Convert CLIP models into new folder

codes/data/audio/fast_paired_dataset.py

@@ -58,6 +58,7 @@ class FastPairedVoiceDataset(torch.utils.data.Dataset):
         self.sample_rate = hparams.sample_rate
         self.aligned_codes_to_audio_ratio = 443 * self.sample_rate // 22050
         self.max_wav_len = opt_get(hparams, ['max_wav_length'], None)
+        self.load_aligned_codes = opt_get(hparams, ['load_aligned_codes'], False)
         if self.max_wav_len is not None:
             self.max_aligned_codes = self.max_wav_len // self.aligned_codes_to_audio_ratio
         self.max_text_len = opt_get(hparams, ['max_text_length'], None)
@@ -198,8 +199,6 @@ class FastPairedVoiceDataset(torch.utils.data.Dataset):
         res = {
             'real_text': text,
             'padded_text': tseq,
-            'aligned_codes': aligned_codes,
-            'aligned_codes_lengths': orig_aligned_code_length,
             'text_lengths': torch.tensor(orig_text_len, dtype=torch.long),
             'wav': wav,
             'wav_lengths': torch.tensor(orig_output, dtype=torch.long),
@@ -210,6 +209,9 @@ class FastPairedVoiceDataset(torch.utils.data.Dataset):
         if self.load_conditioning:
             res['conditioning'] = cond
             res['conditioning_contains_self'] = cond_is_self
+        if self.load_aligned_codes:
+            res['aligned_codes']: aligned_codes
+            res['aligned_codes_lengths']: orig_aligned_code_length
         if self.produce_ctc_metadata:
             res.update(self.get_ctc_metadata(raw_codes))
 

