mirror of https://github.com/coqui-ai/TTS.git
Update XTTS docs
This commit is contained in:
Edresson Casanova
Eren G??lge
parent
8479a3702c
commit
5df8f76b0c
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@ -88,7 +88,7 @@ class XTTSDataset(torch.utils.data.Dataset):
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self.sample_rate = sample_rate
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self.max_wav_len = model_args.max_wav_length
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self.max_text_len = model_args.max_text_length
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self.use_masking_gt_as_prompt = model_args.gpt_use_masking_gt_as_prompt
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self.use_masking_gt_prompt_approach = model_args.gpt_use_masking_gt_prompt_approach
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assert self.max_wav_len is not None and self.max_text_len is not None
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self.samples = samples
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@ -141,7 +141,7 @@ class XTTSDataset(torch.utils.data.Dataset):
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# Ultra short clips are also useless (and can cause problems within some models).
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raise ValueError
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if self.use_masking_gt_as_prompt:
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if self.use_masking_gt_prompt_approach:
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# get a slice from GT to condition the model
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cond, _, cond_idxs = get_prompt_slice(
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audiopath, self.max_conditioning_length, self.min_conditioning_length, self.sample_rate, self.is_eval
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@ -112,7 +112,7 @@ def load_discrete_vocoder_diffuser(
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return SpacedDiffusion(
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use_timesteps=space_timesteps(trained_diffusion_steps, [desired_diffusion_steps]),
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model_mean_type="epsilon",
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model_var_type="learned_range",
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model_vgpt_type="learned_range",
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loss_type="mse",
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betas=get_named_beta_schedule("linear", trained_diffusion_steps),
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conditioning_free=cond_free,
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@ -192,16 +192,19 @@ class XttsArgs(Coqpit):
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use_hifigan (bool, optional): Whether to use hifigan or diffusion + univnet as a decoder. Defaults to True.
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For GPT model:
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ar_max_audio_tokens (int, optional): The maximum mel tokens for the autoregressive model. Defaults to 604.
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ar_max_text_tokens (int, optional): The maximum text tokens for the autoregressive model. Defaults to 402.
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ar_max_prompt_tokens (int, optional): The maximum prompt tokens or the autoregressive model. Defaults to 70.
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ar_layers (int, optional): The number of layers for the autoregressive model. Defaults to 30.
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ar_n_model_channels (int, optional): The model dimension for the autoregressive model. Defaults to 1024.
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ar_n_heads (int, optional): The number of heads for the autoregressive model. Defaults to 16.
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ar_number_text_tokens (int, optional): The number of text tokens for the autoregressive model. Defaults to 255.
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ar_start_text_token (int, optional): The start text token for the autoregressive model. Defaults to 255.
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gpt_max_audio_tokens (int, optional): The maximum mel tokens for the autoregressive model. Defaults to 604.
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gpt_max_text_tokens (int, optional): The maximum text tokens for the autoregressive model. Defaults to 402.
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gpt_max_prompt_tokens (int, optional): The maximum prompt tokens or the autoregressive model. Defaults to 70.
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gpt_layers (int, optional): The number of layers for the autoregressive model. Defaults to 30.
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gpt_n_model_channels (int, optional): The model dimension for the autoregressive model. Defaults to 1024.
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gpt_n_heads (int, optional): The number of heads for the autoregressive model. Defaults to 16.
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gpt_number_text_tokens (int, optional): The number of text tokens for the autoregressive model. Defaults to 255.
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gpt_start_text_token (int, optional): The start text token for the autoregressive model. Defaults to 255.
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gpt_checkpointing (bool, optional): Whether to use checkpointing for the autoregressive model. Defaults to False.
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ar_train_solo_embeddings (bool, optional): Whether to train embeddings for the autoregressive model. Defaults to False.
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gpt_train_solo_embeddings (bool, optional): Whether to train embeddings for the autoregressive model. Defaults to False.
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gpt_code_stride_len (int, optional): The hop_size of dvae and consequently of the gpt output. Defaults to 1024.
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gpt_use_masking_gt_prompt_approach (bool, optional): If True, it will use ground truth as prompt and it will mask the loss to avoid repetition. Defaults to True.
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gpt_use_perceiver_resampler (bool, optional): If True, it will use perceiver resampler from flamingo paper - https://arxiv.org/abs/2204.14198. Defaults to False.
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For DiffTTS model:
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diff_model_channels (int, optional): The number of channels for the DiffTTS model. Defaults to 1024.
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@ -241,7 +244,8 @@ class XttsArgs(Coqpit):
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gpt_num_audio_tokens: int = 8194
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gpt_start_audio_token: int = 8192
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gpt_stop_audio_token: int = 8193
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gpt_use_masking_gt_as_prompt: bool = True
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gpt_code_stride_len: int = 1024
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gpt_use_masking_gt_prompt_approach: bool = True
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gpt_use_perceiver_resampler: bool = False
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# Diffusion Decoder params
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@ -261,7 +265,6 @@ class XttsArgs(Coqpit):
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input_sample_rate: int = 22050
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output_sample_rate: int = 24000
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output_hop_length: int = 256
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ar_mel_length_compression: int = 1024
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decoder_input_dim: int = 1024
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d_vector_dim: int = 512
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cond_d_vector_in_each_upsampling_layer: bool = True
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@ -319,6 +322,7 @@ class Xtts(BaseTTS):
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start_audio_token=self.args.gpt_start_audio_token,
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stop_audio_token=self.args.gpt_stop_audio_token,
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use_perceiver_resampler=self.args.gpt_use_perceiver_resampler,
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code_stride_len=self.args.gpt_code_stride_len,
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)
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if self.args.use_hifigan:
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@ -326,7 +330,7 @@ class Xtts(BaseTTS):
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input_sample_rate=self.args.input_sample_rate,
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output_sample_rate=self.args.output_sample_rate,
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output_hop_length=self.args.output_hop_length,
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ar_mel_length_compression=self.args.ar_mel_length_compression,
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ar_mel_length_compression=self.args.gpt_code_stride_len,
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decoder_input_dim=self.args.decoder_input_dim,
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d_vector_dim=self.args.d_vector_dim,
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cond_d_vector_in_each_upsampling_layer=self.args.cond_d_vector_in_each_upsampling_layer,
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@ -337,7 +341,7 @@ class Xtts(BaseTTS):
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input_sample_rate=self.args.input_sample_rate,
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output_sample_rate=self.args.output_sample_rate,
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output_hop_length=self.args.output_hop_length,
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ar_mel_length_compression=self.args.ar_mel_length_compression,
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ar_mel_length_compression=self.args.gpt_code_stride_len,
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decoder_input_dim=self.args.decoder_input_dim,
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d_vector_dim=self.args.d_vector_dim,
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cond_d_vector_in_each_upsampling_layer=self.args.cond_d_vector_in_each_upsampling_layer,
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