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(maybe) fixed an issue with using prompt redactions (emotions) on CPU causing a crash, because for some reason the wav2vec_alignment assumed CUDA was always available

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mrq 2023-02-07 07:51:05 -06:00
parent d6b5d67f79
commit 5f934c5feb
3 changed files with 21 additions and 9 deletions
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5
app.py
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@ -181,7 +181,7 @@ def generate(text, delimiter, emotion, prompt, voice, mic_audio, seed, candidate
if sample_voice is not None:
sample_voice = (22050, sample_voice.squeeze().cpu().numpy())
print(f"Saved to '{outdir}'")
print(f"Generation took {info['time']} seconds, saved to '{outdir}'\n")
info['seed'] = settings['use_deterministic_seed']
del info['latents']
@ -332,9 +332,6 @@ def export_exec_settings( share, check_for_updates, low_vram, cond_latent_max_ch
def main():
if not torch.cuda.is_available():
print("CUDA is NOT available for use.")
with gr.Blocks() as webui:
with gr.Tab("Generate"):
with gr.Row():

12
tortoise/api.py
View File

@ -226,13 +226,21 @@ class TextToSpeech:
Default is true.
:param device: Device to use when running the model. If omitted, the device will be automatically chosen.
"""
if not torch.cuda.is_available():
print("CUDA is NOT available for use.")
# minor_optimizations = False
# enable_redaction = False
if device is None:
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.minor_optimizations = minor_optimizations
self.models_dir = models_dir
self.autoregressive_batch_size = pick_best_batch_size_for_gpu() if autoregressive_batch_size is None or autoregressive_batch_size == 0 else autoregressive_batch_size
self.enable_redaction = enable_redaction
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.device = device
if self.enable_redaction:
self.aligner = Wav2VecAlignment()
self.aligner = Wav2VecAlignment(device=self.device)
self.tokenizer = VoiceBpeTokenizer()

13
tortoise/utils/wav2vec_alignment.py
View File

@ -49,7 +49,10 @@ class Wav2VecAlignment:
"""
Uses wav2vec2 to perform audio<->text alignment.
"""
def __init__(self, device='cuda'):
def __init__(self, device=None):
if device is None:
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.model = Wav2Vec2ForCTC.from_pretrained("jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli").cpu()
self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"facebook/wav2vec2-large-960h")
self.tokenizer = Wav2Vec2CTCTokenizer.from_pretrained('jbetker/tacotron-symbols')
@ -59,12 +62,16 @@ class Wav2VecAlignment:
orig_len = audio.shape[-1]
with torch.no_grad():
self.model = self.model.to(self.device)
if torch.cuda.is_available(): # This is unneccessary technically, but it's a placebo
self.model = self.model.to(self.device)
audio = audio.to(self.device)
audio = torchaudio.functional.resample(audio, audio_sample_rate, 16000)
clip_norm = (audio - audio.mean()) / torch.sqrt(audio.var() + 1e-7)
logits = self.model(clip_norm).logits
self.model = self.model.cpu()
if torch.cuda.is_available():
self.model = self.model.cpu()
logits = logits[0]
pred_string = self.tokenizer.decode(logits.argmax(-1).tolist())