ return out_dict
+
+ def get_energy(self, idx):
+ out_dict = self.energy_dataset[idx]
+ item = self.samples[idx]
+ assert item["audio_unique_name"] == out_dict["audio_unique_name"]
+ return out_dict
+
+ @staticmethod
+ def get_attn_mask(attn_file):
+ return np.load(attn_file)
+
+ def get_token_ids(self, idx, text):
+ if self.tokenizer.use_phonemes:
+ token_ids = self.get_phonemes(idx, text)["token_ids"]
+ else:
+ token_ids = self.tokenizer.text_to_ids(text)
+ return np.array(token_ids, dtype=np.int32)
+
+ def load_data(self, idx):
+ item = self.samples[idx]
+
+ raw_text = item["text"]
+
+ wav = np.asarray(self.load_wav(item["audio_file"]), dtype=np.float32)
+
+ # apply noise for augmentation
+ if self.use_noise_augment:
+ wav = noise_augment_audio(wav)
+
+ # get token ids
+ token_ids = self.get_token_ids(idx, item["text"])
+
+ # get pre-computed attention maps
+ attn = None
+ if "alignment_file" in item:
+ attn = self.get_attn_mask(item["alignment_file"])
+
+ # after phonemization the text length may change
+ # this is a shareful 🤭 hack to prevent longer phonemes
+ # TODO: find a better fix
+ if len(token_ids) > self.max_text_len or len(wav) < self.min_audio_len:
+ self.rescue_item_idx += 1
+ return self.load_data(self.rescue_item_idx)
+
+ # get f0 values
+ f0 = None
+ if self.compute_f0:
+ f0 = self.get_f0(idx)["f0"]
+ energy = None
+ if self.compute_energy:
+ energy = self.get_energy(idx)["energy"]
+
+ sample = {
+ "raw_text": raw_text,
+ "token_ids": token_ids,
+ "wav": wav,
+ "pitch": f0,
+ "energy": energy,
+ "attn": attn,
+ "item_idx": item["audio_file"],
+ "speaker_name": item["speaker_name"],
+ "language_name": item["language"],
+ "wav_file_name": os.path.basename(item["audio_file"]),
+ "audio_unique_name": item["audio_unique_name"],
+ }
+ return sample
+
+ @staticmethod
+ def _compute_lengths(samples):
+ new_samples = []
+ for item in samples:
+ audio_length = os.path.getsize(item["audio_file"]) / 16 * 8 # assuming 16bit audio
+ text_lenght = len(item["text"])
+ item["audio_length"] = audio_length
+ item["text_length"] = text_lenght
+ new_samples += [item]
+ return new_samples
+
+ @staticmethod
+ def filter_by_length(lengths: List[int], min_len: int, max_len: int):
+ idxs = np.argsort(lengths) # ascending order
+ ignore_idx = []
+ keep_idx = []
+ for idx in idxs:
+ length = lengths[idx]
+ if length < min_len or length > max_len:
+ ignore_idx.append(idx)
+ else:
+ keep_idx.append(idx)
+ return ignore_idx, keep_idx
+
+ @staticmethod
+ def sort_by_length(samples: List[List]):
+ audio_lengths = [s["audio_length"] for s in samples]
+ idxs = np.argsort(audio_lengths) # ascending order
+ return idxs
+
+ @staticmethod
+ def create_buckets(samples, batch_group_size: int):
+ assert batch_group_size > 0
+ for i in range(len(samples) // batch_group_size):
+ offset = i * batch_group_size
+ end_offset = offset + batch_group_size
+ temp_items = samples[offset:end_offset]
+ random.shuffle(temp_items)
+ samples[offset:end_offset] = temp_items
+ return samples
+
+ @staticmethod
+ def _select_samples_by_idx(idxs, samples):
+ samples_new = []
+ for idx in idxs:
+ samples_new.append(samples[idx])
+ return samples_new
+
+ def preprocess_samples(self):
+ r"""Sort `items` based on text length or audio length in ascending order. Filter out samples out or the length
+ range.
+ """
+ samples = self._compute_lengths(self.samples)
+
+ # sort items based on the sequence length in ascending order
+ text_lengths = [i["text_length"] for i in samples]
+ audio_lengths = [i["audio_length"] for i in samples]
+ text_ignore_idx, text_keep_idx = self.filter_by_length(text_lengths, self.min_text_len, self.max_text_len)
+ audio_ignore_idx, audio_keep_idx = self.filter_by_length(audio_lengths, self.min_audio_len, self.max_audio_len)
+ keep_idx = list(set(audio_keep_idx) & set(text_keep_idx))
+ ignore_idx = list(set(audio_ignore_idx) | set(text_ignore_idx))
+
+ samples = self._select_samples_by_idx(keep_idx, samples)
+
+ sorted_idxs = self.sort_by_length(samples)
+
+ if self.start_by_longest:
+ longest_idxs = sorted_idxs[-1]
+ sorted_idxs[-1] = sorted_idxs[0]
+ sorted_idxs[0] = longest_idxs
+
+ samples = self._select_samples_by_idx(sorted_idxs, samples)
+
+ if len(samples) == 0:
+ raise RuntimeError(" [!] No samples left")
+
+ # shuffle batch groups
+ # create batches with similar length items
+ # the larger the `batch_group_size`, the higher the length variety in a batch.
+ if self.batch_group_size > 0:
+ samples = self.create_buckets(samples, self.batch_group_size)
+
+ # update items to the new sorted items
+ audio_lengths = [s["audio_length"] for s in samples]
+ text_lengths = [s["text_length"] for s in samples]
+ self.samples = samples
+
+ if self.verbose:
+ print(" | > Preprocessing samples")
+ print(" | > Max text length: {}".format(np.max(text_lengths)))
+ print(" | > Min text length: {}".format(np.min(text_lengths)))
+ print(" | > Avg text length: {}".format(np.mean(text_lengths)))
+ print(" | ")
+ print(" | > Max audio length: {}".format(np.max(audio_lengths)))
+ print(" | > Min audio length: {}".format(np.min(audio_lengths)))
+ print(" | > Avg audio length: {}".format(np.mean(audio_lengths)))
+ print(f" | > Num. instances discarded samples: {len(ignore_idx)}")
+ print(" | > Batch group size: {}.".format(self.batch_group_size))
+
+ @staticmethod
+ def _sort_batch(batch, text_lengths):
+ """Sort the batch by the input text length for RNN efficiency.
+
+ Args:
+ batch (Dict): Batch returned by `__getitem__`.
+ text_lengths (List[int]): Lengths of the input character sequences.
+ """
+ text_lengths, ids_sorted_decreasing = torch.sort(torch.LongTensor(text_lengths), dim=0, descending=True)
+ batch = [batch[idx] for idx in ids_sorted_decreasing]
+ return batch, text_lengths, ids_sorted_decreasing
+
+ def collate_fn(self, batch):
+ r"""
+ Perform preprocessing and create a final data batch:
+ 1. Sort batch instances by text-length
+ 2. Convert Audio signal to features.
+ 3. PAD sequences wrt r.
+ 4. Load to Torch.
