diff --git a/requirements.txt b/requirements.txt
index 594507f..c96ba68 100755
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,10 +4,10 @@ transformers==4.19
 tokenizers
 inflect
 progressbar
-einops==0.6.0
+einops
 unidecode
 scipy
-librosa==0.8.0
+librosa==0.8.1
 torchaudio
 threadpoolctl
 appdirs
diff --git a/tortoise/api.py b/tortoise/api.py
index 2df47ea..f1a60f2 100755
--- a/tortoise/api.py
+++ b/tortoise/api.py
@@ -442,19 +442,7 @@ class TextToSpeech:
                 beta=8.555504641634386,
             ).to(device)
 
-            samples = []
-            auto_conds = []
-            for sample in voice_samples:
-                auto_conds.append(format_conditioning(sample, device=device, sampling_rate=self.input_sample_rate))
-                samples.append(resampler(sample)) 
-
-            auto_conds = torch.stack(auto_conds, dim=1)
-
-            self.autoregressive = migrate_to_device( self.autoregressive, device )
-            auto_latent = self.autoregressive.get_conditioning(auto_conds)
-            self.autoregressive = migrate_to_device( self.autoregressive, self.device if self.preloaded_tensors else 'cpu' )
-            
-            diffusion_conds = []
+            samples = [resampler(sample) for sample in voice_samples]
             chunks = []
 
             concat = torch.cat(samples, dim=-1)
@@ -469,15 +457,22 @@ class TextToSpeech:
 
             chunks = torch.chunk(concat, slices, dim=1)
             chunk_size = chunks[0].shape[-1]
+
+            auto_conds = []
+            for chunk in tqdm_override(chunks, verbose=verbose, progress=progress, desc="Computing AR conditioning latents..."):
+                auto_conds.append(format_conditioning(chunk, device=device, sampling_rate=self.input_sample_rate, cond_length=chunk_size))
+            auto_conds = torch.stack(auto_conds, dim=1)
+
+            self.autoregressive = migrate_to_device( self.autoregressive, device )
+            auto_latent = self.autoregressive.get_conditioning(auto_conds)
+            self.autoregressive = migrate_to_device( self.autoregressive, self.device if self.preloaded_tensors else 'cpu' )
             
-            # expand / truncate samples to match the common size
-            # required, as tensors need to be of the same length
-            for chunk in tqdm_override(chunks, verbose=verbose, progress=progress, desc="Computing conditioning latents..."):
+            diffusion_conds = []
+            for chunk in tqdm_override(chunks, verbose=verbose, progress=progress, desc="Computing diffusion conditioning latents..."):
                 check_for_kill_signal()
                 chunk = pad_or_truncate(chunk, chunk_size)
                 cond_mel = wav_to_univnet_mel(migrate_to_device( chunk, device ), do_normalization=False, device=device)
                 diffusion_conds.append(cond_mel)
-
             diffusion_conds = torch.stack(diffusion_conds, dim=1)
 
             self.diffusion = migrate_to_device( self.diffusion, device )