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Shamelessly nabbed from ae80992817 (if this is makes a big enough difference in training i'm going to cum)

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mrq 2023-03-09 03:39:23 +00:00
parent 0ee0f46596
commit 84c8196da5
1 changed files with 9 additions and 3 deletions
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12
codes/models/audio/tts/unified_voice2.py
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@ -243,7 +243,8 @@ class UnifiedVoice(nn.Module):
mel_length_compression=1024, number_text_tokens=256, mel_length_compression=1024, number_text_tokens=256,
start_text_token=255, stop_text_token=0, number_mel_codes=8194, start_mel_token=8192, start_text_token=255, stop_text_token=0, number_mel_codes=8194, start_mel_token=8192,
stop_mel_token=8193, train_solo_embeddings=False, use_mel_codes_as_input=True, stop_mel_token=8193, train_solo_embeddings=False, use_mel_codes_as_input=True,
checkpointing=True, average_conditioning_embeddings=False, freeze_everything_but_position_embeddings=False): checkpointing=True, average_conditioning_embeddings=False, freeze_everything_but_position_embeddings=False,
tortoise_compat=True):
""" """
Args: Args:
layers: Number of layers in transformer stack. layers: Number of layers in transformer stack.
@ -281,6 +282,7 @@ class UnifiedVoice(nn.Module):
self.mel_length_compression = mel_length_compression self.mel_length_compression = mel_length_compression
self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads) self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads)
self.average_conditioning_embeddings = average_conditioning_embeddings self.average_conditioning_embeddings = average_conditioning_embeddings
self.tortoise_compat = tortoise_compat # credit to https://github.com/152334H/DL-Art-School/commit/ae80992817059acf6eef38a680efa5124cee570b
# nn.Embedding # nn.Embedding
self.text_embedding = ml.Embedding(self.number_text_tokens, model_dim) self.text_embedding = ml.Embedding(self.number_text_tokens, model_dim)
if use_mel_codes_as_input: if use_mel_codes_as_input:
@ -301,6 +303,7 @@ class UnifiedVoice(nn.Module):
self.text_head = ml.Linear(model_dim, self.number_text_tokens) self.text_head = ml.Linear(model_dim, self.number_text_tokens)
self.mel_head = ml.Linear(model_dim, self.number_mel_codes) self.mel_head = ml.Linear(model_dim, self.number_mel_codes)
# Initialize the embeddings per the GPT-2 scheme # Initialize the embeddings per the GPT-2 scheme
embeddings = [self.text_embedding] embeddings = [self.text_embedding]
if use_mel_codes_as_input: if use_mel_codes_as_input:
@ -386,6 +389,8 @@ class UnifiedVoice(nn.Module):
If return_attentions is specified, only logits are returned. If return_attentions is specified, only logits are returned.
If return_latent is specified, loss & logits are not computed or returned. Only the predicted latents are returned. If return_latent is specified, loss & logits are not computed or returned. Only the predicted latents are returned.
""" """
if self.tortoise_compat:
wav_lengths *= self.mel_length_compression
# This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by # 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. # chopping the inputs by the maximum actual length.
max_text_len = text_lengths.max() max_text_len = text_lengths.max()
@ -414,14 +419,15 @@ class UnifiedVoice(nn.Module):
mel_emb = self.mel_embedding(mel_inp) mel_emb = self.mel_embedding(mel_inp)
mel_emb = mel_emb + self.mel_pos_embedding(mel_codes) mel_emb = mel_emb + self.mel_pos_embedding(mel_codes)
sub = -2 if self.tortoise_compat else -1
if text_first: if text_first:
text_logits, mel_logits = self.get_logits(conds, text_emb, self.text_head, mel_emb, self.mel_head, get_attns=return_attentions, return_latent=return_latent) text_logits, mel_logits = self.get_logits(conds, text_emb, self.text_head, mel_emb, self.mel_head, get_attns=return_attentions, return_latent=return_latent)
if return_latent: if return_latent:
return mel_logits[:, :-1] # Despite the name, these are not logits. return mel_logits[:, :-sub] # Despite the name, these are not logits.
else: else:
mel_logits, text_logits = self.get_logits(conds, mel_emb, self.mel_head, text_emb, self.text_head, get_attns=return_attentions, return_latent=return_latent) mel_logits, text_logits = self.get_logits(conds, mel_emb, self.mel_head, text_emb, self.text_head, get_attns=return_attentions, return_latent=return_latent)
if return_latent: if return_latent:
return text_logits[:, :-1] # Despite the name, these are not logits return text_logits[:, :-sub] # Despite the name, these are not logits
if return_attentions: if return_attentions:
return mel_logits return mel_logits