+ """
+
+ # Puts each data field into a tensor with outer dimension batch size
+ if isinstance(batch[0], collections.abc.Mapping):
+ token_ids_lengths = np.array([len(d["token_ids"]) for d in batch])
+
+ # sort items with text input length for RNN efficiency
+ batch, token_ids_lengths, ids_sorted_decreasing = self._sort_batch(batch, token_ids_lengths)
+
+ # convert list of dicts to dict of lists
+ batch = {k: [dic[k] for dic in batch] for k in batch[0]}
+
+ # get language ids from language names
+ if self.language_id_mapping is not None:
+ language_ids = [self.language_id_mapping[ln] for ln in batch["language_name"]]
+ else:
+ language_ids = None
+ # get pre-computed d-vectors
+ if self.d_vector_mapping is not None:
+ embedding_keys = list(batch["audio_unique_name"])
+ d_vectors = [self.d_vector_mapping[w]["embedding"] for w in embedding_keys]
+ else:
+ d_vectors = None
+
+ # get numerical speaker ids from speaker names
+ if self.speaker_id_mapping:
+ speaker_ids = [self.speaker_id_mapping[sn] for sn in batch["speaker_name"]]
+ else:
+ speaker_ids = None
+ # compute features
+ mel = [self.ap.melspectrogram(w).astype("float32") for w in batch["wav"]]
+
+ mel_lengths = [m.shape[1] for m in mel]
+
+ # lengths adjusted by the reduction factor
+ mel_lengths_adjusted = [
+ m.shape[1] + (self.outputs_per_step - (m.shape[1] % self.outputs_per_step))
+ if m.shape[1] % self.outputs_per_step
+ else m.shape[1]
+ for m in mel
+ ]
+
+ # compute 'stop token' targets
+ stop_targets = [np.array([0.0] * (mel_len - 1) + [1.0]) for mel_len in mel_lengths]
+
+ # PAD stop targets
+ stop_targets = prepare_stop_target(stop_targets, self.outputs_per_step)
+
+ # PAD sequences with longest instance in the batch
+ token_ids = prepare_data(batch["token_ids"]).astype(np.int32)
+
+ # PAD features with longest instance
+ mel = prepare_tensor(mel, self.outputs_per_step)
+
+ # B x D x T --> B x T x D
+ mel = mel.transpose(0, 2, 1)
+
+ # convert things to pytorch
+ token_ids_lengths = torch.LongTensor(token_ids_lengths)
+ token_ids = torch.LongTensor(token_ids)
+ mel = torch.FloatTensor(mel).contiguous()
+ mel_lengths = torch.LongTensor(mel_lengths)
+ stop_targets = torch.FloatTensor(stop_targets)
+
+ # speaker vectors
+ if d_vectors is not None:
+ d_vectors = torch.FloatTensor(d_vectors)
+
+ if speaker_ids is not None:
+ speaker_ids = torch.LongTensor(speaker_ids)
+
+ if language_ids is not None:
+ language_ids = torch.LongTensor(language_ids)
+
+ # compute linear spectrogram
+ linear = None
+ if self.compute_linear_spec:
+ linear = [self.ap.spectrogram(w).astype("float32") for w in batch["wav"]]
+ linear = prepare_tensor(linear, self.outputs_per_step)
+ linear = linear.transpose(0, 2, 1)
+ assert mel.shape[1] == linear.shape[1]
+ linear = torch.FloatTensor(linear).contiguous()
+
+ # format waveforms
+ wav_padded = None
+ if self.return_wav:
+ wav_lengths = [w.shape[0] for w in batch["wav"]]
+ max_wav_len = max(mel_lengths_adjusted) * self.ap.hop_length
+ wav_lengths = torch.LongTensor(wav_lengths)
+ wav_padded = torch.zeros(len(batch["wav"]), 1, max_wav_len)
+ for i, w in enumerate(batch["wav"]):
+ mel_length = mel_lengths_adjusted[i]
+ w = np.pad(w, (0, self.ap.hop_length * self.outputs_per_step), mode="edge")
+ w = w[: mel_length * self.ap.hop_length]
+ wav_padded[i, :, : w.shape[0]] = torch.from_numpy(w)
+ wav_padded.transpose_(1, 2)
+
+ # format F0
+ if self.compute_f0:
+ pitch = prepare_data(batch["pitch"])
+ assert mel.shape[1] == pitch.shape[1], f"[!] {mel.shape} vs {pitch.shape}"
+ pitch = torch.FloatTensor(pitch)[:, None, :].contiguous() # B x 1 xT
+ else:
+ pitch = None
+ # format energy
+ if self.compute_energy:
+ energy = prepare_data(batch["energy"])
+ assert mel.shape[1] == energy.shape[1], f"[!] {mel.shape} vs {energy.shape}"
+ energy = torch.FloatTensor(energy)[:, None, :].contiguous() # B x 1 xT
+ else:
+ energy = None
+ # format attention masks
+ attns = None
+ if batch["attn"][0] is not None:
+ attns = [batch["attn"][idx].T for idx in ids_sorted_decreasing]
+ for idx, attn in enumerate(attns):
+ pad2 = mel.shape[1] - attn.shape[1]
+ pad1 = token_ids.shape[1] - attn.shape[0]
+ assert pad1 >= 0 and pad2 >= 0, f"[!] Negative padding - {pad1} and {pad2}"
+ attn = np.pad(attn, [[0, pad1], [0, pad2]])
+ attns[idx] = attn
+ attns = prepare_tensor(attns, self.outputs_per_step)
+ attns = torch.FloatTensor(attns).unsqueeze(1)
+
+ return {
+ "token_id": token_ids,
+ "token_id_lengths": token_ids_lengths,
+ "speaker_names": batch["speaker_name"],
+ "linear": linear,
+ "mel": mel,
+ "mel_lengths": mel_lengths,
+ "stop_targets": stop_targets,
+ "item_idxs": batch["item_idx"],
+ "d_vectors": d_vectors,
+ "speaker_ids": speaker_ids,
+ "attns": attns,
+ "waveform": wav_padded,
+ "raw_text": batch["raw_text"],
+ "pitch": pitch,
+ "energy": energy,
+ "language_ids": language_ids,
+ "audio_unique_names": batch["audio_unique_name"],
+ }
+
+ raise TypeError(
+ (
+ "batch must contain tensors, numbers, dicts or lists;\
+ found {}".format(
+ type(batch[0])
+ )
+ )
+ )
+
+
+class PhonemeDataset(Dataset):
+ """Phoneme Dataset for converting input text to phonemes and then token IDs
+
+ At initialization, it pre-computes the phonemes under `cache_path` and loads them in training to reduce data
+ loading latency. If `cache_path` is already present, it skips the pre-computation.
+
+ Args:
+ samples (Union[List[List], List[Dict]]):
+ List of samples. Each sample is a list or a dict.
+
+ tokenizer (TTSTokenizer):
+ Tokenizer to convert input text to phonemes.
+
+ cache_path (str):
+ Path to cache phonemes. If `cache_path` is already present or None, it skips the pre-computation.
+
+ precompute_num_workers (int):
+ Number of workers used for pre-computing the phonemes. Defaults to 0.
+ """
+
+ def __init__(
+ self,
+ samples: Union[List[Dict], List[List]],
+ tokenizer: "TTSTokenizer",
+ cache_path: str,
+ precompute_num_workers=0,
+ ):
+ self.samples = samples
+ self.tokenizer = tokenizer
+ self.cache_path = cache_path
+ if cache_path is not None and not os.path.exists(cache_path):
+ os.makedirs(cache_path)
+ self.precompute(precompute_num_workers)
+
+ def __getitem__(self, index):
+ item = self.samples[index]
+ ids = self.compute_or_load(string2filename(item["audio_unique_name"]), item["text"], item["language"])
+ ph_hat = self.tokenizer.ids_to_text(ids)
+ return {"text": item["text"], "ph_hat": ph_hat, "token_ids": ids, "token_ids_len": len(ids)}
+
+ def __len__(self):
+ return len(self.samples)
+
+ def compute_or_load(self, file_name, text, language):
+ """Compute phonemes for the given text.
