Create configuration_speech_encoder.py

configuration_speech_encoder.py

@@ -0,0 +1,49 @@
+from transformers import PretrainedConfig
+
+class SpeechEncoderConfig(PretrainedConfig):
+    model_type = "gslm-speech-encoder"
+
+    def __init__(
+        self,
+        # Backend for dense features: "fairseq" (your uploaded .pt) or "transformers"
+        hubert_backend: str = "fairseq",
+        # If backend=fairseq: filename of your uploaded HuBERT checkpoint (e.g. "hubert_base_ls960.pt")
+        hubert_ckpt: str = "hubert_base_ls960.pt",
+        # If backend=transformers: HF model id (e.g. "facebook/hubert-base-ls960")
+        hubert_hf_name: str = "facebook/hubert-base-ls960",
+        hubert_layer: int = 9,               # which layer features to use
+        expected_sample_rate: int = 16000,   # HuBERT-base default
+        code_hop_size: int = 320,            # 20 ms at 16 kHz
+        # Quantizer file (K-Means centers)
+        quantizer_file: str = "kmeans_100.pt",
+        # Optional key to find centers inside a .pt/.pkl
+        quantizer_key: str = "",
+        # Interface flags (mirror textless SpeechEncoder)
+        deduplicate: bool = True,
+        add_bos_eos: bool = False,
+        need_f0: bool = False,               # keep False (F0 pipeline not bundled here)
+        # BOS/EOS ids (if left None, we will place them at vocab_size and vocab_size+1)
+        bos_id: int | None = None,
+        eos_id: int | None = None,
+        # Feature normalization before KMeans (None | "unit" | "layernorm")
+        feature_norm: str | None = None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.hubert_backend = hubert_backend
+        self.hubert_ckpt = hubert_ckpt
+        self.hubert_hf_name = hubert_hf_name
+        self.hubert_layer = int(hubert_layer)
+        self.expected_sample_rate = int(expected_sample_rate)
+        self.code_hop_size = int(code_hop_size)
+
+        self.quantizer_file = quantizer_file
+        self.quantizer_key = quantizer_key
+
+        self.deduplicate = bool(deduplicate)
+        self.add_bos_eos = bool(add_bos_eos)
+        self.need_f0 = bool(need_f0)
+        self.bos_id = bos_id
+        self.eos_id = eos_id
+
+        self.feature_norm = feature_norm