mrq/tortoise-tts
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option to decouple sample batch size from CLVP candidate selection size (currently just unsqueezes the batches)

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mrq 2023-03-21 21:33:46 +00:00
parent d1ad634ea9
commit 0bcdf81d04
1 changed files with 10 additions and 2 deletions
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12
tortoise/api.py
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@ -267,8 +267,9 @@ class TextToSpeech:
def __init__(self, autoregressive_batch_size=None, models_dir=MODELS_DIR, enable_redaction=True, device=None, def __init__(self, autoregressive_batch_size=None, models_dir=MODELS_DIR, enable_redaction=True, device=None,
minor_optimizations=True, minor_optimizations=True,
unsqueeze_sample_batches=False,
input_sample_rate=22050, output_sample_rate=24000, input_sample_rate=22050, output_sample_rate=24000,
autoregressive_model_path=None, diffusion_model_path=None, vocoder_model=None, tokenizer_json=None autoregressive_model_path=None, diffusion_model_path=None, vocoder_model=None, tokenizer_json=None,
): ):
""" """
Constructor Constructor
@ -289,6 +290,7 @@ class TextToSpeech:
self.input_sample_rate = input_sample_rate self.input_sample_rate = input_sample_rate
self.output_sample_rate = output_sample_rate self.output_sample_rate = output_sample_rate
self.minor_optimizations = minor_optimizations self.minor_optimizations = minor_optimizations
self.unsqueeze_sample_batches = unsqueeze_sample_batches
# for clarity, it's simpler to split these up and just predicate them on requesting VRAM-consuming optimizations # for clarity, it's simpler to split these up and just predicate them on requesting VRAM-consuming optimizations
self.preloaded_tensors = minor_optimizations self.preloaded_tensors = minor_optimizations
@ -697,8 +699,14 @@ class TextToSpeech:
if not self.preloaded_tensors: if not self.preloaded_tensors:
self.autoregressive = migrate_to_device( self.autoregressive, 'cpu' ) self.autoregressive = migrate_to_device( self.autoregressive, 'cpu' )
clip_results = [] if self.unsqueeze_sample_batches:
new_samples = []
for batch in samples:
for i in range(batch.shape[0]):
new_samples.append(batch[i].unsqueeze(0))
samples = new_samples
clip_results = []
if auto_conds is not None: if auto_conds is not None:
auto_conditioning = migrate_to_device( auto_conditioning, self.device ) auto_conditioning = migrate_to_device( auto_conditioning, self.device )