Merge pull request #37 from lucasnewman/null-cond-no-grad

Disable gradients for null conditioning when CFG is enabled

voicebox_pytorch/voicebox_pytorch.py

@@ -137,12 +137,12 @@ def mask_from_frac_lengths(
     seq_len: int,
     frac_lengths: Tensor
 ):
-    device = frac_lengths
+    device = frac_lengths.device
 
     lengths = (frac_lengths * seq_len).long()
     max_start = seq_len - lengths
 
-    rand = torch.zeros_like(frac_lengths).float().uniform_(0, 1)
+    rand = torch.zeros_like(frac_lengths, device = device).float().uniform_(0, 1)
     start = (max_start * rand).clamp(min = 0)
     end = start + lengths
 
@@ -562,9 +562,13 @@ def encode(self, audio):
         encoded_audio, _, _ = self.encodec(audio, return_encoded = True)
         return encoded_audio
 
-    def decode(self, latents):
+    def decode_to_codes(self, latents):
         _, codes, _ = self.encodec.rq(latents)
         codes = rearrange(codes, 'b n q -> b q n')
+        return codes
+
+    def decode(self, latents):
+        codes = self.decode_to_codes(latents)
 
         all_audios = []
         for code in codes:
@@ -631,7 +635,7 @@ def __init__(
 
         self.to_embed = nn.Linear(dim + dim_phoneme_emb, dim)
 
-        self.null_cond = nn.Parameter(torch.zeros(dim))
+        self.null_cond = nn.Parameter(torch.zeros(dim), requires_grad = False)
 
         self.conv_embed = ConvPositionEmbed(
             dim = dim,
@@ -835,7 +839,7 @@ def forward(
         else:
             loss_mask = self_attn_mask
 
-        if not exists(mask):
+        if not exists(loss_mask):
             return F.l1_loss(x, target)
 
         loss = F.l1_loss(x, target, reduction = 'none')
@@ -848,7 +852,7 @@ def forward(
         loss = num / den
         loss = loss.mean()
 
-        if not should_align:
+        if not return_aligned_phoneme_ids:
             return loss
 
         #aligner loss
@@ -920,7 +924,7 @@ def __init__(
 
         self.to_embed = nn.Linear(dim_in * 2 + dim_cond_emb, dim)
 
-        self.null_cond = nn.Parameter(torch.zeros(dim_in))
+        self.null_cond = nn.Parameter(torch.zeros(dim_in), requires_grad = False)
 
         self.conv_embed = ConvPositionEmbed(
             dim = dim,
@@ -1174,6 +1178,7 @@ def sample(
         steps = 3,
         cond_scale = 1.,
         decode_to_audio = True,
+        decode_to_codes = False,
         max_semantic_token_ids = 2048,
         spec_decode = False,
         spec_decode_gamma = 5 # could be higher, since speech is probably easier than text, needs to be tested
@@ -1311,6 +1316,9 @@ def fn(t, x, *, packed_shape = None):
             sampled = sol.ys[:, -1]
             sampled = unpack_one(sampled, packed_shape, 'b *')
 
+        if decode_to_codes and exists(self.voicebox.audio_enc_dec):
+            return self.voicebox.audio_enc_dec.decode_to_codes(sampled)
+
         if not decode_to_audio or not exists(self.voicebox.audio_enc_dec):
             return sampled