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Fix usage of `aux_input["min_input_length"]` when running `test_run()` during training

This commit is contained in:
Jindrich Matousek 2023-03-09 16:32:29 +01:00
parent 687789558e
commit fcfecf6310
2 changed files with 6 additions and 6 deletions
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2
TTS/tts/models/vits.py
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@ -1175,7 +1175,7 @@ class Vits(BaseTTS):
) )
# JMa: set minimum duration if required # JMa: set minimum duration if required
# w = self._set_min_inference_length(torch.exp(logw) * x_mask, aux_input["min_input_length"]) if aux_input["min_input_length"] else torch.exp(logw) * x_mask # w = self._set_min_inference_length(torch.exp(logw) * x_mask, aux_input["min_input_length"]) if aux_input["min_input_length"] else torch.exp(logw) * x_mask
w = self._set_min_inference_length(x_input, torch.exp(logw) * x_mask, aux_input["min_input_length"]) if aux_input["min_input_length"] else torch.exp(logw) * x_mask w = self._set_min_inference_length(x_input, torch.exp(logw) * x_mask, aux_input["min_input_length"]) if aux_input.get("min_input_length", 0) else torch.exp(logw) * x_mask
# JMa: length scale for the given sentence-like input # JMa: length scale for the given sentence-like input
length_scale = aux_input["length_scale"] if aux_input["length_scale"] else self.length_scale length_scale = aux_input["length_scale"] if aux_input["length_scale"] else self.length_scale
w *= length_scale w *= length_scale

10
TTS/tts/utils/synthesis.py
View File

@ -30,7 +30,7 @@ def run_model_torch(
style_text: str = None, style_text: str = None,
d_vector: torch.Tensor = None, d_vector: torch.Tensor = None,
language_id: torch.Tensor = None, language_id: torch.Tensor = None,
aux_input: Dict = {"durations": None, "length_scale": None, "min_input_length": 0}, aux_input: Dict = {},
) -> Dict: ) -> Dict:
"""Run a torch model for inference. It does not support batch inference. """Run a torch model for inference. It does not support batch inference.
@ -49,9 +49,9 @@ def run_model_torch(
_func = model.module.inference _func = model.module.inference
else: else:
_func = model.inference _func = model.inference
# JMa: propagate `durations``, `length_scale``, and `min_input_length` to `aux_input` # JMa: propagate other inputs like `durations``, `length_scale``, and `min_input_length`
# to enable changing length (durations) per each input text (sentence) and to set # to `aux_input` to enable changing length (durations) per each input text (sentence)
# minimum allowed length of each input char/phoneme # and to set minimum allowed length of each input char/phoneme
# - `length_scale` changes length of the whole generated wav # - `length_scale` changes length of the whole generated wav
# - `durations` sets up duration (in frames) for each input text ID # - `durations` sets up duration (in frames) for each input text ID
# - minimum allowed length (in frames) per input ID (char/phoneme) during inference # - minimum allowed length (in frames) per input ID (char/phoneme) during inference
@ -114,7 +114,7 @@ def apply_griffin_lim(inputs, input_lens, CONFIG, ap):
return wavs return wavs
# JMa: add `aux_input` to enable extra input (length_scale, durations) # JMa: add `aux_input` to enable extra input (like length_scale, durations)
def synthesis( def synthesis(
model, model,
text, text,