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more fixes

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This commit is contained in:
James Betker 2022-05-02 16:44:47 -06:00
parent 4836e1f792
commit ccf16f978e
4 changed files with 20 additions and 79 deletions
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15
tortoise/api.py
View File

@ -194,8 +194,7 @@ class TextToSpeech:
self.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30,
model_dim=1024,
heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False,
train_solo_embeddings=False,
average_conditioning_embeddings=True).cpu().eval()
train_solo_embeddings=False).cpu().eval()
self.autoregressive.load_state_dict(torch.load(f'{models_dir}/autoregressive.pth'))
self.diffusion = DiffusionTts(model_channels=1024, num_layers=10, in_channels=100, out_channels=200,
@ -244,7 +243,7 @@ class TextToSpeech:
kwargs.update(presets[preset])
return self.tts(text, **kwargs)
def get_conditioning_latents(self, voice_samples):
def get_conditioning_latents(self, voice_samples, return_mels=False):
"""
Transforms one or more voice_samples into a tuple (autoregressive_conditioning_latent, diffusion_conditioning_latent).
These are expressive learned latents that encode aspects of the provided clips like voice, intonation, and acoustic
@ -268,7 +267,7 @@ class TextToSpeech:
# The diffuser operates at a sample rate of 24000 (except for the latent inputs)
sample = torchaudio.functional.resample(sample, 22050, 24000)
sample = pad_or_truncate(sample, 102400)
cond_mel = wav_to_univnet_mel(sample.to(voice_samples.device), do_normalization=False)
cond_mel = wav_to_univnet_mel(sample.to('cuda'), do_normalization=False)
diffusion_conds.append(cond_mel)
diffusion_conds = torch.stack(diffusion_conds, dim=1)
@ -276,7 +275,10 @@ class TextToSpeech:
diffusion_latent = self.diffusion.get_conditioning(diffusion_conds)
self.diffusion = self.diffusion.cpu()
return auto_latent, diffusion_latent, auto_conds
if return_mels:
return auto_latent, diffusion_latent, auto_conds, diffusion_conds
else:
return auto_latent, diffusion_latent
def get_random_conditioning_latents(self):
# Lazy-load the RLG models.
@ -295,7 +297,6 @@ class TextToSpeech:
def tts(self, text, voice_samples=None, conditioning_latents=None, k=1, verbose=True,
# autoregressive generation parameters follow
num_autoregressive_samples=512, temperature=.8, length_penalty=1, repetition_penalty=2.0, top_p=.8, max_mel_tokens=500,
typical_sampling=False, typical_mass=.9,
# CLVP & CVVP parameters
clvp_cvvp_slider=.5,
# diffusion generation parameters follow
@ -354,7 +355,7 @@ class TextToSpeech:
auto_conds = None
if voice_samples is not None:
auto_conditioning, diffusion_conditioning, auto_conds = self.get_conditioning_latents(voice_samples)
auto_conditioning, diffusion_conditioning, auto_conds, _ = self.get_conditioning_latents(voice_samples, return_mels=True)
elif conditioning_latents is not None:
auto_conditioning, diffusion_conditioning = conditioning_latents
else:

4
tortoise/get_conditioning_latents.py
View File

@ -11,8 +11,8 @@ other ML models, or can be augmented manually and fed back into Tortoise to affe
"""
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--voice', type=str, help='Selects the voice to convert to conditioning latents', default='pat')
parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/conditioning_latents')
parser.add_argument('--voice', type=str, help='Selects the voice to convert to conditioning latents', default='pat2')
parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='../results/conditioning_latents')
args = parser.parse_args()
os.makedirs(args.output_path, exist_ok=True)