+
+ If the phonemes are already cached, load them from cache.
+ """
+ file_ext = "_phoneme.npy"
+ cache_path = os.path.join(self.cache_path, file_name + file_ext)
+ try:
+ ids = np.load(cache_path)
+ except FileNotFoundError:
+ ids = self.tokenizer.text_to_ids(text, language=language)
+ np.save(cache_path, ids)
+ return ids
+
+ def get_pad_id(self):
+ """Get pad token ID for sequence padding"""
+ return self.tokenizer.pad_id
+
+ def precompute(self, num_workers=1):
+ """Precompute phonemes for all samples.
+
+ We use pytorch dataloader because we are lazy.
+ """
+ print("[*] Pre-computing phonemes...")
+ with tqdm.tqdm(total=len(self)) as pbar:
+ batch_size = num_workers if num_workers > 0 else 1
+ dataloder = torch.utils.data.DataLoader(
+ batch_size=batch_size, dataset=self, shuffle=False, num_workers=num_workers, collate_fn=self.collate_fn
+ )
+ for _ in dataloder:
+ pbar.update(batch_size)
+
+ def collate_fn(self, batch):
+ ids = [item["token_ids"] for item in batch]
+ ids_lens = [item["token_ids_len"] for item in batch]
+ texts = [item["text"] for item in batch]
+ texts_hat = [item["ph_hat"] for item in batch]
+ ids_lens_max = max(ids_lens)
+ ids_torch = torch.LongTensor(len(ids), ids_lens_max).fill_(self.get_pad_id())
+ for i, ids_len in enumerate(ids_lens):
+ ids_torch[i, :ids_len] = torch.LongTensor(ids[i])
+ return {"text": texts, "ph_hat": texts_hat, "token_ids": ids_torch}
+
+ def print_logs(self, level: int = 0) -> None:
+ indent = "\t" * level
+ print("\n")
+ print(f"{indent}> PhonemeDataset ")
+ print(f"{indent}| > Tokenizer:")
+ self.tokenizer.print_logs(level + 1)
+ print(f"{indent}| > Number of instances : {len(self.samples)}")
+
+
+class F0Dataset:
+ """F0 Dataset for computing F0 from wav files in CPU
+
+ Pre-compute F0 values for all the samples at initialization if `cache_path` is not None or already present. It
+ also computes the mean and std of F0 values if `normalize_f0` is True.
+
+ Args:
+ samples (Union[List[List], List[Dict]]):
+ List of samples. Each sample is a list or a dict.
+
+ ap (AudioProcessor):
+ AudioProcessor to compute F0 from wav files.
+
+ cache_path (str):
+ Path to cache F0 values. If `cache_path` is already present or None, it skips the pre-computation.
+ Defaults to None.
+
+ precompute_num_workers (int):
+ Number of workers used for pre-computing the F0 values. Defaults to 0.
+
+ normalize_f0 (bool):
+ Whether to normalize F0 values by mean and std. Defaults to True.
+ """
+
+ def __init__(
+ self,
+ samples: Union[List[List], List[Dict]],
+ ap: "AudioProcessor",
+ verbose=False,
+ cache_path: str = None,
+ precompute_num_workers=0,
+ normalize_f0=True,
+ ):
+ self.samples = samples
+ self.ap = ap
+ self.verbose = verbose
+ self.cache_path = cache_path
+ self.normalize_f0 = normalize_f0
+ self.pad_id = 0.0
+ self.mean = None
+ self.std = None
+ if cache_path is not None and not os.path.exists(cache_path):
+ os.makedirs(cache_path)
+ self.precompute(precompute_num_workers)
+ if normalize_f0:
+ self.load_stats(cache_path)
+
+ def __getitem__(self, idx):
+ item = self.samples[idx]
+ f0 = self.compute_or_load(item["audio_file"], string2filename(item["audio_unique_name"]))
+ if self.normalize_f0:
+ assert self.mean is not None and self.std is not None, " [!] Mean and STD is not available"
+ f0 = self.normalize(f0)
+ return {"audio_unique_name": item["audio_unique_name"], "f0": f0}
+
+ def __len__(self):
+ return len(self.samples)
+
+ def precompute(self, num_workers=0):
+ print("[*] Pre-computing F0s...")
+ with tqdm.tqdm(total=len(self)) as pbar:
+ batch_size = num_workers if num_workers > 0 else 1
+ # we do not normalize at preproessing
+ normalize_f0 = self.normalize_f0
+ self.normalize_f0 = False
+ dataloder = torch.utils.data.DataLoader(
+ batch_size=batch_size, dataset=self, shuffle=False, num_workers=num_workers, collate_fn=self.collate_fn
+ )
+ computed_data = []
+ for batch in dataloder:
+ f0 = batch["f0"]
+ computed_data.append(f for f in f0)
+ pbar.update(batch_size)
+ self.normalize_f0 = normalize_f0
+
+ if self.normalize_f0:
+ computed_data = [tensor for batch in computed_data for tensor in batch] # flatten
+ pitch_mean, pitch_std = self.compute_pitch_stats(computed_data)
+ pitch_stats = {"mean": pitch_mean, "std": pitch_std}
+ np.save(os.path.join(self.cache_path, "pitch_stats"), pitch_stats, allow_pickle=True)
+
+ def get_pad_id(self):
+ return self.pad_id
+
+ @staticmethod
+ def create_pitch_file_path(file_name, cache_path):
+ pitch_file = os.path.join(cache_path, file_name + "_pitch.npy")
+ return pitch_file
+
+ @staticmethod
+ def _compute_and_save_pitch(ap, wav_file, pitch_file=None):
+ wav = ap.load_wav(wav_file)
+ pitch = ap.compute_f0(wav)
+ if pitch_file:
+ np.save(pitch_file, pitch)
+ return pitch
+
+ @staticmethod
+ def compute_pitch_stats(pitch_vecs):
+ nonzeros = np.concatenate([v[np.where(v != 0.0)[0]] for v in pitch_vecs])
+ mean, std = np.mean(nonzeros), np.std(nonzeros)
+ return mean, std
+
+ def load_stats(self, cache_path):
+ stats_path = os.path.join(cache_path, "pitch_stats.npy")
+ stats = np.load(stats_path, allow_pickle=True).item()
+ self.mean = stats["mean"].astype(np.float32)
+ self.std = stats["std"].astype(np.float32)
+
+ def normalize(self, pitch):
+ zero_idxs = np.where(pitch == 0.0)[0]
+ pitch = pitch - self.mean
+ pitch = pitch / self.std
+ pitch[zero_idxs] = 0.0
+ return pitch
+
+ def denormalize(self, pitch):
+ zero_idxs = np.where(pitch == 0.0)[0]
+ pitch *= self.std
+ pitch += self.mean
+ pitch[zero_idxs] = 0.0
+ return pitch
+
+ def compute_or_load(self, wav_file, audio_unique_name):
+ """
+ compute pitch and return a numpy array of pitch values
+ """
+ pitch_file = self.create_pitch_file_path(audio_unique_name, self.cache_path)
+ if not os.path.exists(pitch_file):
+ pitch = self._compute_and_save_pitch(self.ap, wav_file, pitch_file)
+ else:
+ pitch = np.load(pitch_file)
+ return pitch.astype(np.float32)
+
+ def collate_fn(self, batch):
+ audio_unique_name = [item["audio_unique_name"] for item in batch]
+ f0s = [item["f0"] for item in batch]
+ f0_lens = [len(item["f0"]) for item in batch]
+ f0_lens_max = max(f0_lens)
+ f0s_torch = torch.LongTensor(len(f0s), f0_lens_max).fill_(self.get_pad_id())
+ for i, f0_len in enumerate(f0_lens):
+ f0s_torch[i, :f0_len] = torch.LongTensor(f0s[i])
+ return {"audio_unique_name": audio_unique_name, "f0": f0s_torch, "f0_lens": f0_lens}
+
+ def print_logs(self, level: int = 0) -> None:
+ indent = "\t" * level
+ print("\n")
+ print(f"{indent}> F0Dataset ")
+ print(f"{indent}| > Number of instances : {len(self.samples)}")
+
+
+class EnergyDataset:
+ """Energy Dataset for computing Energy from wav files in CPU
+
+ Pre-compute Energy values for all the samples at initialization if `cache_path` is not None or already present. It
+ also computes the mean and std of Energy values if `normalize_Energy` is True.
