Track bundled vendor runtime sources

vendor/ComfyUI/comfy/audio_encoders/audio_encoders.py

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+from .wav2vec2 import Wav2Vec2Model
+from .whisper import WhisperLargeV3
+import comfy.model_management
+import comfy.ops
+import comfy.utils
+import logging
+import torchaudio
+
+
+class AudioEncoderModel():
+    def __init__(self, config):
+        self.load_device = comfy.model_management.text_encoder_device()
+        offload_device = comfy.model_management.text_encoder_offload_device()
+        self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
+        model_type = config.pop("model_type")
+        model_config = dict(config)
+        model_config.update({
+            "dtype": self.dtype,
+            "device": offload_device,
+            "operations": comfy.ops.manual_cast
+        })
+
+        if model_type == "wav2vec2":
+            self.model = Wav2Vec2Model(**model_config)
+        elif model_type == "whisper3":
+            self.model = WhisperLargeV3(**model_config)
+        self.model.eval()
+        self.patcher = comfy.model_patcher.CoreModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
+        self.model_sample_rate = 16000
+        comfy.model_management.archive_model_dtypes(self.model)
+
+    def load_sd(self, sd):
+        return self.model.load_state_dict(sd, strict=False, assign=self.patcher.is_dynamic())
+
+    def get_sd(self):
+        return self.model.state_dict()
+
+    def encode_audio(self, audio, sample_rate):
+        comfy.model_management.load_model_gpu(self.patcher)
+        audio = torchaudio.functional.resample(audio, sample_rate, self.model_sample_rate)
+        out, all_layers = self.model(audio.to(self.load_device))
+        outputs = {}
+        outputs["encoded_audio"] = out
+        outputs["encoded_audio_all_layers"] = all_layers
+        outputs["audio_samples"] = audio.shape[2]
+        return outputs
+
+
+def load_audio_encoder_from_sd(sd, prefix=""):
+    sd = comfy.utils.state_dict_prefix_replace(sd, {"wav2vec2.": ""})
+    if "encoder.layer_norm.bias" in sd: #wav2vec2
+        embed_dim = sd["encoder.layer_norm.bias"].shape[0]
+        if embed_dim == 1024:# large
+            config = {
+                "model_type": "wav2vec2",
+                "embed_dim": 1024,
+                "num_heads": 16,
+                "num_layers": 24,
+                "conv_norm": True,
+                "conv_bias": True,
+                "do_normalize": True,
+                "do_stable_layer_norm": True
+                }
+        elif embed_dim == 768: # base
+            config = {
+                "model_type": "wav2vec2",
+                "embed_dim": 768,
+                "num_heads": 12,
+                "num_layers": 12,
+                "conv_norm": False,
+                "conv_bias": False,
+                "do_normalize": False, # chinese-wav2vec2-base has this False
+                "do_stable_layer_norm": False
+            }
+        else:
+            raise RuntimeError("ERROR: audio encoder file is invalid or unsupported embed_dim: {}".format(embed_dim))
+    elif "model.encoder.embed_positions.weight" in sd:
+        sd = comfy.utils.state_dict_prefix_replace(sd, {"model.": ""})
+        config = {
+            "model_type": "whisper3",
+        }
+    else:
+        raise RuntimeError("ERROR: audio encoder not supported.")
+
+    audio_encoder = AudioEncoderModel(config)
+    m, u = audio_encoder.load_sd(sd)
+    if len(m) > 0:
+        logging.warning("missing audio encoder: {}".format(m))
+    if len(u) > 0:
+        logging.warning("unexpected audio encoder: {}".format(u))
+
+    return audio_encoder