[WIP] Add YuE model

src/transformers/models/yue/feature_extraction_yue.py

@@ -0,0 +1,122 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Feature extractor class for YuE."""
+
+import numpy as np
+
+from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
+from ...feature_extraction_utils import BatchFeature
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class YuEFeatureExtractor(SequenceFeatureExtractor):
+    model_input_names = ["input_values", "padding_mask"]
+
+    def __init__(
+        self,
+        feature_size=1,
+        sampling_rate=16000,
+        padding_value=0.0,
+        hop_length=320,
+        **kwargs,
+    ):
+        super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs)
+        self.hop_length = hop_length
+
+    def __call__(
+        self,
+        raw_audio,
+        padding=None,
+        truncation=False,
+        max_length=None,
+        return_tensors="pt",
+        sampling_rate=None,
+        pad_to_multiple_of=None,
+    ):
+        if sampling_rate is not None:
+            if sampling_rate != self.sampling_rate:
+                raise ValueError(
+                    f"Expected {self.sampling_rate} Hz audio but got {sampling_rate} Hz,"
+                    f"please make sure that the provided audio input was sampled with {self.sampling_rate}."
+                )
+
+        else:
+            logger.warning(
+                f"It is strongly recommended to pass the `sampling_rate` argument to `{self.__class__.__name__}()`. "
+                "Failing to do so can result in silent errors that might be hard to debug."
+            )
+
+        if padding and truncation:
+            raise ValueError("Both padding and truncation were set. Set only one.")
+
+        elif padding is None:
+            padding = True
+
+        is_batched = (
+            isinstance(raw_audio, (list, tuple))
+            and len(raw_audio) > 0
+            and isinstance(raw_audio[0], (np.ndarray, list, tuple))
+        )
+
+        if is_batched:
+            raw_audio = [np.asarray(_audio, dtype=np.float32) for _audio in raw_audio]
+
+        elif not isinstance(raw_audio, np.ndarray):
+            raw_audio = np.asarray(raw_audio, dtype=np.float32)
+
+        if not is_batched:
+            raw_audio = [raw_audio]
+
+        for i, audio in enumerate(raw_audio):
+            if audio.ndim > 2:
+                raise ValueError(f"Expected input shape (channels, length) but got shape {audio.shape}")
+
+            if self.feature_size == 1 and audio.ndim == 2:
+                logger.warning(
+                    "The model corresponding to this feature extractor expects a mono channel audio."
+                    "We're averaging the audio signals into mono."
+                )
+
+                audio = np.mean(audio, -1)
+
+            raw_audio[i] = audio
+
+        batch = BatchFeature({"input_values": raw_audio})
+
+        padded = self.pad(
+            batch,
+            max_length=max_length,
+            truncation=truncation,
+            padding=padding,
+            return_attention_mask=True,
+            pad_to_multiple_of=pad_to_multiple_of,
+        )
+
+        padded["padding_mask"] = padded.pop("attention_mask")
+
+        values = []
+
+        for example in padded.pop("input_values"):
+            example = np.asarray(example, dtype=np.float32)
+            values.append(example[None, :])
+        padded["input_values"] = values
+
+        if return_tensors is not None:
+            padded = padded.convert_to_tensors(return_tensors)
+
+        return padded

src/transformers/models/yue/modular_yue.py

@@ -0,0 +1,16 @@
+# coding=utf-8
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" YuE model."""
+