+
+ Args:
+ samples (Union[List[List], List[Dict]]):
+ List of samples. Each sample is a list or a dict.
+
+ ap (AudioProcessor):
+ AudioProcessor to compute Energy from wav files.
+
+ cache_path (str):
+ Path to cache Energy values. If `cache_path` is already present or None, it skips the pre-computation.
+ Defaults to None.
+
+ precompute_num_workers (int):
+ Number of workers used for pre-computing the Energy values. Defaults to 0.
+
+ normalize_Energy (bool):
+ Whether to normalize Energy values by mean and std. Defaults to True.
+ """
+
+ def __init__(
+ self,
+ samples: Union[List[List], List[Dict]],
+ ap: "AudioProcessor",
+ verbose=False,
+ cache_path: str = None,
+ precompute_num_workers=0,
+ normalize_energy=True,
+ ):
+ self.samples = samples
+ self.ap = ap
+ self.verbose = verbose
+ self.cache_path = cache_path
+ self.normalize_energy = normalize_energy
+ self.pad_id = 0.0
+ self.mean = None
+ self.std = None
+ if cache_path is not None and not os.path.exists(cache_path):
+ os.makedirs(cache_path)
+ self.precompute(precompute_num_workers)
+ if normalize_energy:
+ self.load_stats(cache_path)
+
+ def __getitem__(self, idx):
+ item = self.samples[idx]
+ energy = self.compute_or_load(item["audio_file"], string2filename(item["audio_unique_name"]))
+ if self.normalize_energy:
+ assert self.mean is not None and self.std is not None, " [!] Mean and STD is not available"
+ energy = self.normalize(energy)
+ return {"audio_unique_name": item["audio_unique_name"], "energy": energy}
+
+ def __len__(self):
+ return len(self.samples)
+
+ def precompute(self, num_workers=0):
+ print("[*] Pre-computing energys...")
+ with tqdm.tqdm(total=len(self)) as pbar:
+ batch_size = num_workers if num_workers > 0 else 1
+ # we do not normalize at preproessing
+ normalize_energy = self.normalize_energy
+ self.normalize_energy = False
+ dataloder = torch.utils.data.DataLoader(
+ batch_size=batch_size, dataset=self, shuffle=False, num_workers=num_workers, collate_fn=self.collate_fn
+ )
+ computed_data = []
+ for batch in dataloder:
+ energy = batch["energy"]
+ computed_data.append(e for e in energy)
+ pbar.update(batch_size)
+ self.normalize_energy = normalize_energy
+
+ if self.normalize_energy:
+ computed_data = [tensor for batch in computed_data for tensor in batch] # flatten
+ energy_mean, energy_std = self.compute_energy_stats(computed_data)
+ energy_stats = {"mean": energy_mean, "std": energy_std}
+ np.save(os.path.join(self.cache_path, "energy_stats"), energy_stats, allow_pickle=True)
+
+ def get_pad_id(self):
+ return self.pad_id
+
+ @staticmethod
+ def create_energy_file_path(wav_file, cache_path):
+ file_name = os.path.splitext(os.path.basename(wav_file))[0]
+ energy_file = os.path.join(cache_path, file_name + "_energy.npy")
+ return energy_file
+
+ @staticmethod
+ def _compute_and_save_energy(ap, wav_file, energy_file=None):
+ wav = ap.load_wav(wav_file)
+ energy = calculate_energy(wav, fft_size=ap.fft_size, hop_length=ap.hop_length, win_length=ap.win_length)
+ if energy_file:
+ np.save(energy_file, energy)
+ return energy
+
+ @staticmethod
+ def compute_energy_stats(energy_vecs):
+ nonzeros = np.concatenate([v[np.where(v != 0.0)[0]] for v in energy_vecs])
+ mean, std = np.mean(nonzeros), np.std(nonzeros)
+ return mean, std
+
+ def load_stats(self, cache_path):
+ stats_path = os.path.join(cache_path, "energy_stats.npy")
+ stats = np.load(stats_path, allow_pickle=True).item()
+ self.mean = stats["mean"].astype(np.float32)
+ self.std = stats["std"].astype(np.float32)
+
+ def normalize(self, energy):
+ zero_idxs = np.where(energy == 0.0)[0]
+ energy = energy - self.mean
+ energy = energy / self.std
+ energy[zero_idxs] = 0.0
+ return energy
+
+ def denormalize(self, energy):
+ zero_idxs = np.where(energy == 0.0)[0]
+ energy *= self.std
+ energy += self.mean
+ energy[zero_idxs] = 0.0
+ return energy
+
+ def compute_or_load(self, wav_file, audio_unique_name):
+ """
+ compute energy and return a numpy array of energy values
+ """
+ energy_file = self.create_energy_file_path(audio_unique_name, self.cache_path)
+ if not os.path.exists(energy_file):
+ energy = self._compute_and_save_energy(self.ap, wav_file, energy_file)
+ else:
+ energy = np.load(energy_file)
+ return energy.astype(np.float32)
+
+ def collate_fn(self, batch):
+ audio_unique_name = [item["audio_unique_name"] for item in batch]
+ energys = [item["energy"] for item in batch]
+ energy_lens = [len(item["energy"]) for item in batch]
+ energy_lens_max = max(energy_lens)
+ energys_torch = torch.LongTensor(len(energys), energy_lens_max).fill_(self.get_pad_id())
+ for i, energy_len in enumerate(energy_lens):
+ energys_torch[i, :energy_len] = torch.LongTensor(energys[i])
+ return {"audio_unique_name": audio_unique_name, "energy": energys_torch, "energy_lens": energy_lens}
+
+ def print_logs(self, level: int = 0) -> None:
+ indent = "\t" * level
+ print("\n")
+ print(f"{indent}> energyDataset ")
+ print(f"{indent}| > Number of instances : {len(self.samples)}")
diff --git a/TTS/tts/datasets/formatters.py b/TTS/tts/datasets/formatters.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb216c5eff2f79b40a5348ca4983ee0caf82e9b2
--- /dev/null
+++ b/TTS/tts/datasets/formatters.py
@@ -0,0 +1,603 @@
+import os
+import re
+import xml.etree.ElementTree as ET
+from glob import glob
+from pathlib import Path
+from typing import List
+
+import pandas as pd
+from tqdm import tqdm
+
+########################
+# DATASETS
+########################
+
+
+def coqui(root_path, meta_file, ignored_speakers=None):
+ """Interal dataset formatter."""
