mirror of https://github.com/coqui-ai/TTS.git
Remove unused code
Edresson Casanova
parent
b146de4ce8
commit
09fb317e6d
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@ -111,78 +111,6 @@ def pad_or_truncate(t, length):
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return tp
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def load_discrete_vocoder_diffuser(
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trained_diffusion_steps=4000,
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desired_diffusion_steps=200,
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cond_free=True,
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cond_free_k=1,
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sampler="ddim",
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):
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"""
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Load a GaussianDiffusion instance configured for use as a decoder.
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Args:
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trained_diffusion_steps (int): The number of diffusion steps used during training.
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desired_diffusion_steps (int): The number of diffusion steps to use during inference.
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cond_free (bool): Whether to use a conditioning-free model.
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cond_free_k (int): The number of samples to use for conditioning-free models.
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sampler (str): The name of the sampler to use.
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Returns:
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A SpacedDiffusion instance configured with the given parameters.
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"""
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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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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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conditioning_free_k=cond_free_k,
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sampler=sampler,
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)
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def do_spectrogram_diffusion(
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diffusion_model,
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diffuser,
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latents,
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conditioning_latents,
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temperature=1,
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):
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"""
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Generate a mel-spectrogram using a diffusion model and a diffuser.
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Args:
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diffusion_model (nn.Module): A diffusion model that converts from 22kHz spectrogram codes to a 24kHz spectrogram signal.
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diffuser (Diffuser): A diffuser that generates a mel-spectrogram from noise.
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latents (torch.Tensor): A tensor of shape (batch_size, seq_len, code_size) containing the input spectrogram codes.
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conditioning_latents (torch.Tensor): A tensor of shape (batch_size, code_size) containing the conditioning codes.
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temperature (float, optional): The temperature of the noise used by the diffuser. Defaults to 1.
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Returns:
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torch.Tensor: A tensor of shape (batch_size, mel_channels, mel_seq_len) containing the generated mel-spectrogram.
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"""
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with torch.no_grad():
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output_seq_len = (
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latents.shape[1] * 4 * 24000 // 22050
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) # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal.
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output_shape = (latents.shape[0], 100, output_seq_len)
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precomputed_embeddings = diffusion_model.timestep_independent(
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latents, conditioning_latents, output_seq_len, False
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)
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noise = torch.randn(output_shape, device=latents.device) * temperature
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mel = diffuser.sample_loop(
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diffusion_model,
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output_shape,
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noise=noise,
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model_kwargs={"precomputed_aligned_embeddings": precomputed_embeddings},
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progress=False,
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)
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return denormalize_tacotron_mel(mel)[:, :, :output_seq_len]
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@dataclass
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class XttsAudioConfig(Coqpit):
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"""
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@ -563,27 +491,6 @@ class Xtts(BaseTTS):
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gpt_cond_len: (int) Length of the audio used for cloning. If audio is shorter, then audio length is used
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else the first `gpt_cond_len` secs is used. Defaults to 6 seconds.
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decoder_iterations: (int) Number of diffusion steps to perform. [0,4000]. More steps means the network has
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more chances to iteratively refine the output, which should theoretically mean a higher quality output.
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Generally a value above 250 is not noticeably better, however. Defaults to 100.
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cond_free: (bool) Whether or not to perform conditioning-free diffusion. Conditioning-free diffusion
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performs two forward passes for each diffusion step: one with the outputs of the autoregressive model
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and one with no conditioning priors. The output of the two is blended according to the cond_free_k
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value below. Conditioning-free diffusion is the real deal, and dramatically improves realism.
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Defaults to True.
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cond_free_k: (float) Knob that determines how to balance the conditioning free signal with the
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conditioning-present signal. [0,inf]. As cond_free_k increases, the output becomes dominated by the
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conditioning-free signal. Defaults to 2.0.
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diffusion_temperature: (float) Controls the variance of the noise fed into the diffusion model. [0,1].
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Values at 0 re the "mean" prediction of the diffusion network and will sound bland and smeared.
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Defaults to 1.0.
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decoder: (str) Selects the decoder to use between ("hifigan", "diffusion")
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Defaults to hifigan
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hf_generate_kwargs: (**kwargs) The huggingface Transformers generate API is used for the autoregressive
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transformer. Extra keyword args fed to this function get forwarded directly to that API. Documentation
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here: https://huggingface.co/docs/transformers/internal/generation_utils
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@ -610,12 +517,6 @@ class Xtts(BaseTTS):
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top_k=top_k,
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top_p=top_p,
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do_sample=do_sample,
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decoder_iterations=decoder_iterations,
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cond_free=cond_free,
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cond_free_k=cond_free_k,
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diffusion_temperature=diffusion_temperature,
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decoder_sampler=decoder_sampler,
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decoder=decoder,
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**hf_generate_kwargs,
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)
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@ -633,13 +534,6 @@ class Xtts(BaseTTS):
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top_k=50,
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top_p=0.85,
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do_sample=True,
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# Decoder inference
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decoder_iterations=100,
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cond_free=True,
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cond_free_k=2,
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diffusion_temperature=1.0,
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decoder_sampler="ddim",
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decoder="hifigan",
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num_beams=1,
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**hf_generate_kwargs,
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):
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@ -734,8 +628,6 @@ class Xtts(BaseTTS):
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top_k=50,
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top_p=0.85,
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do_sample=True,
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# Decoder inference
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decoder="hifigan",
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**hf_generate_kwargs,
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):
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text = text.strip().lower()
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