codes/models/clip/mel_text_clip.py

@@ -0,0 +1,130 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from torch import einsum
+
+from models.lucidrains.dalle.transformer import Transformer
+from trainer.networks import register_model
+from utils.util import opt_get
+
+
+def exists(val):
+    return val is not None
+
+
+def masked_mean(t, mask, dim = 1):
+    t = t.masked_fill(~mask[:, :, None], 0.)
+    return t.sum(dim = 1) / mask.sum(dim = 1)[..., None]
+
+
+class MelTextCLIP(nn.Module):
+    """
+    CLIP model retrofitted for performing contrastive evaluation between MEL data and the corresponding
+    transcribed text.
+
+    Originally from https://github.com/lucidrains/DALLE-pytorch/blob/main/dalle_pytorch/dalle_pytorch.py
+    """
+
+    def __init__(
+            self,
+            *,
+            dim_text=512,
+            dim_speech=512,
+            dim_latent=512,
+            num_text_tokens=256,
+            text_enc_depth=6,
+            text_seq_len=120,
+            text_heads=8,
+            num_speech_tokens=8192,
+            speech_enc_depth=6,
+            speech_heads=8,
+            speech_seq_len=250,
+            text_mask_percentage=0,
+            voice_mask_percentage=0,
+            mel_compression=256,
+    ):
+        super().__init__()
+        self.text_emb = nn.Embedding(num_text_tokens, dim_text)
+        self.text_pos_emb = nn.Embedding(text_seq_len, dim_text)
+        self.text_transformer = Transformer(causal=False, seq_len=text_seq_len, dim=dim_text, depth=text_enc_depth,
+                                            heads=text_heads, rotary_emb=False)
+        self.to_text_latent = nn.Linear(dim_text, dim_latent, bias=False)
+
+        self.speech_enc = nn.Conv1d(80, dim_speech, kernel_size=3, padding=1)
+        self.speech_pos_emb = nn.Embedding(num_speech_tokens, dim_speech)
+        self.speech_transformer = Transformer(causal=False, seq_len=speech_seq_len, dim=dim_speech,
+                                              depth=speech_enc_depth, heads=speech_heads, rotary_emb=False)
+        self.to_speech_latent = nn.Linear(dim_speech, dim_latent, bias=False)
+
+        self.temperature = nn.Parameter(torch.tensor(1.))
+        self.text_mask_percentage = text_mask_percentage
+        self.voice_mask_percentage = voice_mask_percentage
+        self.mel_compression = mel_compression
+
+    def forward(
+            self,
+            text,
+            text_lengths,
+            mel,
+            wav_lengths,
+            return_loss=False
+    ):
+        # 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 = text[:, :max_text_len]
+        max_mel_len = wav_lengths.max() // self.mel_compression
+        mel = mel[:, :, :max_mel_len]
+
+        b, device = text.shape[0], text.device
+        if self.training:
+            text_mask = torch.rand_like(text.float()) > self.text_mask_percentage
+            voice_mask = torch.rand_like(mel[:,0,:].float()) > self.voice_mask_percentage
+        else:
+            text_mask = torch.ones_like(text.float()).bool()
+            voice_mask = torch.ones_like(mel[:,0,:].float()).bool()
+
+        text_emb = self.text_emb(text)
+        text_emb += self.text_pos_emb(torch.arange(text.shape[1], device=device))
+
+        speech_emb = self.speech_enc(mel).permute(0,2,1)
+        speech_emb += self.speech_pos_emb(torch.arange(speech_emb.shape[1], device=device))
+
+        # Only autocast the transformer part. The MEL encoder loses accuracy if you autcast it.
+        with torch.autocast(speech_emb.device.type):
+            enc_text = self.text_transformer(text_emb, mask=text_mask)
+            enc_speech = self.speech_transformer(speech_emb, mask=voice_mask)
+
+            text_latents = masked_mean(enc_text, text_mask, dim=1)
+            speech_latents = masked_mean(enc_speech, voice_mask, dim=1)
+
+        text_latents = self.to_text_latent(text_latents).float()
+        speech_latents = self.to_speech_latent(speech_latents).float()
+
+        text_latents, speech_latents = map(lambda t: F.normalize(t, p=2, dim=-1), (text_latents, speech_latents))
+
+        temp = self.temperature.exp()
+
+        if not return_loss:
+            sim = einsum('n d, n d -> n', text_latents, speech_latents) * temp
+            return sim
+
+        sim = einsum('i d, j d -> i j', text_latents, speech_latents) * temp
+        labels = torch.arange(b, device=device)
+        loss = (F.cross_entropy(sim, labels) + F.cross_entropy(sim.t(), labels)) / 2
+        return loss
+
+
+@register_model
+def register_mel_text_clip(opt_net, opt):
+    return MelTextCLIP(**opt_get(opt_net, ['kwargs'], {}))
+
+
+if __name__ == '__main__':
+    clip = MelTextCLIP(text_mask_percentage=.2, voice_mask_percentage=.2)
+    clip(torch.randint(0,256,(2,120)),
+         torch.tensor([50,100]),
+         torch.randn(2,80,400),
+         torch.tensor([10100,10200]),
+         return_loss=True)

codes/train.py

@@ -316,7 +316,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_wav2vec_mass_initial_annealing.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_clip_text_to_voice.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
     args = parser.parse_args()

codes/trainer/steps.py

@@ -117,12 +117,7 @@ class ConfigurableStep(Module):
             params_names_weights = sorted(list(all_param_names ^ param_names_notweights))
             params_weights = [param_map[k] for k in params_names_weights]
 
-            if 'optimizer' not in self.step_opt.keys() or self.step_opt['optimizer'] == 'adam':
-                opt = torch.optim.Adam(list(optim_params.values()), lr=opt_config['lr'],
-                                       weight_decay=opt_config['weight_decay'],
-                                       betas=(opt_config['beta1'], opt_config['beta2']))
-                opt._group_names = sorted(list(all_param_names))
-            elif self.step_opt['optimizer'] == 'adamw':
+            if 'optimizer' not in self.step_opt.keys() or self.step_opt['optimizer'] == 'adamw':
                 groups = [
                     { 'params': params_weights, 'weight_decay': opt_get(opt_config, ['weight_decay'], 0) },
                     { 'params': params_notweights, 'weight_decay': 0 }