Try length parameter

app.py

@@ -55,7 +55,7 @@ class Tango:
     def generate(self, prompt, steps = 100, guidance = 3, samples = 1, disable_progress = True):
         # Generate audio for a single prompt string
         with torch.no_grad():
-            latents = self.model.inference([prompt], self.scheduler, steps, guidance, samples, disable_progress = disable_progress)
+            latents = self.model.inference([prompt], self.scheduler, steps, guidance, samples, disable_progress = disable_progress, length = 20)
             mel = self.vae.decode_first_stage(latents)
             wave = self.vae.decode_to_waveform(mel)
         return wave
@@ -66,7 +66,7 @@ class Tango:
         for k in tqdm(range(0, len(prompts), batch_size)):
             batch = prompts[k: k + batch_size]
             with torch.no_grad():
-                latents = self.model.inference(batch, self.scheduler, steps, guidance, samples, disable_progress = disable_progress, length = 20)
+                latents = self.model.inference(batch, self.scheduler, steps, guidance, samples, disable_progress = disable_progress)
                 mel = self.vae.decode_first_stage(latents)
                 wave = self.vae.decode_to_waveform(mel)
                 outputs += [item for item in wave]