+ filepath = os.path.join(root_path, meta_file)
+ # ensure there are 4 columns for every line
+ with open(filepath, "r", encoding="utf8") as f:
+ lines = f.readlines()
+ num_cols = len(lines[0].split("|")) # take the first row as reference
+ for idx, line in enumerate(lines[1:]):
+ if len(line.split("|")) != num_cols:
+ print(f" > Missing column in line {idx + 1} -> {line.strip()}")
+ # load metadata
+ metadata = pd.read_csv(os.path.join(root_path, meta_file), sep="|")
+ assert all(x in metadata.columns for x in ["audio_file", "text"])
+ speaker_name = None if "speaker_name" in metadata.columns else "coqui"
+ emotion_name = None if "emotion_name" in metadata.columns else "neutral"
+ items = []
+ not_found_counter = 0
+ for row in metadata.itertuples():
+ if speaker_name is None and ignored_speakers is not None and row.speaker_name in ignored_speakers:
+ continue
+ audio_path = os.path.join(root_path, row.audio_file)
+ if not os.path.exists(audio_path):
+ not_found_counter += 1
+ continue
+ items.append(
+ {
+ "text": row.text,
+ "audio_file": audio_path,
+ "speaker_name": speaker_name if speaker_name is not None else row.speaker_name,
+ "emotion_name": emotion_name if emotion_name is not None else row.emotion_name,
+ "root_path": root_path,
+ }
+ )
+ if not_found_counter > 0:
+ print(f" | > [!] {not_found_counter} files not found")
+ return items
+
+
+def tweb(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
+ """Normalize TWEB dataset.
+ https://www.kaggle.com/bryanpark/the-world-english-bible-speech-dataset
+ """
+ txt_file = os.path.join(root_path, meta_file)
+ items = []
+ speaker_name = "tweb"
+ with open(txt_file, "r", encoding="utf-8") as ttf:
+ for line in ttf:
+ cols = line.split("\t")
+ wav_file = os.path.join(root_path, cols[0] + ".wav")
+ text = cols[1]
+ items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
+ return items
+
+
+def mozilla(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
+ """Normalizes Mozilla meta data files to TTS format"""
+ txt_file = os.path.join(root_path, meta_file)
+ items = []
+ speaker_name = "mozilla"
+ with open(txt_file, "r", encoding="utf-8") as ttf:
+ for line in ttf:
+ cols = line.split("|")
+ wav_file = cols[1].strip()
+ text = cols[0].strip()
+ wav_file = os.path.join(root_path, "wavs", wav_file)
+ items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
+ return items
+
+
+def mozilla_de(root_path, meta_file, **kwargs): # pylint: disable=unused-argument
+ """Normalizes Mozilla meta data files to TTS format"""
+ txt_file = os.path.join(root_path, meta_file)
+ items = []
+ speaker_name = "mozilla"
+ with open(txt_file, "r", encoding="ISO 8859-1") as ttf:
+ for line in ttf:
+ cols = line.strip().split("|")
+ wav_file = cols[0].strip()
+ text = cols[1].strip()
+ folder_name = f"BATCH_{wav_file.split('_')[0]}_FINAL"
+ wav_file = os.path.join(root_path, folder_name, wav_file)
+ items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
+ return items
+
+
+def mailabs(root_path, meta_files=None, ignored_speakers=None):
+ """Normalizes M-AI-Labs meta data files to TTS format
+
+ Args:
+ root_path (str): root folder of the MAILAB language folder.
+ meta_files (str): list of meta files to be used in the training. If None, finds all the csv files
+ recursively. Defaults to None
+ """
+ speaker_regex = re.compile(f"by_book{os.sep}(male|female){os.sep}(?P
+
+
+ }})
+
+
+ + {%if use_multi_speaker%} + Choose a speaker: +
+ {%endif%} + + {%if use_multi_language%} + Choose a language: +
+ {%endif%} + + + {%if show_details%} +
+ {%endif%} + + +
+
+
+ -
+
+ + {%if use_multi_speaker%} + Choose a speaker: +
+ {%endif%} + + {%if use_multi_language%} + Choose a language: +
+ {%endif%} + + + {%if show_details%} +
+ {%endif%} + + +
\n", + " | - audio1.wav
\n", + " | - audio2.wav
\n", + " | - audio3.wav
\n", + " ...
\n", + "\n", + "\n", + "and a **metadata.csv** file will have the audio file name in parallel to the transcript, delimited by `|`: \n", + " \n", + "\n", + "# metadata.csv
\n", + "audio1|This is my sentence.
\n", + "audio2|This is maybe my sentence.
\n", + "audio3|This is certainly my sentence.
\n", + "audio4|Let this be your sentence.
\n", + "...\n", + "\n", + "\n", + "In the end, we should have the following **folder structure**:\n", + "\n", + "\n", + "/MyTTSDataset
\n", + " |
\n", + " | -> metadata.csv
\n", + " | -> /wavs
\n", + " | -> audio1.wav
\n", + " | -> audio2.wav
\n", + " | ...
\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "69501a10-3b53-4e75-ae66-90221d6f2271", + "metadata": {}, + "source": [ + "🐸TTS already provides tooling for the _LJSpeech_. if you use the same format, you can start training your models right away.
\n", + "\n", + "After you collect and format your dataset, you need to check two things. Whether you need a **_formatter_** and a **_text_cleaner_**.
