removed the erroneous ‘’continual'' implementation (#1865)

Author: JinZr

egs/wenetspeech4tts/TTS/valle/infer.py

@@ -118,13 +118,6 @@ def get_args():
         help="The temperature of AR Decoder top_k sampling.",
     )
 
-    parser.add_argument(
-        "--continual",
-        type=str2bool,
-        default=False,
-        help="Do continual task.",
-    )
-
     parser.add_argument(
         "--repetition-aware-sampling",
         type=str2bool,
@@ -262,29 +255,21 @@ def main():
         )
 
         # synthesis
-        if args.continual:
-            assert text == ""
-            encoded_frames = model.continual(
-                text_tokens.to(device),
-                text_tokens_lens.to(device),
-                audio_prompts,
-            )
-        else:
-            enroll_x_lens = None
-            if text_prompts:
-                _, enroll_x_lens = text_collater(
-                    [tokenize_text(text_tokenizer, text=f"{text_prompts}".strip())]
-                )
-            encoded_frames = model.inference(
-                text_tokens.to(device),
-                text_tokens_lens.to(device),
-                audio_prompts,
-                enroll_x_lens=enroll_x_lens,
-                top_k=args.top_k,
-                temperature=args.temperature,
-                top_p=args.top_p,
-                ras=args.repetition_aware_sampling,
+        enroll_x_lens = None
+        if text_prompts:
+            _, enroll_x_lens = text_collater(
+                [tokenize_text(text_tokenizer, text=f"{text_prompts}".strip())]
             )
+        encoded_frames = model.inference(
+            text_tokens.to(device),
+            text_tokens_lens.to(device),
+            audio_prompts,
+            enroll_x_lens=enroll_x_lens,
+            top_k=args.top_k,
+            temperature=args.temperature,
+            top_p=args.top_p,
+            ras=args.repetition_aware_sampling,
+        )
 
         if audio_prompts != []:
             samples = audio_tokenizer.decode([(encoded_frames.transpose(2, 1), None)])

egs/wenetspeech4tts/TTS/valle/valle.py

@@ -1564,103 +1564,6 @@ def inference(
         assert len(codes) == self.num_quantizers
         return torch.stack(codes, dim=-1)
 
-    def continual(
-        self,
-        x: torch.Tensor,
-        x_lens: torch.Tensor,
-        y: torch.Tensor,
-    ) -> torch.Tensor:
-        """
-        Args:
-          x:
-            A 2-D tensor of shape (1, S).
-          x_lens:
-            A 1-D tensor of shape (1,). It contains the number of tokens in `x`
-            before padding.
-          y:
-            A 3-D tensor of shape (1, T, 8).
-        Returns:
-          Return the predicted audio code matrix.
-        """
-        assert x.ndim == 2, x.shape
-        assert x_lens.ndim == 1, x_lens.shape
-        assert y.ndim == 3, y.shape
-        assert y.shape[0] == 1, y.shape
-
-        assert torch.all(x_lens > 0)
-        assert self.num_quantizers == 8
-
-        # NOTE: x has been padded in TextTokenCollater
-        text = x
-        x = self.ar_text_embedding(text)
-        x = self.ar_text_prenet(x)
-        x = self.ar_text_position(x)
-
-        text_len = x_lens.max()
-
-        prefix_len = min(int(y.shape[1] * 0.5), 3 * 75)
-
-        # AR Decoder
-        prompts = y[:, :prefix_len]
-
-        codes = [y[:, prefix_len:, 0]]
-        # Non-AR Decoders
-        x = self.nar_text_embedding(text)
-        x = self.nar_text_prenet(x)
-        x = self.nar_text_position(x)
-
-        y_emb = self.nar_audio_embeddings[0](y[..., 0])
-
-        if self.prefix_mode == 0:
-            for i, (predict_layer, embedding_layer) in enumerate(
-                zip(
-                    self.nar_predict_layers,
-                    self.nar_audio_embeddings[1:],
-                )
-            ):
-                y_pos = self.nar_audio_position(y_emb)
-                y_pos = self.nar_audio_prenet(y_pos)
-                xy_pos = torch.concat([x, y_pos], dim=1)
-
-                xy_dec, _ = self.nar_decoder(
-                    (xy_pos, self.nar_stage_embeddings[i].weight)
-                )
-                logits = predict_layer(xy_dec[:, text_len + prefix_len :])
-
-                samples = torch.argmax(logits, dim=-1)
-                codes.append(samples)
-
-                if i < 6:
-                    y_emb[:, :prefix_len] += embedding_layer(prompts[..., i + 1])
-                    y_emb[:, prefix_len:] += embedding_layer(samples)
-        else:
-            for j in range(1, 8):
-                y_emb[:, :prefix_len] += self.nar_audio_embeddings[j](prompts[..., j])
-
-            for i, (predict_layer, embedding_layer) in enumerate(
-                zip(
-                    self.nar_predict_layers,
-                    self.nar_audio_embeddings[1:],
-                )
-            ):
-                y_pos = self.nar_audio_prenet(y_emb)
-                y_pos = self.nar_audio_position(y_pos)
-                xy_pos = torch.concat([x, y_pos], dim=1)
-
-                xy_dec, _ = self.nar_decoder(
-                    (xy_pos, self.nar_stage_embeddings[i].weight)
-                )
-                logits = predict_layer(xy_dec[:, text_len + prefix_len :])
-
-                samples = torch.argmax(logits, dim=-1)
-                codes.append(samples)
-
-                if i < 6:
-                    y_emb[:, prefix_len:] += embedding_layer(samples)
-
-        assert len(codes) == 8
-        return torch.stack(codes, dim=-1)
-
     def visualize(
         self,
         predicts: Tuple[torch.Tensor],