67
tortoise/models/autoregressive.py
View File

@ -280,8 +280,7 @@ class UnifiedVoice(nn.Module):
mel_length_compression=1024, number_text_tokens=256,
start_text_token=None, number_mel_codes=8194, start_mel_token=8192,
stop_mel_token=8193, train_solo_embeddings=False, use_mel_codes_as_input=True,
checkpointing=True, average_conditioning_embeddings=False,
types=1):
checkpointing=True, types=1):
"""
Args:
layers: Number of layers in transformer stack.
@ -300,7 +299,6 @@ class UnifiedVoice(nn.Module):
train_solo_embeddings:
use_mel_codes_as_input:
checkpointing:
average_conditioning_embeddings: Whether or not conditioning embeddings should be averaged, instead of fed piecewise into the model.
"""
super().__init__()
@ -318,7 +316,6 @@ class UnifiedVoice(nn.Module):
self.max_conditioning_inputs = max_conditioning_inputs
self.mel_length_compression = mel_length_compression
self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads)
self.average_conditioning_embeddings = average_conditioning_embeddings
self.text_embedding = nn.Embedding(self.number_text_tokens*types+1, model_dim)
if use_mel_codes_as_input:
self.mel_embedding = nn.Embedding(self.number_mel_codes, model_dim)
@ -397,8 +394,7 @@ class UnifiedVoice(nn.Module):
for j in range(speech_conditioning_input.shape[1]):
conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
conds = torch.stack(conds, dim=1)
if self.average_conditioning_embeddings:
conds = conds.mean(dim=1).unsqueeze(1)
conds = conds.mean(dim=1)
return conds
def forward(self, speech_conditioning_latent, text_inputs, text_lengths, mel_codes, wav_lengths, types=None, text_first=True, raw_mels=None, return_attentions=False,
@ -461,65 +457,6 @@ class UnifiedVoice(nn.Module):
loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
return loss_text.mean(), loss_mel.mean(), mel_logits
def text_forward(self, speech_conditioning_input, text_inputs, text_lengths):
"""
Performs autoregressive modeling on only text. Still requires a speech_conditioning_input due to the way the
model inputs are formatted. Just provide any audio clip (arguably, zeros could be provided).
"""
assert self.max_text_tokens >= text_inputs.shape[1], f'{text_inputs.shape[1]}'
# This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
# chopping the inputs by the maximum actual length.
max_text_len = text_lengths.max()
text_inputs = F.pad(text_inputs[:, :max_text_len], (0,1), value=self.stop_text_token)
speech_conditioning_input = speech_conditioning_input.unsqueeze(1) if len(speech_conditioning_input.shape) == 3 else speech_conditioning_input
conds = []
for j in range(speech_conditioning_input.shape[1]):
conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
conds = torch.stack(conds, dim=1)
if self.average_conditioning_embeddings:
conds = conds.mean(dim=1).unsqueeze(1)
text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs) + self.text_solo_embedding
text_logits = self.get_logits(conds, text_emb, self.text_head)
loss_text = F.cross_entropy(text_logits, text_targets.long())
return loss_text.mean()
def speech_forward(self, speech_conditioning_input, mel_codes, wav_lengths, raw_mels=None):
"""
Performs autoregressive modeling on only speech data.
"""
assert self.max_mel_tokens >= mel_codes.shape[1], f'{mel_codes.shape[1]}'
# This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
# chopping the inputs by the maximum actual length.
max_mel_len = wav_lengths.max() // self.mel_length_compression
mel_codes = F.pad(mel_codes[:, :max_mel_len], (0,1), value=self.stop_mel_token)
mel_codes = self.set_mel_padding(mel_codes, wav_lengths)
if raw_mels is not None:
raw_mels = raw_mels[:, :, :max_mel_len*4]
speech_conditioning_input = speech_conditioning_input.unsqueeze(1) if len(speech_conditioning_input.shape) == 3 else speech_conditioning_input
conds = []
for j in range(speech_conditioning_input.shape[1]):
conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
conds = torch.stack(conds, dim=1)
if self.average_conditioning_embeddings:
conds = conds.mean(dim=1).unsqueeze(1)
mel_codes, mel_targets = self.build_aligned_inputs_and_targets(mel_codes, self.start_mel_token, self.stop_mel_token)
if raw_mels is not None:
mel_inp = F.pad(raw_mels, (0, 4))
else:
mel_inp = mel_codes
mel_emb = self.mel_embedding(mel_inp)
mel_emb = mel_emb + self.mel_pos_embedding(mel_codes) + self.mel_solo_embedding
mel_logits = self.get_logits(conds, mel_emb, self.mel_head)
loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
return loss_mel.mean()
def inference_speech(self, speech_conditioning_latent, text_inputs, input_tokens=None, num_return_sequences=1,
max_generate_length=None, typical_sampling=False, typical_mass=.9, **hf_generate_kwargs):
seq_length = self.max_mel_tokens + self.max_text_tokens + 2

9
tortoise/utils/audio.py
View File

@ -87,7 +87,7 @@ def get_voices():
for sub in subs:
subj = os.path.join('voices', sub)
if os.path.isdir(subj):
voices[sub] = list(glob(f'{subj}/*.wav')) + list(glob(f'{subj}/*.mp3'))
voices[sub] = list(glob(f'{subj}/*.wav')) + list(glob(f'{subj}/*.mp3')) + list(glob(f'{subj}/*.pth'))
return voices
@ -111,6 +111,9 @@ def load_voices(voices):
latents = []
clips = []
for voice in voices:
if voice == 'random':
print("Cannot combine a random voice with a non-random voice. Just using a random voice.")
return None, None
latent, clip = load_voice(voice)
if latent is None:
assert len(latents) == 0, "Can only combine raw audio voices or latent voices, not both. Do it yourself if you want this."
@ -119,10 +122,10 @@ def load_voices(voices):
assert len(voices) == 0, "Can only combine raw audio voices or latent voices, not both. Do it yourself if you want this."
latents.append(latent)
if len(latents) == 0:
return clips
return clips, None
else:
latents = torch.stack(latents, dim=0)
return latents.mean(dim=0)
return None, latents.mean(dim=0)
class TacotronSTFT(torch.nn.Module):