The **_formatter_** loads the text file (created above) as a list and the **_text_cleaner_** performs a sequence of text normalization operations that converts the raw text into the spoken representation (e.g. converting numbers to text, acronyms, and symbols to the spoken format).\n", + "\n", + "If you use a different dataset format then the LJSpeech or the other public datasets that 🐸TTS supports, then you need to write your own **_formatter_** and **_text_cleaner_**." + ] + }, + { + "cell_type": "markdown", + "id": "e7f226c8-4e55-48fa-937b-8415d539b17c", + "metadata": {}, + "source": [ + "## ⏳️ Loading your dataset\n", + "Load one of the dataset supported by 🐸TTS.\n", + "\n", + "We will start by defining dataset config and setting LJSpeech as our target dataset and define its path.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3cb0191-b8fc-4158-bd26-8423c2a8ba66", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# BaseDatasetConfig: defines name, formatter and path of the dataset.\n", + "from TTS.tts.configs.shared_configs import BaseDatasetConfig\n", + "\n", + "output_path = \"tts_train_dir\"\n", + "if not os.path.exists(output_path):\n", + " os.makedirs(output_path)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae6b7019-3685-4b48-8917-c152e288d7e3", + "metadata": {}, + "outputs": [], + "source": [ + "# Download and extract LJSpeech dataset.\n", + "\n", + "!wget -O $output_path/LJSpeech-1.1.tar.bz2 https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2 \n", + "!tar -xf $output_path/LJSpeech-1.1.tar.bz2 -C $output_path" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "76cd3ab5-6387-45f1-b488-24734cc1beb5", + "metadata": {}, + "outputs": [], + "source": [ + "dataset_config = BaseDatasetConfig(\n", + " formatter=\"ljspeech\", meta_file_train=\"metadata.csv\", path=os.path.join(output_path, \"LJSpeech-1.1/\")\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ae82fd75", + "metadata": {}, + "source": [ + "## ✅ Train a new model\n", + "\n", + "Let's kick off a training run 🚀🚀🚀.\n", + "\n", + "Deciding on the model architecture you'd want to use is based on your needs and available resources. Each model architecture has it's pros and cons that define the run-time efficiency and the voice quality.\n", + "We have many recipes under `TTS/recipes/` that provide a good starting point. For this tutorial, we will be using `GlowTTS`." + ] + }, + { + "cell_type": "markdown", + "id": "f5876e46-2aee-4bcf-b6b3-9e3c535c553f", + "metadata": {}, + "source": [ + "We will begin by initializing the model training configuration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5483ca28-39d6-49f8-a18e-4fb53c50ad84", + "metadata": {}, + "outputs": [], + "source": [ + "# GlowTTSConfig: all model related values for training, validating and testing.\n", + "from TTS.tts.configs.glow_tts_config import GlowTTSConfig\n", + "config = GlowTTSConfig(\n", + " batch_size=32,\n", + " eval_batch_size=16,\n", + " num_loader_workers=4,\n", + " num_eval_loader_workers=4,\n", + " run_eval=True,\n", + " test_delay_epochs=-1,\n", + " epochs=100,\n", + " text_cleaner=\"phoneme_cleaners\",\n", + " use_phonemes=True,\n", + " phoneme_language=\"en-us\",\n", + " phoneme_cache_path=os.path.join(output_path, \"phoneme_cache\"),\n", + " print_step=25,\n", + " print_eval=False,\n", + " mixed_precision=True,\n", + " output_path=output_path,\n", + " datasets=[dataset_config],\n", + " save_step=1000,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "b93ed377-80b7-447b-bd92-106bffa777ee", + "metadata": {}, + "source": [ + "Next we will initialize the audio processor which is used for feature extraction and audio I/O." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1b12f61-f851-4565-84dd-7640947e04ab", + "metadata": {}, + "outputs": [], + "source": [ + "from TTS.utils.audio import AudioProcessor\n", + "ap = AudioProcessor.init_from_config(config)\n", + "# Modify sample rate if for a custom audio dataset:\n", + "# ap.sample_rate = 22050\n" + ] + }, + { + "cell_type": "markdown", + "id": "1d461683-b05e-403f-815f-8007bda08c38", + "metadata": {}, + "source": [ + "Next we will initialize the tokenizer which is used to convert text to sequences of token IDs. If characters are not defined in the config, default characters are passed to the config." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "014879b7-f18d-44c0-b24a-e10f8002113a", + "metadata": {}, + "outputs": [], + "source": [ + "from TTS.tts.utils.text.tokenizer import TTSTokenizer\n", + "tokenizer, config = TTSTokenizer.init_from_config(config)" + ] + }, + { + "cell_type": "markdown", + "id": "df3016e1-9e99-4c4f-94e3-fa89231fd978", + "metadata": {}, + "source": [ + "Next we will load data samples. Each sample is a list of ```[text, audio_file_path, speaker_name]```. You can define your custom sample loader returning the list of samples." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cadd6ada-c8eb-4f79-b8fe-6d72850af5a7", + "metadata": {}, + "outputs": [], + "source": [ + "from TTS.tts.datasets import load_tts_samples\n", + "train_samples, eval_samples = load_tts_samples(\n", + " dataset_config,\n", + " eval_split=True,\n", + " eval_split_max_size=config.eval_split_max_size,\n", + " eval_split_size=config.eval_split_size,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "db8b451e-1fe1-4aa3-b69e-ab22b925bd19", + "metadata": {}, + "source": [ + "Now we're ready to initialize the model.\n", + "\n", + "Models take a config object and a speaker manager as input. Config defines the details of the model like the number of layers, the size of the embedding, etc. Speaker manager is used by multi-speaker models." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac2ffe3e-ad0c-443e-800c-9b076ee811b4", + "metadata": {}, + "outputs": [], + "source": [ + "from TTS.tts.models.glow_tts import GlowTTS\n", + "model = GlowTTS(config, ap, tokenizer, speaker_manager=None)" + ] + }, + { + "cell_type": "markdown", + "id": "e2832c56-889d-49a6-95b6-eb231892ecc6", + "metadata": {}, + "source": [ + "Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training, distributed training, etc." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f609945-4fe0-4d0d-b95e-11d7bfb63ebe", + "metadata": {}, + "outputs": [], + "source": [ + "from trainer import Trainer, TrainerArgs\n", + "trainer = Trainer(\n", + " TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5b320831-dd83-429b-bb6a-473f9d49d321", + "metadata": {}, + "source": [ + "### AND... 3,2,1... START TRAINING 🚀🚀🚀" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4c07f99-3d1d-4bea-801e-9f33bbff0e9f", + "metadata": {}, + "outputs": [], + "source": [ + "trainer.fit()" + ] + }, + { + "cell_type": "markdown", + "id": "4cff0c40-2734-40a6-a905-e945a9fb3e98", + "metadata": {}, + "source": [ + "#### 🚀 Run the Tensorboard. 🚀\n", + "On the notebook and Tensorboard, you can monitor the progress of your model. Also Tensorboard provides certain figures and sample outputs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a85cd3b-1646-40ad-a6c2-49323e08eeec", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install tensorboard\n", + "!tensorboard --logdir=tts_train_dir" + ] + }, + { + "cell_type": "markdown", + "id": "9f6dc959", + "metadata": {}, + "source": [ + "## ✅ Test the model\n", + "\n", + "We made it! 🙌\n", + "\n", + "Let's kick off the testing run, which displays performance metrics.\n", + "\n", + "We're committing the cardinal sin of ML 😈 (aka - testing on our training data) so you don't want to deploy this model into production. In this notebook we're focusing on the workflow itself, so it's forgivable 😇\n", + "\n", + "You can see from the test output that our tiny model has overfit to the data, and basically memorized this one sentence.\n", + "\n", + "When you start training your own models, make sure your testing data doesn't include your training data 😅" + ] + }, + { + "cell_type": "markdown", + "id": "99fada7a-592f-4a09-9369-e6f3d82de3a0", + "metadata": {}, + "source": [ + "Let's get the latest saved checkpoint. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6dd47ed5-da8e-4bf9-b524-d686630d6961", + "metadata": {}, + "outputs": [], + "source": [ + "import glob, os\n", + "output_path = \"tts_train_dir\"\n", + "ckpts = sorted([f for f in glob.glob(output_path+\"/*/*.pth\")])\n", + "configs = sorted([f for f in glob.glob(output_path+\"/*/*.json\")])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd42bc7a", + "metadata": {}, + "outputs": [], + "source": [ + " !tts --text \"Text for TTS\" \\\n", + " --model_path $test_ckpt \\\n", + " --config_path $test_config \\\n", + " --out_path out.wav" + ] + }, + { + "cell_type": "markdown", + "id": "81cbcb3f-d952-469b-a0d8-8941cd7af670", + "metadata": {}, + "source": [ + "## 📣 Listen to the synthesized wave 📣" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e0000bd6-6763-4a10-a74d-911dd08ebcff", + "metadata": {}, + "outputs": [], + "source": [ + "import IPython\n", + "IPython.display.Audio(\"out.wav\")" + ] + }, + { + "cell_type": "markdown", + "id": "13914401-cad1-494a-b701-474e52829138", + "metadata": {}, + "source": [ + "## 🎉 Congratulations! 🎉 You now have trained your first TTS model! \n", + "Follow up with the next tutorials to learn more advanced material." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "950d9fc6-896f-4a2c-86fd-8fd1fcbbb3f7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/dataset_analysis/AnalyzeDataset.ipynb b/notebooks/dataset_analysis/AnalyzeDataset.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7fc51a3adfd209951752b9c0d03062d3a3998d75 --- /dev/null +++ b/notebooks/dataset_analysis/AnalyzeDataset.ipynb @@ -0,0 +1,427 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "# TTS_PATH = \"/home/erogol/projects/\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "import os\n", + "import sys\n", + "import librosa\n", + "import numpy as np\n", + "import pandas as pd\n", + "from scipy.stats import norm\n", + "from tqdm import tqdm_notebook as tqdm\n", + "from multiprocessing import Pool\n", + "from matplotlib import pylab as plt\n", + "from collections import Counter\n", + "from TTS.config.shared_configs import BaseDatasetConfig\n", + "from TTS.tts.datasets import load_tts_samples\n", + "from TTS.tts.datasets.formatters import *\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "NUM_PROC = 8\n", + "DATASET_CONFIG = BaseDatasetConfig(\n", + " name=\"ljspeech\", meta_file_train=\"metadata.csv\", path=\"/absolute/path/to/your/dataset/\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def formatter(root_path, meta_file, **kwargs): # pylint: disable=unused-argument\n", + " txt_file = os.path.join(root_path, meta_file)\n", + " items = []\n", + " speaker_name = \"myspeaker\"\n", + " with open(txt_file, \"r\", encoding=\"utf-8\") as ttf:\n", + " for line in ttf:\n", + " cols = line.split(\"|\")\n", + " wav_file = os.path.join(root_path, \"wavs\", cols[0] + \".wav\") \n", + " text = cols[1]\n", + " items.append({\"text\": text, \"audio_file\": wav_file, \"speaker_name\": speaker_name})\n", + " return items" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "# use your own preprocessor at this stage - TTS/datasets/proprocess.py\n", + "train_samples, eval_samples = load_tts_samples(DATASET_CONFIG, eval_split=True, formatter=formatter)\n", + "if eval_samples is not None:\n", + " items = train_samples + eval_samples\n", + "else:\n", + " items = train_samples\n", + "print(\" > Number of audio files: {}\".format(len(items)))\n", + "print(items[1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "# check wavs if exist\n", + "wav_files = []\n", + "for item in items:\n", + " wav_file = item[\"audio_file\"].strip()\n", + " wav_files.append(wav_file)\n", + " if not os.path.exists(wav_file):\n", + " print(waf_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "# show duplicate items\n", + "c = Counter(wav_files)\n", + "print([item for item, count in c.items() if count > 1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "item" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "def load_item(item):\n", + " text = item[\"text\"].strip()\n", + " file_name = item[\"audio_file\"].strip()\n", + " audio, sr = librosa.load(file_name, sr=None)\n", + " audio_len = len(audio) / sr\n", + " text_len = len(text)\n", + " return file_name, text, text_len, audio, audio_len\n", + "\n", + "# This will take a while depending on size of dataset\n", + "if NUM_PROC == 1:\n", + " data = []\n", + " for m in tqdm(items):\n", + " data += [load_item(m)]\n", + "else:\n", + " with Pool(8) as p:\n", + " data = list(tqdm(p.imap(load_item, items), total=len(items)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "# count words in the dataset\n", + "w_count = Counter()\n", + "for item in tqdm(data):\n", + " text = item[1].lower().strip()\n", + " for word in text.split():\n", + " w_count[word] += 1\n", + "print(\" > Number of words: {}\".format(len(w_count)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "text_vs_durs = {} # text length vs audio duration\n", + "text_len_counter = Counter() # number of sentences with the keyed length\n", + "for item in tqdm(data):\n", + " text = item[1].lower().strip()\n", + " text_len = len(text)\n", + " text_len_counter[text_len] += 1\n", + " audio_len = item[-1]\n", + " try:\n", + " text_vs_durs[text_len] += [audio_len]\n", + " except:\n", + " text_vs_durs[text_len] = [audio_len]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "# text_len vs avg_audio_len, median_audio_len, std_audio_len\n", + "text_vs_avg = {}\n", + "text_vs_median = {}\n", + "text_vs_std = {}\n", + "for key, durs in text_vs_durs.items():\n", + " text_vs_avg[key] = np.mean(durs)\n", + " text_vs_median[key] = np.median(durs)\n", + " text_vs_std[key] = np.std(durs)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false" + }, + "source": [ + "### Avg audio length per char" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "for item in data:\n", + " if item[-1] < 2:\n", + " print(item)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "sec_per_chars = []\n", + "for item in data:\n", + " text = item[1]\n", + " dur = item[-1]\n", + " sec_per_char = dur / len(text)\n", + " sec_per_chars.append(sec_per_char)\n", + "# sec_per_char /= len(data)\n", + "# print(sec_per_char)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "mean = np.mean(sec_per_chars)\n", + "std = np.std(sec_per_chars)\n", + "print(mean)\n", + "print(std)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "dist = norm(mean, std)\n", + "\n", + "# find irregular instances long or short voice durations\n", + "for item in data:\n", + " text = item[1]\n", + " dur = item[-1]\n", + " sec_per_char = dur / len(text)\n", + " pdf =norm.pdf(sec_per_char)\n", + " if pdf < 0.39:\n", + " print(item)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false" + }, + "source": [ + "### Plot Dataset Statistics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "plt.title(\"text length vs mean audio duration\")\n", + "plt.scatter(list(text_vs_avg.keys()), list(text_vs_avg.values()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "plt.title(\"text length vs median audio duration\")\n", + "plt.scatter(list(text_vs_median.keys()), list(text_vs_median.values()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "plt.title(\"text length vs STD\")\n", + "plt.scatter(list(text_vs_std.keys()), list(text_vs_std.values()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "plt.title(\"text length vs # instances\")\n", + "plt.scatter(list(text_len_counter.keys()), list(text_len_counter.values()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "Collapsed": "false" + }, + "source": [ + "### Check words frequencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "w_count_df = pd.DataFrame.from_dict(w_count, orient='index')\n", + "w_count_df.sort_values(0, ascending=False, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false", + "scrolled": true + }, + "outputs": [], + "source": [ + "w_count_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "# check a certain word\n", + "w_count_df.at['minute', 0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "Collapsed": "false" + }, + "outputs": [], + "source": [ + "# fequency bar plot - it takes time!!\n", + "w_count_df.plot.bar()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/dataset_analysis/CheckDatasetSNR.ipynb b/notebooks/dataset_analysis/CheckDatasetSNR.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..18c48d0bd181830fe096852dfdfe30cba1bc47b4 --- /dev/null +++ b/notebooks/dataset_analysis/CheckDatasetSNR.ipynb @@ -0,0 +1,210 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook computes the average SNR a given Voice Dataset. If the SNR is too low, that might reduce the performance or prevent model to learn. SNR paper can be seen here: https://www.cs.cmu.edu/~robust/Papers/KimSternIS08.pdf\n", + "\n", + "To use this notebook, you need:\n", + "- WADA SNR estimation: http://www.cs.cmu.edu/~robust/archive/algorithms/WADA_SNR_IS_2008/\n", + " 1. extract in the same folder as this notebook\n", + " 2. under MacOS you'll have to rebuild the executable. In the build folder: 1) remove existing .o files and 2) run make\n", + "\n", + "\n", + "- FFMPEG: ```sudo apt-get install ffmpeg ``` \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import glob\n", + "import subprocess\n", + "import IPython\n", + "import soundfile as sf\n", + "import numpy as np\n", + "from tqdm import tqdm\n", + "from multiprocessing import Pool\n", + "from matplotlib import pylab as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set the meta parameters\n", + "DATA_PATH = \"/home/erogol/Data/m-ai-labs/de_DE/by_book/female/eva_k/\"\n", + "NUM_PROC = 1\n", + "CURRENT_PATH = os.getcwd()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def compute_file_snr(file_path):\n", + " \"\"\" Convert given file to required format with FFMPEG and process with WADA.\"\"\"\n", + " _, sr = sf.read(file_path)\n", + " new_file = file_path.replace(\".wav\", \"_tmp.wav\")\n", + " if sr != 16000:\n", + " command = f'ffmpeg -i \"{file_path}\" -ac 1 -acodec pcm_s16le -y -ar 16000 \"{new_file}\"'\n", + " else:\n", + " command = f'cp \"{file_path}\" \"{new_file}\"'\n", + " os.system(command)\n", + " command = [f'\"{CURRENT_PATH}/WadaSNR/Exe/WADASNR\"', f'-i \"{new_file}\"', f'-t \"{CURRENT_PATH}/WadaSNR/Exe/Alpha0.400000.txt\"', '-ifmt mswav']\n", + " output = subprocess.check_output(\" \".join(command), shell=True)\n", + " try:\n", + " output = float(output.split()[-3].decode(\"utf-8\"))\n", + " except:\n", + " raise RuntimeError(\" \".join(command))\n", + " os.system(f'rm \"{new_file}\"')\n", + " return output, file_path\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "wav_file = \"/home/erogol/Data/LJSpeech-1.1/wavs/LJ001-0001.wav\"\n", + "output = compute_file_snr(wav_file)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "wav_files = glob.glob(f\"{DATA_PATH}/**/*.wav\", recursive=True)\n", + "print(f\" > Number of wav files {len(wav_files)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if NUM_PROC == 1:\n", + " file_snrs = [None] * len(wav_files) \n", + " for idx, wav_file in tqdm(enumerate(wav_files)):\n", + " tup = compute_file_snr(wav_file)\n", + " file_snrs[idx] = tup\n", + "else:\n", + " with Pool(NUM_PROC) as pool:\n", + " file_snrs = list(tqdm(pool.imap(compute_file_snr, wav_files), total=len(wav_files)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "snrs = [tup[0] for tup in file_snrs]\n", + "\n", + "error_idxs = np.where(np.isnan(snrs) == True)[0]\n", + "error_files = [wav_files[idx] for idx in error_idxs]\n", + "\n", + "file_snrs = [i for j, i in enumerate(file_snrs) if j not in error_idxs]\n", + "file_names = [tup[1] for tup in file_snrs]\n", + "snrs = [tup[0] for tup in file_snrs]\n", + "file_idxs = np.argsort(snrs)\n", + "\n", + "\n", + "print(f\" > Average SNR of the dataset:{np.mean(snrs)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def output_snr_with_audio(idx):\n", + " file_idx = file_idxs[idx]\n", + " file_name = file_names[file_idx]\n", + " wav, sr = sf.read(file_name)\n", + " # multi channel to single channel\n", + " if len(wav.shape) == 2:\n", + " wav = wav[:, 0]\n", + " print(f\" > {file_name} - snr:{snrs[file_idx]}\")\n", + " IPython.display.display(IPython.display.Audio(wav, rate=sr))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# find worse SNR files\n", + "N = 10 # number of files to fetch\n", + "for i in range(N):\n", + " output_snr_with_audio(i)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# find best recordings\n", + "N = 10 # number of files to fetch\n", + "for i in range(N):\n", + " output_snr_with_audio(-i-1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.hist(snrs, bins=100)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/dataset_analysis/CheckPitch.ipynb b/notebooks/dataset_analysis/CheckPitch.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..72afbc64a17e2a46d1d2d5336990f01eb620ca20 --- /dev/null +++ b/notebooks/dataset_analysis/CheckPitch.ipynb @@ -0,0 +1,179 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 9, + "source": [ + "import numpy as np\n", + "import glob\n", + "from TTS.utils.audio import AudioProcessor\n", + "from TTS.config.shared_configs import BaseAudioConfig\n", + "from TTS.tts.utils.visual import plot_pitch" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 13, + "source": [ + "pitch_path = \"/home/ubuntu/TTS/recipes/ljspeech/fast_pitch/f0_cache\"\n", + "wav_path = \"/home/ubuntu/TTS/recipes/ljspeech/LJSpeech-1.1/wavs\"\n", + "wav_files = glob.glob(\"/home/ubuntu/TTS/recipes/ljspeech/LJSpeech-1.1/wavs/*.wav\")\n", + "print(len(wav_files))" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "13100\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 20, + "source": [ + "ap = AudioProcessor(**BaseAudioConfig( sample_rate=22050,\n", + " do_trim_silence=True,\n", + " trim_db=60.0,\n", + " signal_norm=False,\n", + " mel_fmin=0.0,\n", + " mel_fmax=8000,\n", + " spec_gain=1.0,\n", + " log_func=\"np.log\",\n", + " ref_level_db=20,\n", + " preemphasis=0.0,))" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " > Setting up Audio Processor...\n", + " | > sample_rate:22050\n", + " | > resample:False\n", + " | > num_mels:80\n", + " | > log_func:np.log\n", + " | > min_level_db:-100\n", + " | > frame_shift_ms:None\n", + " | > frame_length_ms:None\n", + " | > ref_level_db:20\n", + " | > fft_size:1024\n", + " | > power:1.5\n", + " | > preemphasis:0.0\n", + " | > griffin_lim_iters:60\n", + " | > signal_norm:False\n", + " | > symmetric_norm:True\n", + " | > mel_fmin:0\n", + " | > mel_fmax:8000\n", + " | > spec_gain:1.0\n", + " | > stft_pad_mode:reflect\n", + " | > max_norm:4.0\n", + " | > clip_norm:True\n", + " | > do_trim_silence:True\n", + " | > trim_db:60.0\n", + " | > do_sound_norm:False\n", + " | > do_amp_to_db_linear:True\n", + " | > do_amp_to_db_mel:True\n", + " | > stats_path:None\n", + " | > base:2.718281828459045\n", + " | > hop_length:256\n", + " | > win_length:1024\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 15, + "source": [ + "pitch_files = [wf.replace(\".wav\", \"_pitch.npy\").replace(wav_path, pitch_path) for wf in wav_files]" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 30, + "source": [ + "idx = 100\n", + "# wav_file = wav_files[idx]\n", + "# pitch_file = pitch_files[idx]\n", + "wav_file = \"/home/ubuntu/TTS/recipes/ljspeech/fast_pitch/../LJSpeech-1.1/wavs/LJ011-0097.wav\"\n", + "pitch_file = \"/home/ubuntu/TTS/recipes/ljspeech/fast_pitch/f0_cache/LJ011-0097_pitch.npy\"\n", + "pitch = np.load(pitch_file)\n", + "wav = ap.load_wav(wav_file)\n", + "spec = ap.melspectrogram(wav)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 31, + "source": [ + "plot_pitch(pitch, spec.T)" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "image/png": 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", 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